Borrowing strength from external trials in a meta-analysis

Optimal exercise intensity for improving executive function in patients with attention deficit hyperactivity disorder: systematic review and network meta-analysis

This study aimed to compare and rank the effectiveness of optimal exercise intensity in improving executive function in patients with ADHD (Attention deficit hyperactivity disorder, ADHD) through a comprehensive comparison of direct and indirect evidence. A systematic search was performed in five electronic databases to explore the optimal exercise intensity for improving executive function in patients with ADHD by directly and indirectly comparing a variety of exercise intervention intensities. In addition, the isolated effects of exercise on improving executive function in patients with ADHD were explored through classical meta-analysis of paired direct comparisons. Twenty-nine studies were retrieved and included in this study. Classical paired meta-analysis showed that for the patients with ADHD in the age group of 7-17 years, statistical difference was observed for all the parameters of exercise interventions (intensity, frequency, period, and training method), the three dimensions of executive function, the use of medication or not, the high and low quality of the methodological approach. Network meta-analysis showed that high-intensity exercise training was optimal for improving working memory (97.4%) and inhibitory function (85.7%) in patients with ADHD. Meanwhile, moderate-intensity exercise training was optimal for improving cognitive flexibility (77.3%) in patients with ADHD. Moderate to high intensity exercise training shows potential for improving executive function in these patients. Therefore, we recommend applying high-intensity exercise intervention to improve executive function in patients with ADHD to achieve substantial improvement.

Network meta-analysis made simple: a composite likelihood approach

Network meta-analysis, also known as mixed treatments comparison meta-analysis or multiple treatments meta-analysis, extends conventional pairwise meta-analysis by simultaneously synthesizing multiple interventions in a single integrated analysis. Despite the growing popularity of network metaanalysis within comparative effectiveness research, it comes with potential challenges. For example, within-study correlations among treatment comparisons are rarely reported in the published literature. Yet, these correlations are pivotal for valid statistical inference. As demonstrated in earlier studies, ignoring these correlations can inflate mean squared errors of the resulting point estimates and lead to inaccurate standard error estimates. This paper introduces a composite likelihood-based approach that ensures accurate statistical inference without requiring knowledge of the within-study correlations. The proposed method is computationally robust and efficient, with substantially reduced computational time compared to the state-of-the-science methods implemented in R packages. The proposed method was evaluated through extensive simulations and applied to two important applications including a network meta-analysis comparing interventions for primary open-angle glaucoma, and another comparing treatments for chronic prostatitis and chronic pelvic pain syndrome.

Peripheral Nerve Blocks in the Preoperative Management of Hip Fractures: A Systematic Review and Network Meta-Analysis

STUDY OBJECTIVE

We conducted a systematic review and network meta-analysis to evaluate the comparative efficacy of peripheral nerve block types for preoperative pain management of hip fractures.

METHODS

We searched Cochrane, Central Register of Controlled Trials, MEDLINE, EMBASE, ICTRP, ClinicalTrials.gov, and Google Scholar for randomized clinical trials. We included participants aged more than 16 years with hip fractures who received peripheral nerve blocks or analgesics for preoperative pain management. The primary outcomes were defined as absolute pain score 2 hours after block placement, preoperative consumption of morphine equivalents, and length of hospital stay. We used a random-effects network meta-analysis conceptualized in the Bayesian framework. Confidence of evidence was assessed using Confidence in Network Meta-Analysis (CINeMA).

RESULTS

We included 63 randomized controlled studies (4,778 participants), of which only a few had a low risk of bias. The femoral nerve block, 3-in-1 block, fascia iliaca compartment block, and pericapsular nerve group block yielded significantly lowered pain scores at 2 hours after block placement compared with those with no block (standardized mean differences [SMD]: -1.1; 95% credible interval [CrI]: -1.7 to -0.48, [confidence of evidence: low]; SMD: -1.8; 95% CrI: -3.0 to -0.55, [low]; SMD: -1.4; 95% CrI: -2.0 to -0.72, [low]; SMD: -2.3; 95% CrI: -3.2 to -1.4, [moderate], respectively). The pericapsular nerve group block, 3-in-1 block, fascia iliaca compartment block, and femoral nerve block resulted in lower pain scores than the no-block group. Additionally, the pericapsular nerve group block yielded a lower pain score than femoral nerve block or fascia iliaca compartment block (SMD: -1.21; 95% CrI: -2.18 to -0.23, [very low]: SMD: -0.92; 95% CrI: -1.70 to -0.16, [low]). However, both the fascia iliaca compartment block and femoral nerve block did not show a reduction in morphine consumption compared with no block. To our knowledge, no studies have compared the pericapsular nerve group block with other methods regarding morphine consumption. Furthermore, no significant difference was observed between peripheral nerve blocks and no block in terms of the length of hospital stay.

CONCLUSIONS

Compared with no block, preoperative peripheral nerve blocks for hip fractures appear to reduce pain 2 hours after block placement. Comparing different blocks, pericapsular nerve group block might be superior to fascia iliaca compartment block and femoral nerve block for pain relief, though the confidence evidence was low in most comparisons because of the moderate to high risk of bias in many of the included studies and the high heterogeneity of treatment strategies across studies. Therefore, further high-quality research is needed.

The Effect of Induction Therapy on Antibody-Mediated Rejection in Kidney Transplantation: A Network Meta-Analysis Using Recent Data

BACKGROUND

Various induction regimens are available for kidney transplantation (KT); however, which is superior remains unclear. Moreover, although the induction regimens are effective and important for reducing side effects, their respective relationships with antibody-mediated rejection (AMR) after transplantation remain unclear. Therefore, this study aimed to elucidate the most effective induction regimen for AMR reduction through network analysis.

METHODS

We performed a comprehensive search of databases, including basiliximab, alemtuzumab, antithymocyte globulin (ATG), and daclizumab as induction regimens for KT from inception to September 1, 2022. Using a network meta-analysis, we investigated the priorities of 5 induction regimens for patient survival, graft failure, and graft rejection after ABO-incompatible KT.

RESULTS

In total, 25 studies comprising 1768 people were included in this network meta-analysis. The primary outcome was the AMR rate of other induction regimens compared with that of basiliximab, whereas the secondary outcomes were heart failure, stroke, hospitalization, peripheral artery disease, myocardial infarction, anemia, leukopenia, herpes zoster, or adverse events. Notably, ATG reduced the AMR rate by 59% (odds ratio, 0.41; 95% credible interval, 0.20-0.90), whereas the other drugs did not show statistical significance. Furthermore, secondary outcomes did not significantly differ between the induction regimens.

CONCLUSION

ATG is widely used in KT induction regimens. Our results showed that ATG reduced the risk of AMR in KT recipients when compared with other induction drugs; therefore, it appears to be an efficient choice of induction regimen to reduce AMR after KT.

Effect of Induction Therapy Dose on Survival in Abo-Incompatible Kidney Transplantation: A Network Meta-Analysis Using Recent Data

BACKGROUND

Rituximab is an essential induction immunosuppressant for ABO-incompatible kidney transplantation (KT) (ABOi-KT). However, studies on the optimal dose of rituximab are insufficient, and there are dosage differences between transplant centers and countries. Therefore, we conducted a study to determine the survival outcomes of patients receiving the most effective and safe dose of rituximab during ABOi-KT.

METHODS

Studies on rituximab dose were divided into four groups: ABO compatible, 1) placebo, 2) rituximab 200 mg, 3) rituximab 200-500 mg, and 4) rituximab 500 mg. We searched the CENTRAL, MEDLINE, EMBASE, and Science Citation Index Expanded databases from 1970 to February 2022.9 . The inclusion criteria were adult patients (>18 years old). Reviews, observational studies, and clinical trials that did not clearly define outcomes or that did not have graft failure as an outcome were excluded. We performed direct and indirect network meta-analyses using Bayesian models and ranked different rituximab doses using a generation mixed treatment comparison (GeMTC) and Stata version 13. The NMA approach was evaluated using the GRADE framework, which specifies four levels of certainty for a given result: high, moderate, low, and very low. The outcomes included patient survival, graft failure, and bacterial and viral infections.

RESULTS

Twenty-five trials, including 5,378 subjects, were divided into the following four groups: 1) placebo, 2) rituximab 200 mg, 3) rituximab 200-500 mg, and 4) rituximab 500 mg. We focused on survival outcomes according to the dose of rituximab when patients received induction therapy for ABOi-KT. The mortality rate was significantly lower in the ABO-compatible and rituximab 200 mg groups (odds ratio [OR] 0.27, 95% CrI: 0.071-0.91 and OR 0.14, 95% CrI 0.036-0.47), compared with that in the placebo group.

CONCLUSIONS

We found that low-dose rituximab in ABO-i KT was effective compared to the high-dose and placebo in maintaining the survival rate. However, large-scale and long-term data are necessary for further validation of our findings. Additionally, the use of smaller doses of rituximab will require further discussion.

Network meta analysis to predict the efficacy of an approved treatment in a new indication

Drug repurposing refers to the process of discovering new therapeutic uses for existing medicines. Compared to traditional drug discovery, drug repurposing is attractive for its speed, cost, and reduced risk of failure. However, existing approaches for drug repurposing involve complex, computationally-intensive analytical methods that are not widely used in practice. Instead, repurposing decisions are often based on subjective judgments from limited empirical evidence. In this article, we develop a novel Bayesian network meta-analysis (NMA) framework that can predict the efficacy of an approved treatment in a new indication and thereby identify candidate treatments for repurposing. We obtain predictions using two main steps: first, we use standard NMA modeling to estimate average relative effects from a network comprised of treatments studied in both indications in addition to one treatment studied in only one indication. Then, we model the correlation between relative effects using various strategies that differ in how they model treatments across indications and within the same drug class. We evaluate the predictive performance of each model using a simulation study and find that the model minimizing root mean squared error of the posterior median for the candidate treatment depends on the amount of available data, the level of correlation between indications, and whether treatment effects differ, on average, by drug class. We conclude by discussing an illustrative example in psoriasis and psoriatic arthritis and find that the candidate treatment has a high probability of success in a future trial.

Twenty years of network meta-analysis: Continuing controversies and recent developments

Network meta-analysis (NMA) is an extension of pairwise meta-analysis (PMA) which combines evidence from trials on multiple treatments in connected networks. NMA delivers internally consistent estimates of relative treatment efficacy, needed for rational decision making. Over its first 20 years NMA's use has grown exponentially, with applications in both health technology assessment (HTA), primarily re-imbursement decisions and clinical guideline development, and clinical research publications. This has been a period of transition in meta-analysis, first from its roots in educational and social psychology, where large heterogeneous datasets could be explored to find effect modifiers, to smaller pairwise meta-analyses in clinical medicine on average with less than six studies. This has been followed by narrowly-focused estimation of the effects of specific treatments at specific doses in specific populations in sparse networks, where direct comparisons are unavailable or informed by only one or two studies. NMA is a powerful and well-established technique but, in spite of the exponential increase in applications, doubts about the reliability and validity of NMA persist. Here we outline the continuing controversies, and review some recent developments. We suggest that heterogeneity should be minimized, as it poses a threat to the reliability of NMA which has not been fully appreciated, perhaps because it has not been seen as a problem in PMA. More research is needed on the extent of heterogeneity and inconsistency in datasets used for decision making, on formal methods for making recommendations based on NMA, and on the further development of multi-level network meta-regression.

