AI Article Synopsis
- The paper discusses a method for analyzing clinical trial data with dichotomous outcomes and missing data, particularly when the missing data may be informative.
- It introduces a mathematical approach to adjust for missing data by redistributing counts to better reflect both favorable and unfavorable outcomes.
- The methodology includes adjustments for treatment comparisons over time and is illustrated with a practical example, with an appendix extending the approach to ordinal data.
Article Abstract
Dichotomous endpoints in clinical trials have only two possible outcomes, either directly or via categorization of an ordinal or continuous observation. It is common to have missing data for one or more visits during a multi-visit study. This paper presents a closed form method for sensitivity analysis of a randomized multi-visit clinical trial that possibly has missing not at random (MNAR) dichotomous data. Counts of missing data are redistributed to the favorable and unfavorable outcomes mathematically to address possibly informative missing data. Adjusted proportion estimates and their closed form covariance matrix estimates are provided. Treatment comparisons over time are addressed with Mantel-Haenszel adjustment for a stratification factor and/or randomization-based adjustment for baseline covariables. The application of such sensitivity analyses is illustrated with an example. An appendix outlines an extension of the methodology to ordinal endpoints.
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| http://dx.doi.org/10.1080/10543406.2017.1289955 | DOI Listing |
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