The use of single armed observational data to closing the gap in otherwise disconnected evidence networks: a network meta-analysis in multiple myeloma

Postoperative mobilization after terrible triad injury: systematic review and single-arm meta-analysis

BACKGROUND

Terrible triad injury is a complex injury of the elbow, involving elbow dislocation with associated fracture of the radial head, avulsion or tear of the lateral ulnar collateral ligament, and fracture of the coronoid. These injuries are commonly managed surgically with fixation or replacement of the radial head and repair of the collateral ligaments with or without fixation of the coronoid. Postoperative mobilization is a significant factor that may affect patient outcomes; however, the optimal postoperative mobilization protocol is unclear. This study aimed to systematically review the available literature regarding postoperative rehabilitation of terrible triad injuries to aid clinical decision making.

METHODS

We systematically reviewed the PubMed, Embase, Cochrane, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) databases in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The inclusion criteria were studies with populations aged ≥16 years with terrible triad injury in which operative treatment was performed, a clear postoperative mobilization protocol was defined, and the Mayo Elbow Performance Score (MEPS) was reported. Secondary outcomes were pain, instability, and range of motion (ROM). Postoperative mobilization was classified as either "early," defined as active ROM commencement before or up to 14 days, or "late," defined as active ROM commencement after 14 days.

RESULTS

A total of 119 articles were identified from the initial search, of which 11 (301 patients) were included in the final review. The most common protocols (6 studies) favored early mobilization, whereas 5 studies undertook late mobilization. Meta-regression analysis including mobilization as a covariate showed an estimated mean difference in the pooled mean MEPS between early and late mobilization of 6.1 (95% confidence interval, 0.2-12) with a higher pooled mean MEPS for early mobilization (MEPS, 91.2) than for late mobilization (MEPS, 85; P = .041). Rates of instability reported ranged from 4.5% to 19% (8%-11.5% for early mobilization and 4.5%-19% for late mobilization).

CONCLUSION

Our findings suggest that early postoperative mobilization may confer a benefit in terms of functional outcomes following surgical management of terrible triad injuries without appearing to confer an increased instability risk. Further research in the form of randomized controlled trials between early and late mobilization is advised to provide a higher level of evidence.

Evaluation of a prototype machine learning tool to semi-automate data extraction for systematic literature reviews

BACKGROUND

Evidence-based medicine requires synthesis of research through rigorous and time-intensive systematic literature reviews (SLRs), with significant resource expenditure for data extraction from scientific publications. Machine learning may enable the timely completion of SLRs and reduce errors by automating data identification and extraction.

METHODS

We evaluated the use of machine learning to extract data from publications related to SLRs in oncology (SLR 1) and Fabry disease (SLR 2). SLR 1 predominantly contained interventional studies and SLR 2 observational studies. Predefined key terms and data were manually annotated to train and test bidirectional encoder representations from transformers (BERT) and bidirectional long-short-term memory machine learning models. Using human annotation as a reference, we assessed the ability of the models to identify biomedical terms of interest (entities) and their relations. We also pretrained BERT on a corpus of 100,000 open access clinical publications and/or enhanced context-dependent entity classification with a conditional random field (CRF) model. Performance was measured using the F1 score, a metric that combines precision and recall. We defined successful matches as partial overlap of entities of the same type.

RESULTS

For entity recognition, the pretrained BERT+CRF model had the best performance, with an F1 score of 73% in SLR 1 and 70% in SLR 2. Entity types identified with the highest accuracy were metrics for progression-free survival (SLR 1, F1 score 88%) or for patient age (SLR 2, F1 score 82%). Treatment arm dosage was identified less successfully (F1 scores 60% [SLR 1] and 49% [SLR 2]). The best-performing model for relation extraction, pretrained BERT relation classification, exhibited F1 scores higher than 90% in cases with at least 80 relation examples for a pair of related entity types.

CONCLUSIONS

The performance of BERT is enhanced by pretraining with biomedical literature and by combining with a CRF model. With refinement, machine learning may assist with manual data extraction for SLRs.

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study

BACKGROUND

Network meta-analysis (NMA) allows estimating and ranking the effects of several interventions for a clinical condition. Component network meta-analysis (CNMA) is an extension of NMA which considers the individual components of multicomponent interventions. CNMA allows to "reconnect" a disconnected network with common components in subnetworks. An additive CNMA assumes that component effects are additive. This assumption can be relaxed by including interaction terms in the CNMA.