A new approach to evaluating loop inconsistency in network meta-analysis

In network meta-analysis, studies evaluating multiple treatment comparisons are modeled simultaneously, and estimation is informed by a combination of direct and indirect evidence. Network meta-analysis relies on an assumption of consistency, meaning that direct and indirect evidence should agree for each treatment comparison. Here we propose new local and global tests for inconsistency and demonstrate their application to three example networks. Because inconsistency is a property of a loop of treatments in the network meta-analysis, we locate the local test in a loop. We define a model with one inconsistency parameter that can be interpreted as loop inconsistency. The model builds on the existing ideas of node-splitting and side-splitting in network meta-analysis. To provide a global test for inconsistency, we extend the model across multiple independent loops with one degree of freedom per loop. We develop a new algorithm for identifying independent loops within a network meta-analysis. Our proposed models handle treatments symmetrically, locate inconsistency in loops rather than in nodes or treatment comparisons, and are invariant to choice of reference treatment, making the results less dependent on model parameterization. For testing global inconsistency in network meta-analysis, our global model uses fewer degrees of freedom than the existing design-by-treatment interaction approach and has the potential to increase power. To illustrate our methods, we fit the models to three network meta-analyses varying in size and complexity. Local and global tests for inconsistency are performed and we demonstrate that the global model is invariant to choice of independent loops.

Pharmacological treatments in panic disorder in adults: a network meta-analysis

BACKGROUND

A panic attack is a discrete period of fear or anxiety that has a rapid onset and reaches a peak within 10 minutes. The main symptoms involve bodily systems, such as racing heart, chest pain, sweating, shaking, dizziness, flushing, churning stomach, faintness and breathlessness. Other recognised panic attack symptoms involve fearful cognitions, such as the fear of collapse, going mad or dying, and derealisation (the sensation that the world is unreal). Panic disorder is common in the general population with a prevalence of 1% to 4%. The treatment of panic disorder includes psychological and pharmacological interventions, including antidepressants and benzodiazepines.

OBJECTIVES

To compare, via network meta-analysis, individual drugs (antidepressants and benzodiazepines) or placebo in terms of efficacy and acceptability in the acute treatment of panic disorder, with or without agoraphobia. To rank individual active drugs for panic disorder (antidepressants, benzodiazepines and placebo) according to their effectiveness and acceptability. To rank drug classes for panic disorder (selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), tricyclic antidepressants (TCAs), mono-amine oxidase inhibitors (MAOIs) and benzodiazepines (BDZs) and placebo) according to their effectiveness and acceptability. To explore heterogeneity and inconsistency between direct and indirect evidence in a network meta-analysis.

SEARCH METHODS

We searched the Cochrane Common Mental Disorders Specialised Register, CENTRAL, CDSR, MEDLINE, Ovid Embase and PsycINFO to 26 May 2022.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) of people aged 18 years or older of either sex and any ethnicity with clinically diagnosed panic disorder, with or without agoraphobia. We included trials that compared the effectiveness of antidepressants and benzodiazepines with each other or with a placebo.

DATA COLLECTION AND ANALYSIS

Two authors independently screened titles/abstracts and full texts, extracted data and assessed risk of bias. We analysed dichotomous data and continuous data as risk ratios (RRs), mean differences (MD) or standardised mean differences (SMD): response to treatment (i.e. substantial improvement from baseline as defined by the original investigators: dichotomous outcome), total number of dropouts due to any reason (as a proxy measure of treatment acceptability: dichotomous outcome), remission (i.e. satisfactory end state as defined by global judgement of the original investigators: dichotomous outcome), panic symptom scales and global judgement (continuous outcome), frequency of panic attacks (as recorded, for example, by a panic diary; continuous outcome), agoraphobia (dichotomous outcome). We assessed the certainty of evidence using threshold analyses.

MAIN RESULTS

Overall, we included 70 trials in this review. Sample sizes ranged between 5 and 445 participants in each arm, and the total sample size per study ranged from 10 to 1168. Thirty-five studies included sample sizes of over 100 participants. There is evidence from 48 RCTs (N = 10,118) that most medications are more effective in the response outcome than placebo. In particular, diazepam, alprazolam, clonazepam, paroxetine, venlafaxine, clomipramine, fluoxetine and adinazolam showed the strongest effect, with diazepam, alprazolam and clonazepam ranking as the most effective. We found heterogeneity in most of the comparisons, but our threshold analyses suggest that this is unlikely to impact the findings of the network meta-analysis. Results from 64 RCTs (N = 12,310) suggest that most medications are associated with either a reduced or similar risk of dropouts to placebo. Alprazolam and diazepam were associated with a lower dropout rate compared to placebo and were ranked as the most tolerated of all the medications examined. Thirty-two RCTs (N = 8569) were included in the remission outcome. Most medications were more effective than placebo, namely desipramine, fluoxetine, clonazepam, diazepam, fluvoxamine, imipramine, venlafaxine and paroxetine, and their effects were clinically meaningful. Amongst these medications, desipramine and alprazolam were ranked highest. Thirty-five RCTs (N = 8826) are included in the continuous outcome reduction in panic scale scores. Brofaromine, clonazepam and reboxetine had the strongest reductions in panic symptoms compared to placebo, but results were based on either one trial or very small trials. Forty-one RCTs (N = 7853) are included in the frequency of panic attack outcome. Only clonazepam and alprazolam showed a strong reduction in the frequency of panic attacks compared to placebo, and were ranked highest. Twenty-six RCTs (N = 7044) provided data for agoraphobia. The strongest reductions in agoraphobia symptoms were found for citalopram, reboxetine, escitalopram, clomipramine and diazepam, compared to placebo. For the pooled intervention classes, we examined the two primary outcomes (response and dropout). The classes of medication were: SSRIs, SNRIs, TCAs, MAOIs and BDZs. For the response outcome, all classes of medications examined were more effective than placebo. TCAs as a class ranked as the most effective, followed by BDZs and MAOIs. SSRIs as a class ranked fifth on average, while SNRIs were ranked lowest. When we compared classes of medication with each other for the response outcome, we found no difference between classes. Comparisons between MAOIs and TCAs and between BDZs and TCAs also suggested no differences between these medications, but the results were imprecise. For the dropout outcome, BDZs were the only class associated with a lower dropout compared to placebo and were ranked first in terms of tolerability. The other classes did not show any difference in dropouts compared to placebo. In terms of ranking, TCAs are on average second to BDZs, followed by SNRIs, then by SSRIs and lastly by MAOIs. BDZs were associated with lower dropout rates compared to SSRIs, SNRIs and TCAs. The quality of the studies comparing antidepressants with placebo was moderate, while the quality of the studies comparing BDZs with placebo and antidepressants was low.

AUTHORS' CONCLUSIONS

In terms of efficacy, SSRIs, SNRIs (venlafaxine), TCAs, MAOIs and BDZs may be effective, with little difference between classes. However, it is important to note that the reliability of these findings may be limited due to the overall low quality of the studies, with all having unclear or high risk of bias across multiple domains. Within classes, some differences emerged. For example, amongst the SSRIs paroxetine and fluoxetine seem to have stronger evidence of efficacy than sertraline. Benzodiazepines appear to have a small but significant advantage in terms of tolerability (incidence of dropouts) over other classes.

Improved methods to construct prediction intervals for network meta-analysis

Network meta-analysis has played an important role in evidence-based medicine for assessing the comparative effectiveness of multiple available treatments. The prediction interval has been one of the standard outputs in recent network meta-analysis as an effective measure that enables simultaneous assessment of uncertainties in treatment effects and heterogeneity among studies. To construct the prediction interval, a large-sample approximating method based on the t-distribution has generally been applied in practice; however, recent studies have shown that similar t-approximation methods for conventional pairwise meta-analyses can substantially underestimate the uncertainty under realistic situations. In this article, we performed simulation studies to assess the validity of the current standard method for network meta-analysis, and we show that its validity can also be violated under realistic situations. To address the invalidity issue, we developed two new methods to construct more accurate prediction intervals through bootstrap and Kenward-Roger-type adjustment. In simulation experiments, the two proposed methods exhibited better coverage performance and generally provided wider prediction intervals than the ordinary t-approximation. We also developed an R package, PINMA (https://cran.r-project.org/web/packages/PINMA/), to perform the proposed methods using simple commands. We illustrate the effectiveness of the proposed methods through applications to two real network meta-analyses.

Comparative efficacy and safety of non-polymyxin antibiotics against nosocomial pneumonia, complicated intra-abdominal infection, or complicated urinary tract infection: A network meta-analysis of randomised clinical trials

OBJECTIVES

The increasing epidemic of infections caused by drug-resistant Gram-negative bacteria has led to the development of several antibiotic therapies. Owing to the scarcity of head-to-head comparisons of current and emerging antibiotics, the present network meta-analysis aimed to compare the efficacy and safety of antibiotics in patients with nosocomial pneumonia, complicated intra-abdominal infection, or complicated urinary tract infection.

METHODS

Two independent researchers systematically searched databases up to August 2022 and included 26 randomised controlled trials that fulfilled the inclusion criteria. The protocol was registered in the Prospective Register of Systematic Reviews, PROSPERO (CRD42021237798). The frequentist random effects model (R version 3.5.1, netmeta package) was utilized. The DerSimonian-Laird random effects model was used to estimate heterogeneity. The calculated P-score was applied to rank the interventions. Additionally, inconsistencies, publication bias, and subgroup effects were assessed in the present study to avoid bias.

RESULTS

There was no significant difference among included antibiotics in terms of clinical response and mortality, probably because most antibiotic trials were designed to be non-inferior. In terms of P-score ranking, carbapenems may be the recommended choice considering both adverse events and clinical responses. On the other hand, for carbapenem-sparing options, ceftolozane-tazobactam was the preferred antibiotic for nosocomial pneumonia; eravacycline, for complicated intra-abdominal infection; and cefiderocol, for complicated urinary tract infection.

CONCLUSION

Carbapenems may be preferable options in terms of safety and efficacy for the treatment of Gram-negative bacterial complicated infections. However, to preserve the effectiveness of carbapenems, it is important to consider carbapenem-sparing regimens.

Kenward-Roger-type corrections for inference methods of network meta-analysis and meta-regression

Network meta-analysis has been an essential methodology of systematic reviews for comparative effectiveness research. The restricted maximum likelihood (REML) method is one of the current standard inference methods for multivariate, contrast-based meta-analysis models, but recent studies have revealed the resultant confidence intervals of average treatment effect parameters in random-effects models can seriously underestimate statistical errors; that is, the actual coverage probability of a true parameter cannot retain the nominal level (e.g., 95%). In this article, we provided improved inference methods for the network meta-analysis and meta-regression models using higher-order asymptotic approximations based on the approach of Kenward and Roger (Biometrics 1997;53:983-997). We provided two corrected covariance matrix estimators for the REML estimator and improved approximations for its sample distribution using a t-distribution with adequate degrees of freedom. All of the proposed procedures can be implemented using only simple matrix calculations. In simulation studies under various settings, the REML-based Wald-type confidence intervals seriously underestimated the statistical errors, especially in cases of small numbers of trials meta-analyzed. By contrast, the proposed Kenward-Roger-type inference methods consistently showed accurate coverage properties under all the settings considered in our experiments. We also illustrated the effectiveness of the proposed methods through applications to two real network meta-analysis datasets.

