A framework for identifying treatment-covariate interactions in individual participant data network meta-analysis

Comparative efficacy and complications of long-acting and intermediate-acting insulin regimens for adults with type 1 diabetes: an individual patient data network meta-analysis

OBJECTIVE

To examine the comparative efficacy and complications of long-acting and intermediate-acting insulin for different patient characteristics for type 1 diabetes mellitus (T1DM).

DESIGN

Systematic review and individual patient data (IPD) network meta-analysis (NMA).

DATA SOURCES

MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials were searched through June 2015.

ELIGIBILITY CRITERIA

Randomised controlled trials (RCTs) on adults with T1DM assessing glycosylated haemoglobin (A1c) and severe hypoglycaemia in long-acting and intermediate-acting insulin regimens.

DATA EXTRACTION AND SYNTHESIS

We requested IPD from authors and funders. When IPD were not available, we used aggregate data. We conducted a random-effects model, and specifically a one-stage IPD-NMA for those studies providing IPD and a two-stage IPD-NMA to incorporate those studies not providing IPD.

RESULTS

We included 28 RCTs plus one companion report, after screening 6680 titles/abstracts and 205 full-text articles. Of the 28 RCTs, 27 studies provided data for the NMA with 7394 participants, of which 12 RCTs had IPD on 4943 participants. The IPD-NMA for A1c suggested that glargine once daily (mean difference [MD]=-0.31, 95% confidence interval [CI]: -0.48 to -0.14) and detemir once daily (MD=-0.25, 95% CI: -0.41 to -0.09) were superior to neutral protamine Hagedorn (NPH) once daily. NPH once/two times per day improved A1c compared with NPH once daily (MD=-0.30, 95% CI: -0.50 to -0.11). Results regarding complications in severe hypoglycaemia should be considered with great caution due to inconsistency in the evidence network. Accounting for missing data, there was no evidence of inconsistency and long-acting insulin regimens ranked higher regarding reducing severe hypoglycaemia compared with intermediate-acting insulin regimens (two-stage NMA: glargine two times per day SUCRA (Surface Under the Cumulative Ranking curve)=89%, detemir once daily SUCRA=77%; one-stage NMA: detemir once daily/two times per day SUCRA=85%). Using multiple imputations and IPD only, complications in severe hypoglycaemia increased with diabetes-related comorbidities (regression coefficient: 1.03, 95% CI: 1.02 to 1.03).

CONCLUSIONS

Long-acting insulin regimens reduced A1c compared with intermediate-acting insulin regimens and were associated with lower severe hypoglycaemia. Of the observed differences, only glargine once daily achieved a clinically significant reduction of 0.30%. Results should be interpreted with caution due to very low quality of evidence.

PROSPERO REGISTRATION NUMBER

CRD42015023511.

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.

Individual Patient Data Meta-Analysis and Network Meta-Analysis

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

Penalized Poisson model for network meta-analysis of individual patient time-to-event data

Network meta-analysis (NMA) allows the combination of direct and indirect evidence from a set of randomized clinical trials. Performing NMA using individual patient data (IPD) is considered as a "gold standard" approach as it provides several advantages over NMA based on aggregate data. For example, it allows to perform advanced modeling of covariates or covariate-treatment interactions. An important issue in IPD NMA is the selection of influential parameters among terms that account for inconsistency, covariates, covariate-by-treatment interactions or nonproportionality of treatments effect for time to event data. This issue has not been deeply studied in the literature yet and in particular not for time-to-event data. A major difficulty is to jointly account for between-trial heterogeneity which could have a major influence on the selection process. The use of penalized generalized mixed effect model is a solution, but existing implementations have several shortcomings and an important computational cost that precludes their use for complex IPD NMA. In this article, we propose a penalized Poisson regression model to perform IPD NMA of time-to-event data. It is based only on fixed effect parameters which improve its computational cost over the use of random effects. It could be easily implemented using existing penalized regression package. Computer code is shared for implementation. The methods were applied on simulated data to illustrate the importance to take into account between trial heterogeneity during the selection procedure. Finally, it was applied to an IPD NMA of overall survival of chemotherapy and radiotherapy in nasopharyngeal carcinoma.

Progress and challenges of network meta-analysis

In the past years, network meta-analysis (NMA) has been widely used among clinicians, guideline makers, and health technology assessment agencies and has played an important role in clinical decision-making and guideline development. To inform further development of NMAs, we conducted a bibliometric analysis to assess the current status of published NMA methodological studies, summarized the methodological progress of seven types of NMAs, and discussed the current challenges of NMAs.

Statistical analyses and quality of individual participant data network meta-analyses were suboptimal: a cross-sectional study

BACKGROUND

Network meta-analyses using individual participant data (IPD-NMAs) have been increasingly used to compare the effects of multiple interventions. Although there have been many studies on statistical methods for IPD-NMAs, it is unclear whether there are statistical defects in published IPD-NMAs and whether the reporting of statistical analyses has improved. This study aimed to investigate statistical methods used and assess the reporting and methodological quality of IPD-NMAs.

METHODS

We searched four bibliographic databases to identify published IPD-NMAs. The methodological quality was assessed using AMSTAR-2 and reporting quality assessed based on PRISMA-IPD and PRISMA-NMA. We performed stratified analyses and correlation analyses to explore the factors that might affect quality.

RESULTS

We identified 21 IPD-NMAs. Only 23.8% of the included IPD-NMAs reported statistical techniques used for missing participant data, 42.9% assessed the consistency, and none assessed the transitivity. None of the included IPD-NMAs reported sources of funding for trials included, only 9.5% stated pre-registration of protocols, and 28.6% assessed the risk of bias in individual studies. For reporting quality, compliance rates were lower than 50.0% for more than half of the items. Less than 15.0% of the IPD-NMAs reported data integrity, presented the network geometry, or clarified risk of bias across studies. IPD-NMAs with statistical or epidemiological authors often better assessed the inconsistency (P = 0.017). IPD-NMAs with a priori protocol were associated with higher reporting quality in terms of search (P = 0.046), data collection process (P = 0.031), and syntheses of results (P = 0.006).