METHODS

We evaluate a forward model selection strategy for component network meta-analysis to relax the additivity assumption that can be used in connected or disconnected networks. In addition, we describe a procedure to create disconnected networks in order to evaluate the properties of the model selection in connected and disconnected networks. We apply the methods to simulated data and a Cochrane review on interventions for postoperative nausea and vomiting in adults after general anaesthesia. Model performance is compared using average mean squared errors and coverage probabilities.

RESULTS

CNMA models provide good performance for connected networks and can be an alternative to standard NMA if additivity holds. For disconnected networks, we recommend to use additive CNMA only if strong clinical arguments for additivity exist.

CONCLUSIONS

CNMA methods are feasible for connected networks but questionable for disconnected networks.

[177Lu]Lu-PSMA-Radioligand Therapy Efficacy Outcomes in Taxane-Naïve Versus Taxane-Treated Patients with Metastatic Castration-Resistant Prostate Cancer: A Systematic Review and Metaanalysis

Radioligand therapy (RLT) with ¹⁷⁷Lu-prostate-specific membrane antigen (PSMA) inhibitors ([¹⁷⁷Lu]Lu-PSMA) is currently approved for patients with metastatic castration-resistant prostate cancer (mCRPC) after progression with at least 1 taxane and 1 androgen-receptor-pathway inhibitor. However, the impact of prior chemotherapy on [¹⁷⁷Lu]Lu-PSMA-RLT outcomes is debatable, with various studies showing inconsistent results. This study was conducted to precisely evaluate the impact of prior taxane chemotherapy on response and survival outcomes in mCRPC patients after [¹⁷⁷Lu]Lu-PSMA-RLT. Methods: This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Searches in PubMed, Scopus, and Embase were made using relevant key words, and articles up to December 2022 were included. The endpoints included prostate-specific antigen (PSA) response rate (RR), progression-free survival, and overall survival (OS). Individual patient data were pooled when feasible. Univariate odds ratios (ORs) and hazard ratios (HRs) were extracted from the individual articles, and pooled estimates and 95% CIs were generated using metaanalysis. Results: Thirteen articles comprising 2,068 patients were included. In 6 articles (553 patients), taxane-naïve patients had significantly better odds of biochemical response after [¹⁷⁷Lu]Lu-PSMA-RLT (pooled OR, 1.82; 95% CI, 1.21-2.71). Individual patient data metaanalysis for PSA RRs in 3 articles revealed a significantly higher PSA RR in the taxane-naïve versus taxane-treated patients (57.1% vs. 39.5%; difference, 17.6%; 95% CI, 5.6%-28.9%). Further, taxane-naïve status was also a predictor of significantly better progression-free survival (5 articles; 1,027 patients; pooled HR, 0.60; 95% CI, 0.51-0.69) and OS (8 articles; 1,594 patients; pooled HR, 0.54; 95% CI, 0.43-0.68) after [¹⁷⁷Lu]Lu-PSMA-RLT. There was no evidence of publication bias. Conclusion: mCRPC patients with no prior taxanes had significantly better outcomes after [¹⁷⁷Lu]Lu-PSMA-RLT than did taxane-treated patients. Further trials evaluating [¹⁷⁷Lu]Lu-PSMA-RLT in the taxane-naïve setting are now required.

Autoresuscitation after circulatory arrest: an updated systematic review

PURPOSE

Current practice in organ donation after death determination by circulatory criteria (DCD) advises a five-minute observation period following circulatory arrest, monitoring for unassisted resumption of spontaneous circulation (i.e., autoresuscitation). In light of newer data, the objective of this updated systematic review was to determine whether a five-minute observation time was still adequate for death determination by circulatory criteria.

SOURCE

We searched four electronic databases from inception to 28 August 2021, for studies evaluating or describing autoresuscitation events after circulatory arrest. Citation screening and data abstraction were conducted independently and in duplicate. We assessed certainty in evidence using the GRADE framework.

PRINCIPAL FINDINGS

Eighteen new studies on autoresuscitation were identified, consisting of 14 case reports and four observational studies. Most studies evaluated adults (n = 15, 83%) and patients with unsuccessful resuscitation following cardiac arrest (n = 11, 61%). Overall, autoresuscitation was reported to occur between one and 20 min after circulatory arrest. Among all eligible studies identified by our reviews (n = 73), seven observational studies were identified. In observational studies of controlled withdrawal of life-sustaining measures with or without DCD (n = 6), 19 autoresuscitation events were reported in 1,049 patients (incidence 1.8%; 95% confidence interval, 1.1 to 2.8). All resumptions occurred within five minutes of circulatory arrest and all patients with autoresuscitation died.