Summarizing empirical information on between-study heterogeneity for Bayesian random-effects meta-analysis

In Bayesian meta-analysis, the specification of prior probabilities for the between-study heterogeneity is commonly required, and is of particular benefit in situations where only few studies are included. Among the considerations in the set-up of such prior distributions, the consultation of available empirical data on a set of relevant past analyses sometimes plays a role. How exactly to summarize historical data sensibly is not immediately obvious; in particular, the investigation of an empirical collection of heterogeneity estimates will not target the actual problem and will usually only be of limited use. The commonly used normal-normal hierarchical model for random-effects meta-analysis is extended to infer a heterogeneity prior. Using an example data set, we demonstrate how to fit a distribution to empirically observed heterogeneity data from a set of meta-analyses. Considerations also include the choice of a parametric distribution family. Here, we focus on simple and readily applicable approaches to then translate these into (prior) probability distributions.

Exercise treatments for chronic low back pain: a network meta‐analysis

This is a protocol for a Cochrane Review (intervention). The objectives are as follows:

The objectives of this systematic review, conducted using a collaborative review model, are to:

Assess the effectiveness of exercise treatment (overall) in adults with chronic non‐specific low back pain on important individual health outcomes: pain, functional limitations, health‐related quality of life, depression, and adverse effects versus comparison treatments: (a) placebo, sham, or attention control, (b) no trial treatment (including waiting lists, control groups described as having no treatment provided, usual/normal care not controlled by the trial available to all treatment groups, or when the exercise and comparison groups receive the same co‐interventions, allowing the effect of exercise treatment to be isolated), and (c) other conservative treatments (eight categories).  Estimate the treatment effects and associated uncertainty for comparisons of different specific types of exercise treatment in adults with chronic non‐specific low back pain to each other, and to each comparison treatment, using direct and indirect evidence with network meta‐analysis. Estimate the treatment effects and associated uncertainty for comparisons of treatments composed of different exercise type categories, design, delivery, dose, and additional treatment components, and their combinations, using direct and indirect evidence with component network meta‐analysis. 

Comparative effectiveness and safety of analgesic medicines for adults with acute non-specific low back pain: systematic review and network meta-analysis

OBJECTIVE

To evaluate the comparative effectiveness and safety of analgesic medicines for acute non-specific low back pain.

DESIGN

Systematic review and network meta-analysis.

DATA SOURCES

Medline, PubMed, Embase, CINAHL, CENTRAL, ClinicalTrials.gov, clinicialtrialsregister.eu, and World Health Organization's International Clinical Trials Registry Platform from database inception to 20 February 2022.

ELIGIBILITY CRITERIA FOR STUDY SELECTION

Randomised controlled trials of analgesic medicines (eg, non-steroidal anti-inflammatory drugs, paracetamol, opioids, anti-convulsant drugs, skeletal muscle relaxants, or corticosteroids) compared with another analgesic medicine, placebo, or no treatment. Adults (≥18 years) who reported acute non-specific low back pain (for less than six weeks).

DATA EXTRACTION AND SYNTHESIS

Primary outcomes were low back pain intensity (0-100 scale) at end of treatment and safety (number of participants who reported any adverse event during treatment). Secondary outcomes were low back specific function, serious adverse events, and discontinuation from treatment. Two reviewers independently identified studies, extracted data, and assessed risk of bias. A random effects network meta-analysis was done and confidence was evaluated by the Confidence in Network Meta-Analysis method.

RESULTS

98 randomised controlled trials (15 134 participants, 49% women) included 69 different medicines or combinations. Low or very low confidence was noted in evidence for reduced pain intensity after treatment with tolperisone (mean difference -26.1 (95% confidence intervals -34.0 to -18.2)), aceclofenac plus tizanidine (-26.1 (-38.5 to -13.6)), pregabalin (-24.7 (-34.6 to -14.7)), and 14 other medicines compared with placebo. Low or very low confidence was noted for no difference between the effects of several of these medicines. Increased adverse events had moderate to very low confidence with tramadol (risk ratio 2.6 (95% confidence interval 1.5 to 4.5)), paracetamol plus sustained release tramadol (2.4 (1.5 to 3.8)), baclofen (2.3 (1.5 to 3.4)), and paracetamol plus tramadol (2.1 (1.3 to 3.4)) compared with placebo. These medicines could increase the risk of adverse events compared with other medicines with moderate to low confidence. Moderate to low confidence was also noted for secondary outcomes and secondary analysis of medicine classes.

CONCLUSIONS

The comparative effectiveness and safety of analgesic medicines for acute non-specific low back pain are uncertain. Until higher quality randomised controlled trials of head-to-head comparisons are published, clinicians and patients are recommended to take a cautious approach to manage acute non-specific low back pain with analgesic medicines.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42019145257.

Explainable domain transfer of distant supervised cancer subtyping model via imaging-based rules extraction

Image texture analysis has for decades represented a promising opportunity for cancer assessment and disease progression evaluation, evolving in a discipline, i.e., radiomics. However, the road to a complete translation into clinical practice is still hampered by intrinsic limitations. As purely supervised classification models fail in devising robust imaging-based biomarkers for prognosis, cancer subtyping approaches would benefit from the employment of distant supervision, for instance exploiting survival/recurrence information. In this work, we assessed, tested, and validated the domain-generality of our previously proposed Distant Supervised Cancer Subtyping model on Hodgkin Lymphoma. We evaluate the model performance on two independent datasets coming from two hospitals, comparing and analyzing the results. Although successful and consistent, the comparison confirmed the instability of radiomics due to an across-center lack of reproducibility, leading to explainable results in one center and poor interpretability in the other. We thus propose a Random Forest-based Explainable Transfer Model for testing the domain-invariance of imaging biomarkers extracted from retrospective cancer subtyping. In doing so, we tested the predictive ability of cancer subtyping in a validation and perspective setting, which led to successful results and supported the domain-generality of the proposed approach. On the other hand, the extraction of decision rules enables to draw of risk factors and robust biomarkers to inform clinical decisions. This work shows the potentialities of the Distant Supervised Cancer Subtyping model to be further evaluated in larger multi-center datasets, to reliably translate radiomics into medical practice. The code is available at this GitHub repository.

Adjusted closed-form confidence interval formulas for network meta-analysis with a small number of studies

We derive simple formulas for closed-form confidence intervals for the Wald statistic, likelihood ratio statistic, and score statistic for network meta-analysis (NMA). Additionally, we consider resolutions of concerns that network meta-analyzes with a small number of studies cannot maintain a nominal confidence level. For bias adjustment in analyzes with a small number of studies, the Bartlett-type adjustment is a well-known method. Many Bartlett-type adjustment-type methods are based on maximum likelihood estimators (MLEs). However, NMA often uses restricted MLEs that have not been extensively discussed with respect to the Bartlett-type adjustment. In this article, we propose a Bartlett-type adjustment method for the Wald statistic, likelihood ratio statistic, and score statistic when nuisance parameters are estimated by not only the maximum likelihood method but also the restricted maximum likelihood method. We can compute closed-form confidence intervals adjusted using the Bartlett-type adjustment immediately without any numerical calculations (eg, bootstrap method). Additionally, we propose a higher-order adjustment by applying the bootstrap method to Bartlett-type adjusted statistics. Using a computer simulation, we confirmed that the adjusted confidence intervals maintained a nominal confidence level. Additionally, we confirmed that the confidence intervals of the Wald statistic, likelihood ratio statistic, and score statistic based on the restricted maximum likelihood method performed well without further bootstrap adjustment and the performances of the three adjusted confidence intervals were comparable. Finally, we demonstrated that confidence intervals were adjusted for actual NMA. In the actual NMA, the adjusted confidence intervals of the Wald statistic were wider, the adjusted confidence intervals of the likelihood ratio statistic were also wider, and the adjusted confidence intervals of the score statistic were narrower. We recommend using the likelihood ratio test statistic with the restricted maximum likelihood estimator; however, just in case, we recommend applying the Bartlett-type adjustment to remove the second order bias. From demonstrations in actual studies, we confirmed that the adjusted confidence intervals improved compared with the naive confidence intervals.

Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-regression to a Network of Treatments for Plaque Psoriasis

BACKGROUND

Network meta-analysis (NMA) and indirect comparisons combine aggregate data (AgD) from multiple studies on treatments of interest but may give biased estimates if study populations differ. Population adjustment methods such as multilevel network meta-regression (ML-NMR) aim to reduce bias by adjusting for differences in study populations using individual patient data (IPD) from 1 or more studies under the conditional constancy assumption. A shared effect modifier assumption may also be necessary for identifiability. This article aims to demonstrate how the assumptions made by ML-NMR can be assessed in practice to obtain reliable treatment effect estimates in a target population.

METHODS

We apply ML-NMR to a network of evidence on treatments for plaque psoriasis with a mix of IPD and AgD trials reporting ordered categorical outcomes. Relative treatment effects are estimated for each trial population and for 3 external target populations represented by a registry and 2 cohort studies. We examine residual heterogeneity and inconsistency and relax the shared effect modifier assumption for each covariate in turn.

RESULTS

Estimated population-average treatment effects were similar across study populations, as differences in the distributions of effect modifiers were small. Better fit was achieved with ML-NMR than with NMA, and uncertainty was reduced by explaining within- and between-study variation. We found little evidence that the conditional constancy or shared effect modifier assumptions were invalid.

CONCLUSIONS

ML-NMR extends the NMA framework and addresses issues with previous population adjustment approaches. It coherently synthesizes evidence from IPD and AgD studies in networks of any size while avoiding aggregation bias and noncollapsibility bias, allows for key assumptions to be assessed or relaxed, and can produce estimates relevant to a target population for decision-making.

HIGHLIGHTS

Multilevel network meta-regression (ML-NMR) extends the network meta-analysis framework to synthesize evidence from networks of studies providing individual patient data or aggregate data while adjusting for differences in effect modifiers between studies (population adjustment). We apply ML-NMR to a network of treatments for plaque psoriasis with ordered categorical outcomes.We demonstrate for the first time how ML-NMR allows key assumptions to be assessed. We check for violations of conditional constancy of relative effects (such as unobserved effect modifiers) through residual heterogeneity and inconsistency and the shared effect modifier assumption by relaxing this for each covariate in turn.Crucially for decision making, population-adjusted treatment effects can be produced in any relevant target population. We produce population-average estimates for 3 external target populations, represented by the PsoBest registry and the PROSPECT and Chiricozzi 2019 cohort studies.

Multivariate Bayesian Arm-Based Network Meta-Analysis of Pharmacological Interventions for the Treatment of Acute Bipolar Mania in Adults

BACKGROUND

In a network meta-analysis (NMA), multiple treatments can be compared simultaneously by aggregating pieces of evidence from direct as well as indirect treatment comparisons in different randomized controlled trials (RCTs). Conventional NMA are performed using a normal approximation approach and can be applied for arm-level binary outcome data as well. This study aimed to estimate the treatment effects within a Bayesian framework using a binomial likelihood for a multivariate NMA model.

