Statistical methods in medical research
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We wish to deal with investigator bias in a statistical context. We sketch how a textbook solution to the problem of "outliers" which avoids one sort of investigator bias, creates the temptation for another sort. ⋯ Finally, we offer tentative suggestions to deal with the problem of investigator bias which follow from our account. As we have given a very sparse and stylized account of investigator bias, we ask what might be done to overcome this limitation.
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Stat Methods Med Res · Jun 2008
ReviewRandomized trials for the real world: making as few and as reasonable assumptions as possible.
The strength of the randomized trial to yield conclusions not dependent on assumptions applies only in an ideal setting. In the real world various complications such as loss-to-follow-up, missing outcomes, noncompliance and nonrandom selection into a trial force a reliance on assumptions. To handle real world complications, it is desirable to make as few and as reasonable assumptions as possible. This article reviews four techniques for using a few reasonable assumptions to design or analyse randomized trials in the presence of specific real world complications: 1) a double sampling design for survival data to avoid strong assumptions about informative censoring, 2) sensitivity analysis for partially missing binary outcomes that uses the randomization to reduce the number of parameters specified by the investigator, 3) an estimate of the effect of treatment received in the presence of all-or-none compliance that requires reasonable assumptions, and 4) statistics for binary outcomes that avoid some assumptions for generalizing results to a target population.