The Permutation Distancing Test for dependent single-case observational AB-phase design data: A Monte Carlo simulation study.

ABSTRACT: The Permutation Distancing Test (PDT) is a nonparametric test for evaluating treatment effects in dependent single-case observational design (SCOD) AB-phase data without linear trends. Monte Carlo methods were used to estimate the PDT power and type I error rate, and to compare them to those of the Single-Case Randomization Test (SCRT) assuming a randomly determined intervention point and the traditional permutation test assuming full exchangeability. Data were simulated without linear trends for five treatment effect levels (- 2, - 1, 0, 1, 2), five autocorrelation levels (0, .15, .30, .45, .60), and four observation number levels (30, 60, 90, 120). The power was calculated multiple times for all combinations of factor levels each generating 1000 replications. With 30 observations, the PD

SUBMITTER: Vroegindeweij A 

PROVIDER: S-EPMC10991042 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature