More data, less information? Potential for nonmonotonic information growth using GEE.

ABSTRACT: Statistical intuition suggests that increasing the total number of observations available for analysis should increase the precision with which parameters can be estimated. Such monotonic growth of statistical information is of particular importance when data are analyzed sequentially, such as in confirmatory clinical trials. However, monotonic information growth is not always guaranteed, even when using a valid, but inefficient estimator. In this article, we demonstrate the theoretical possibility of nonmonotonic information growth when using generalized estimating equations (GEE) to estimate a slope and provide intuition for why this possibility exists. We use theoretical and simulation-based results to characterize situations that may result in nonmonotonic information growth. Nonmonoto

SUBMITTER: Shoben AB 

PROVIDER: S-EPMC5620018 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature