Abstract
BackgroundResearchers and funders should consider the statistical power of planned Individual Participant Data (IPD) meta-analysis projects, as they are often time-consuming and costly. We propose simulation-based power calculations utilising a two-stage framework, and illustrate the approach for a planned IPD meta-analysis of randomised trials with continuous outcomes where the aim is to identify treatment-covariate interactions.MethodsThe simulation approach has four steps: (i) specify an underlying (data generating) statistical model for trials in the IPD meta-analysis; (ii) use readily available information (e.g. from publications) and prior knowledge (e.g. number of studies promising IPD) to specify model parameter values (e.g. control group mean, intervention effect, treatment-covariate interaction); (iii) simulate an IPD meta-analysis dataset of a particular size from the model, and apply a two-stage IPD meta-analysis to obtain the summary estimate of interest (e.g. interaction effect) and its associated p-value; (iv) repeat the previous step (e.g. thousands of times), then estimate the power to detect a genuine effect by the proportion of summary estimates with a significant p-value.ResultsIn a planned IPD meta-analysis of lifestyle interventions to reduce weight gain in pregnancy, 14 trials (1183 patients) promised their IPD to examine a treatment-BMI interaction (i.e. whether baseline BMI modifies intervention effect on weight gain). Using our simulation-based approach, a two-stage IPD meta-analysis has < 60% power to detect a reduction of 1 kg weight gain for a 10-unit increase in BMI. Additional IPD from ten other published trials (containing 1761 patients) would improve power to over 80%, but only if a fixed-effect meta-analysis was appropriate. Pre-specified adjustment for prognostic factors would increase power further. Incorrect dichotomisation of BMI would reduce power by over 20%, similar to immediately throwing away IPD from ten trials.ConclusionsSimulation-based power calculations could inform the planning and funding of IPD projects, and should be used routinely.
Highlights
Researchers and funders should consider the statistical power of planned Individual Participant Data (IPD) meta-analysis projects, as they are often time-consuming and costly
The two-stage approach to IPD meta-analysis We introduce the two-stage approach to IPD metaanalysis of continuous outcomes, which was recently described by Burke et al [18]
We demonstrated the approach for continuous outcomes, using a planned IPD meta-analysis of pregnancy trials (i-WIP), and showed that IPD from 14 trials was unlikely to have adequate power to detect a treatmentBMI interaction unless the effect was very large (Fig. 1)
Summary
Researchers and funders should consider the statistical power of planned Individual Participant Data (IPD) meta-analysis projects, as they are often time-consuming and costly. A project that pools the IPD from multiple trials is highly appealing to funders to substantially increase the power to detect genuine treatment-covariate interactions. At the time of developing the grant application, 14 of the trials (containing 1183 patients) included in the aforementioned aggregate data meta-analysis had provisionally agreed to provide their IPD. These are summarised, including information about the weight gain in each treatment group, and the distribution of baseline BMI values. No formal power calculation was originally performed for the i-WIP grant application, but it was noted that in order to detect treatment-covariate interactions “our IPD meta-analysis provides an efficient way to substantially increase the sample size, without the need for a new trial”
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