CONCLUSION

A five-minute observation time is sufficient for controlled DCD (moderate certainty). An observation time greater than five minutes may be needed for uncontrolled DCD (low certainty). The findings of this systematic review will be incorporated into a Canadian guideline on death determination.

STUDY REGISTRATION

PROSPERO (CRD42021257827); registered 9 July 2021.

Decision Curve Analysis for Personalized Treatment Choice between Multiple Options

BACKGROUND

Decision curve analysis can be used to determine whether a personalized model for treatment benefit would lead to better clinical decisions. Decision curve analysis methods have been described to estimate treatment benefit using data from a single randomized controlled trial.

OBJECTIVES

Our main objective is to extend the decision curve analysis methodology to the scenario in which several treatment options exist and evidence about their effects comes from a set of trials, synthesized using network meta-analysis (NMA).

METHODS

We describe the steps needed to estimate the net benefit of a prediction model using evidence from studies synthesized in an NMA. We show how to compare personalized versus one-size-fit-all treatment decision-making strategies, such as "treat none" or "treat all patients with a specific treatment" strategies. First, threshold values for each included treatment need to be defined (i.e., the minimum risk difference compared with control that renders a treatment worth taking). The net benefit per strategy can then be plotted for a plausible range of threshold values to reveal the most clinically useful strategy. We applied our methodology to an NMA prediction model for relapsing-remitting multiple sclerosis, which can be used to choose between natalizumab, dimethyl fumarate, glatiramer acetate, and placebo.

RESULTS

We illustrated the extended decision curve analysis methodology using several threshold value combinations for each available treatment. For the examined threshold values, the "treat patients according to the prediction model" strategy performs either better than or close to the one-size-fit-all treatment strategies. However, even small differences may be important in clinical decision making. As the advantage of the personalized model was not consistent across all thresholds, improving the existing model (by including, for example, predictors that will increase discrimination) is needed before advocating its clinical usefulness.

CONCLUSIONS

This novel extension of decision curve analysis can be applied to NMA-based prediction models to evaluate their use to aid treatment decision making.

HIGHLIGHTS

Decision curve analysis is extended into a (network) meta-analysis framework.Personalized models predicting treatment benefit are evaluated when several treatment options are available and evidence about their effects comes from a set of trials.Detailed steps to compare personalized versus one-size-fit-all treatment decision-making strategies are outlined.This extension of decision curve analysis can be applied to (network) meta-analysis-based prediction models to evaluate their use to aid treatment decision making.

Comparative effectiveness of medical treatment vs. metabolic surgery for histologically proven non-alcoholic steatohepatitis and fibrosis: a matched network meta-analysis

Background

Non-alcoholic steatohepatitis (NASH) comprises a major healthcare problem affecting up to 30% of patients with obesity and the associated risk for cardiovascular and liver-related mortality. Several new drugs for NASH-treatment are currently investigated. No study thus far directly compared surgical and non-surgical therapies for NASH. This network meta-analysis compares for the first time the effectiveness of different therapies for NASH using a novel statistical approach.

Methods

The study was conducted according to the PRISMA guidelines for network meta-analysis. PubMed, CENTRAL and Web of Science were searched without restriction of time or language using a validated search strategy. Studies investigating therapies for NASH in adults with liver biopsies at baseline and after at least 12 months were selected. Patients with liver cirrhosis were excluded. Risk of bias was assessed with ROB-2 and ROBINS-I-tools. A novel method for population-adjusted indirect comparison to include and compare single-arm trials was applied. Main outcomes were NASH-resolution and improvement of fibrosis.

Results

Out of 7,913 studies, twelve randomized non-surgical studies and twelve non-randomized surgical trials were included. NASH-resolution after non-surgical intervention was 29% [95% confidence interval (CI): 23-40%] and 79% (95% CI: 72-88%) after surgery. The network meta-analysis showed that surgery had a higher chance of NASH-resolution than medication [odds ratio (OR) =2.68; 95% CI: 1.44-4.97] while drug treatment was superior to placebo (OR =2.24; 95% CI: 1.55-3.24). Surgery (OR =2.18; 95% CI: 1.34-3.56) and medication (OR =1.79; 95% CI: 1.39-2.31) were equally effective to treat fibrosis compared to placebo without difference between them. The results did not change when only new drugs specifically developed for the treatment of NASH were included.

Conclusions

Metabolic surgery has a higher effectiveness for NASH-therapy than medical therapy while both were equally effective regarding improvement of fibrosis. Trials directly comparing surgery with medication must be urgently conducted. Patients with NASH should be informed about surgical treatment options.

