[t]he panel will use as its main information-gathering resource a workshop that will include participation from multiple stakeholders, including clinical trialists, statistical researchers, appropriate experts from the National Institutes of Health and the pharmaceutical industry, regulators from FDA, and participants in the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH).

In both the workshop and report, the panel will strive to identify ways in which FDA guidance should be augmented to facilitate the cost-effective use of appropriate methods for missingness by the designers and implementers of clinical trials. Such guidance would usefully distinguish between types of clinical trials and missingness situations. For example, it could be useful to provide guidance on such questions as:

  1. When missingness is likely to result in an appreciable bias such that sophisticated methods for reducing bias would be needed, and, conversely, under what circumstances simple methods such as case deletion could be an acceptable practice, and

  2. How to use the leading techniques for variance estimation for each primary estimation method, along with suggestions for implementing these often complex techniques in software packages.

RANDOMIZATION AND MISSING DATA

A key feature of a randomized clinical trial is comparison with a control group, with the assignment to either the control or the treatment group carried out using a random process. This eliminates intentional or unintentional bias from affecting the treatment assignment. Randomization also (probabilistically) balances the control and treatment groups for known and, more importantly, unknown factors that could be associated with the response or outcome of interest. By using randomization, the comparison between the treatment and control groups is made as fair as possible. Thus, randomization provides a basis for inference in the assessment of whether the observed average outcome for the treatment group is or is not sufficiently different than that for the control group to assert that the measured difference is or is not due to random variation. That is, randomization permits generalizations about outcomes.

Unfortunately, this key advantage, derived from the use of random selection for treatment and control groups, is jeopardized when some of the outcome measurements are missing. By missing data we mean when an outcome value that is meaningful for analysis was not collected. So, for example, a quality-of-life measure after death is not meaningful for analysis and should not be referred to as a missing outcome. Since whether or not data are missing can be related to the assigned treatment and to the



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