1. the assessment of goodness-of-fit for the parametric models used to analyze data from clinical trials (when there is missing data),

  2. the performance of double-robust procedures in comparison to more commonly used procedures,

  3. the impact of missingness in auxiliary variables on the various current methods, and ways of reducing the associated bias,

  4. methods of sensitivity analysis in clinical trials, particularly for nonmonotone patterns in longitudinal data,

  5. methods for assessing and limiting the impact of informative censoring for time-to-event outcomes, and

  6. 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 National Science Foundation. There remain several important areas where progress is particularly needed, namely: (1) methods for sensitivity analysis and principled decision making based on the results from sensitivity analyses, (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 after treatment discontinuation (degree of sampling, how many attempts are made, etc.), and (5) doable robust methods, to more clearly understand their strengths and vulnerabilities in practical settings. The development of software that supports coherent missing data analyses is also a high priority.



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