Some t-tests for N-of-1 trials with serial correlation

TL;DR

This paper introduces new t-tests that account for serial correlation in N-of-1 trials, improving inference accuracy from single-subject data and outperforming traditional methods in simulations and real examples.

Contribution

The paper develops and evaluates t-tests that incorporate serial correlation for single-subject trials, providing tools for more accurate analysis and planning.

Findings

Serial t-tests outperform usual t-tests in error control.

Accounting for serial correlation can significantly alter inferences.

The methods are easy to implement and useful for clinical trial analysis.

Abstract

N-of-1 trials allow inference between two treatments given to a single individual. Most often, clinical investigators analyze an individual's N-of-1 trial data with usual t-tests or simple nonparametric methods. These simple methods do not account for serial correlation in repeated observations coming from the individual. Existing methods accounting for serial correlation require simulation, multiple N-of-1 trials, or both. Here, we develop t-tests that account for serial correlation in a single individual. The development includes effect size and precision calculations, both of which are useful for study planning. We then evaluate and compare their Type I and II errors and interval estimators to those of usual t-tests analogues via Monte Carlo simulation. The serial t-tests clearly outperform the usual t-tests commonly used in reporting N-of-1 results. Examples from N-of-1 clinical trials in fibromyalgia patients and from a behavioral health setting exhibit how accounting for serial correlation can change inferences. These t-tests are easily implemented and more appropriate than simple methods commonly used; however, caution is needed when analyzing only a few observations. Keywords: Autocorrelation; Cross-over studies; Repeated measures analysis; Single-case experimental design; Time-series