Broberg Per, Miller Frank
Division of Cancer Epidemiology, Department of Clinical Sciences Lund, Lund University, Skane University Hospital, Lund, Sweden.
Department of Statistics, Stockholm University, 10691 Stockholm, Sweden.
Biometrics. 2017 Sep;73(3):895-904. doi: 10.1111/biom.12642. Epub 2017 Jan 18.
We consider conditional estimation in two-stage sample size adjustable designs and the consequent bias. More specifically, we consider a design which permits raising the sample size when interim results look rather promising, and which retains the originally planned sample size when results look very promising. The estimation procedures reported comprise the unconditional maximum likelihood, the conditionally unbiased Rao-Blackwell estimator, the conditional median unbiased estimator, and the conditional maximum likelihood with and without bias correction. We compare these estimators based on analytical results and a simulation study. We show how they can be applied in a real clinical trial setting.
我们考虑两阶段样本量可调整设计中的条件估计及其随之而来的偏差。更具体地说，我们考虑一种设计，当期中结果看起来很有希望时允许增加样本量，而当结果看起来非常有希望时保持原计划的样本量。报告的估计程序包括无条件最大似然估计、条件无偏的 Rao-Blackwell 估计器、条件中位数无偏估计器以及有无偏差校正的条件最大似然估计。我们基于分析结果和模拟研究对这些估计器进行比较。我们展示了它们如何应用于实际的临床试验环境中。
