Department of Biostatistics, S-2323 Medical Center North, Vanderbilt University,Nashville, TN 37232-2158, USA.
Am J Epidemiol. 2011 Sep 1;174(5):613-20. doi: 10.1093/aje/kwr143. Epub 2011 Jul 12.
Propensity scores are widely used in cohort studies to improve performance of regression models when considering large numbers of covariates. Another type of summary score, the disease risk score (DRS), which estimates disease probability conditional on nonexposure, has also been suggested. However, little is known about how it compares with propensity scores. Monte Carlo simulations were conducted comparing regression models using the DRS and the propensity score with models that directly adjust for all of the individual covariates. The DRS was calculated in 2 ways: from the unexposed population and from the full cohort. Compared with traditional multivariable outcome regression models, all 3 summary scores had comparable performance for moderate correlation between exposure and covariates and, for strong correlation, the full-cohort DRS and propensity score had comparable performance. When traditional methods had model misspecification, propensity scores and the full-cohort DRS had superior performance. All 4 models were affected by the number of events per covariate, with propensity scores and traditional multivariable outcome regression least affected. These data suggest that, for cohort studies for which covariates are not highly correlated with exposure, the DRS, particularly that calculated from the full cohort, is a useful tool.
倾向评分在考虑大量协变量的队列研究中被广泛用于提高回归模型的性能。另一种汇总评分，即疾病风险评分（DRS），它估计了在无暴露条件下的疾病概率，也已被提出。然而，对于它与倾向评分的比较，我们知之甚少。通过比较直接调整所有个体协变量的使用 DRS 和倾向评分的回归模型与使用模型，进行了蒙特卡罗模拟。DRS 以两种方式计算：从不暴露人群和整个队列。与传统的多变量结局回归模型相比，所有 3 种汇总评分在暴露与协变量之间存在中度相关性时表现相当，而在强相关性时，全队列 DRS 和倾向评分的表现相当。当传统方法存在模型误定时，倾向评分和全队列 DRS 的表现更好。所有 4 种模型都受到每个协变量的事件数的影响，倾向评分和传统的多变量结局回归受影响最小。这些数据表明，对于协变量与暴露没有高度相关的队列研究，DRS，特别是从整个队列计算的 DRS，是一种有用的工具。