METHODS

The dataset consists of 57 RCTs comparing the effect of ten pharmacological drugs and a placebo for acute bipolar mania in adults. The binary outcomes of interest were treatment response and all-cause dropouts measured three weeks from the baseline. Binomial distribution was adopted for the number of events and the probability of event occurrence modeled on the logit scale. Jeffrey's Beta prior was considered for the heterogeneity and inconsistency of standard deviation (SD) parameters. Cholesky and spherical decomposition strategies were adopted for the between-study variance-covariance matrix. Deviance information criterion (DIC) indices were computed to determine the model fit. All results pertaining to Markov chain Monte Carlo simulations and all analyses were carried out in WinBUGS software.

RESULTS

The estimated common heterogeneity SDs were similar, and the DIC values did not provide any evidence for superiority between the two decomposition strategies. The correlation (95% credible interval) between the outcomes was estimated as -0.31 (-0.71, -0.02) and -0.37 (-0.73, -0.03) for the Cholesky and spherical decompositions, respectively. Gelman-Rubin convergence statistics were stable, and Monte Carlo errors for all the parameters were around 0.005. Overall, olanzapine, paliperidone, and quetiapine were both significantly more effective and acceptable than a placebo when both the study outcomes were considered simultaneously.

CONCLUSIONS

The findings favoring olanzapine, paliperidone, and quetiapine possess an excellent concordance with the one adopted in clinical practice, and the Canadian Network for Mood and Anxiety Treatments and Royal Australian and New Zealand College of Psychiatrists guidelines recommend these as first-line drugs for treating bipolar disorder.

Identifying relative efficacy of components of prehabilitation in adult surgical patients: protocol for a systematic review and component network meta-analysis

INTRODUCTION

Prehabilitation is a high-priority intervention for patients, the public, clinicians and health systems. However, existing knowledge syntheses are generally low quality and do not provide insights regarding the relative efficacy of different prehabilitation components (eg, exercise, nutrition, psychosocial or cognitive interventions). The objective of the planned review is to evaluate the relative efficacy of different prehabilitation components to inform current care, implementation and future research.

METHODS AND ANALYSIS

We will perform a systematic review and component network meta-analysis (CNMA). We will use a peer-reviewed search strategy to identify all randomised trials of prehabilitation in adult surgical patients from Ovid Medline, Embase, the CINAHL, PsycINFO, Web of Science and the Cochrane Central Register of Controlled Trials, along with grey literature. All stages of the review and data extraction process will be performed in duplicate, following recommended best practices. To compare the relative efficacy of different prehabilitation components (prespecified as exercise, nutrition, psychosocial or cognitive interventions), we will use CNMA, an extension of network meta-analysis that allows estimation of the contributions to efficacy of each component of a multicomponent intervention through direct and indirect comparisons. We will use additive CNMA models for critical outcomes (postoperative complications, patient-reported recovery, physical recovery and length of stay); standard care will be the common reference condition. Pre-specified sensitivity and subgroup analyses will be conducted.

ETHICS AND DISSEMINATION

This review of published data does not require ethical review. Results will be disseminated via scientific conferences, peer-reviewed publications, social and traditional media and via our research network to target partners and organisations.

Catheter ablation for treatment of bradycardia-tachycardia syndrome: is it time to consider it the therapy of choice? A systematic review and meta-analysis

BACKGROUND

Atrial fibrillation catheter ablation (AFCA) should be considered as a strategy to avoid pacemaker (PM) implantation for patients with bradycardia-tachycardia syndrome (BTS), but lack of evidence is remarkable.

METHODS

Our aim was to conduct a random-effects model meta-analysis on safety and efficacy data from controlled trials and observational studies. We compared atrial fibrillation (AF) recurrence, AF progression, procedural complication, additional procedure, cardiovascular death, cardiovascular hospitalization, heart failure and stroke in patients undergoing AFCA vs. PM implantation.

RESULTS

PubMed/MEDLINE, Cochrane Database and Google Scholar were screened, and four retrospective studies were selected. A total of 776 patients (371 in the AFCA group, 405 in the PM group) were included. After a median follow-up of 67.5 months, lower AF recurrence [odds ratio (OR) 0.06, confidence interval (CI) 0.02-0.18, I2 = 82.42%, P < 0.001], AF progression (OR 0.12, CI 0.06-0.26, I2 = 0%, P < 0.001), heart failure (OR 0.12, CI 0.04-0.34, I2 = 0%, P < 0.001), and stroke (OR 0.30, CI 0.15-0.61, I2 = 0%, P = 0.001) were observed in the AFCA group. No differences were observed in cardiovascular death and hospitalization (OR 0.48, CI 0.10-2.28, I2 = 0%, P = 0.358 and OR 0.43, CI 0.14-1.29, I2 = 87.52%, P = 0.134, respectively). Higher need for additional procedures in the AFCA group was highlighted (OR 3.65, CI 1.51-8.84, I2 = 53.75%, P < 0.001). PM implantation was avoided in 91% of BTS patients undergoing AFCA.

CONCLUSIONS

AFCA in BTS patients seems to be more effective than PM implantation in reducing AF recurrence and PM implantation may be waived in most BTS patients treated by AFCA. Need for additional procedures in AFCA patients is balanced by long-term benefit in clinical end points.

Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews

OBJECTIVES

Multivariate meta-analysis allows the joint synthesis of multiple outcomes accounting for their correlation. This enables borrowing of strength (BoS) across outcomes, which may lead to greater efficiency and even different conclusions compared to separate univariate meta-analyses. However, multivariate meta-analysis is complex to apply, so guidance is needed to flag (in advance of analysis) when the approach is most useful.

STUDY DESIGN AND SETTING

We use 43 Cochrane intervention reviews to empirically investigate the characteristics of meta-analysis datasets that are associated with a larger BoS statistic (from 0 to 100%) when applying a bivariate meta-analysis of binary outcomes.

RESULTS

Four characteristics were identified as strongly associated with BoS: the total number of studies, the number of studies with the outcome of interest, the percentage of studies missing the outcome of interest, and the largest absolute within-study correlation. Using these characteristics, we then develop a model for predicting BoS in a new dataset, which is shown to have good performance (an adjusted R² of 50%). Applied examples are used to illustrate the use of the BoS prediction model.

CONCLUSIONS

Cochrane reviewers mainly use univariate meta-analysis methods, but the identified characteristics associated with BoS and our subsequent prediction model for BoS help to flag when a multivariate meta-analysis may also be beneficial in Cochrane reviews with multiple binary outcomes. Extension to non-Cochrane reviews and other outcome types is still required.

Performance of Antigen Detection Tests for SARS-CoV-2: A Systematic Review and Meta-Analysis

Coronavirus disease 2019 (COVID-19) initiated global health care challenges such as the necessity for new diagnostic tests. Diagnosis by real-time PCR remains the gold-standard method, yet economical and technical issues prohibit its use in points of care (POC) or for repetitive tests in populations. A lot of effort has been exerted in developing, using, and validating antigen-based tests (ATs). Since individual studies focus on few methodological aspects of ATs, a comparison of different tests is needed. Herein, we perform a systematic review and meta-analysis of data from articles in PubMed, medRxiv and bioRxiv. The bivariate method for meta-analysis of diagnostic tests pooling sensitivities and specificities was used. Most of the AT types for SARS-CoV-2 were lateral flow immunoassays (LFIA), fluorescence immunoassays (FIA), and chemiluminescence enzyme immunoassays (CLEIA). We identified 235 articles containing data from 220,049 individuals. All ATs using nasopharyngeal samples show better performance than those with throat saliva (72% compared to 40%). Moreover, the rapid methods LFIA and FIA show about 10% lower sensitivity compared to the laboratory-based CLEIA method (72% compared to 82%). In addition, rapid ATs show higher sensitivity in symptomatic patients compared to asymptomatic patients, suggesting that viral load is a crucial parameter for ATs performed in POCs. Finally, all methods perform with very high specificity, reaching around 99%. LFIA tests, though with moderate sensitivity, appear as the most attractive method for use in POCs and for performing seroprevalence studies.

How to observe the principle of concurrent control in an arm-based meta-analysis using SAS procedures GLIMMIX and BGLIMM

Network meta-analysis is a popular method to synthesize the information obtained in a systematic review of studies (e.g. randomized clinical trials) involving subsets of multiple treatments of interest. The dominant method of analysis employs within-study information on treatment contrasts and integrates this over a network of studies. One advantage of this approach is that all inference is protected by within-study randomization. By contrast, arm-based analyses have been criticized in the past because they may also recover inter-study information when studies are modelled as random, which is the dominant practice, hence violating the principle of concurrent control, requiring treated individuals to only be compared directly with randomized controls. This issue arises regardless of whether analysis is implemented within a frequentist or a Bayesian framework. Here, we argue that recovery of inter-study information can be prevented in an arm-based analysis by adding a fixed study main effect. This simple device means that it is possible to honour the principle of concurrent control in a two-way analysis-of-variance approach that is very easy to implement using generalized linear mixed model procedures and hence may be particularly welcome to those not well versed in the more intricate coding required for a contrast-based analysis. This article is protected by copyright. All rights reserved.

Network meta-analysis and random walks

Network meta-analysis (NMA) is a central tool for evidence synthesis in clinical research. The results of an NMA depend critically on the quality of evidence being pooled. In assessing the validity of an NMA, it is therefore important to know the proportion contributions of each direct treatment comparison to each network treatment effect. The construction of proportion contributions is based on the observation that each row of the hat matrix represents a so-called "evidence flow network" for each treatment comparison. However, the existing algorithm used to calculate these values is associated with ambiguity according to the selection of paths. In this article, we present a novel analogy between NMA and random walks. We use this analogy to derive closed-form expressions for the proportion contributions. A random walk on a graph is a stochastic process that describes a succession of random "hops" between vertices which are connected by an edge. The weight of an edge relates to the probability that the walker moves along that edge. We use the graph representation of NMA to construct the transition matrix for a random walk on the network of evidence. We show that the net number of times a walker crosses each edge of the network is related to the evidence flow network. By then defining a random walk on the directed evidence flow network, we derive analytically the matrix of proportion contributions. The random-walk approach has none of the associated ambiguity of the existing algorithm.

Investigating the efficiency and tolerability of traditional Chinese formulas combined with antiarrhythmic agents for paroxysmal atrial fibrillation: A systematic review and Bayesian network meta-analysis

BACKGROUND

The combination of antiarrhythmic drugs with traditional Chinese formulas are used treatments for the management of paroxysmal atrial fibrillation (PAF). However, the most effective treatment for PAF has yet to be been determined. A Bayesian network meta-analysis study was thus performed for comparing the relative efficacy and tolerability of different treatment alternatives.

METHODS

A comprehensive literature review of randomized controlled trials (RCTs) is performed from eight database. Maintenance rate of sinus rhythm (MRSR), p-wave dispersion (Pd), left atrium diameter (LAD), left ventricular ejection fraction (LVEF), and adverse events (AEs) were used as outcomes. We also estimated treatment rank based on the surface under the cumulative ranking curve (SUCRA). This study was performed using a Bayesian network meta-analysis with a random-effects model.