Network Meta-analysis on Disconnected Evidence Networks When Only Aggregate Data Are Available: Modified Methods to Include Disconnected Trials and Single-Arm Studies while Minimizing Bias

BACKGROUND

Network meta-analysis (NMA) requires a connected network of randomized controlled trials (RCTs) and cannot include single-arm studies. Regulators or academics often have only aggregate data. Two aggregate data methods for analyzing disconnected networks are random effects on baseline and aggregate-level matching (ALM). ALM has been used only for single-arm studies, and both methods may bias effect estimates.

METHODS

We modified random effects on baseline to separate RCTs connected to and disconnected from the reference and any single-arm studies, minimizing the introduction of bias. We term our modified method reference prediction. We similarly modified ALM and extended it to include RCTs disconnected from the reference. We tested these methods using constructed data and a simulation study.

RESULTS

In simulations, bias for connected treatments for ALM ranged from -0.0158 to 0.051 and for reference prediction from -0.0107 to 0.083. These were low compared with the true mean effect of 0.5. Coverage ranged from 0.92 to 1.00. In disconnected treatments, bias of ALM ranged from -0.16 to 0.392 and of reference prediction from -0.102 to 0.40, whereas coverage of ALM ranged from 0.30 to 0.82 and of reference prediction from 0.64 to 0.94. Under fixed study effects for disconnected evidence, bias was similar, but coverage was 0.81 to 1.00 for reference prediction and 0.18 to 0.76 for ALM. Trends of similar bias but greater coverage for reference prediction with random study effects were repeated in constructed data.

CONCLUSIONS

Both methods with random study effects seem to minimize bias in treatment connected to the reference. They can estimate treatment effects for disconnected treatments but may be biased. Reference prediction has greater coverage and may be recommended overall.

HIGHLIGHTS

Two methods were modified for network meta-analysis on disconnected networks and for including single-arm observational or interventional studies in network meta-analysis using only aggregate data and for minimizing the bias of effect estimates for treatments only in trials connected to the reference.Reference prediction was developed as a modification of random effects on baseline that keeps analyses of trials connected to the reference separately from those disconnected from the reference and from single-arm studies. The method was further modified to account for correlation in trials with more than 2 arms and, under random study effects, to estimate variance in heterogeneity separately in connected and disconnected evidence.Aggregate-level matching was extended to include trials disconnected from the reference, rather than only single-arm studies. The method was further modified to separately estimate treatment effects and heterogeneity variance in the connected and disconnected evidence and to account for the correlation between arms in trials with more than 2 arms.Performance was assessed using a constructed data example and simulation study.The methods were found to have similar, and sometimes low, bias when estimating the relative effects for disconnected treatments, but reference prediction with random study effects had the greatest coverage.The use of reference prediction with random study effects for disconnected networks is recommended if no individual patient data or alternative real-world evidence is available.

Bayesian network meta-analysis methods for combining individual participant data and aggregate data from single arm trials and randomised controlled trials

BACKGROUND

Increasingly in network meta-analysis (NMA), there is a need to incorporate non-randomised evidence to estimate relative treatment effects, and in particular in cases with limited randomised evidence, sometimes resulting in disconnected networks of treatments. When combining different sources of data, complex NMA methods are required to address issues associated with participant selection bias, incorporating single-arm trials (SATs), and synthesising a mixture of individual participant data (IPD) and aggregate data (AD). We develop NMA methods which synthesise data from SATs and randomised controlled trials (RCTs), using a mixture of IPD and AD, for a dichotomous outcome.

METHODS

We propose methods under both contrast-based (CB) and arm-based (AB) parametrisations, and extend the methods to allow for both within- and across-trial adjustments for covariate effects. To illustrate the methods, we use an applied example investigating the effectiveness of biologic disease-modifying anti-rheumatic drugs for rheumatoid arthritis (RA). We applied the methods to a dataset obtained from a literature review consisting of 14 RCTs and an artificial dataset consisting of IPD from two SATs and AD from 12 RCTs, where the artificial dataset was created by removing the control arms from the only two trials assessing tocilizumab in the original dataset.

RESULTS

Without adjustment for covariates, the CB method with independent baseline response parameters (CBunadjInd) underestimated the effectiveness of tocilizumab when applied to the artificial dataset compared to the original dataset, albeit with significant overlap in posterior distributions for treatment effect parameters. The CB method with exchangeable baseline response parameters produced effectiveness estimates in agreement with CBunadjInd, when the predicted baseline response estimates were similar to the observed baseline response. After adjustment for RA duration, there was a reduction in across-trial heterogeneity in baseline response but little change in treatment effect estimates.

