tion of interventions is considered impractical, as for example in some trials of devices or surgical procedures. These trials do not possess the balancing property of randomization with respect to the distribution of observed or unmeasured covariates, and hence are subject to potential bias if there are important differences in these distributions across intervention groups.

The threat to validity from missing data is similar for nonrandomized and randomized trials—in fact the threat is potentially greater given the inability to mask the treatments—so the principles of missing data analysis described in this report apply in a similar fashion to nonrandomized trials. These include the need to design and conduct trials to minimize the amount of missing data, the need to use principled missing data adjustments based on scientifically plausible assumptions, the need to conduct sensitivity analyses for potential deviations from the primary assumed mechanisms of missing data, and the need to collect covariate information that is predictive of missingness and the study outcomes. The need for good covariate information is, if anything, even greater for nonrandomized trials, since this information can also be used to reduce differences in intervention groups arising from the nonrandomized allocation of interventions.

This study included only four panel meetings, one of which was a workshop, and therefore cannot be comprehensive. The focus was on identifying principles that could be applied in a wide variety of settings. We recognize that there are a wide variety of types of clinical trials for a wide variety of health issues and that there will always be idiosyncratic situations that will require specialized techniques not directly covered here. Also, it is important to point out that we focus on the assessment of various forms of intervention efficacy: this report does not do any more than touch on the assessment of the safety of medical interventions.

The next two chapters provide details and recommendations on trial designs and trial conduct that are useful for reducing the frequency of missing data. Chapters 4 and 5 describe methods of analysis for data from clinical trials in which some of the values for the outcome or outcomes of interest are missing: Chapter 4 considers drawing inferences when there are missing data, and Chapter 5 considers sensitivity analyses. The final chapter presents the panel’s recommendations.



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