FINDINGS

After screening, 59 RCTs involving 5,543 patients and 16 treatments were included. The results showed that Shensong-Yangxin capsule (SSYX) plus amiodarone (81%) was the most effective treatment for MRSR according to the value of SUCRA, followed by Wenxin-Keli granules (WXKL) plus amiodarone (73%). Meanwhile, SSYX plus amiodarone (7%) was most likely to reduce Pd, followed by SSYX plus metoprolol (23%), WXKL plus amiodarone (26%), WXKL plus bisoprolol (27%). Furthermore, SSYX plus amiodarone (4%) was more effective in improving LAD. WXKL plus amiodarone was preferred because it had the lowest toxicity. For benefit-risk ratio, amiodarone combined with WXKL or SSYX appeared to be the best option.

CONCLUSION

Antiarrhythmic agents combined with traditional Chinese formulas had higher efficacy and lower toxicity than other treatment alternatives. This study might provide reference to help find the better treatment options for PAF.

Tezepelumab compared with other biologics for the treatment of severe asthma: a systematic review and indirect treatment comparison

AIMS

To compare the efficacy of tezepelumab with other approved biologics via indirect treatment comparisons (ITCs) in patients aged ≥ 12 years with severe uncontrolled asthma.

MATERIALS AND METHODS

Data from randomized controlled trials (RCTs) identified from a systematic literature review were synthesized using two different ITC approaches: network meta-analysis (NMA) and simulated treatment comparison (STC). Outcomes of interest were annualized asthma exacerbation rate (AAER) and AAER for exacerbations leading to hospitalization. To address potential heterogeneity between study populations, various subgroup analyses were performed for the NMA (based on blood eosinophil count, fractional exhaled nitric oxide level, and presence of allergic asthma), and for the STC, models were adjusted for potential treatment effect modifiers. Sensitivity analyses were performed to assess the impact of study design (exclusion of non-placebo-controlled studies and non-phase 3 or 4 studies). Results were reported as rate ratios (RRs) with 95% credible/confidence intervals and ranking statistics were computed for the NMAs.

RESULTS

Sixteen RCTs were included in at least one of the ITCs. All biologics (tezepelumab, dupilumab, benralizumab, mepolizumab, reslizumab, and omalizumab) had similar efficacy, with no statistically significant RRs for either exacerbation outcome; however, tezepelumab was favorably associated with numerically lower AAERs and was ranked first in the network for both types of exacerbation outcome. This trend was consistent in the subgroup and sensitivity analyses. As with the primary NMA, the STC results did not demonstrate any significant differences between biologics, but point estimates were favorable towards tezepelumab.

LIMITATIONS

Heterogeneity between trials was observed among eligibility criteria and clinically important patient characteristics; however, the impact on findings is expected to be low, based on consistency across analyses.

CONCLUSIONS

Findings from both ITCs (NMA and STC) support the use of tezepelumab in a broad patient population of severe uncontrolled asthma of any phenotype.

A Revised Framework to Evaluate the Consistency Assumption Globally in a Network of Interventions

Background

The unrelated mean effects (UME) model has been proposed for evaluating the consistency assumption globally in the network of interventions. However, the UME model does not accommodate multiarm trials properly and omits comparisons between nonbaseline interventions in the multiarm trials not investigated in 2-arm trials.

Methods

We proposed a refinement of the UME model that tackles the limitations mentioned above. We also accompanied the scatterplots on the posterior mean deviance contributions of the trial arms under the network meta-analysis (NMA) and UME models with Bland-Altman plots to detect outlying trials contributing to poor model fit. We applied the refined and original UME models to 2 networks with multiarm trials.

Results

The original UME model omitted more than 20% of the observed comparisons in both networks. The thorough inspection of the individual data points’ deviance contribution using complementary plots in conjunction with the measures of model fit and the estimated between-trial variance indicated that the refined and original UME models revealed possible inconsistency in both examples.

Conclusions

The refined UME model allows proper accommodation of the multiarm trials and visualization of all observed evidence in complex networks of interventions. Furthermore, considering several complementary plots to investigate deviance helps draw informed conclusions on the possibility of global inconsistency in the network.

Highlights

Next-Generation Meta-analysis for Next-Generation Questions: Introducing the Prevention Science Special Issue on Modern Meta-analytic Methods

This commentary accompanies the special issue of Prevention Science on modern meta-analytic methods. The papers that comprise this special issue are considered in terms of the next-generation meta-analytic questions they support: questions about multivariate relationships, drawing on real-life data structures, with improved usability, and answered openly. The contributions to this special issue illustrate a range of methods to address these questions, including meta-analytic structural equation modelling; robust variance estimation and network meta-analysis methods; transportability and causal inference; Bayesian methods; and open science. This special issue collectively represents a step forward in the field's ability to address questions of use to improving human welfare through preventing ill health, supporting uptake of these next-generation methods by applied researchers in prevention science. Future methodological developments in meta-analysis should be synergistic with the questions prevention scientists seek to answer, both creating new possibilities and meeting the challenges of improving human health and wellbeing.

A non-parametric approach for jointly combining evidence on progression free and overall survival time in network meta-analysis

Randomised controlled trials of cancer treatments typically report progression free survival (PFS) and overall survival (OS) outcomes. Existing methods to synthesise evidence on PFS and OS either rely on the proportional hazards assumption or make parametric assumptions which may not capture the diverse survival curve shapes across studies and treatments. Furthermore, PFS and OS are not independent: OS is the sum of PFS and post-progression survival (PPS). Our aim was to develop a non-parametric approach for jointly synthesising evidence from published Kaplan-Meier survival curves of PFS and OS without assuming proportional hazards. Restricted mean survival times (RMST) are estimated by the area under the survival curves (AUCs) up to a restricted follow-up time. The correlation between AUCs due to the constraint that OS>PFS is estimated using bootstrap re-sampling. Network meta-analysis models are given for RMST for PFS and PPS and ensure that OS=PFS + PPS. Both additive and multiplicative network meta-analysis models are presented to obtain relative treatment effects as either differences or ratios of RMST. The methods are illustrated with a network meta-analysis of treatments for Stage IIIA-N2 Non-Small Cell Lung Cancer. The approach has implications for health economic models of cancer treatments which require estimates of the mean time spent in the PFS and PPS health-states. The methods can be applied to a single time-to-event outcome, and so have wide applicability in any field where time-to-event outcomes are reported, the proportional hazards assumption is in doubt, and survival curve shapes differ across studies and interventions. This article is protected by copyright. All rights reserved.

Methods to assess evidence consistency in dose-response model based network meta-analysis

Network meta-analysis (NMA) simultaneously estimates multiple relative treatment effects based on evidence that forms a network of treatment comparisons. Heterogeneity in treatment definitions, such as dose, can lead to a violation of the consistency assumption that underpins NMA. Model-based NMA (MBNMA) methods have been proposed that allow functional dose-response relationships to be estimated within an NMA, which avoids lumping different doses together and thereby reduces the likelihood of inconsistency. Dose-response MBNMA relies on appropriate specification of the dose-response relationship as well as consistency of relative effects. In this article we describe methods to check for inconsistency in dose-response MBNMA models. Global and local (node-splitting) tests for inconsistency are described that account for studies with ≥3 arms that are typical in dose-finding trials. We show that consistency needs to be assessed with respect to the choice of dose-response function. We illustrate the methods using a network comparing biologics for moderate-to-severe psoriasis. By comparing results from an Emax and an exponential dose-response function we show that failure to correctly characterise the dose-response can introduce apparent inconsistency. The number of comparisons for which node-splitting is possible is also shown to be dependent on the complexity of the selected dose-response function. We highlight that the nature of dose-finding studies, which typically compare multiple doses of the same agent, provide limited scope to assess inconsistency, but these study designs help guard against inconsistency in the first place. We demonstrate the importance of assessing consistency to obtain robust relative effects to inform drug-development and policy decisions.

Effectiveness of Exercise Training on Male Factor Infertility: A Systematic Review and Network Meta-analysis

CONTEXT

Mounting evidence from the literature suggests that different types of training interventions can be successful at improving several aspects of male reproductive function in both fertile and infertile populations.

OBJECTIVE

The aim of this study was to evaluate the effectiveness of exercise training on male factor infertility and seminal markers of inflammation.

DATA SOURCES

We searched PubMed, CISCOM, Springer, Elsevier Science, Cochrane Central Register of Controlled Trials, Scopus, PEDro, Ovid (Medline, EMBASE, PsycINFO), Sport Discus, Orbis, CINAHL, Web of Science, ProQuest, and the ClinicalTrials.gov registry for randomized controlled trials (RCTs) that analyzed the impacts of selected types of exercise interventions on markers of male reproductive function and reproductive performance.

STUDY SELECTION

A total of 336 records were identified, of which we included 7 trials reporting on 2641 fertile and infertile men in the systematic review and network meta-analysis.

LEVEL OF EVIDENCE

Level 1 (because this is a systematic review of RCTs).

DATA EXTRACTION

The data included the study design, participant characteristics, inclusion and exclusion, intervention characteristics, outcome measures, and the main results of the study.

RESULTS

The results of network meta-analysis showed that, compared with a nonintervention control group, the top-ranking interventions for pregnancy rate were for combined aerobic and resistance training (CET) (relative risk [RR] = 27.81), moderate-intensity continuous training (MICT) (RR = 26.67), resistance training (RT) (RR = 12.54), high-intensity continuous training (HICT) (RR = 5.55), and high-intensity interval training (HIIT) (RR = 4.63). While the top-ranking interventions for live birth rate were for MICT (RR = 10.05), RT (RR = 4.92), HIIT (RR = 4.38), CET (RR = 2.20), and HICT (RR = 1.55). Also, with the following order of effectiveness, 5 training strategies were significantly better at improving semen quality parameters (CET > MICT > HICT > RT > HIIT), seminal markers of oxidative stress (CET > MICT > HIIT > HICT > RT), seminal markers of inflammation (CET > MICT > HIIT > RT > HICT), as well as measures of body composition and VO₂max (CET > HICT > MICT > HIIT > RT).

CONCLUSION

The review recommends that the intervention with the highest probability of being the best approach out of all available options for improving the male factor infertility was for CET.

Bayesian model-averaged meta-analysis in medicine

We outline a Bayesian model-averaged (BMA) meta-analysis for standardized mean differences in order to quantify evidence for both treatment effectiveness δ and across-study heterogeneity τ . We construct four competing models by orthogonally combining two present-absent assumptions, one for the treatment effect and one for across-study heterogeneity. To inform the choice of prior distributions for the model parameters, we used 50% of the Cochrane Database of Systematic Reviews to specify rival prior distributions for δ and τ . The relative predictive performance of the competing models and rival prior distributions was assessed using the remaining 50% of the Cochrane Database. On average, ℋ 1 r -the model that assumes the presence of a treatment effect as well as across-study heterogeneity-outpredicted the other models, but not by a large margin. Within ℋ 1 r , predictive adequacy was relatively constant across the rival prior distributions. We propose specific empirical prior distributions, both for the field in general and for each of 46 specific medical subdisciplines. An example from oral health demonstrates how the proposed prior distributions can be used to conduct a BMA meta-analysis in the open-source software R and JASP. The preregistered analysis plan is available at https://osf.io/zs3df/.

