Prioritizing covariates in the planning of future studies in the meta-analytic framework

TL;DR

This paper introduces a formal framework for prioritizing covariates in the design of future studies within a meta-analytic context, optimizing decision criteria to enhance scientific impact.

Contribution

It develops a systematic approach for covariate prioritization using various statistical criteria, aiding in the design of more effective future studies based on existing evidence.

Findings

Application to genome-wide association studies for C-reactive protein.

Recommendations for covariate selection to improve study design.

Framework adaptable to different statistical decision criteria.

Abstract

Science can be seen as a sequential process where each new study augments evidence to the existing knowledge. To have the best prospects to make an impact in this process, a new study should be designed optimally taking into account the previous studies and other prior information. We propose a formal approach for the covariate prioritization, i.e., the decision about the covariates to be measured in a new study. The decision criteria can be based on conditional power, change of the p-value, change in lower confidence limit, Kullback-Leibler divergence, Bayes factors, Bayesian false discovery rate or difference between prior and posterior expectation. The criteria can be also used for decisions on the sample size. As an illustration, we consider covariate prioritization based on genome-wide association studies for C-reactive protein levels and make suggestions on the genes to be studied further. keywords: design; evidence-based medicine; meta-analysis; power; scientific method