The Prevention and Treatment of Missing Data in Clinical Trials (2010) / Chapter Skim
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6 Conclusions and Recommendations
6 Conclusions and Recommendations
Pages 107-114
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From page 107... ...
One is for the FDA and the National Institutes of Health to use their extensive database to develop a better understanding of the various causes of dropout from clinical trials, the typical extent of missing data in different types of trials, and the reductions in the rates of missing data that can be anticipated from the application of various alternative trial designs and techniques for trial conduct. A second recommendation is for the training of analysts in the latest techniques for the treatment of missing data in clinical trials.
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From page 108... ...
Recommendation 2: Investigators, sponsors, and regulators should design clinical trials consistent with the goal of maximizing the number of participants who are maintained on the protocol-specified interven tion until the outcome data are collected. There is a key distinction between treatment dropout and analysis dropout, and although there are trials in which treatment dropout will understandably be substantial, there is very little reason for substantial amount of missing data, that is, analysis dropouts.
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From page 109... ...
In particular, the trial protocol should contain a section that addresses missing data issues, including the anticipated amount of missing data, and steps taken in trial design and trial conduct to monitor and limit the impact of missing data. Recommendation 7: Informed consent documents should emphasize the importance of collecting outcome data from individuals who choose to discontinue treatment during the study, and they should encourage participants to provide this information whether or not they complete the anticipated course of study treatment.
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From page 110... ...
Therefore, it is important that the assumptions underlying any selected analysis technique be clearly articulated so that they can be evaluated by clinicians as well as by statistical analysts. Recommendation 9: Statistical methods for handling missing data should be specified by clinical trial sponsors in study protocols, and their associated assumptions stated in a way that can be understood by clinicians.
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From page 111... ...
In addition, investigators should seriously consider following up all or a random sample of trial dropouts, who have not withdrawn consent, to ask them to indicate why they dropped out of the study, and, if they are willing, to collect outcome measurements from them. Given that the assumptions for the missing data mechanism cannot be validated, the sensitivity of inferences for treatment effects in clinical trials to those assumptions needs to be assessed.
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From page 112... ...
A pharmaceutical company that has been researching interventions in a particular area for a long time may have internal data that can provide some of this information. However, if a company is small or has limited prior experience, having access to information from prior clinical trials would be extremely useful in trial design.
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From page 113... ...
and drug, device, and biologic companies that sponsor clinical trials should carry out continued training of their analysts to keep abreast of up-to-date techniques for missing data analysis. FDA should also encourage continued training of their clinical reviewers to make them broadly familiar with missing data terminology and missing data methods.
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From page 114... ...
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 statis tical research, such as the National Institutes of Health and the National Science Foundation.
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