Abstract
Increased availability of data and accessibility of computational tools in recent years have created an unprecedented upsurge of scientific studies driven by statistical analysis. Limitations inherent to statistics impose constraints on the reliability of conclusions drawn from data, so misuse of statistical methods is a growing concern. Hypothesis and significance testing, and the accompanying P-values are being scrutinized as representing the most widely applied and abused practices. One line of critique is that P-values are inherently unfit to fulfill their ostensible role as measures of credibility for scientific hypotheses. It has also been suggested that while P-values may have their role as summary measures of effect, researchers underappreciate the degree of randomness in the P-value. High variability of P-values would suggest that having obtained a small P-value in one study, one is, ne\tvertheless, still likely to obtain a much larger P-value in a similarly powered replication study. Thus, “replicability of P-value” is in itself questionable. To characterize P-value variability, one can use prediction intervals whose endpoints reflect the likely spread of P-values that could have been obtained by a replication study. Unfortunately, the intervals currently in use, the frequentist P-intervals, are based on unrealistic implicit assumptions. Namely, P-intervals are constructed with the assumptions that imply substantial chances of encountering large values of effect size in an observational study, which leads to bias. The long-run frequentist probability provided by P-intervals is similar in interpretation to that of the classical confidence intervals, but the endpoints of any particular interval lack interpretation as probabilistic bounds for the possible spread of future P-values that may have been obtained in replication studies. Along with classical frequentist intervals, there exists a Bayesian viewpoint toward interval construction in which the endpoints of an interval have a meaningful probabilistic interpretation. We propose Bayesian intervals for prediction of P-value variability in prospective replication studies. Contingent upon approximate prior knowledge of the effect size distribution, our proposed Bayesian intervals have endpoints that are directly interpretable as probabilistic bounds for replication P-values, and they are resistant to selection bias. We showcase our approach by its application to P-values reported for five psychiatric disorders by the Psychiatric Genomics Consortium group.
Highlights
Poor replicability has been plaguing observational studies
The observed P-value was based on a twosample Z-test and was thresholded according to the following selection rules: (i) no selection, i.e., a prediction interval is constructed for a randomly observed P-value; (ii) selection of P-values around a value, e.g., P % 0:05, i.e., prediction intervals are constructed only for P-values that were close to the 5% significance level; (iii) selection of P-values that are smaller than a threshold, e.g., P
Researchers want to know whether a statistic used for summarizing their data supports their scientific hypothesis and to what degree
Summary
Poor replicability has been plaguing observational studies. The “replicability crisis” is largely statistical and while there are limits to what statistics can do, a serious concernVsevolozhskaya et al Translational Psychiatry (2017)7:1271 introduced by Killeen[1], to mean the P-value obtained from subsequent, replicate experiments with the same sample size, taken from the same population. It has been suggested that P-intervals may serve the purpose of improving P-value interpretability, especially in largescale genomic studies with many tests or in other studies utilizing modern high-throughput technologies[5,6] In their discussion of P-values and their prediction intervals, Lazzeroni and colleagues[6] argued that the P-values “will continue to have an important role in research” and that “no other statistic fills this particular niche.”. They present P-intervals not as a way to expose alleged weaknesses of P-values but rather as a tool for assessing the real uncertainty inherent in P-values In their discussion of P-values and their prediction intervals, Lazzeroni and colleagues[6] argued that the P-values “will continue to have an important role in research” and that “no other statistic fills this particular niche.” They present P-intervals not as a way to expose alleged weaknesses of P-values but rather as a tool for assessing the real uncertainty inherent in P-values
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