Biostatistical issues in the design and analysis of experimental and observational studies, and in evaluating test accuracy

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This research concerns principles and applications in observational studies and randomised experiments. It is motivated by important practical questions, and the results should be widely applicable in research in health, and natural and social sciences. The major themes will be: 1.1 Experimental designs recognising participant preferences: In conventional experiments, all participants are randomised. Alternative designs have been proposed to allow some people to choose their intervention. Suitable data analysis can then estimate the effects of 1) treatment, 2) the participant's ideal treatment, and 3) whether the preferred treatment is the one received. The two latter effects are potentially important, but they are not estimable in conventional experiments. We will investigate optimization of the alternative designs; efficiency; sample size; power; and practical implementation. 1.2 Measurement error: We demonstrated earlier that in balanced randomised experiments, correction for measurement error in a baseline variable induces bias in the treatment effect. In contrast, in observational studies intuition suggests that failing to correct for measurement error may yield bias. Comparing these scenarios, we infer that there must be a threshold on the baseline difference, defining when correction is worthwhile. We propose to compare the corrected and uncorrected estimators of treatment effect, define the decision threshold, and provide practical advice about when to correct for measurement error. 2. Evaluation of test accuracy There is often no gold standard available for evaluating a new classifier (such as a diagnostic or screening test), so an imperfect reference standard must be substituted. It is not clear what impact this may have on assessing the new test. We will investigate how the apparent test accuracy may be biased when the reference standard is imperfect.