CONCLUSIONS

Our findings suggest incorporating SATs in NMA may be useful in some situations where a treatment is disconnected from a network of comparator treatments, due to a lack of comparative evidence, to estimate relative treatment effects. The reliability of effect estimates based on data from SATs may depend on adjustment for covariate effects, although further research is required to understand this in more detail.

Multinomial network meta-analysis using response rates: relapsed/refractory multiple myeloma treatment rankings differ depending on the choice of outcome

Background

Due to the fast growing relapsed/refractory multiple myeloma (RRMM) treatment landscape, a comparison of all the available treatments was warranted. For clinical practice it is important to consider both immediate effects such as response quality and prolonged benefits such as progression-free survival (PFS) in a meta-analysis. The objective of this study was to assess the impact of the choice of outcome on the treatment rankings in RRMM.

Methods

A multinomial logistic network meta-analysis was conducted to estimate the ranking of sixteen treatments based on both complete and objective response rates (CRR and ORR). Seventeen phase III randomized controlled trials from a previously performed systematic literature review were included. Treatment ranking was based on the surface under the cumulative ranking curve (SUCRA). Sensitivity analysis was conducted.

Results

The ranking of treatments differed when comparing PFS hazard ratios rankings with rankings based on CRR. Pomalidomide, bortezomib and dexamethasone ranked highest, while a substantial lower ranking was observed for the triplet elotuzumab, lenalidomide, dexamethasone. The ranking of treatments did not differ when comparing PFS hazard ratios and ORR. The scenario analyses showed that the results were robust. In all scenarios the top three was dominated by the same triplets. The treatment with the highest probability of having the best PFS and ORR was the triplet daratumumab, lenalidomide plus dexamethasone in the base case.

Conclusion

This analysis shows that depending on the chosen outcome treatment rankings in RRMM may differ. When conducting NMAs, the response rate, a clinically recognized outcome, should therefore be more frequently considered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09571-8.

BRIDGING RANDOMIZED CONTROLLED TRIALS AND SINGLE-ARM TRIALS USING COMMENSURATE PRIORS IN ARM-BASED NETWORK META-ANALYSIS

Network meta-analysis (NMA) is a powerful tool to compare multiple treatments directly and indirectly by combining and contrasting multiple independent clinical trials. Because many NMAs collect only a few eligible randomized controlled trials (RCTs), there is an urgent need to synthesize different sources of information, e.g., from both RCTs and single-arm trials. However, single-arm trials and RCTs may have different populations and quality, so that assuming they are exchangeable may be inappropriate. This article presents a novel method using a commensurate prior on variance (CPV) to borrow variance (rather than mean) information from single-arm trials in an arm-based (AB) Bayesian NMA. We illustrate the advantages of this CPV method by reanalyzing an NMA of immune checkpoint inhibitors in cancer patients. Comprehensive simulations investigate the impact on statistical inference of including single-arm trials. The simulation results show that the CPV method provides efficient and robust estimation even when the two sources of information are moderately inconsistent.

A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier

Introduction

Individual patient data (IPD) present particular advantages in network meta-analysis (NMA) because interactions may lead an aggregated data (AD)-based model to wrong a treatment effect (TE) estimation. However, fewer works have been conducted for IPD with time-to-event contrary to binary outcomes. We aimed to develop a general frequentist one-step model for evaluating TE in the presence of interaction in a three-node NMA for time-to-event data.

Methods

One-step, frequentist, IPD-based Cox and Poisson generalized linear mixed models were proposed. We simulated a three-node network with or without a closed loop with (1) no interaction, (2) covariate-treatment interaction, and (3) covariate distribution heterogeneity and covariate-treatment interaction. These models were applied to the NMA (Meta-analyses of Chemotherapy in Head and Neck Cancer [MACH-NC] and Radiotherapy in Carcinomas of Head and Neck [MARCH]), which compared the addition of chemotherapy or modified radiotherapy (mRT) to loco-regional treatment with two direct comparisons. AD-based (contrast and meta-regression) models were used as reference.

Results

In the simulated study, no IPD models failed to converge. IPD-based models performed well in all scenarios and configurations with small bias. There were few variations across different scenarios. In contrast, AD-based models performed well when there were no interactions, but demonstrated some bias when interaction existed and a larger one when the modifier was not distributed evenly. While meta-regression performed better than contrast-based only, it demonstrated a large variability in estimated TE. In the real data example, Cox and Poisson IPD-based models gave similar estimations of the model parameters. Interaction decomposition permitted by IPD explained the ecological bias observed in the meta-regression.

