Department of Epidemiology, Erasmus Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
Eur J Epidemiol. 2010 Apr;25(4):245-51. doi: 10.1007/s10654-010-9451-7. Epub 2010 Apr 1.
In pharmaco-epidemiology, the use of drugs is the determinant of interest when studying exposure-outcome associations. The increased availability of computerized information about drug use on an individual basis has greatly facilitated analyses of drug effects on a population-based scale. It seems likely that many negative findings in the early days of pharmaco-epidemiology can be explained by non-differential misclassification because of too simple (yes/no) exposure measures. In this paper, the authors discuss the importance of an adequate definition of drug exposure in pharmaco-epidemiological research and how this time-varying determinant can be analyzed in cohort studies. To reduce the risk of non-differential misclassification, a precise definition of exposure is mandatory and it is important to distinguish the complete follow-up period of a population into mutually exclusive episodes of non-use, past use and current use for each individual. By analyzing exposure to drugs as a time-dependent variable in a Cox regression model, cohort studies with complete coverage of all filled prescriptions can provide us with valid and precise risk estimates of drug-outcome associations. However, such estimates may be biased in the presence of time-dependent confounders which are themselves affected by prior exposure.
在药物流行病学中，当研究暴露-结局关联时，药物使用是感兴趣的决定因素。由于个体基础上可获得更多关于药物使用的计算机化信息，因此极大地促进了基于人群的药物效应分析。在药物流行病学的早期，许多负面发现可能可以归因于由于过于简单的（是/否）暴露测量而导致的非差异错误分类。本文作者讨论了在药物流行病学研究中充分定义药物暴露的重要性，以及如何在队列研究中分析这种时变决定因素。为了降低非差异错误分类的风险，必须对暴露进行精确定义，并且重要的是要将人群的整个随访期划分为每个个体的非使用、过去使用和当前使用的互斥阶段。通过在 Cox 回归模型中分析药物暴露作为时间依赖性变量，具有完整涵盖所有已填处方的队列研究可以为我们提供药物-结局关联的有效且精确的风险估计。然而，在存在自身受先前暴露影响的时间依赖性混杂因素的情况下，这些估计可能存在偏差。
