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Even if the necessary aggregate data are available for a trial, it may have been obtained using an inappropriate analytic method. For example, a treatment effect measured by an odds ratio derived from a logistic regression analysis may have been reported for a trial, when a hazard ratio based on a Cox regression would have been more appropriate, due to the time‐to‐event nature of the data; or a cluster randomised trial may have been analysed without accounting for the clustering. Another issue is that trial analyses may not provide the estimate of interest. For example, when conditional treatment effects are of interest, a trial may have derived treatment effect estimates without adjustment for key prognostic factors (e.g. if continuous outcome values at the end of follow‐up were analysed without adjustment for the continuous outcome values at baseline). Analyses may also not be sufficiently comprehensive; for example, a Cox regression model may have been fitted without examining the proportional hazards assumption for the treatment variable, even when non‐constant hazard ratios are a concern (e.g. from overlapping Kaplan‐Meier curves), or missing data may not have been handled appropriately (Chapter 18).

Analyses may also be inconsistent across trials. For example, even in situations where estimates of treatment‐covariate interactions are reported for each trial, they may differ in their definitions of categorical covariates, handling of continuous covariates (e.g. age might be dichotomised in some trials, but not in others) and the assumed relationships (e.g. linear or non‐linear trends).

All of these issues give rise to concern that a meta‐analysis based on aggregate data would not be robust or adequate for answering the research question of interest, and that an IPD meta‐analysis would be more comprehensive and flexible. Again, this can be determined only by first appraising the individual trial analyses, and evaluating which methods trial investigators used and the extent of aggregate data available.