Comparative Efficacy of Multiple Therapies for the Treatment of Patients With Subthreshold Depression: A Systematic Review and Network Meta-Analysis

Background: Subthreshold depression (SD) is considered to be the precursor stage of major depression, which is correlated with functional impairment and increased suicide rate. Although there are multiple therapies for the treatment of SD, the comparison and efficacy of various methods has yet to be evaluated. This study aimed to evaluate the efficacy of different therapies by performing a Bayesian network meta-analysis.

Methods: We searched eight databases on April 3, 2021. Center for Epidemiologic Studies Depression Scale (CES-D), Beck Depression Inventory scale (BDI), the Patient Health Questionnaire-9 (PHQ-9), and the Kessler Screening Scale for Psychological Distress (K-6) were used as efficacy outcomes. This Bayesian network meta-analysis used a fixed-effects model.

Findings: Twenty-one randomized controlled trials involving 5,048 participants were included in this study. The results suggested that electroacupuncture (MD −12.00, 95% CrI −15.00, −10.00), conventional acupuncture plus wheat-grain moxibustion (MD −9.70, 95% CrI −14.00, −5.30), and the Chinese traditional peripateticism pill plus group counseling (MD −9.00, 95% CrI −11.00, −6.70) had better efficacy than the control group (CG) in improving CES-D. For BDI outcome, bright light therapy (MD −9.70, 95% CrI −13.00, −6.00), behavioral activation program (MD −5.70, 95% CrI −6.10, −5.40), and dim light therapy (MD −6.30, 95% CrI −10.00, −2.20) were better than the CG. Tai chi (MD −3.00, 95% CrI −4.00, −2.00) was better than CG for PHQ-9 outcomes. Telephone-based cognitive behavioral treatment (MD −2.50 95% CrI −2.70, −2.30) was better than the CG for K-6 scores.

Conclusion: Our results suggest that electroacupuncture or bright light therapy appear to be the better choices in the treatment of SD. This study provide new insights into clinical treatment selection and may aid the development of guidelines for the management of SD.

Predictive P-score for treatment ranking in Bayesian network meta-analysis

Background

Network meta-analysis (NMA) is a widely used tool to compare multiple treatments by synthesizing different sources of evidence. Measures such as the surface under the cumulative ranking curve (SUCRA) and the P-score are increasingly used to quantify treatment ranking. They provide summary scores of treatments among the existing studies in an NMA. Clinicians are frequently interested in applying such evidence from the NMA to decision-making in the future. This prediction process needs to account for the heterogeneity between the existing studies in the NMA and a future study.

Methods

This article introduces the predictive P-score for informing treatment ranking in a future study via Bayesian models. Two NMAs were used to illustrate the proposed measure; the first assessed 4 treatment strategies for smoking cessation, and the second assessed treatments for all-grade treatment-related adverse events. For all treatments in both NMAs, we obtained their conventional frequentist P-scores, Bayesian P-scores, and predictive P-scores.

Results

In the two examples, the Bayesian P-scores were nearly identical to the corresponding frequentist P-scores for most treatments, while noticeable differences existed for some treatments, likely owing to the different assumptions made by the frequentist and Bayesian NMA models. Compared with the P-scores, the predictive P-scores generally had a trend to converge toward a common value of 0.5 due to the heterogeneity. The predictive P-scores’ numerical estimates and the associated plots of posterior distributions provided an intuitive way for clinicians to appraise treatments for new patients in a future study.

Conclusions

The proposed approach adapts the existing frequentist P-score to the Bayesian framework. The predictive P-score can help inform medical decision-making in future studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01397-5.

Some types of exercise are more effective than others in people with chronic low back pain: a network meta-analysis

QUESTION

What are the effects of specific types of exercise treatments on pain intensity and functional limitation outcomes for adults with chronic low back pain?

DESIGN

Systematic review with network meta-analysis of randomised controlled trials.

PARTICIPANTS

Adults with non-specific low back pain for ≥ 12 weeks.

INTERVENTION

Exercise treatments prescribed or planned by a health professional that involved conducting specific activities, postures and/or movements with a goal to improve low back pain outcomes.

OUTCOME MEASURES

Pain intensity (eg, visual analogue scale or numerical rating scale) and back-related functional limitations (eg, Roland Morris Disability Questionnaire or Oswestry Disability Index), each standardised to range from 0 to 100.

RESULTS

This review included 217 randomised controlled trials with 20,969 participants and 507 treatment groups. Most exercise types were more effective than minimal treatment for pain and functional limitation outcomes. Network meta-analysis results were compatible with moderate to clinically important treatment effects for Pilates, McKenzie therapy, and functional restoration (pain only) and flexibility exercises (function only) compared with minimal treatment, other effective treatments and other exercise types. The estimated mean differences for these exercise types compared with minimal treatment ranged from -15 to -19 for pain and from -10 to -12 for functional limitation.

CONCLUSION

This review found evidence that Pilates, McKenzie therapy and functional restoration were more effective than other types of exercise treatment for reducing pain intensity and functional limitations. Nevertheless, people with chronic low back pain should be encouraged to perform the exercise that they enjoy to promote adherence.

REGISTRATION

DOI:10.1002/14651858.CD009790.

Individual Patient Data Meta-Analysis and Network Meta-Analysis

Meta-analyses are often conducted using trial-level summary data. However, when individual patient data (IPD ) is available, there is greater flexibility in the analysis and a wider range of statistical models that can be fitted. There are two approaches to fitting IPD models. The traditional two-stage approach involves analyzing each trial individually in the first stage and then combining trial estimates of treatment effectiveness in the second stage using methods developed for aggregate data meta-analysis. Growing in popularity is the one-stage approach in which trials are analyzed and synthesized within one statistical model whilst the clustering of patients within trials is accounted for. This chapter outlines both fixed effect and random effects one- and two-stage meta-analysis models for continuous, binary, and time-to-event outcomes. The meta-analysis framework is then extended to the scenario where there are more than two treatments and network meta-analysis models are described.The availability of IPD provides greater statistical power for investigating interactions between treatments and covariates. Treatment-covariate interactions contain both within- and across-trial information where the across-trial information may be subject to ecological bias. This chapter presents network meta-analysis models separating out the within- and across-trial information and finishes by considering practical solutions for dealing with missing covariate data, assessing the consistency assumption, combining IPD and aggregate data and specific considerations for time-to-event outcomes.

Comparing and combining evidence of treatment effects in randomized and nonrandomized studies on the use of misoprostol to prevent postpartum hemorrhage

OBJECTIVE

Postpartum hemorrhage (PPH) is a preventable condition and the main cause of maternal death worldwide. Evidence on the effectiveness of misoprostol in the prevention of PPH has been generated from both randomized controlled trials (RCTs) and nonrandomized studies (NRS). This study aimed to compare the results of RCTs and NRS, and to compare Classical and Bayesian approaches of combining the results of RCTs and NRS on the use of misoprostol versus placebo in the prevention of PPH.

METHODS

We searched MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials for appropriate studies. We pooled estimates of effects from RCTs and NRS seperately, using random-effects models, then merged them using classical and Bayesian random effects meta-analysis.

RESULTS

A total of 34 studies (20 RCTs and 14 NRS) involving 74 204 participants were identified. The summary odds ratio (OR) from RCTs for the use of misoprostol in the prevention of PPH was 0.69 (95% confidence interval [CI]: 0.59 to 0.80). The summary OR from NRS was 0.46 (95% CI: 0.36 to 0.63). Classical and Bayesian approaches of combining the two study designs both showed benefit of misoprostol in preventing PPH, with similar effects.

CONCLUSIONS

Both RCTs and NRS show comparable significant benefit for the use of misoprostol in the prevention of PPH.

Beds, overlays and mattresses for preventing and treating pressure ulcers: an overview of Cochrane Reviews and network meta-analysis

BACKGROUND

Pressure ulcers (also known as pressure injuries, pressure sores and bed sores) are localised injuries to the skin or underlying soft tissue, or both, caused by unrelieved pressure, shear or friction. Specific kinds of beds, overlays and mattresses are widely used with the aim of preventing and treating pressure ulcers.

OBJECTIVES

To summarise evidence from Cochrane Reviews that assess the effects of beds, overlays and mattresses on reducing the incidence of pressure ulcers and on increasing pressure ulcer healing in any setting and population. To assess the relative effects of different types of beds, overlays and mattresses for reducing the incidence of pressure ulcers and increasing pressure ulcer healing in any setting and population. To cumulatively rank the different treatment options of beds, overlays and mattresses in order of their effectiveness in pressure ulcer prevention and treatment.

METHODS

In July 2020, we searched the Cochrane Library. Cochrane Reviews reporting the effectiveness of beds, mattresses or overlays for preventing or treating pressure ulcers were eligible for inclusion in this overview. Two review authors independently screened search results and undertook data extraction and risk of bias assessment using the ROBIS tool. We summarised the reported evidence in an overview of reviews. Where possible, we included the randomised controlled trials from each included review in network meta-analyses. We assessed the relative effectiveness of beds, overlays and mattresses for preventing or treating pressure ulcers and their probabilities of being, comparably, the most effective treatment. We assessed the certainty of the evidence using the GRADE approach.

