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

本研究涉及观察性研究和随机实验的原理和应用。 它的动机是重要的实际问题，其结果应广泛适用于健康，自然和社会科学的研究。 主要主题将是： 1.1识别参与者偏好的实验设计：在传统实验中，所有参与者都是随机的。已经提出了替代设计，以允许一些人选择他们的干预。 然后，适当的数据分析可以估计1）治疗的效果，2）参与者的理想治疗，以及3）首选治疗是否是接受的治疗。 后两种效应可能很重要，但在常规实验中无法估计。我们将研究替代设计的优化;效率;样本量;功效;和实际实施。 1.2测量误差：我们在前面已经证明，在平衡随机实验中，对基线变量测量误差的校正会导致治疗效果的偏倚。相反，在观察性研究中，直觉表明，未能纠正测量误差可能会产生偏倚。 通过比较这些情景，我们推断，基线差异必须有一个阈值，定义何时值得进行纠正。我们建议比较治疗效果的校正和未校正估计量，定义决策阈值，并提供有关何时校正测量误差的实用建议。 2.测试准确性的评估通常没有金标准可用于评估新的分类器（例如诊断或筛选测试），因此必须用不完美的参考标准来替代。目前尚不清楚这可能对评估新测试产生什么影响。我们将研究当参比标准品不完美时，表观检测准确度可能会出现偏倚。

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.