Power Calculations for Individual Participant Data (IPD) Meta-Analysis Projects скачать в хорошем качестве

Power Calculations for Individual Participant Data (IPD) Meta-Analysis Projects

Individual participant data (IPD) meta-analysis projects herald much promise (www.ipdma.co.uk), but are potentially time-consuming, often taking upwards of two years to engage with trial investigators; obtain, clean, harmonise and meta-analyse the IPD; and publish and disseminate results. Therefore, before embarking on an IPD meta-analysis project, researchers and funders need to be reassured that the time and resources required are worth their investment. This should include consideration of how many trials are likely to provide their IPD and, based on this, estimation of the potential power of the planned IPD meta-analysis. In this tale, I explain the rationale for power calculations in IPD meta-analysis projects, and describe approaches to estimate the power in advance of collecting IPD. In particular, I focus on how how to estimate the power of a planned IPD meta-analysis of randomised trials to examine treatment-covariate interactions at the participant level (i.e. treatment effect modifiers). The talk is based on our published work, as follows: Riley RD, et al. Calculating the power to examine treatment-covariate interactions when planning an individual participant data meta-analysis of randomized trials with a binary outcome. Stat Med 2022;41:4822-37. https://onlinelibrary.wiley.com/doi/f... Riley R.D, Ensor J. Power Calculations for Planning an IPD Meta-analysis. In: Riley RD, Tierney J, Stewart LA, eds. Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Chichester, West Sussex: Wiley 2021. https://www.ipdma.co.uk/textbook Ensor J, et al. Simulation-based power calculations for planning a two-stage individual participant data meta-analysis. BMC Med Res Methodol 2018;18(1):41. https://bmcmedresmethodol.biomedcentr... Riley RD, et al. Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: Statistical recommendations for conduct and planning. Stat Med 2020;39(15):2115-37. https://onlinelibrary.wiley.com/doi/f... See also an introduction to IPD meta-analysis projects here:    • Individual Participant Data (IPD) Meta-Ana...