MAIN RESULTS

We include six Cochrane Reviews in this overview of reviews, all at low or unclear risk of bias. Pressure ulcer prevention: four reviews (of 68 studies with 18,174 participants) report direct evidence for 27 pairwise comparisons between 12 types of support surface on the following outcomes: pressure ulcer incidence, time to pressure ulcer incidence, patient comfort response, adverse event rates, health-related quality of life, and cost-effectiveness. Here we focus on outcomes with some evidence at a minimum of low certainty. (1) Pressure ulcer incidence: our overview includes direct evidence for 27 comparisons that mostly (19/27) have very low-certainty evidence concerning reduction of pressure ulcer risk. We included 40 studies (12,517 participants; 1298 participants with new ulcers) in a network meta-analysis involving 13 types of intervention. Data informing the network are sparse and this, together with the high risk of bias in most studies informing the network, means most network contrasts (64/78) yield evidence of very low certainty. There is low-certainty evidence that, compared with foam surfaces (reference treatment), reactive air surfaces (e.g. static air overlays) (risk ratio (RR) 0.46, 95% confidence interval (CI) 0.29 to 0.75), alternating pressure (active) air surfaces (e.g. alternating pressure air mattresses, large-celled ripple mattresses) (RR 0.63, 95% CI 0.42 to 0.93), and reactive gel surfaces (e.g. gel pads used on operating tables) (RR 0.47, 95% CI 0.22 to 1.01) may reduce pressure ulcer incidence. The ranking of treatments in terms of effectiveness is also of very low certainty for all interventions. It is unclear which treatment is best for preventing ulceration. (2) Time to pressure ulcer incidence: four reviews had direct evidence on this outcome for seven comparisons. We included 10 studies (7211 participants; 699 participants with new ulcers) evaluating six interventions in a network meta-analysis. Again, data from most network contrasts (13/15) are of very low certainty. There is low-certainty evidence that, compared with foam surfaces (reference treatment), reactive air surfaces may reduce the hazard of developing new pressure ulcers (hazard ratio (HR) 0.20, 95% CI 0.04 to 1.05). The ranking of all support surfaces for preventing pressure ulcers in terms of time to healing is uncertain. (3) Cost-effectiveness: this overview includes direct evidence for three comparisons. For preventing pressure ulcers, alternating pressure air surfaces are probably more cost-effective than foam surfaces (moderate-certainty evidence). Pressure ulcer treatment: two reviews (of 12 studies with 972 participants) report direct evidence for five comparisons on: complete pressure ulcer healing, time to complete pressure ulcer healing, patient comfort response, adverse event rates, and cost-effectiveness. Here we focus on outcomes with some evidence at a minimum of low certainty. (1) Complete pressure ulcer healing: our overview includes direct evidence for five comparisons. There is uncertainty about the relative effects of beds, overlays and mattresses on ulcer healing. The corresponding network meta-analysis (with four studies, 397 participants) had only three direct contrasts and a total of six network contrasts. Again, most network contrasts (5/6) have very low-certainty evidence. There was low-certainty evidence that more people with pressure ulcers may heal completely using reactive air surfaces than using foam surfaces (RR 1.32, 95% CI 0.96 to 1.80). We are uncertain which surfaces have the highest probability of being the most effective (all very low-certainty evidence). (2) Time to complete pressure ulcer healing: this overview includes direct evidence for one comparison: people using reactive air surfaces may be more likely to have healed pressure ulcers compared with those using foam surfaces in long-term care settings (HR 2.66, 95% CI 1.34 to 5.17; low-certainty evidence). (3) Cost-effectiveness: this overview includes direct evidence for one comparison: compared with foam surfaces, reactive air surfaces may cost an extra 26 US dollars for every ulcer-free day in the first year of use in long-term care settings (low-certainty evidence).

AUTHORS' CONCLUSIONS

Compared with foam surfaces, reactive air surfaces may reduce pressure ulcer risk and may increase complete ulcer healing. Compared with foam surfaces, alternating pressure air surfaces may reduce pressure ulcer risk and are probably more cost-effective in preventing pressure ulcers. Compared with foam surfaces, reactive gel surfaces may reduce pressure ulcer risk, particularly for people in operating rooms and long-term care settings. There are uncertainties for the relative effectiveness of other support surfaces for preventing and treating pressure ulcers, and their efficacy ranking. More high-quality research is required; for example, for the comparison of reactive air surfaces with alternating pressure air surfaces. Future studies should consider time-to-event outcomes and be designed to minimise any risk of bias.

Nicardipine Prolonged Release Implants for Prevention of Delayed Cerebral Ischemia after Aneurysmal Subarachnoid Hemorrhage: A Meta-Analysis

OBJECTIVES

A paucity of treatments to prevent delayed cerebral ischemia (DCI) has stymied recovery after aneurysmal subarachnoid hemorrhage (aSAH). Nicardipine has long been recognized as a potent cerebrovascular vasodilator with a history off-label use to prevent vasospasm and DCI. Multiple centers have developed nicardipine prolonged release implants (NPRI) that are directly applied during clip ligation to locally deliver nicardipine throughout the vasospasm window. Here we perform a systematic review and meta-analysis to assess whether NPRI confers protection against DCI and improves functional outcomes after aSAH.

MATERIALS AND METHODS

A systematic search of PubMed, Ovid Embase, and Cochrane databases was performed for studies reporting the use of NPRI after aSAH published after January 1, 1980. We included all studies assessing the association of NPRI with DCI and or functional outcomes. Findings from studies with control arms were analyzed using a random effects model. A separate network meta-analysis was performed, including controlled NPRI studies, single-arm NPRI reports, and the control-arms of modern aSAH randomized clinical trials as additional comparators.

RESULTS

The search identified 214 unique citations. Three studies with 284 patients met criteria for the random effects model. The pooled summary odds ratio for the association of NPRI and DCI was 0.21 (95% CI 0.09-0.49, p = 0.0002) with no difference in functional outcomes (OR 1.80, 95% CI 0.63 - 5.16, p = 0.28). 10 studies of 866 patients met criteria for the network meta-analysis. The pooled summary odds ratio for the association of NPRI and DCI was 0.30 (95% CI 0.13-0.89,p = 0.017) with a trend towards improved functional outcomes (OR 1.68, 0.63 - 4.13 95% CI, p = 0.101).

CONCLUSIONS

In these meta-analyses, NPRI decreases the incidence of DCI with a non-significant trend towards improvement in functional outcomes. Randomized trials on the role of intrathecal calcium channel blockers are warranted to evaluate these observations in a prospective manner.

Minimally invasive treatments for lower urinary tract symptoms in men with benign prostatic hyperplasia: a network meta-analysis

BACKGROUND

A variety of minimally invasive treatments are available as an alternative to transurethral resection of the prostate (TURP) for management of lower urinary tract symptoms (LUTS) in men with benign prostatic hyperplasia (BPH). However, it is unclear which treatments provide better results.

OBJECTIVES

Our primary objective was to assess the comparative effectiveness of minimally invasive treatments for lower urinary tract symptoms in men with BPH through a network meta-analysis. Our secondary objective was to obtain an estimate of relative ranking of these minimally invasive treatments, according to their effects.

SEARCH METHODS

We performed a comprehensive search of multiple databases (CENTRAL, MEDLINE, Embase, Scopus, Web of Science and LILACS), trials registries, other sources of grey literature, and conference proceedings, up to 24 February 2021. We had no restrictions on language of publication or publication status.

SELECTION CRITERIA

We included parallel-group randomized controlled trials assessing the effects of the following minimally invasive treatments, compared to TURP or sham treatment, on men with moderate to severe LUTS due to BPH: convective radiofrequency water vapor therapy (CRFWVT); prostatic arterial embolization (PAE); prostatic urethral lift (PUL); temporary implantable nitinol device (TIND); and transurethral microwave thermotherapy (TUMT).

DATA COLLECTION AND ANALYSIS

Two review authors independently screened the literature, extracted data, and assessed risk of bias. We performed statistical analyses using a random-effects model for pair-wise comparisons and a frequentist network meta-analysis for combined estimates. We interpreted them according to Cochrane methods. We planned subgroup analyses by age, prostate volume, and severity of baseline symptoms. We used risk ratios (RRs) with 95% confidence intervals (CIs) to express dichotomous data and mean differences (MDs) with 95% CIs to express continuous data. We used the GRADE approach to rate the certainty of evidence.

MAIN RESULTS

We included 27 trials involving 3017 men, mostly over age 50, with severe LUTS due to BPH. The overall certainty of evidence was low to very low due to concerns regarding bias, imprecision, inconsistency (heterogeneity), and incoherence. Based on the network meta-analysis, results for our main outcomes were as follows. Urologic symptoms (19 studies, 1847 participants): PUL and PAE may result in little to no difference in urologic symptoms scores (MD of International Prostate Symptoms Score [IPSS]) compared to TURP (3 to 12 months; MD range 0 to 35; higher scores indicate worse symptoms; PUL: 1.47, 95% CI -4.00 to 6.93; PAE: 1.55, 95% CI -1.23 to 4.33; low-certainty evidence). CRFWVT, TUMT, and TIND may result in worse urologic symptoms scores compared to TURP at short-term follow-up, but the CIs include little to no difference (CRFWVT: 3.6, 95% CI -4.25 to 11.46; TUMT: 3.98, 95% CI 0.85 to 7.10; TIND: 7.5, 95% CI -0.68 to 15.69; low-certainty evidence). Quality of life (QoL) (13 studies, 1459 participants): All interventions may result in little to no difference in the QoL scores, compared to TURP (3 to 12 months; MD of IPSS-QoL score; MD range 0 to 6; higher scores indicate worse symptoms; PUL: 0.06, 95% CI -1.17 to 1.30; PAE: 0.09, 95% CI -0.57 to 0.75; CRFWVT: 0.37, 95% CI -1.45 to 2.20; TUMT: 0.65, 95% CI -0.48 to 1.78; TIND: 0.87, 95% CI -1.04 to 2.79; low-certainty evidence). Major adverse events (15 studies, 1573 participants): TUMT probably results in a large reduction of major adverse events compared to TURP (RR 0.20, 95% CI 0.09 to 0.43; moderate-certainty evidence). PUL, CRFWVT, TIND and PAE may also result in a large reduction in major adverse events, but CIs include substantial benefits and harms at three months to 36 months; PUL: RR 0.30, 95% CI 0.04 to 2.22; CRFWVT: RR 0.37, 95% CI 0.01 to 18.62; TIND: RR 0.52, 95% CI 0.01 to 24.46; PAE: RR 0.65, 95% CI 0.25 to 1.68; low-certainty evidence). Retreatment (10 studies, 799 participants): We are uncertain about the effects of PAE and PUL on retreatment compared to TURP (12 to 60 months; PUL: RR 2.39, 95% CI 0.51 to 11.1; PAE: RR 4.39, 95% CI 1.25 to 15.44; very low-certainty evidence). TUMT may result in higher retreatment rates (RR 9.71, 95% CI 2.35 to 40.13; low-certainty evidence). Erectile function (six studies, 640 participants): We are very uncertain of the effects of minimally invasive treatments on erectile function (MD of International Index of Erectile Function [IIEF-5]; range 5 to 25; higher scores indicates better function; CRFWVT: 6.49, 95% CI -8.13 to 21.12; TIND: 5.19, 95% CI -9.36 to 19.74; PUL: 3.00, 95% CI -5.45 to 11.44; PAE: -0.03, 95% CI -6.38, 6.32; very low-certainty evidence). Ejaculatory dysfunction (eight studies, 461 participants): We are uncertain of the effects of PUL, PAE and TUMT on ejaculatory dysfunction compared to TURP (3 to 12 months; PUL: RR 0.05, 95 % CI 0.00 to 1.06; PAE: RR 0.35, 95% CI 0.13 to 0.92; TUMT: RR 0.34, 95% CI 0.17 to 0.68; low-certainty evidence). TURP is the reference treatment with the highest likelihood of being the most efficacious for urinary symptoms, QoL and retreatment, but the least favorable in terms of major adverse events, erectile function and ejaculatory function. Among minimally invasive procedures, PUL and PAE have the highest likelihood of being the most efficacious for urinary symptoms and QoL, TUMT for major adverse events, PUL for retreatment, CRFWVT and TIND for erectile function and PUL for ejaculatory function.

AUTHORS' CONCLUSIONS

Minimally invasive treatments may result in similar or worse effects concerning urinary symptoms and QoL compared to TURP at short-term follow-up. They may result in fewer major adverse events, especially in the case of PUL and PAE; resulting in better rankings for symptoms scores. PUL may result in fewer retreatments compared to other interventions, especially TUMT, which had the highest retreatment rates at long-term follow-up. We are very uncertain about the effects of these interventions on erectile function. There was limited long-term data, especially for CRFWVT and TIND. Future high-quality studies with more extended follow-up, comparing different, active treatment modalities, and adequately reporting critical outcomes relevant to patients, including those related to sexual function, could provide more information on the relative effectiveness of these interventions.