Conclusion

The proposed general one-step frequentist Cox and Poisson models had small bias in the evaluation of a three-node network with interactions. They performed as well or better than AD-based models and should also be undertaken whenever possible.

Evolving role of 225Ac-PSMA radioligand therapy in metastatic castration-resistant prostate cancer-a systematic review and meta-analysis

BACKGROUND

Targeted radionuclide therapy with Actinium-225-labeled prostate-specific membrane antigen ligands (²²⁵Ac-PSMA) has emerged as a promising treatment modality in the management of metastatic castration-resistant prostate cancer (mCRPC). With its high linear energy transfer and short path length, ²²⁵Ac induces double-stranded DNA breaks and is expected to have excellent efficacy and safety profile. This systematic review was conducted to precisely evaluate the role of ²²⁵Ac-PSMA radioligand therapy (RLT) in mCRPC.

METHODS

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Searches were made using relevant keywords in the PubMed, Embase, and Scopus databases, and articles up to December 2020 were included. Data on efficacy and toxicity were extracted from the individual articles. Random-effects model was used for generating pooled estimates through meta-analysis.

RESULTS

Ten articles comprising 256 patients were included. Overall, 62.8% (95% confidence interval, CI: 53.4-71.7%) of the patients treated with ²²⁵Ac-PSMA RLT achieved biochemical response, i.e., ≥50% decline in the serum prostate-specific antigen levels from baseline. Molecular response on Gallium-68 PSMA positron emission tomography/computed tomography was noted in 74% (95% CI: 50.1-92.1%) of the patients. The pooled estimates of median progression-free survival and overall survival were 9.1 months (95% CI: 3.6-14.5 months) and 12.8 months (95% CI: 4.5-21.0 months), respectively. The most commonly reported adverse event was xerostomia, which was observed in 72.7% (95% CI: 50.5-90.1%) of the patients. However, clinically significant toxicity was limited with grade ≥3 xerostomia, anemia, leucopenia, thrombocytopenia, and nephrotoxicity occurring in 1.2%, 12.3%, 8.3%, 6.3%, and 3.8% of the patients, respectively. Treatment discontinuation due to adverse events was noted in 20/208 patients.

CONCLUSIONS

²²⁵Ac-PSMA RLT is an efficacious and safe treatment option for patients with mCRPC. Future randomized controlled trials are required to establish its therapeutic efficacy and survival benefit vis-à-vis other approved treatment modalities.

Incorporating single-arm studies in meta-analysis of randomised controlled trials: a simulation study

Background

Use of real world data (RWD) from non-randomised studies (e.g. single-arm studies) is increasingly being explored to overcome issues associated with data from randomised controlled trials (RCTs). We aimed to compare methods for pairwise meta-analysis of RCTs and single-arm studies using aggregate data, via a simulation study and application to an illustrative example.

Methods

We considered contrast-based methods proposed by Begg & Pilote (1991) and arm-based methods by Zhang et al (2019). We performed a simulation study with scenarios varying (i) the proportion of RCTs and single-arm studies in the synthesis (ii) the magnitude of bias, and (iii) between-study heterogeneity. We also applied methods to data from a published health technology assessment (HTA), including three RCTs and 11 single-arm studies.

Results

Our simulation study showed that the hierarchical power and commensurate prior methods by Zhang et al provided a consistent reduction in uncertainty, whilst maintaining over-coverage and small error in scenarios where there was limited RCT data, bias and differences in between-study heterogeneity between the two sets of data. The contrast-based methods provided a reduction in uncertainty, but performed worse in terms of coverage and error, unless there was no marked difference in heterogeneity between the two sets of data.

Conclusions

The hierarchical power and commensurate prior methods provide the most robust approach to synthesising aggregate data from RCTs and single-arm studies, balancing the need to account for bias and differences in between-study heterogeneity, whilst reducing uncertainty in estimates. This work was restricted to considering a pairwise meta-analysis using aggregate data.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12874-021-01301-1).

Systematic review and network meta-analysis of the efficacy of existing treatments for patients with recurrent glioblastoma

Background

Despite advances in the treatment of cancers over the last years, treatment options for patients with recurrent glioblastoma (rGBM) remain limited with poor outcomes. Many regimens have been investigated in clinical trials; however, there is a lack of knowledge on comparative effectiveness. The aim of this systematic review is to provide an overview of existing treatment strategies and to estimate the relative efficacy of these regimens in terms of progression-free survival (PFS) and overall survival (OS).

