Individual Participant Data Meta-Analysis

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Individual Participant Data Meta-Analysis: краткое содержание, описание и аннотация

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Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research Split into five parts, the book chapters take the reader through the journey from initiating and planning IPD projects to obtaining, checking, and meta-analysing IPD, and appraising and reporting findings. The book initially focuses on the synthesis of IPD from randomised trials to evaluate treatment effects, including the evaluation of participant-level effect modifiers (treatment-covariate interactions). Detailed extension is then made to specialist topics such as diagnostic test accuracy, prognostic factors, risk prediction models, and advanced statistical topics such as multivariate and network meta-analysis, power calculations, and missing data.
Intended for a broad audience, the book will enable the reader to:
Understand the advantages of the IPD approach and decide when it is needed over a conventional systematic review Recognise the scope, resources and challenges of IPD meta-analysis projects Appreciate the importance of a multi-disciplinary project team and close collaboration with the original study investigators Understand how to obtain, check, manage and harmonise IPD from multiple studies Examine risk of bias (quality) of IPD and minimise potential biases throughout the project Understand fundamental statistical methods for IPD meta-analysis, including two-stage and one-stage approaches (and their differences), and statistical software to implement them Clearly report and disseminate IPD meta-analyses to inform policy, practice and future research Critically appraise existing IPD meta-analysis projects Address specialist topics such as effect modification, multiple correlated outcomes, multiple treatment comparisons, non-linear relationships, test accuracy at multiple thresholds, multiple imputation, and developing and validating clinical prediction models Detailed examples and case studies are provided throughout.

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Once a trial’s IPD has been processed, checked and all issues resolved as far as possible, it is useful to give trial investigators a final opportunity to verify their data prior to it being used in any meta‐analysis. This is most easily achieved by sending a descriptive summary of the finalised data for each trial, any amendments made to the original data provided, and results of the primary analyses ( Figure 4.13gives an example), but offering the option to supply a copy of the full checked IPD, if required. Such a document may be useful if investigators raise queries about differences between trial data presented in the original trial publication and in the IPD meta‐analysis.

4.9 Merging IPD Ready for Meta‐Analysis

After verification, the finalised dataset for each trial is ready to be used within subsequent IPD meta‐analyses. At this stage, it is helpful to merge the final IPD from all trials into a single dataset. Although statistical methods for IPD meta‐analysis can still be applied if trial datasets are located locally in different files, a single dataset that houses all the IPD is more convenient and potentially makes analyses faster. For example, Box 1.1( Chapter 1) shows an example dataset containing IPD from 10 trials after data checking, harmonisation, verification and merging. Most statistical packages, such as Stata, SAS and R, have built‐in commands for merging datasets from different files, and these generally require the datasets to share common variable names, which will have been achieved at the data harmonisation stage ( Section 4.5.3). Not all variables in every trial dataset need to be merged, and the statistician can restrict the variables to just those to be used in particular statistical analyses. It is important to check that no errors are introduced when merging the datasets. Therefore, it is sensible to calculate summary statistics (e.g. number of participants in each group, mean age, proportion of women, overall treatment effect) for each trial before (in the pre‐merge dataset) and after (in the merged dataset), to ensure they agree exactly. A single dataset comprising IPD from all trials will not be achievable if the IPD for some can only be accessed remotely, but the central research team can still proceed with a two-stage meta‐analysis as described in Chapter 5.

Figure 413Example of items to include in summary of finalised trial IPD for - фото 17

Figure 4.13Example of items to include in summary of finalised trial IPD for verification by a trial investigator.

Source: Ruth Walker and Lesley Stewart.

4.10 Concluding Remarks

There is no doubt that development of the project protocol, managing a large‐scale collaboration and carefully collecting, processing and checking data is a lengthy and resource‐intensive phase of an IPD meta‐analysis project. However, these aspects are key to ensuring that the approach is scientifically rigorous and truly collaborative, and that the privacy of the participants in the included trials is maintained. Importantly, it also ensures that the collated IPD is as accurate, up to date, reliable and comprehensive as it can be, and that it is well understood by the central research team. In all, this provides a necessary solid foundation for the statistical analysis part of the IPD project that follows.

Part II Fundamental Statistical Methods and Principles

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