Abstract

Kawaguchi, Koch, and Wang (2011) provide methodology and applications for a stratified Mann-Whitney estimator that addresses the same comparison between two randomized groups for a strictly ordinal response variable as the van Elteren test statistic for randomized clinical trials with strata. The sanon package provides the implementation of the method within the R programming environment. The usage of sanon is illustrated with five examples. The first example is a randomized clinical trial with eight strata and a univariate ordinal response variable. The second example is a randomized clinical trial with four strata, two covariables, and four ordinal response variables. The third example is a crossover design randomized clinical trial with two strata, one covariable, and two ordinal response variables. The fourth example is a randomized clinical trial with seven strata (which are managed as a categorical covariable), three ordinal covariables with missing values, and three ordinal response variables with missing values. The fifth example is a randomized clinical trial with six strata, a categorical covariable with three levels, and three ordinal response variables with missing values.

Highlights

  • The primary analyses for confirmatory randomized clinical trials should have protocol specified methods that have minimal assumptions

  • The R package sanon contains functions to implement the methods for stratified Mann-Whitney estimators

  • These methods address the same comparisons between two randomized groups for a strictly ordinal response variable as the van Elteren test statistic

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Summary

Introduction

The primary analyses for confirmatory randomized clinical trials (and those with regulatory objectives) should have protocol specified methods that have minimal assumptions. For confirmatory randomized clinical trials with stratified designs for comparing two treatments, Kawaguchi et al (2011) proposed stratified multivariate Mann-Whitney estimators as a useful structure for the analysis of strictly ordinal response variables. Their scope can address strata with at least minimal sample sizes (e.g., ≥ 16), and randomization-based covariance adjustment is possible. The method is based on the Mann-Whitney estimator for the probability that a randomly selected patient from one treatment group has better status for a response variable within a stratum than a randomly selected patient from the other treatment group (with ties being randomly broken with probability 0.5) Such Mann-Whitney estimators can be combined across the strata to provide stratified estimator counterparts that address the same comparisons between the two treatment groups as the van Elteren test statistic.

Methods
Stratified multivariate Mann-Whitney estimator
Randomization Based Covariance Adjustment
Management of Missing Data
Illustrative data sets
Chronic pain data
Respiratory disorder data
Relief of heartburn data
Seborrheic dermatitis data
Skin conditions data
Design Matrix:
Findings
Summary
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