Hop to It! A Systematic Review and Longitudinal Meta-analysis of Hop Performance After ACL Reconstruction

BACKGROUND

Hop testing is widely used by clinicians to monitor rehabilitation and decide when to return to sport following anterior cruciate ligament reconstruction (ACLR); however, the trajectory of long-term hop performance has not been summarised.

OBJECTIVE

To investigate hop performance change over time after ACLR.

DESIGN

Systematic review with longitudinal meta-analysis.

DATA SOURCES

MEDLINE, EMBASE, CINAHL, Scopus, Cochrane CENTRAL and SPORTDiscus to 28 February 2023.

ELIGIBILITY CRITERIA

Studies with ≥ 50 participants following primary ACLR, with mean participant age of 18-40 years, reporting a quantitative measure of hop performance (e.g. single forward hop distance). Results had to be reported for the ACLR limb and compared with (1) the contralateral limb (within person) and/or (2) an uninjured control limb (between person).

RESULTS

We included 136 studies of 23,360 participants. Performance was similar across different hop tests, with steep initial improvements in within-person symmetry, tailing off after 18-24 months. ACLR limb hop performance was 5-10% lower compared with the contralateral limb at 1 year post-surgery, with largest deficits observed for vertical hop [87.0% contralateral limb (95% CI 85.3-88.8) compared with single forward hop 93.8% (95% CI 92.8-94.9)]. By 3-5 years, results were similar between ACLR and contralateral limbs. There were limited data for between-person comparisons (n = 17 studies). Exploratory analyses showed deficits in all forward hopping tests to be very strongly correlated with each other [e.g. single forward and triple hop rho = 0.96 (95% CI 0.90-0.99)], though there was discordance in the relationship between single forward hop and vertical hop performance [rho = 0.27 (95% CI - 0.53 to 0.79)].

CONCLUSIONS

Hop performance is comparable to the uninjured limb by 3-5 years post-ACLR, with the greatest deficits in within-person symmetry present in vertical and side hop tests. Assessment of hopping in multiple planes and comparison with uninjured controls, may provide the most complete evaluation of functional performance.

Tale of quadriceps and hamstring muscle strength after ACL reconstruction: a systematic review with longitudinal and multivariate meta-analysis

OBJECTIVE

This study aimed to investigate how knee extensor and flexor strength change over time after anterior cruciate ligament reconstruction (ACLR).

DESIGN

Systematic review with longitudinal meta-analysis.

DATA SOURCES

Medline, Embase, CINAHL, Scopus, Cochrane CENTRAL and SPORTDiscus to 28 February 2023.

ELIGIBILITY CRITERIA

Studies of primary ACLR (n≥50), with mean participant age 18-40 years, reporting a quantitative measure of knee extensor or flexor strength were eligible. Muscle strength had to be reported for the ACL limb and compared with: (1) the contralateral limb (within-person); and/or (2) an uninjured control limb (between-person).

RESULTS

We included 232 studies of 34 220 participants. Knee extensor and flexor strength showed sharp initial improvement postoperatively before tailing off at approximately 12-18 months post surgery with minimal change thereafter. Knee extensor strength was reduced by more than 10% compared with the contralateral limb and approximately 20% compared with uninjured controls at 1 year for slow concentric, fast concentric and isometric contractions. Knee flexor strength showed smaller deficits but was still 5%-7% lower than the contralateral limb at 1 year for slow concentric, fast concentric and isometric contractions. Between-person comparisons showed larger deficits than within-person comparisons.

CONCLUSION

Knee extensor muscle strength is meaningfully reduced (>10%) at 1 year, with limited improvement after this time up to and beyond 5 years post surgery. Many people likely experience persistent and potentially long-term strength deficits after ACLR. Comparison within person (to the contralateral limb) likely underestimates strength deficits in contrast to uninjured controls.

Time points of outcome are often neglected in acupuncture meta-analyses: a methodological survey

OBJECTIVES

To systematically understand the transparency of outcome measurement time point reporting in meta-analyses of acupuncture.

STUDY DESIGN AND SETTING

We searched for meta-analyses of acupuncture published between 2013 and 2022 in PubMed, Embase, and Cochrane Library. A team of method-trained investigators screened studies for eligibility and collected data using pilot-tested standardized questionnaires. We documented in detail the reporting of outcome measurement time points in acupuncture meta-analyses.

RESULTS

A total of 224 acupuncture meta-analyses were included. Of these, 98 (43.8%) studies did not specify the time points of primary outcome. Among 126 (56.3%) meta-analyses which reported the time points of primary outcome, only 22 (17.5%) meta-analyses specified time points in corresponding protocol. Among 48 (38.1%) meta-analyses that estimated treatment effects of multiple time points, 11 (22.9%) meta-analyses used inappropriate meta-analysis method (subgroup analysis) to pool effect size, and none of the meta-analyses used advanced methods for pooling effect sizes at different time points.

CONCLUSION

Transparency in reporting outcome time points for acupuncture meta-analyses and appropriate methods to pool the effect size of multiple time points were lacking. For future systematic reviews, the transparency of outcome measurement time points should be emphasized in the protocols and final reports. Furthermore, advanced methods should be considered for pooling effect sizes at multiple time points.

