Oden N L
Department of Preventive Medicine, Health Sciences Center, State University of New York, Stony Brook.
Stat Med. 1991 Aug;10(8):1303-11. doi: 10.1002/sim.4780100813.
A common error in statistical analysis of ophthalmic data is the lack of accounting for the positive correlation generally present between observations made in fellow eyes. The alternative of data analysis from only one eye in each patient may lead to loss of power and unrealistically large confidence intervals. This paper discusses a method to estimate kappa, a measure of agreement between two graders, when both graders rate the same set of pairs of eyes. The method assumes that the true left-eye and right-eye kappa values are equal and makes use of the correlated binocular data to estimate confidence intervals for the common kappa. Simulations show that the new estimators are better than the estimator based on only one eye; new confidence intervals had the correct coverage probability, but were usually only about 70 per cent as wide as single-eye intervals. The general methodology described here applies to analysis of grader agreement in rating other paired body structures.
眼科数据统计分析中一个常见的错误是,未考虑在双眼进行的观察之间通常存在的正相关性。在每位患者中仅分析一只眼睛的数据,可能会导致检验效能降低以及置信区间大得不符合实际。本文讨论了一种在两名分级者对同一组双眼进行评分时,估计kappa值(一种衡量两名分级者之间一致性的指标)的方法。该方法假定左眼和右眼的真实kappa值相等,并利用相关的双眼数据来估计共同kappa值的置信区间。模拟结果表明,新的估计方法优于仅基于一只眼睛的估计方法;新的置信区间具有正确的覆盖概率,但通常只有单眼区间宽度的70%左右。这里描述的一般方法适用于分析分级者在对其他成对身体结构进行评分时的一致性。