Analysis of serial measurements in medical research.

TLDR

Serial data collection in medical research is common, yet conventional analyses—often relying on repeated group comparisons with t tests—are frequently inadequate, failing to address clinically relevant questions and producing statistically invalid results. The authors propose a two‑stage remedy that employs summary measures of individual responses. First, each subject’s response is condensed into a summary metric such as a rate of change or area under the curve; second, these metrics are treated as raw data and analyzed with simple statistical techniques, guiding study design to ensure sufficient subjects and critical time points. This approach is statistically valid, more clinically relevant, and offers a straightforward, useful tool for analyzing serial measurements in medical research.

Abstract

In medical research data are often collected serially on subjects. The statistical analysis of such data is often inadequate in two ways: it may fail to settle clinically relevant questions and it may be statistically invalid. A commonly used method which compares groups at a series of time points, possibly with t tests, is flawed on both counts. There may, however, be a remedy, which takes the form of a two stage method that uses summary measures. In the first stage a suitable summary of the response in an individual, such as a rate of change or an area under a curve, is identified and calculated for each subject. In the second stage these summary measures are analysed by simple statistical techniques as though they were raw data. The method is statistically valid and likely to be more relevant to the study questions. If this method is borne in mind when the experiment is being planned it should promote studies with enough subjects and sufficient observations at critical times to enable useful conclusions to be drawn. Use of summary measures to analyse serial measurements, though not new, is potentially a useful and simple tool in medical research.