Environmental drivers of increased ecosystem respiration in a warming tundra

Arctic and alpine tundra ecosystems are large reservoirs of organic carbon1,2. Climate warming may stimulate ecosystem respiration and release carbon into the atmosphere3,4. The magnitude and persistency of this stimulation and the environmental mechanisms that drive its variation remain uncertain5-7. This hampers the accuracy of global land carbon-climate feedback projections7,8. Here we synthesize 136 datasets from 56 open-top chamber in situ warming experiments located at 28 arctic and alpine tundra sites which have been running for less than 1 year up to 25 years. We show that a mean rise of 1.4 °C [confidence interval (CI) 0.9-2.0 °C] in air and 0.4 °C [CI 0.2-0.7 °C] in soil temperature results in an increase in growing season ecosystem respiration by 30% [CI 22-38%] (n = 136). Our findings indicate that the stimulation of ecosystem respiration was due to increases in both plant-related and microbial respiration (n = 9) and continued for at least 25 years (n = 136). The magnitude of the warming effects on respiration was driven by variation in warming-induced changes in local soil conditions, that is, changes in total nitrogen concentration and pH and by context-dependent spatial variation in these conditions, in particular total nitrogen concentration and the carbon:nitrogen ratio. Tundra sites with stronger nitrogen limitations and sites in which warming had stimulated plant and microbial nutrient turnover seemed particularly sensitive in their respiration response to warming. The results highlight the importance of local soil conditions and warming-induced changes therein for future climatic impacts on respiration.

Changes in patient care through flexible and integrated treatment programs in German psychiatric hospitals: meta-analyses based on a series of controlled claims-based cohort studies

BACKGROUND

Global treatment budgets, i.e. predefined budgets for patients treated in hospital independent of the setting within the hospital, together with flexible and integrated treatment (FIT) have been introduced in some German psychiatric hospitals since 2013. We investigated pooled changes in inpatient, day-care, outpatient treatment, and continuity of care for patients with mental disorders in 12 FIT-hospitals.

METHODS

We conducted a series of 12 controlled cohort studies regarding FIT hospitals using anonymized patient claims data from more than 70 German statutory health insurance funds. Each study compared one FIT-hospital to matched patients from equivalent non-FIT-hospitals (routine care). We included only those patients without treatment in the respective hospital within two years prior to first hospital treatment (either FIT or routine care). We contrasted results between the year prior to with the first and second year after patient's first treatment (treatment continuity: only group comparison) using multivariate multi-level models. To approximate the difference-in-difference effect in the meta-analysis, we used the interaction terms group (FIT hospital vs. routine care) x time (year before vs. first or second patient year after study inclusion) in the Poisson models.

RESULTS

The 12 studies included 36,069 patients with 2,358 patients from a Department of child and adolescent psychiatry. The pooled effect revealed a 5.1 days lower increase in inpatient treatment in FIT-hospitals during the first patient year compared to routine care. Results were statistically significant for adult care FIT-hospitals but not for child and adolescent FIT-hospitals. Utilization of day-care treatment increased more in most FIT-hospitals during the first year, while outpatient contacts increased in some and decreased in others. The odds of treatment continuity increased by 1.4 in FIT-hospitals compared to non-FIT-hospitals.

CONCLUSIONS

Global treatment budgets lead to the intended changes in mental health care in the majority of FIT-hospitals compared to routine care in this large real-world evidence study from Germany. For child and adolescent psychiatry, more evidence is needed to draw firm conclusions.

TRIAL REGISTRATION

This study was registered in the database "Health Services Research Germany" (trial number: VVfD_EVA64_15_003713).

Is cognitive reserve associated with the prevention of cognitive decline after stroke? A Systematic review and meta-analysis

OBJECTIVE

To conduct a systematic review and meta-analyses of the effect of socio-behavioral cognitive reserve (CR) proxies on cognitive decline after stroke.

METHOD

Three journal search and indexing databases (PubMed, Scopus and Web of Sciences) were crossed to examine the scientific evidence systematically. In addition, meta-analytic techniques, using mixed-effect methods, were carried out to estimate the impact (pooled effect size) of CR proxies on either dementia incidence or cognitive decline after stroke.

RESULTS

Twenty-two studies were included in the systematic revision, whereas nineteen of them were eligible for the meta-analysis. The findings showed that high education is associated with a decreased rate of post-stroke dementia. Moreover, other CR proxies (e.g., occupation, bilingualism or social interaction) demonstrate a protective effect against non-dementia cognitive decline after stroke, although some inconsistencies were found in the literature. Regarding the meta-analysis, occupational attainment and education) showed a protective effect against post-stroke cognitive impairment diagnosis in comparison with a mixed category of different CR proxies. Second, a main cognitive change effect was found, pointing to greater cognitive change after stroke in those with low vs. high CR.

CONCLUSIONS

Our findings emphasize that CR may prevent cognitive decline after stroke, but this effect can be modulated by different factors such the CR proxy and individual characteristics such as age or type of lesion. The methodological divergences of the studies (i.e., follow-up intervals, cognitive outcomes) need unification to diminish external sources of variability for predicting rates of cognitive decline after stroke.

Efficacy of Topical Essential Oils in Musculoskeletal Disorders: Systematic Review and Meta-Analysis of Randomized Controlled Trials

Essential oils (EOs) are widely used topically in musculoskeletal disorders (MSDs); however, their clinical efficacy is controversial. Our aim was to find evidence that topical EOs are beneficial as an add-on treatment in MSDs. We performed a systematic review and meta-analysis to summarize the evidence on the available data of randomized controlled trials (RCTs). The protocol of this work was registered on PROSPERO. We used Web of Science, EMBASE, PubMed, Central Cochrane Library and Scopus electronic databases for systematic search. Eight RCTs were included in the quantitative analysis. In conclusion, EO therapy had a favorable effect on pain intensity (primary outcome) compared to placebo. The greatest pain-relieving effect of EO therapy was calculated immediately after the intervention (MD of pain intensity = −0.87; p = 0.014). EO therapy had a slightly better analgesic effect than placebo one week after the intervention (MD of pain intensity = −0.58; p = 0.077) and at the four-week follow-up as well (MD of pain intensity = −0.52; p = 0.049). EO therapy had a beneficial effect on stiffness (a secondary outcome) compared to the no intervention group (MD = −0.77; p = 0.061). This systematic review and meta-analysis showed that topical EOs are beneficial as an add-on treatment in reducing pain and stiffness in the investigated MSDs.

A Matter of Metals: Copper but Not Cadmium Affects the Microbial Alpha-Diversity of Soils and Sediments - a Meta-analysis

Heavy metal (HM) accumulation in soil affects plants and soil fauna, yet the effect on microbial alpha-diversity remains unclear, mainly due to the absence of dedicated research synthesis (e.g. meta-analysis). Here, we report the first meta-analysis of the response of soil microbial alpha-diversity to the experimental addition of cadmium (Cd) and copper (Cu). We considered studies conducted between 2013 and 2022 using DNA metabarcoding of bacterial and fungal communities to overcome limitations of other cultivation- and electrophoresis-based techniques. Fungi were discarded due to the limited study number (i.e. 6 studies). Bacterial studies resulted in 66 independent experiments reported in 32 primary papers from four continents. We found a negative dose-dependent response for Cu but not for Cd for bacterial alpha-diversity in the environments, only for Cu additions exceeding 29.6 mg kg^-1 (first loss of - 0.06% at 30 mg kg^-1). The maximal loss of bacterial alpha-diversity registered was 13.89% at 3837 mg kg^-1. Our results first highlight that bacterial communities behave differently to soil pollution depending on the metal. Secondly, our study suggests that even extreme doses of Cu do not cause a dramatic loss in alpha-diversity, highlighting how the behaviour of bacterial communities diverges from soil macro-organisms.

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.

Challenges of modelling approaches for network meta-analysis of time-to-event outcomes in the presence of non-proportional hazards to aid decision making: Application to a melanoma network

BACKGROUND

Synthesis of clinical effectiveness from multiple trials is a well-established component of decision-making. Time-to-event outcomes are often synthesised using the Cox proportional hazards model assuming a constant hazard ratio over time. However, with an increasing proportion of trials reporting treatment effects where hazard ratios vary over time and with differing lengths of follow-up across trials, alternative synthesis methods are needed.

OBJECTIVES

To compare and contrast five modelling approaches for synthesis of time-to-event outcomes and provide guidance on key considerations for choosing between the modelling approaches.

METHODS

The Cox proportional hazards model and five other methods of estimating treatment effects from time-to-event outcomes, which relax the proportional hazards assumption, were applied to a network of melanoma trials reporting overall survival: restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models.

RESULTS

All models fitted the melanoma network acceptably well. However, there were important differences in extrapolations of the survival curve and interpretability of the modelling constraints demonstrating the potential for different conclusions from different modelling approaches.

CONCLUSION

The restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models can accommodate non-proportional hazards and differing lengths of trial follow-up within a network meta-analysis of time-to-event outcomes. We recommend that model choice is informed using available and relevant prior knowledge, model transparency, graphically comparing survival curves alongside observed data to aid consideration of the reliability of the survival estimates, and consideration of how the treatment effect estimates can be incorporated within a decision model.

Weight and body mass index increase in children and adolescents exposed to antipsychotic drugs in non-interventional settings: a meta-analysis and meta-regression

Antipsychotics increase weight, BMI and waist size, particularly in pediatric patients. Switching antipsychotics is common practice, thus defining the risk for each antipsychotic in real-life settings can be important for clinical guidance. We conducted a meta-analysis on antipsychotic-related changes in body measures in pediatric observational studies. Of 934 publications found on PubMed, we analyzed 38, including nine treatment arms: no treatment, mixed antipsychotic treatment, first-generation antipsychotics, aripiprazole, clozapine, olanzapine, quetiapine, risperidone and ziprasidone. Changes in weight, BMI, BMI-Z and waist size were meta-analyzed according to the duration of clinical observations: 6, 12, > 12 months. Meta-regressions probed influencing factors. Weight in Kg was increased at 6, 12, > 12 months by olanzapine [+ 10.91, + 10.7, data not available (n/a)], mixed antipsychotic treatment (n/a, + 9.42, + 12.59), quetiapine (+ 5.84, n/a, n/a) and risperidone (+ 4.47, + 6.01, + 9.51) and without treatment (n/a, + 2.3, n/a). BMI was increased at 6, 12, > 12 months by olanzapine (+ 3.47, + 3.42, n/a), clozapine (n/a, + 3, n/a) mixed antipsychotic treatment (+ 3.37, + 2.95, + 3.32), risperidone (+ 2, + 2.13, + 2.16), quetiapine (+ 1.5, + 1.82, n/a), aripiprazole (n/a, + 1.7, + 2.1) and without treatment (n/a, + 0.75, n/a). BMI-Z was increased at 6, 12, > 12 months by olanzapine (+ 0.94, + 0.98, + 0.89), clozapine (n/a, + 0.8, n/a), risperidone (+ 0.62, + 0.61, + 0.48), quetiapine (+ 0.57, + 0.54, n/a), mixed antipsychotic treatment (+ 0.51, + 0.94, + 0.44), without treatment (n/a, + 0.37, n/a) and aripiprazole (no gain, + 0.31, n/a). Waist size in cm was increased at 6, 12 months by risperidone (+ 8.8, + 11.5), mixed antipsychotics treatment (+ 9.1, + 10.2) and quetiapine (+ 6.9, + 9.1). Overall, olanzapine and clozapine displayed maximum risk, followed by risperidone, quetiapine and aripiprazole (more risky at longer terms); ziprasidone was associated with no gains. No time-based trends emerged, suggesting a drug-specific risk magnitude. Meta-regressions evidenced variable roles for persistence in therapy and follow-up length, increased risk for drug-naïve patients, and a ceiling effect determined by higher baseline BMI/BMI-Z values.

Multivariate meta-analysis of critical care meta-analyses: a meta-epidemiological study

Background

Meta-analyses typically consider multiple outcomes and report univariate effect sizes considered as independent. Multivariate meta-analysis (MVMA) incorporates outcome correlation and synthesises direct evidence and related outcome estimates within a single analysis. In a series of meta-analyses from the critically ill literature, the current study contrasts multiple univariate effect estimates and their precision with those derived from MVMA.

