The following HTML text is provided to enhance online
readability. Many aspects of typography translate only awkwardly to HTML.
Please use the page image
as the authoritative form to ensure accuracy.
The Prevention and Treatment of Missing Data in Clinical Trials
the assessment of goodness-of-fit for the parametric models used to analyze data from clinical trials (when there is missing data),
the performance of double-robust procedures in comparison to more commonly used procedures,
the impact of missingness in auxiliary variables on the various current methods, and ways of reducing the associated bias,
methods of sensitivity analysis in clinical trials, particularly for nonmonotone patterns in longitudinal data,
methods for assessing and limiting the impact of informative censoring for time-to-event outcomes, and
how to develop effective decision rules based on the input from sensitivity analyses.
We have collected the highest priority of these calls for additional research in a final recommendation, adding to that a call for the development of the associated software tools.
Recommendation 18: The treatment of missing data in clinical trials,being a crucial issue, should have a higher priority for sponsors of statistical research, such as the National Institutes of Health and the NationalScience Foundation. There remain several important areas where progress is particularly needed, namely: (1) methods for sensitivity analysisand principled decision making based on the results from sensitivityanalyses, (2) analysis of data where the missingness pattern is non-monotone, (3) sample size calculations in the presence of missing data,and (4) design of clinical trials, in particular plans for follow-up aftertreatment discontinuation (degree of sampling, how many attempts aremade, etc.), and (5) doable robust methods, to more clearly understandtheir strengths and vulnerabilities in practical settings. The developmentof software that supports coherent missing data analyses is also a highpriority.