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Plea for routinely presenting prediction intervals in meta-analysis
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  • Published on:
    Prediction intervals - the beginning of an effectful future?
    • Hao Zhang, Biostatistician McGill University
    • Other Contributors:
      • Tibor Schuster, Assistant Professor

    There is considerable debate going on questioning the practical usefulness of a priori power calculations suggesting that “underpowered” studies are not unethical and that little scientific projection would be still better than no projection at all [1-4]. Some authors argue that “being underpowered is unethical” is a “widespread misconception which is only plausible when presented in vague, qualitative terms but does not hold when examined in detail” [1, 2]. Further review of the arguments reveals that the crucial assumptions implied in the reasoning do not reflect actual scientific practice. The main theoretical arguments assume a perfect “frequentist world” that may allow substitution of one big trial by a corresponding number of small trials that would, once being aggregated in a formal evidence synthesis i.e. meta-analysis, cumulate the same information as the big one [2, 4]. If the individual studies are non-representative samples of the target population, the practical value of estimating a pooled effect that is a weighted average of potentially disparate effects in different subpopulations is questionable.

    A widely considered answer to the threat of effect heterogeneity in meta-analyses are random-effect confidence intervals that are often assumed to better reflect variation in the effects across subpopulations than fixed-effects confidence intervals. However, while such intervals offer a valid solution to inference regarding the average effect across all c...

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    Conflict of Interest:
    None declared.