CONCLUSIONS

The reporting of statistical methods and compliance rates of methodological and reporting items of IPD-NMAs were suboptimal. Authors of future IPD-NMAs should address the identified flaws and strictly adhere to methodological and reporting guidelines.

Individual participant data meta-analysis of intervention studies with time-to-event outcomes: A review of the methodology and an applied example

Many randomized trials evaluate an intervention effect on time-to-event outcomes. Individual participant data (IPD) from such trials can be obtained and combined in a so-called IPD meta-analysis (IPD-MA), to summarize the overall intervention effect. We performed a narrative literature review to provide an overview of methods for conducting an IPD-MA of randomized intervention studies with a time-to-event outcome. We focused on identifying good methodological practice for modeling frailty of trial participants across trials, modeling heterogeneity of intervention effects, choosing appropriate association measures, dealing with (trial differences in) censoring and follow-up times, and addressing time-varying intervention effects and effect modification (interactions).We discuss how to achieve this using parametric and semi-parametric methods, and describe how to implement these in a one-stage or two-stage IPD-MA framework. We recommend exploring heterogeneity of the effect(s) through interaction and non-linear effects. Random effects should be applied to account for residual heterogeneity of the intervention effect. We provide further recommendations, many of which specific to IPD-MA of time-to-event data from randomized trials examining an intervention effect.We illustrate several key methods in a real IPD-MA, where IPD of 1225 participants from 5 randomized clinical trials were combined to compare the effects of Carbamazepine and Valproate on the incidence of epileptic seizures.

Prostate Radiotherapy for Metastatic Hormone-sensitive Prostate Cancer: A STOPCAP Systematic Review and Meta-analysis

Background

Many trials are evaluating therapies for men with metastatic hormone-sensitive prostate cancer (mHSPC).

Objective

To systematically review trials of prostate radiotherapy.

Design, setting, and participants

Using a prospective framework (framework for adaptive meta-analysis [FAME]), we prespecified methods before any trial results were known. We searched extensively for eligible trials and asked investigators when results would be available. We could then anticipate that a definitive meta-analysis of the effects of prostate radiotherapy was possible. We obtained prepublication, unpublished, and harmonised results from investigators.

Intervention

We included trials that randomised men to prostate radiotherapy and androgen deprivation therapy (ADT) or ADT only.

Outcome measurements and statistical analysis

Hazard ratios (HRs) for the effects of prostate radiotherapy on survival, progression-free survival (PFS), failure-free survival (FFS), biochemical progression, and subgroup interactions were combined using fixed-effect meta-analysis.

Results and limitations

We identified one ongoing (PEACE-1) and two completed (HORRAD and STAMPEDE) eligible trials. Pooled results of the latter (2126 men; 90% of those eligible) showed no overall improvement in survival (HR = 0.92, 95% confidence interval [CI] 0.81–1.04, p = 0.195) or PFS (HR = 0.94, 95% CI 0.84–1.05, p = 0.238) with prostate radiotherapy. There was an overall improvement in biochemical progression (HR = 0.74, 95% CI 0.67–0.82, p = 0.94 × 10⁻⁸) and FFS (HR = 0.76, 95% CI 0.69–0.84, p = 0.64 × 10⁻⁷), equivalent to ∼10% benefit at 3 yr. The effect of prostate radiotherapy varied by metastatic burden—a pattern consistent across trials and outcome measures, including survival (<5, ≥5; interaction HR = 1.47, 95% CI 1.11–1.94, p = 0.007). There was 7% improvement in 3-yr survival in men with fewer than five bone metastases.

Conclusions

Prostate radiotherapy should be considered for men with mHSPC with a low metastatic burden.

Patient summary

Prostate cancer that has spread to other parts of the body (metastases) is usually treated with hormone therapy. In men with fewer than five bone metastases, addition of prostate radiotherapy helped them live longer and should be considered.

A framework for identifying treatment-covariate interactions in individual participant data network meta-analysis

BACKGROUND

Stratified medicine seeks to identify patients most likely to respond to treatment. Individual participant data (IPD) network meta-analysis (NMA) models have greater power than individual trials to identify treatment-covariate interactions (TCIs). Treatment-covariate interactions contain "within" and "across" trial interactions, where the across-trial interaction is more susceptible to confounding and ecological bias.

METHODS

We considered a network of IPD from 37 trials (5922 patients) for cervical cancer (2394 events), where previous research identified disease stage as a potential interaction covariate. We compare 2 models for NMA with TCIs: (1) 2 effects separating within- and across-trial interactions and (2) a single effect combining within- and across-trial interactions. We argue for a visual assessment of consistency of within- and across-trial interactions and consider more detailed aspects of interaction modelling, eg, common vs trial-specific effects of the covariate. This leads us to propose a practical framework for IPD NMA with TCIs.

RESULTS

Following our framework, we found no evidence in the cervical cancer network for a treatment-stage interaction on the basis of the within-trial interaction. The NMA provided additional power for an across-trial interaction over and above the pairwise evidence. Following our proposed framework, we found that the within- and across-trial interactions should not be combined.

CONCLUSION

Across-trial interactions are susceptible to confounding and ecological bias. It is important to separate the sources of evidence to check their consistency and identify which sources of evidence are driving the conclusion. Our framework provides practical guidance for researchers, reducing the risk of unduly optimistic interpretation of TCIs.