Premium
Semi‐parametric and non‐parametric methods for the analysis of repeated measurements with applications to clinical trials
Author(s) -
Davis Charles S.
Publication year - 1991
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780101210
Subject(s) - categorical variable , covariate , parametric statistics , missing data , univariate , computer science , time point , parametric model , nonparametric statistics , binary data , variable (mathematics) , statistics , econometrics , mathematics , multivariate statistics , binary number , machine learning , mathematical analysis , philosophy , arithmetic , aesthetics
Techniques applicable for the analysis of longitudinal data when the response variable is non‐normal are not nearly as comprehensive as for normally‐distributed outcomes. However, there have been several recent developments. Semi‐parametric and non‐parametric methodology for the analysis of repeated measurements is reviewed. The commonly encountered design in which, for each subject, one assesses a univariate response variable at multiple fixed time points, is considered. The types of outcomes considered include binary, ordered categorical, and continuous (but extremely non‐normal) response variables. All of the methods considered allow for incomplete data due to the occurrence of missing observations. In addition, discrete and/or continuous covariates, which may be time‐dependent, are accommodated by some of the approaches. The methods are demonstrated using data from three clinical trials.