Prognosis research strategy (PROGRESS) 4: Stratified medicine research

Hingorani, Aroon D.; Windt, D.A.W.M. van der; Riley, Richard D; Abrams, Keith R.; Moons, Karel G.M.; Steyerberg, Ewout W.; Schroter, Sara; Sauerbrei, Willi; Altman, Douglas G.; Hemingway, Harry

doi:10.1136/bmj.e5793

Cited by 386 publications

(358 citation statements)

References 53 publications

(36 reference statements)

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“…This is especially important when the power of any across‐trial information is comparable with that for within‐trial information, which occurs when the variation across‐trials in the mean covariate values is similar to, or bigger than, the variation in individual covariate values 7. The consequences of amalgamating within‐trial and across‐trial interactions may be substantial with false predictors of treatment effect being wrongly identified as important, or conversely genuine predictors of treatment effect being missed or discarded prematurely 1. Our epilepsy example demonstrates how the magnitude of treatment effect modification for age and its statistical significance depends heavily on whether within‐trial associations are amalgamated or separated from across‐trial associations; in particular, separating within‐trial and across‐trial information leads to less dramatic clinical and statistical conclusions.…”

Section: Discussionmentioning

confidence: 99%

“…There is an increasing interest in personalized or stratified medicine, where the aim is to tailor treatments to individuals or to groups of similar individuals based on their particular characteristics 1. This allows clinicians to optimize treatment decisions and reduce unnecessary costs, in order to select treatments for individual patients that are most likely to benefit (or least likely to harm) them.…”

Section: Introductionmentioning

confidence: 99%

“…A key component of stratified medicine research is to identify individual‐level characteristics (covariates) that are associated with a differential treatment effect 1. These are referred to as treatment‐covariate interactions in this article, but other names include effect‐modifiers, moderators, subgroup effects and predictive markers.…”

Section: Introductionmentioning

confidence: 99%

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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

et al. 2016

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

et al. 2016

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“…1,2 It is increasingly recognized that clinical translation of a pharmacogenomic marker is strongly dependent on its clinical utility -that is, whether routine testing improves patients' outcomes. 3,4 Towards this end, it is important to understand whether treatment response differs between pharmacogenomic subgroups.…”

mentioning

confidence: 99%

“…However, it is often not well appreciated that interpreting the clinical utility (and consequently the cost-effectiveness) of a pharmacogenomic marker is not straightforward based on such evidence. 1 This paper therefore aims to provide insight into this issue using a range of illustrative examples.…”

mentioning

confidence: 99%

Interpreting the clinical utility of a pharmacogenomic marker based on observational association studies

Sorich

Coory

2013

Pharmacogenomics J

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Preferred Reporting Items for a Systematic Review and Meta-analysis of Individual Participant Data

Stewart

Clarke

Rovers

et al. 2015

JAMA

1,560

1,197

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IMPORTANCE Systematic reviews and meta-analyses of individual participant data (IPD) aim to collect, check, and reanalyze individual-level data from all studies addressing a particular research question and are therefore considered a gold standard approach to evidence synthesis. They are likely to be used with increasing frequency as current initiatives to share clinical trial data gain momentum and may be particularly important in reviewing controversial therapeutic areas. OBJECTIVE To develop PRISMA-IPD as a stand-alone extension to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement, tailored to the specific requirements of reporting systematic reviews and meta-analyses of IPD. Although developed primarily for reviews of randomized trials, many items will apply in other contexts, including reviews of diagnosis and prognosis. DESIGN Development of PRISMA-IPD followed the EQUATOR Network framework guidance and used the existing standard PRISMA Statement as a starting point to draft additional relevant material. A web-based survey informed discussion at an international workshop that included researchers, clinicians, methodologists experienced in conducting systematic reviews and meta-analyses of IPD, and journal editors. The statement was drafted and iterative refinements were made by the project, advisory, and development groups. The PRISMA-IPD Development Group reached agreement on the PRISMA-IPD checklist and flow diagram by consensus. FINDINGS Compared with standard PRISMA, the PRISMA-IPD checklist includes 3 new items that address (1) methods of checking the integrity of the IPD (such as pattern of randomization, data consistency, baseline imbalance, and missing data), (2) reporting any important issues that emerge, and (3) exploring variation (such as whether certain types of individual benefit more from the intervention than others). A further additional item was created by reorganization of standard PRISMA items relating to interpreting results. Wording was modified in 23 items to reflect the IPD approach. CONCLUSIONS AND RELEVANCE PRISMA-IPD provides guidelines for reporting systematic reviews and meta-analyses of IPD.

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Prognosis research strategy (PROGRESS) 4: Stratified medicine research

Cited by 386 publications

References 53 publications

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

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

Interpreting the clinical utility of a pharmacogenomic marker based on observational association studies

Preferred Reporting Items for a Systematic Review and Meta-analysis of Individual Participant Data

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