Stamey James D, Beavers Daniel P, Faries Douglas, Price Karen L, Seaman John W
Department of Statistical Science, Baylor University, Waco, TX, USA.
Pharm Stat. 2014 Jan-Feb;13(1):94-100. doi: 10.1002/pst.1604. Epub 2013 Nov 13.
Unmeasured confounding is a common problem in observational studies. Failing to account for unmeasured confounding can result in biased point estimators and poor performance of hypothesis tests and interval estimators. We provide examples of the impacts of unmeasured confounding on cost-effectiveness analyses using observational data along with a Bayesian approach to correct estimation. Assuming validation data are available, we propose a Bayesian approach to correct cost-effectiveness studies for unmeasured confounding. We consider the cases where both cost and effectiveness are assumed to have a normal distribution and when costs are gamma distributed and effectiveness is normally distributed. Simulation studies were conducted to determine the impact of ignoring the unmeasured confounder and to determine the size of the validation data required to obtain valid inferences.
未测量的混杂因素是观察性研究中常见的问题。未能考虑未测量的混杂因素可能导致点估计有偏差，以及假设检验和区间估计的性能不佳。我们提供了未测量的混杂因素对使用观察性数据进行成本效益分析的影响示例，以及一种用于校正估计的贝叶斯方法。假设可获得验证数据，我们提出一种贝叶斯方法来校正未测量的混杂因素对成本效益研究的影响。我们考虑了成本和效益均假定为正态分布的情况，以及成本为伽马分布且效益为正态分布的情况。进行了模拟研究，以确定忽略未测量的混杂因素的影响，并确定获得有效推断所需的验证数据量。