Methods

A previous meta-epidemiological study was used to identify meta-analyses with either one or two secondary outcomes providing sufficient detail to structure bivariate or tri-variate MVMA, with mortality as primary outcome. Analysis was performed using a random effects model for both odds ratio (OR) and risk ratio (RR); borrowing of strength (BoS) between multivariate outcome estimates was reported. Estimate comparisons, β coefficients, standard errors (SE) and confidence interval (CI) width, univariate versus multivariate, were performed using Lin’s concordance correlation coefficient (CCC).

Results

In bivariate meta-analyses, for OR (n = 49) and RR (n = 48), there was substantial concordance (≥ 0.69) between estimates; but this was less so for tri-variate meta-analyses for both OR (n = 25; ≥ 0.38) and RR (≥ -0.10; n = 22). A variable change in the multivariate precision of primary mortality outcome estimates compared with univariate was present for both bivariate and tri-variate meta-analyses and for metrics. For second outcomes, precision tended to decrease and CI width increase for bivariate meta-analyses, but was variable in the tri-variate. For third outcomes, precision increased and CI width decreased. In bivariate meta-analyses, OR coefficient significance reversal, univariate versus MVMA, occurred once for mortality and 6 cases for second outcomes. RR coefficient significance reversal occurred in 4 cases; 2 were discordant with OR. For tri-variate OR meta-analyses reversal of coefficient estimate significance occurred in two cases for mortality, nine cases for second and 7 cases for third outcomes. In RR meta-analyses significance reversals occurred for mortality in 2 cases, 6 cases for second and 3 cases for third; there were 7 discordances with OR. BoS was greater in trivariate MVMAs compared with bivariate and for OR versus RR.

Conclusions

MVMA would appear to be the preferred solution to multiple univariate analyses; parameter significance changes may occur. Analytic metric appears to be a determinant.

Meta-analysis with Robust Variance Estimation: Expanding the Range of Working Models

In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single meta-regression model, even when the exact form of the dependence is unknown. RVE uses a working model of the dependence structure, but the two currently available working models are limited to each describing a single type of dependence. Drawing on flexible tools from multilevel and multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer potential benefits in terms of better capturing the types of data structures that occur in practice and, under some circumstances, improving the efficiency of meta-regression estimates. We describe how the methods can be implemented using existing software (the "metafor" and "clubSandwich" packages for R), illustrate the proposed approach in a meta-analysis of randomized trials on the effects of brief alcohol interventions for adolescents and young adults, and report findings from a simulation study evaluating the performance of the new methods.

Multivariate Meta-Analysis of the Mortality Effect of Prone Positioning in the Acute Respiratory Distress Syndrome

BACKGROUND

The efficacy of prone positioning (PP) as therapy of the acute respiratory distress syndrome (ARDS) has varied in recent meta-analyses. The efficacy question was reviewed using a cohesive multivariate meta-analysis model incorporating all available common time-point data.

METHODS

Data from a core group of 8 randomized controlled trials (2001-2013) utilized in 8 current meta-analyses (2014-2017) was extracted for common time points. Multivariate meta-analysis and meta-regression models for prone-hours per day, mechanical ventilation tidal-volume and baseline patient PaO₂/FiO₂, considered as continuous and categorical predictors, determined the pooled relative risk (RR) of mortality for prone versus supine positioning.

RESULTS

Mortality RR at 28-30 days, 2-3 months and 6-months was not significant overall (P > 0.05). Meta-regression of categorical predictors indicated significant mortality reduction (P ≤ 0.001) for ≥ 12 prone-hours (versus < 12), lung protective ventilation (versus none) and moderate-severe ARDS (versus all ARDS). Meta-regressions of continuous predictors were also significant (P ≤ 0.021) and yielded treatment inflection points of efficacious therapy for ≥ 12 prone-hours per day, ≤ 8.5 mL/kg tidal volume and ≤ PaO₂/FiO₂ ratio of 130.

CONCLUSIONS

The mortality treatment effect of PP in ARDS, was not demonstrated in the unadjusted meta-analysis model. Moderator effects indicated consistent significant benefit of prone positioning. In the absence of individual patient data, multivariate models provide more decisive conclusions than individual time point analyses.

Impact of surgical margin on survival in extremity soft tissue sarcoma: A systematic review and meta-analysis

BACKGROUND

The impact of surgical margin status on the survival of patients with extremity soft tissue sarcoma (STS) remains to be clearly defined. The evidence regarding the impact of surgical margins on survival is limited by retrospective single-institution cohort studies. We conducted a systematic review and meta-analysis to examine the impact of surgical margin status on patient survival in extremity STS.

METHODS

A literature search in the PubMed, EMBASE, and Cochrane Controlled Trials Register electronic databases, and a manual search of reference lists of original studies was performed. The following text words and/or Medical Subject Heading terms were searched: (neoplasm) or/and (sarcoma) and/or (connective tissue) and/or (soft tissue) and/or (extremity) and/or (extremity) and/or (surgical margin).

RESULTS

Six selected studies that reported a total of 2917 cases of extremity STS were published between 1994 and 2013. All the eligible studies were observational cohort studies, and the sample size ranged from 95 to 1261 patients. A meta-analysis of 6 studies showed that a positive surgical margin predicted poor 5-year OS in a random-effects model (summary hazard ratio, 1.56; 95% confidence interval, 1.12-2.17). Moderate heterogeneity was observed among the studies (P < .075; heterogeneity, 45.6%).

CONCLUSIONS

This meta-analysis supports the hypothesis that adequate surgical margins are associated with improved survival in extremity STS.

Behaviour change techniques associated with smoking cessation in intervention and comparator groups of randomized controlled trials: a systematic review and meta-regression

AIMS

To estimate the strengths of associations between use of behaviour change techniques (BCTs) and clusters of BCTs in behavioural smoking cessation interventions and comparators with smoking cessation rates.