Methods

We conducted a systematic review to identify randomized controlled trials (RCTs) investigating any treatment regimen in adult patients suffering from rGBM. Connected studies reporting at least one of our primary outcomes were included in a Bayesian network meta-analysis (NMA) estimating relative treatment effects.

Results

Forty RCTs fulfilled our inclusion criteria evaluating the efficacy of 38 drugs as mono- or combination therapy. Median OS ranged from 2.9 to 18.3 months; median PFS ranged from 0.7 to 6 months. We performed an NMA including 24 treatments that were connected within a large evidence network. Our NMA indicated improvement in PFS with most bevacizumab (BV)-based regimens compared to other regimens. We did not find any differences in OS between treatments.

Conclusion

This systematic review provides a comprehensive overview of existing treatment options for rGBM. The NMA provides relative effects for many of these treatment regimens, which have not been directly compared in RCTs. Overall, outcomes for patients with rGBM remain poor across all treatment options, highlighting the need for innovative treatment options.

Component network meta-analysis compared to a matching method in a disconnected network: A case study

Network meta-analysis is a method to combine evidence from randomized controlled trials (RCTs) that compare a number of different interventions for a given clinical condition. Usually, this requires a connected network. A possible approach to link a disconnected network is to add evidence from nonrandomized comparisons, using propensity score or matching-adjusted indirect comparisons methods. However, nonrandomized comparisons may be associated with an unclear risk of bias. Schmitz et al. used single-arm observational studies for bridging the gap between two disconnected networks of treatments for multiple myeloma. We present a reanalysis of these data using component network meta-analysis (CNMA) models entirely based on RCTs, utilizing the fact that many of the treatments consisted of common treatment components occurring in both networks. We discuss forward and backward strategies for selecting appropriate CNMA models and compare the results to those obtained by Schmitz et al. using their matching method. CNMA models provided a good fit to the data and led to treatment rankings that were similar, though not fully equal to that obtained by Schmitz et al. We conclude that researchers encountering a disconnected network with treatments in different subnets having common components should consider a CNMA model. Such models, exclusively based on evidence from RCTs, are a promising alternative to matching approaches that require additional evidence from observational studies. CNMA models are implemented in the R package netmeta.

Real-world effectiveness and safety of ixazomib-lenalidomide-dexamethasone in relapsed/refractory multiple myeloma

Real-world data on regimens for relapsed/refractory multiple myeloma (RRMM) represent an important component of therapeutic decision-making. This multi-centric, retrospective, observational study conducted by the treating physicians evaluated the effectiveness and safety of ixazomib-lenalidomide-dexamethasone (IRd) in 155 patients who received ixazomib via early access programs in Greece, the UK, and the Czech Republic. Median age was 68 years; 17% had an Eastern Cooperative Oncology Group performance status ≥ 2; median number of prior therapies was 1 (range 1-7); 91%, 47%, and 17% had received prior bortezomib, thalidomide, and lenalidomide, respectively. Median duration of exposure to ixazomib was 9.6 months. Overall response rate was 74%, including 35% very good partial response or better (16% complete response). Median progression-free survival (PFS) was 27.6 months (27.6 and 19.9 months in patients with 1 or > 1 prior lines, respectively). IRd treatment for ≥ 6 months was associated with longer PFS (hazard ratio 0.06). Fourteen patients (9%) discontinued IRd due to adverse events/toxicity in the absence of disease progression. Peripheral neuropathy was reported in 35% of patients (3% grades 3-4). These findings support the results of the phase III TOURMALINE-MM1 trial in a broader real-world RRMM population.

COMPARATIVE EFFICACY OF MEDICAL TREATMENT FOR ACROMEGALY: A SYSTEMATIC REVIEW AND NETWORK META-ANALYSIS OF INTEGRATED RANDOMIZED TRIALS AND OBSERVATIONAL STUDIES

