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Two-period, two-treatment crossover designs subject to non-ignorable missing data
John N S Matthews1, Robin Henderson
1School of Mathematics and Statistics, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
Biostatistics (Oxford, England)
|March 23, 2013
Summary
Missing data in crossover trials can bias treatment effects. This study shows the conventional AB/BA design is robust, but AB, BA, AA, BB designs may be better if dropout depends on response differences.
Area of Science:
- Biostatistics
- Clinical Trial Design
Background:
- Missing data is a common issue in crossover trials, potentially causing loss of efficiency or inestimability of contrasts.
- Existing designs have not adequately addressed bias introduced by the missingness process itself.
Purpose of the Study:
- To investigate the impact of non-ignorable missing data on treatment effect bias in two-treatment, two-period crossover designs.
- To assess the robustness of standard analysis methods under missing at random assumptions when data are non-ignorably missing.
Main Methods:
- Analysis of two-treatment, two-period crossover designs under missing data scenarios.
- Evaluation of the conventional AB/BA design and designs including AA and BB sequences.
- Comparison of robustness under missing at random versus non-ignorably missing data.
Main Results:
- The conventional AB/BA design demonstrates good robustness properties even with non-ignorable missing data.
- Designs incorporating sequences AB, BA, AA, and BB may offer advantages when dropout is related to response differences between periods.
Conclusions:
- The choice of crossover design impacts the potential for bias due to non-ignorable missing data.
- Careful consideration of the missingness mechanism is crucial for selecting robust crossover trial designs.
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