METHOD

Systematic review and meta-regression of biochemically verified smoking cessation rates on BCTs in interventions and comparators in randomized controlled trials, adjusting for a priori-defined potential confounding variables, together with moderation analyses. Studies were drawn from the Cochrane Tobacco Addiction Group Specialised Register. Data were extracted from published and unpublished (i.e. obtained from study authors) study materials by two independent coders. Adequately described intervention (k = 143) and comparator (k = 92) groups were included in the analyses (n = 43 992 participants). Using bivariate mixed-effects meta-regressions, while controlling for key a priori confounders, we regressed smoking cessation on (a) three BCT groupings consistent with dual-process theory (i.e. associative, reflective motivational and self-regulatory), (b) 17 expert-derived BCT groupings (i.e. BCT taxonomy version 1 clusters) and (c) individual BCTs from the BCT taxonomy version 1.

RESULTS

Among person-delivered interventions, higher smoking cessation rates were predicted by BCTs targeting associative and self-regulatory processes (B = 0.034, 0.041, P < 0.05), and by three individual BCTs (prompting commitment, social reward, identity associated with changed behaviour). Among written interventions, BCTs targeting taxonomy cluster 10a (rewards) predicted higher smoking cessation (B = 0.394, P < 0.05). Moderation effects were observed for nicotine dependence, mental health status and mode of delivery.

CONCLUSIONS

Among person-delivered behavioural smoking cessation interventions, specific behaviour change techniques and clusters of techniques are associated with higher success rates.

General destabilizing effects of eutrophication on grassland productivity at multiple spatial scales

Eutrophication is a widespread environmental change that usually reduces the stabilizing effect of plant diversity on productivity in local communities. Whether this effect is scale dependent remains to be elucidated. Here, we determine the relationship between plant diversity and temporal stability of productivity for 243 plant communities from 42 grasslands across the globe and quantify the effect of chronic fertilization on these relationships. Unfertilized local communities with more plant species exhibit greater asynchronous dynamics among species in response to natural environmental fluctuations, resulting in greater local stability (alpha stability). Moreover, neighborhood communities that have greater spatial variation in plant species composition within sites (higher beta diversity) have greater spatial asynchrony of productivity among communities, resulting in greater stability at the larger scale (gamma stability). Importantly, fertilization consistently weakens the contribution of plant diversity to both of these stabilizing mechanisms, thus diminishing the positive effect of biodiversity on stability at differing spatial scales. Our findings suggest that preserving grassland functional stability requires conservation of plant diversity within and among ecological communities.

Eutrophication has been shown to weaken diversity-stability relationships in grasslands, but it is unclear whether the effect depends on scale. Analysing a globally distributed network of grassland sites, the authors show a positive role of beta diversity and spatial asynchrony as drivers of stability but find that nitrogen enrichment weakens the diversity-stability relationships at different spatial scales.

A pseudo-likelihood approach for multivariate meta-analysis of test accuracy studies with multiple thresholds

Multivariate meta-analysis of test accuracy studies when tests are evaluated in terms of sensitivity and specificity at more than one threshold represents an effective way to synthesize results by fully exploiting the data, if compared to univariate meta-analyses performed at each threshold independently. The approximation of logit transformations of sensitivities and specificities at different thresholds through a normal multivariate random-effects model is a recent proposal that straightforwardly extends the bivariate models well recommended for the one threshold case. However, drawbacks of the approach, such as poor estimation of the within-study correlations between sensitivities and between specificities, and severe computational issues can make it unappealing. We propose an alternative method for inference on common diagnostic measures using a pseudo-likelihood constructed under a working independence assumption between sensitivities and between specificities at different thresholds in the same study. The method does not require within-study correlations, overcomes the convergence issues and can be effortlessly implemented. Simulation studies highlight a satisfactory performance of the method, remarkably improving the results from the multivariate normal counterpart under different scenarios. The pseudo-likelihood approach is illustrated in the evaluation of a test used for diagnosis of preeclampsia as a cause of maternal and perinatal morbidity and mortality.

Cognitive and Educational Interventions for Orphans and Vulnerable Children Affected by HIV/AIDS: A Meta-Analysis

Orphans and vulnerable children affected by HIV/AIDS (HIV OVC) are at risk for cognitive difficulties and lack of access to education. Interventions addressing cognitive or educational outcomes for HIV OVC worldwide were examined through systematic searches conducted from October 2016 to 2019. We examined the summative effectiveness of interventions and the specific effects of participant age and intervention length. Interventions targeting cognitive outcomes had a small, significant effect, with no effect of moderating variables. Interventions targeting educational outcomes had significant effects. Educational interventions over 1 year were effective, but effects of intervention length were not significant. Overall, this review provides support for the effectiveness of interventions targeting cognitive and educational outcomes for OVC and highlights the need for more research on such interventions.

Joint synthesis of conditionally related multiple outcomes makes better use of data than separate meta-analyses

BACKGROUND

When there are structural relationships between outcomes reported in different trials, separate analyses of each outcome do not provide a single coherent analysis, which is required for decision-making. For example, trials of intrapartum anti-bacterial prophylaxis (IAP) to prevent early onset group B streptococcal (EOGBS) disease can report three treatment effects: the effect on bacterial colonisation of the newborn, the effect on EOGBS, and the effect on EOGBS conditional on newborn colonisation. These outcomes are conditionally related, or nested, in a multi-state model. This paper shows how to exploit these structural relationships, providing a single coherent synthesis of all the available data, while checking to ensure that different sources of evidence are consistent.

RESULTS

Overall, the use of IAP reduces the risk of EOGBS (RR: 0.03; 95% Credible Interval (CrI): 0.002-0.13). Most of the treatment effect is due to the prevention of colonisation in newborns of colonised mothers (RR: 0.08, 95% CrI: 0.04-0.14). Node-splitting demonstrated that the treatment effect calculated using only direct evidence was consistent with that predicted from the remaining evidence (p = 0.15). The findings accorded with previously published separate meta-analyses of the different outcomes, once these are re-analysed correctly accounting for zero cells.

