Performance of Selected Nonparametric Tests for Discrete Longitudinal Data Under Different Patterns of Missing Data

Abstract

Comparison of changes over time of a continuous response variable between treatment groups is often of main interest in clinical trials. When the distributional properties of the continuous response variable are not regular enough, or when the response is discrete, nonparametric techniques have been used. The relative performances of selected repeated measures nonparametric two-sample tests proposed by Wei and Lachin, Koziol, Wei and Johnson, and the adapted Wilcoxon Rank-Sum test are compared through simulations based on quality of life data. The Wilcoxon Rank-Sum test is the most powerful and is not significantly affected by the different patterns of missing data.

Levels of Tuberculosis Treatment Adherence among Sputum Smear Positive Pulmonary Tuberculosis Patients Attending Care at Zomba Central Hospital, Southern Malawi, 2007–2008

“…Such adherence patterns and how they arise are described elsewhere [34] in form of missing data patterns. This paper has documented the treatment adherence levels in the intensive and continuation phases focusing on their effects on cure from TB at the end of the treatment period.…”