Objective: Comprehensive evidence comparing different medications for acromegaly is scarce. The aim of this study was to perform a network meta-analysis based on evidence from both randomized trials and observational studies of medical treatments for acromegaly. Methods: Electronic databases were searched for both observational studies and randomized trials that enrolled acromegaly patients treated with medications of interest. Simulated trials were generated by a machine learning algorithm and then synthesized with Bayesian random-effects network meta-analyses. The main outcome was the rate of insulin-like growth factor 1 (IGF-1) control after medical treatment. Results: We included 90 studies (100 arms, 4,523 patients) before matching. After matching, 28 simulated trials were generated. Balance of matched arms was checked by spatial distance and correlation matrix. Cotreatment with somatostatin receptor ligands and pegvisomant was the most effective treatment compared with other treatments. In unselected patients, pegvisomant was better than octreotide long-acting release (logOR, 0.85; 95% credible interval [CrI], 0.05 to 1.65) or lanreotide (logOR, 1.09, 95% CrI, 0.05 to 2.14), and the mean absolute IGF-1 control rate ranged from 40 to 60%. In partially responsive patients, cotreatment with somatostatin receptor ligands and pegvisomant was similar to pegvisomant monotherapy, ranking as the most two effective treatments, and the mean absolute IGF-1 control rate was over 60%. Conclusion: Our analysis suggested that the combination of data from observational studies and randomized trials in network meta-analysis was feasible. The findings of this network meta-analysis provided robust evidence supporting the current guidelines in treatment strategy for acromegaly. Abbreviations: CrI = credible interval; DA = dopamine agonist; GH = growth hormone; IGF-1 = insulin-like growth factor 1; ITT = intention-to-treat; LAN = lanreotide; LAN-ATG = lanreotide autogel; OCT = octreotide; OCT-LAR = octreotide long acting repeatable; OR = odds ratio; PEG = pegvisomant; PP = per-protocol; SRL = somatostatin receptor ligand.

Efficacy of first-line treatments for multiple myeloma patients not eligible for stem cell transplantation: a network meta-analysis

Decision making for patients with multiple myeloma (MM) not transplant eligible (NTE) is complicated by a lack of head-to-head comparisons of standards of care, the increase in the choice of treatment modalities, and the promising results that are rapidly evolving from studies with novel regimens. To support evidence-based decision making, we performed a network meta-analysis for NTE MM patients that synthesizes direct and indirect evidence and enables a comparison of all treatments. Relevant randomized clinical trials were identified by a systematic literature review in EMBASE®, MEDLINE®, MEDLINE®-in-Process and the Cochrane Central Register of Controlled Trials for January 1999 to March 2016. Efficacy outcomes [i.e. the hazard ratio (HR) and 95% confidence interval (95%CI) for progression-free survival] were extracted and synthesized in a random effects network-meta analysis. In total, 24 studies were identified including 21 treatments. According to the network-meta analysis, the HR for progression-free survival was favorable for all NTE MM treatments compared to dexamethasone (HR: 0.19-0.90). Daratumumab-bortezomib-melphalan-prednisone and bortezomib-melphalan-prednisone-thalidomide with bortezomib-thalidomide maintenance were identified as the most effective treatments (HR: 0.19, 95%CI: 0.08-0.45 and HR: 0.22, 95%CI: 0.10-0.51, respectively). HR and 95%CI for currently recommended treatments, bortezomib-lenalidomide-dexamethasone, bortezomib-melphalan-prednisone, and lenalidomide-dexamethasone compared to dexamethasone, were 0.31 (0.16-0.59), 0.39 (0.20-0.75), and 0.44 (0.29-0.65), respectively. In addition to identifying the most effective treatment options, we illustrate the additional value and evidence of network meta-analysis in clinical practice. In the current treatment landscape, the results of network meta-analysis may support evidence-based decisions and ultimately help to optimize treatment and outcomes of NTE MM patients.

Incorporating single-arm evidence into a network meta-analysis using aggregate level matching: Assessing the impact

Increasingly, single-armed evidence is included in health technology assessment submissions when companies are seeking reimbursement for new drugs. While it is recognized that randomized controlled trials provide a higher standard of evidence, these are not available for many new agents that have been granted licenses in recent years. Therefore, it is important to examine whether alternative strategies for assessing this evidence may be used. In this work, we examine approaches to incorporating single-armed evidence formally in the evaluation process. We consider matching aggregate level covariates to comparator arms or trials and including this evidence in a network meta-analysis. We consider two methods of matching: (i) we include the chosen matched arm in the data set itself as a comparator for the single-arm trial; (ii) we use the baseline odds of an event in a chosen matched trial to use as a plug-in estimator for the single-arm trial. We illustrate that the synthesis of evidence resulting from such a setup is sensitive to the between-study variability, formulation of the prior for the between-design effect, weight given to the single-arm evidence, and extent of the bias in single-armed evidence. We provide a flowchart for the process involved in such a synthesis and highlight additional sensitivity analyses that should be carried out. This work was motivated by a hepatitis C data set, where many agents have only been examined in single-arm studies. We present the results of our methods applied to this data set.