CONCLUSION

Multiple outcomes should be synthesised together where possible, taking account of their structural relationships. This generates an internally coherent analysis, suitable for decision making, in which estimates of each of the treatment effects are based on all available evidence (direct and indirect). Separate meta-analyses of each outcome have none of these properties.

AHRQ EPC Series on Improving Translation of Evidence: Web-Based Interactive Presentation of Systematic Review Reports

Systematic reviews are used by a diverse range of users to address an ever-expanding set of questions and needs. It is unlikely that a single static report will efficiently satisfy the different needs of diverse users.

METHODS

An open-source Web-based interactive report presentation of a systematic review was developed to allow users to generate their own "reports" from the information produced by the review. Data from a broad-scope systematic review were used with network meta-analysis conducted on nonsurgical treatments of urinary incontinence (UI) in women. Stakeholders informed and piloted the tool and assessed its usefulness.

RESULTS

The final tool allows users to obtain descriptive and analytic results for a network of treatment categories and various outcomes (cure, improvement, satisfaction, quality of life, adverse events) across several subgroups (all women, older women, or those with stress or urgency UI), along with study-level information, and overall conclusions. The stakeholders were satisfied with the functionality of the tool and proposed a number of improvements regarding presentation (for example, present information on numbers of trials in figures), analyses (for example, allow on-the-fly subgroup analyses, explore trade-offs between several outcomes), and information sharing (for example, provide ability to import/export data from/to other software).

CONCLUSION

A prototype tool to present customized analyses from broad-scope systematic reviews is presented. Further improvements are suggested to develop a scalable tool to make systematic reviews useful to increasingly diverse user groups.

Empirical comparison of univariate and multivariate meta-analyses in Cochrane Pregnancy and Childbirth reviews with multiple binary outcomes

BACKGROUND

Multivariate meta-analysis (MVMA) jointly synthesizes effects for multiple correlated outcomes. The MVMA model is potentially more difficult and time-consuming to apply than univariate models, so if its use makes little difference to parameter estimates, it could be argued that it is redundant.

METHODS

We assessed the applicability and impact of MVMA in Cochrane Pregnancy and Childbirth (CPCB) systematic reviews. We applied MVMA to CPCB reviews published between 2011 and 2013 with two or more binary outcomes with at least three studies and compared findings with results of univariate meta-analyses. Univariate random effects meta-analysis models were fitted using restricted maximum likelihood estimation (REML).

RESULTS

Eighty CPCB reviews were published. MVMA could not be applied in 70 of these reviews. MVMA was not feasible in three of the remaining 10 reviews because the appropriate models failed to converge. Estimates from MVMA agreed with those of univariate analyses in most of the other seven reviews. Statistical significance changed in two reviews: In one, this was due to a very small change in P value; in the other, the MVMA result for one outcome suggested that previous univariate results may be vulnerable to small-study effects and that the certainty of clinical conclusions needs consideration.

CONCLUSIONS

MVMA methods can be applied only in a minority of reviews of interventions in pregnancy and childbirth and can be difficult to apply because of missing correlations or lack of convergence. Nevertheless, clinical and/or statistical conclusions from MVMA may occasionally differ from those from univariate analyses.

Modelling time-course relationships with multiple treatments: Model-based network meta-analysis for continuous summary outcomes

BACKGROUND

Model-based meta-analysis (MBMA) is increasingly used to inform drug-development decisions by synthesising results from multiple studies to estimate treatment, dose-response, and time-course characteristics. Network meta-analysis (NMA) is used in Health Technology Appraisals for simultaneously comparing effects of multiple treatments, to inform reimbursement decisions. Recently, a framework for dose-response model-based network meta-analysis (MBNMA) has been proposed that combines, often nonlinear, MBMA modelling with the statistically robust properties of NMA. Here, we aim to extend this framework to time-course models.

METHODS

We propose a Bayesian time-course MBNMA modelling framework for continuous summary outcomes that allows for nonlinear modelling of multiparameter time-course functions, accounts for residual correlation between observations, preserves randomisation by modelling relative effects, and allows for testing of inconsistency between direct and indirect evidence on the time-course parameters. We demonstrate our modelling framework using an illustrative dataset of 23 trials investigating treatments for pain in osteoarthritis.

RESULTS

Of the time-course functions that we explored, the E_max model gave the best fit to the data and has biological plausibility. Some simplifying assumptions were needed to identify the ET₅₀ , due to few observations at early follow-up times. Treatment estimates were robust to the inclusion of correlations in the likelihood.

CONCLUSIONS

Time-course MBNMA provides a statistically robust framework for synthesising evidence on multiple treatments at multiple time points. The use of placebo-controlled studies in drug-development means there is limited potential for inconsistency. The methods can inform drug-development decisions and provide the rigour needed in the reimbursement decision-making process.

Response Time Reduction Due to Retesting in Mental Speed Tests: A Meta-Analysis

As retest effects in cognitive ability tests have been investigated by various primary and meta-analytic studies, most studies from this area focus on score gains as a result of retesting. To the best of our knowledge, no meta-analytic study has been reported that provides sizable estimates of response time (RT) reductions due to retesting. This multilevel meta-analysis focuses on mental speed tasks, for which outcome measures often consist of RTs. The size of RT reduction due to retesting in mental speed tasks for up to four test administrations was analyzed based on 36 studies including 49 samples and 212 outcomes for a total sample size of 21,810. Significant RT reductions were found, which increased with the number of test administrations, without reaching a plateau. Larger RT reductions were observed in more complex mental speed tasks compared to simple ones, whereas age and test-retest interval mostly did not moderate the size of the effect. Although a high heterogeneity of effects exists, retest effects were shown to occur for mental speed tasks regarding RT outcomes and should thus be more thoroughly accounted for in applied and research settings.

Costs of immune responses are related to host body size and lifespan

A central assumption in ecological immunology is that immune responses are costly, with costs manifesting directly (e.g., increases in metabolic rate and increased amino acid usage) or as tradeoffs with other life processes (e.g., reduced growth and reproductive success). Across taxa, host longevity, timing of maturity, and reproductive effort affect the organization of immune systems. It is reasonable, therefore, to expect that these and related factors should also affect immune activation costs. Specifically, species that spread their breeding efforts over a long lifetime should experience lower immune costs than those that mature and breed quickly and die comparatively early. Likewise, body mass should affect immune costs, as body size affects the extent to which hosts are exposed to parasites as well as how hosts can combat infections (via its effects on metabolic rates and other factors). Here, we used phylogenetic meta-regression to reveal that, in general, animals incur costs of immune activation, but small species that are relatively long-lived incur the largest costs. These patterns probably arise because of the relative need for defense when infection risk is comparatively high and fitness can only be realized over a comparatively long period. However, given the diversity of species considered here and the overall modest effects of body mass and life history on immune costs, much more research is necessary before generalizations are appropriate.

Meta-Analysis of Effect Sizes Reported at Multiple Time Points Using General Linear Mixed Model

Meta-analysis of longitudinal studies combines effect sizes measured at pre-determined time points. The most common approach involves performing separate univariate meta-analyses at individual time points. This simplistic approach ignores dependence between longitudinal effect sizes, which might result in less precise parameter estimates. In this paper, we show how to conduct a meta-analysis of longitudinal effect sizes where we contrast different covariance structures for dependence between effect sizes, both within and between studies. We propose new combinations of covariance structures for the dependence between effect size and utilize a practical example involving meta-analysis of 17 trials comparing postoperative treatments for a type of cancer, where survival is measured at 6, 12, 18 and 24 months post randomization. Although the results from this particular data set show the benefit of accounting for within-study serial correlation between effect sizes, simulations are required to confirm these results.

An overview of methods for network meta-analysis using individual participant data: when do benefits arise?

Network meta-analysis (NMA) is a common approach to summarizing relative treatment effects from randomized trials with different treatment comparisons. Most NMAs are based on published aggregate data (AD) and have limited possibilities for investigating the extent of network consistency and between-study heterogeneity. Given that individual participant data (IPD) are considered the gold standard in evidence synthesis, we explored statistical methods for IPD-NMA and investigated their potential advantages and limitations, compared with AD-NMA. We discuss several one-stage random-effects NMA models that account for within-trial imbalances, treatment effect modifiers, missing response data and longitudinal responses. We illustrate all models in a case study of 18 antidepressant trials with a continuous endpoint (the Hamilton Depression Score). All trials suffered from drop-out; missingness of longitudinal responses ranged from 21 to 41% after 6 weeks follow-up. Our results indicate that NMA based on IPD may lead to increased precision of estimated treatment effects. Furthermore, it can help to improve network consistency and explain between-study heterogeneity by adjusting for participant-level effect modifiers and adopting more advanced models for dealing with missing response data. We conclude that implementation of IPD-NMA should be considered when trials are affected by substantial drop-out rate, and when treatment effects are potentially influenced by participant-level covariates.

Identifying effective behavioural components of Intervention and Comparison group support provided in SMOKing cEssation (IC-SMOKE) interventions: a systematic review protocol

BACKGROUND

Systematic reviews of behaviour change interventions for smoking cessation vary in scope, quality, and applicability. The current review aims to generate more accurate and useful findings by (1) a detailed analysis of intervention elements that change behaviour (i.e. behaviour change techniques (BCTs)) and potential moderators of behaviour change (i.e. other intervention and sample characteristics) and (2) assessing and controlling for variability in support provided to comparison groups in smoking cessation trials.

METHODS

A systematic review will be conducted of randomized controlled trials of behaviour change interventions for smoking cessation in adults (with or without pharmacological support), with a minimum follow-up of 6 months, published after 1995. Eligible articles will be identified through the Cochrane Tobacco Addiction Group Specialized Register. Study authors will be asked for detailed descriptions of smoking cessation support provided to intervention and comparison groups. All data will be independently coded by two researchers. The BCT taxonomy v1 (tailored to smoking cessation interventions) and template for intervention description and replication criteria will be used to code intervention characteristics. Data collection will further include sample and trial characteristics and outcome data (smoking cessation rates). Multilevel mixed-effects meta-regression models will be used to examine which BCTs and/or BCT clusters delivered to intervention and comparison groups explain smoking cessation rates in treatment arms (and effect sizes) and what key moderators of behaviour change are. Predicted effect sizes of each intervention will be computed assuming all interventions are compared against comparison groups receiving the same levels of behavioural support (i.e. low, medium, and high levels). Multi-disciplinary advisory board members (policymakers, health care providers, and (ex-)smokers) will provide strategic input throughout the project to ensure the review's applicability to policy and practice.

DISCUSSION

By capturing BCTs in intervention and comparison groups, this systematic review will provide more accurate estimates of the effectiveness of smoking cessation interventions, the most promising BCTs and/or BCT clusters associated with smoking cessation rates in intervention and comparison arms, and important moderators of behaviour change. The results could set new standards for conducting meta-analyses of behaviour change interventions and improve research, service delivery, and training in the area of smoking cessation.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42015025251.

Meta-analysis of time-to-event outcomes from randomized trials using restricted mean survival time: application to individual participant data

Meta-analysis of time-to-event outcomes using the hazard ratio as a treatment effect measure has an underlying assumption that hazards are proportional. The between-arm difference in the restricted mean survival time is a measure that avoids this assumption and allows the treatment effect to vary with time. We describe and evaluate meta-analysis based on the restricted mean survival time for dealing with non-proportional hazards and present a diagnostic method for the overall proportional hazards assumption. The methods are illustrated with the application to two individual participant meta-analyses in cancer. The examples were chosen because they differ in disease severity and the patterns of follow-up, in order to understand the potential impacts on the hazards and the overall effect estimates. We further investigate the estimation methods for restricted mean survival time by a simulation study.

