Tomasson H
University of Iceland, Faculty of Economics and Business Administration, Reykjavik.
Stat Med. 1995 Jun 30;14(12):1331-9. doi: 10.1002/sim.4780141206.
In epidemiology, the risk of disease in terms of a set of covariates is often modelled by logistic regression. The resulting linear predictor can be used to define the extent of risk between extremes, and to calculate an attributable risk for the covariates taken together. As is well known, straightforward use of the linear predictor, on the sample from which it was derived, to obtain estimates the relative and attributable risk will be biased, often seriously. Use of the jack-knife technique is extended to produce asymptotically unbiased estimates of relative and attributable risks. The asymptotic variances associated with these estimates are derived by using the formulae of conditional variances. They are applied to the results of a case-control study of stomach cancer.
在流行病学中，疾病风险通常根据一组协变量通过逻辑回归进行建模。由此产生的线性预测器可用于定义极端情况之间的风险程度，并计算协变量综合起来的归因风险。众所周知，直接在推导线性预测器的样本上使用该预测器来估计相对风险和归因风险会产生偏差，而且往往偏差严重。使用刀切法进行扩展，以产生相对风险和归因风险的渐近无偏估计。通过使用条件方差公式得出与这些估计相关的渐近方差。这些方差被应用于一项胃癌病例对照研究的结果。
