One-stage individual participant data meta-analysis models: estimation of treatment-covariate interactions must avoid ecological bias by separating out within-trial and across-trial information

Stratified medicine utilizes individual-level covariates that are associated with a differential treatment effect, also known as treatment-covariate interactions. When multiple trials are available, meta-analysis is used to help detect true treatment-covariate interactions by combining their data. Meta-regression of trial-level information is prone to low power and ecological bias, and therefore, individual participant data (IPD) meta-analyses are preferable to examine interactions utilizing individual-level information. However, one-stage IPD models are often wrongly specified, such that interactions are based on amalgamating within- and across-trial information. We compare, through simulations and an applied example, fixed-effect and random-effects models for a one-stage IPD meta-analysis of time-to-event data where the goal is to estimate a treatment-covariate interaction. We show that it is crucial to centre patient-level covariates by their mean value in each trial, in order to separate out within-trial and across-trial information. Otherwise, bias and coverage of interaction estimates may be adversely affected, leading to potentially erroneous conclusions driven by ecological bias. We revisit an IPD meta-analysis of five epilepsy trials and examine age as a treatment effect modifier. The interaction is -0.011 (95% CI: -0.019 to -0.003; p = 0.004), and thus highly significant, when amalgamating within-trial and across-trial information. However, when separating within-trial from across-trial information, the interaction is -0.007 (95% CI: -0.019 to 0.005; p = 0.22), and thus its magnitude and statistical significance are greatly reduced. We recommend that meta-analysts should only use within-trial information to examine individual predictors of treatment effect and that one-stage IPD models should separate within-trial from across-trial information to avoid ecological bias. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd

Lessons learnt when accounting for competing events in the external validation of time-to-event prognostic models

Background: External validation of prognostic models is necessary to assess the accuracy and generalizability of the model to new patients. If models are validated in a setting in which competing events occur, these competing risks should be accounted for when comparing predicted risks to observed outcomes.  Methods: We discuss existing measures of calibration and discrimination that incorporate competing events for time-to-event models. These methods are illustrated using a clinical-data example concerning the prediction of kidney failure in a population with advanced chronic kidney disease (CKD), using the guideline-recommended Kidney Failure Risk Equation (KFRE). The KFRE was developed using Cox regression in a diverse population of CKD patients and has been proposed for use in patients with advanced CKD in whom death is a frequent competing event.  Results: When validating the 5-year KFRE with methods that account for competing events, it becomes apparent that the 5-year KFRE considerably overestimates the real-world risk of kidney failure. The absolute overestimation was 10%age points on average and 29%age points in older high-risk patients. Conclusions: It is crucial that competing events are accounted for during external validation to provide a more reliable assessment the performance of a model in clinical settings in which competing risks occur.</p

A systematic review and evaluation of the use of tumour markers in paediatric oncology: Ewing's sarcoma and neuroblastoma

A systematic review and evaluation of the use of tumour markers in paediatric oncology: Ewing's sarcoma and neuroblastom

Subgrouping and TargetEd Exercise pRogrammes for knee and hip OsteoArthritis (STEER OA): a systematic review update and individual participant data meta-analysis protocol.

INTRODUCTION: Knee and hip osteoarthritis (OA) is a leading cause of disability worldwide. Therapeutic exercise is a recommended core treatment for people with knee and hip OA, however, the observed effect sizes for reducing pain and improving physical function are small to moderate. This may be due to insufficient targeting of exercise to subgroups of people who are most likely to respond and/or suboptimal content of exercise programmes. This study aims to identify: (1) subgroups of people with knee and hip OA that do/do not respond to therapeutic exercise and to different types of exercise and (2) mediators of the effect of therapeutic exercise for reducing pain and improving physical function. This will enable optimal targeting and refining the content of future exercise interventions. METHODS AND ANALYSIS: Systematic review and individual participant data meta-analyses. A previous comprehensive systematic review will be updated to identify randomised controlled trials that compare the effects of therapeutic exercise for people with knee and hip OA on pain and physical function to a non-exercise control. Lead authors of eligible trials will be invited to share individual participant data. Trial-level and participant-level characteristics (for baseline variables and outcomes) of included studies will be summarised. Meta-analyses will use a two-stage approach, where effect estimates are obtained for each trial and then synthesised using a random effects model (to account for heterogeneity). All analyses will be on an intention-to-treat principle and all summary meta-analysis estimates will be reported as standardised mean differences with 95% CI. ETHICS AND DISSEMINATION: Research ethical or governance approval is exempt as no new data are being collected and no identifiable participant information will be shared. Findings will be disseminated via national and international conferences, publication in peer-reviewed journals and summaries posted on websites accessed by the public and clinicians. PROSPERO REGISTRATION NUMBER: CRD42017054049.</p