Network meta-analysis of longitudinal data using fractional polynomials

Network meta-analysis of randomized controlled trials (RCTs) are often based on one treatment effect measure per study. However, many studies report data at multiple time points. Furthermore, not all studies measure the outcomes at the same time points. As an alternative to a network meta-analysis based on a synthesis of the results at one time point, a network meta-analysis method is presented that allows for the simultaneous analysis of outcomes at multiple time points. The development of outcomes over time of interventions compared in an RCT is modeled with fractional polynomials, and the differences between the parameters of these polynomials within a trial are synthesized across studies with a Bayesian network meta-analysis. The proposed models are illustrated with an analysis of RCTs evaluating interventions for osteoarthritis of the knee. Fixed and random effects second order fractional polynomials were applied to the case study. Network meta-analysis with models that represent the treatment effects in terms of several parameters using fractional polynomials can be considered a useful addition to models for network meta-analysis of repeated measures previously proposed. When RCTs report treatment effects at multiple follow-up times, these models can be used to synthesize the results even if reporting times differ across the studies.

Multivariate meta-analysis using individual participant data

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models.

A systematic review and meta-analysis of utility-based quality of life for osteoporosis-related conditions

This study aimed to statistically combine multiple health state utility values (HSUVs) reported in the literature for patients with osteoporosis and osteoporotic fractures. Fracture events were associated with decrements in HSUVs which differed between fracture sites. We have provided summary values for use in future health economics analyses in osteoporosis. Osteoporotic fractures have high financial and health burden. Economic evaluations on osteoporotic fracture prevention have been frequently performed in past decades. One of the challenges in the economic evaluations was to identify consistent health state utility values (HSUVs) to use for osteoporotic fracture-related conditions. The objective of this study was to determine summary measures of multiple HSUVs reported in the literature for patients with osteoporosis and osteoporotic fractures. We performed a systematic review, meta-analysis and meta-regression of published literature that reported HSUVs for osteoporotic fracture-related conditions. There were 62 studies representing 142,477 patients included. In total, 362 HSUVs were identified: 106 for pre-fracture; 89 for post-hip fracture; 130 for post-vertebral fracture and 37 for post-wrist fracture. The pooled HSUVs, using a random-effects model were 0.76 (95% CI 0.75, 0.77, I (2) = 0.99) for pre-fracture; 0.57 (95% CI 0.52, 0.63, I (2) =1) for post-hip fracture; 0.59 (95% CI 0.55, 0.62, I (2) = 0.99) for post-vertebral facture and 0.72 (95% CI 0.67, 0.78, I (2) = 1) for post-wrist fracture. Heterogeneities were addressed through meta-regression. HSUVs immediately following hip, vertebral and wrist fractures were 0.31, 0.44 and 0.61, respectively. Patients' HSUVs improved over time following fracture events: HSUVs for the first year after hip, vertebral and wrist fractures were 0.59, 0.55 and 0.78, respectively; and 0.66, 0.66 and 0.81 for subsequent years. Fractures were associated with significant decrements in HSUVs. This study provides a standard set of HSUVs that can be used in health economic assessments in osteoporosis.

Addressing multimorbidity in evidence integration and synthesis

To minimize bias, clinical practice guidelines (CPG) for managing patients with multiple conditions should be informed by well-planned syntheses of the totality of the relevant evidence by means of systematic reviews and meta-analyses. However, deficiencies along the entire evidentiary pathway hinder the development of evidence-based CPGs. Published reports of trials and observational studies often do not provide usable data on treatment effect heterogeneity, perhaps because their design, analysis and presentation is seldom geared towards informing on how multimorbidity modifies the effect of treatments. Systematic reviews and meta-analyses inherit all the limitations of their building blocks and introduce additional of their own, including selection biases at the level of the included studies, ecological biases, and analytical challenges. To generate recommendations to help negotiate some of the challenges in synthesizing the primary literature, so that the results of the evidence synthesis is applicable to the care of those with multiple conditions. Informal group process. We have built upon established general guidance, and provide additional recommendations specific to systematic reviews that could improve the CPGs for multimorbid patients. We suggest that following the additional recommendations is good practice, but acknowledge that not all proposed recommendations are of equal importance, validity and feasibility, and that further work is needed to test and refine the recommendations.

An empirical comparison of univariate and multivariate meta-analyses for categorical outcomes

Treatment effects for multiple outcomes can be meta-analyzed separately or jointly, but no systematic empirical comparison of the two approaches exists. From the Cochrane Library of Systematic Reviews, we identified 45 reviews, including 1473 trials and 258,675 patients, that contained two or three univariate meta-analyses of categorical outcomes for the same interventions that could also be analyzed jointly. Eligible were meta-analyses with at least seven trials reporting all outcomes for which the cross-classification tables were exactly recoverable (e.g., outcomes were mutually exclusive, or one was a subset of the other). This ensured known correlation structures. Outcomes in 40 reviews had an is-subset-of relationship, and those in 5 were mutually exclusive. We analyzed these data with univariate and multivariate models based on discrete and approximate likelihoods. Discrete models were fit in the Bayesian framework using slightly informative priors. The summary effects for each outcome were similar with univariate and multivariate meta-analyses (both using the approximate and discrete likelihoods); however, the multivariate model with the discrete likelihood gave smaller between-study variance estimates, and narrower predictive intervals for new studies. When differences in the summary treatment effects were examined, the multivariate models gave similar summary estimates but considerably longer (shorter) uncertainty intervals because of positive (negative) correlation between outcome treatment effects. It is unclear whether any of the examined reviews would change their overall conclusions based on multivariate versus univariate meta-analyses, because extra-analytical and context-specific considerations contribute to conclusions and, secondarily, because numerical differences were often modest.