Bayesian meta-analysis using SAS PROC BGLIMM

Meta-analysis is commonly used to compare two treatments. Network meta-analysis (NMA) is a powerful extension for comparing and contrasting multiple treatments simultaneously in a systematic review of multiple clinical trials. Although the practical utility of meta-analysis is apparent, it is not always straightforward to implement, especially for those interested in a Bayesian approach. This paper demonstrates that the recently-developed SAS procedure BGLIMM provides an intuitive and computationally efficient means for conducting Bayesian meta-analysis in SAS, using a worked example of a smoking cessation NMA data set. BGLIMM gives practitioners an effective and simple way to implement Bayesian meta-analysis (pairwise and network, either contrast-based or arm-based) without requiring significant background in coding or statistical modeling. Those familiar with generalized linear mixed models, and especially the SAS procedure GLIMMIX, will find this tutorial a useful introduction to Bayesian meta-analysis in SAS.

Efficacy and Safety According to the Dose of Valganciclovir for Cytomegalovirus Prophylaxis in Transplantation: Network Meta-analysis Using Recent Data

BACKGROUND

Valganciclovir is used to prevent posttransplant cytomegalovirus (CMV) infection among patients undergoing kidney transplant. However, the optimal dose remains controversial because continuous use decreases kidney function and can induce leukopenia. The purpose of this study was to identify the appropriate dose of valganciclovir for preventing CMV using network meta-analysis.

METHODS

We searched the Cochrane Central Register, Ovid MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health, and Web of Science databases for studies published through April 15, 2017, evaluating 900-mg and 450-mg valganciclovir. We performed direct and indirect network meta-analysis using Bayesian models and generated rankings of different doses of valganciclovir by generating a mixed-treatment comparison.

RESULTS

Twenty-three studies involving 3478 participants were included. Compared with the control group, there was no difference in the incidence of CMV infection between the low-dose (450 mg) (odds ratio [OR], 0.79; 95% confidence interval [CI], 0.50-1.40) and high-dose (900 mg) (OR, 1.0; 95% CI, 0.61-1.60) groups. Low-dose valganciclovir had the best probability (71.1%) for decreasing CMV infection. Leukopenia was significantly more common in the high-dose group than in the control group (OR, 4.3; 95% CI, 2.69-7.10) and in the low-dose group (OR, 2.9; 95% CI, 1.88-4.67), but there was no significant difference in the incidence of leukopenia between the low-dose and control groups (OR, 1.5; 95% CI, 0.99-2.20).

CONCLUSIONS

The incidence of CMV was not different based on the dose of valganciclovir, although the tendency for CMV infection was decreased at 450 mg. However, the low dose of valganciclovir significantly reduced the incidence of leukopenia.

School-based interventions to prevent anxiety, depression and conduct disorder in children and young people: a systematic review and network meta-analysis

Background

Schools in the UK increasingly have to respond to anxiety, depression and conduct disorder as key causes of morbidity in children and young people.

Objective

The objective was to assess the comparative effectiveness of educational setting-based interventions for the prevention of anxiety, depression and conduct disorder in children and young people.

Design

This study comprised a systematic review, a network meta-analysis and an economic evaluation.

Data sources

The databases MEDLINE, EMBASE™ (Elsevier, Amsterdam, the Netherlands), PsycInfo® (American Psychological Association, Washington, DC, USA) and Cochrane Central Register of Controlled Trials (CENTRAL) were searched to 4 April 2018, and the NHS Economic Evaluation Database (NHS EED) was searched on 22 May 2019 for economic evaluations. No language or date filters were applied.

Main outcomes

The main outcomes were post-intervention self-reported anxiety, depression or conduct disorder symptoms.

Review methods

Randomised/quasi-randomised trials of universal or targeted interventions for the prevention of anxiety, depression or conduct disorder in children and young people aged 4–18 years were included. Screening was conducted independently by two reviewers. Data extraction was conducted by one reviewer and checked by a second. Intervention- and component-level network meta-analyses were conducted in OpenBUGS. A review of the economic literature and a cost–consequence analysis were conducted.

Results

A total of 142 studies were included in the review, and 109 contributed to the network meta-analysis. Of the 109 studies, 57 were rated as having an unclear risk of bias for random sequence generation and allocation concealment. Heterogeneity was moderate. In universal secondary school settings, mindfulness/relaxation interventions [standardised mean difference (SMD) –0.65, 95% credible interval (CrI) –1.14 to –0.19] and cognitive–behavioural interventions (SMD –0.15, 95% CrI –0.34 to 0.04) may be effective for anxiety. Cognitive–behavioural interventions incorporating a psychoeducation component may be effective (SMD –0.30, 95% CrI –0.59 to –0.01) at preventing anxiety immediately post intervention. There was evidence that exercise was effective in preventing anxiety in targeted secondary school settings (SMD –0.47, 95% CrI –0.86 to –0.09). There was weak evidence that cognitive–behavioural interventions may prevent anxiety in universal (SMD –0.07, 95% CrI –0.23 to 0.05) and targeted (SMD –0.38, 95% CrI –0.84 to 0.07) primary school settings. There was weak evidence that cognitive–behavioural (SMD –0.04, 95% CrI –0.16 to 0.07) and cognitive–behavioural + interpersonal therapy (SMD –0.18, 95% CrI –0.46 to 0.08) may be effective in preventing depression in universal secondary school settings. Third-wave (SMD –0.35, 95% CrI –0.70 to 0.00) and cognitive–behavioural interventions (SMD –0.11, 95% CrI –0.28 to 0.05) incorporating a psychoeducation component may be effective at preventing depression immediately post intervention. There was no evidence of intervention effectiveness in targeted secondary, targeted primary or universal primary school settings post intervention. The results for university settings were unreliable because of inconsistency in the network meta-analysis. A narrative summary was reported for five conduct disorder prevention studies, all in primary school settings. None reported the primary outcome at the primary post-intervention time point. The economic evidence review reported heterogeneous findings from six studies. Taking the perspective of a single school budget and based on cognitive–behavioural therapy intervention costs in universal secondary school settings, the cost–consequence analysis estimated an intervention cost of £43 per student.

Limitations

The emphasis on disorder-specific prevention excluded broader mental health interventions and restricted the number of eligible conduct disorder prevention studies. Restricting the study to interventions delivered in the educational setting may have limited the number of eligible university-level interventions.

Conclusions

There was weak evidence of the effectiveness of school-based, disorder-specific prevention interventions, although effects were modest and the evidence not robust. Cognitive–behavioural therapy-based interventions may be more effective if they include a psychoeducation component.

Future work

Future trials for prevention of anxiety and depression should evaluate cognitive–behavioural interventions with and without a psychoeducation component, and include mindfulness/relaxation or exercise comparators, with sufficient follow-up. Cost implications must be adequately measured.

Study registration

This study is registered as PROSPERO CRD42016048184.

Funding

This project was funded by the National Institute for Health Research (NIHR) Public Health Research programme and will be published in full in Public Health Research; Vol. 9, No. 8. See the NIHR Journals Library website for further project information.

Inconsistent results for Peto odds ratios in multi-arm studies, network meta-analysis and indirect comparisons

The Peto odds ratio is a well-known effect measure in meta-analysis of binary outcomes. For pairwise comparisons, the Peto odds ratio estimator can be severely biased in the situation of unbalanced sample sizes in the two treatment groups or large treatment effects. In this publication, we evaluate Peto odds ratio estimators in the setting of multi-arm studies and in network meta-analysis using illustrative examples. We observe that Peto odds ratio estimators in a multi-arm study are inconsistent if the observed event probabilities are different or the sample sizes of treatment groups are unbalanced. The same problem emerges in network meta-analysis including only two-arm studies and translates to indirect comparisons of pairwise meta-analyses. We conclude that the Peto odds ratio should not be used as effect measure in network meta-analysis or indirect comparisons of pairwise meta-analyses.

A method for assessing robustness of the results of a star-shaped network meta-analysis under the unidentifiable consistency assumption

Background

In a star-shaped network, pairwise comparisons link treatments with a reference treatment (often placebo or standard care), but not with each other. Thus, comparisons between non-reference treatments rely on indirect evidence, and are based on the unidentifiable consistency assumption, limiting the reliability of the results. We suggest a method of performing a sensitivity analysis through data imputation to assess the robustness of results with an unknown degree of inconsistency.

Methods

The method involves imputation of data for randomized controlled trials comparing non-reference treatments, to produce a complete network. The imputed data simulate a situation that would allow mixed treatment comparison, with a statistically acceptable extent of inconsistency. By comparing the agreement between the results obtained from the original star-shaped network meta-analysis and the results after incorporating the imputed data, the robustness of the results of the original star-shaped network meta-analysis can be quantified and assessed. To illustrate this method, we applied it to two real datasets and some simulated datasets.

Results

Applying the method to the star-shaped network formed by discarding all comparisons between non-reference treatments from a real complete network, 33% of the results from the analysis incorporating imputed data under acceptable inconsistency indicated that the treatment ranking would be different from the ranking obtained from the star-shaped network. Through a simulation study, we demonstrated the sensitivity of the results after data imputation for a star-shaped network with different levels of within- and between-study variability. An extended usability of the method was also demonstrated by another example where some head-to-head comparisons were incorporated.

Conclusions

Our method will serve as a practical technique to assess the reliability of results from a star-shaped network meta-analysis under the unverifiable consistency assumption.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01290-1.

Classifying information-sharing methods

BACKGROUND

Sparse relative effectiveness evidence is a frequent problem in Health Technology Assessment (HTA). Where evidence directly pertaining to the decision problem is sparse, it may be feasible to expand the evidence-base to include studies that relate to the decision problem only indirectly: for instance, when there is no evidence on a comparator, evidence on other treatments of the same molecular class could be used; similarly, a decision on children may borrow-strength from evidence on adults. Usually, in HTA, such indirect evidence is either included by ignoring any differences ('lumping') or not included at all ('splitting'). However, a range of more sophisticated methods exists, primarily in the biostatistics literature. The objective of this study is to identify and classify the breadth of the available information-sharing methods.

METHODS

Forwards and backwards citation-mining techniques were used on a set of seminal papers on the topic of information-sharing. Papers were included if they specified (network) meta-analytic methods for combining information from distinct populations, interventions, outcomes or study-designs.

RESULTS

Overall, 89 papers were included. A plethora of evidence synthesis methods have been used for information-sharing. Most papers (n=79) described methods that shared information on relative treatment effects. Amongst these, there was a strong emphasis on methods for information-sharing across multiple outcomes (n=42) and treatments (n=25), with fewer papers focusing on study-designs (n=23) or populations (n=8). We categorise and discuss the methods under four 'core' relationships of information-sharing: functional, exchangeability-based, prior-based and multivariate relationships, and explain the assumptions made within each of these core approaches.

CONCLUSIONS

This study highlights the range of information-sharing methods available. These methods often impose more moderate assumptions than lumping or splitting. Hence, the degree of information-sharing that they impose could potentially be considered more appropriate. Our identification of four 'core' methods of information-sharing allows for an improved understanding of the assumptions underpinning the different methods. Further research is required to understand how the methods differ in terms of the strength of sharing they impose and the implications of this for health care decisions.