Measuring morbidity: Disease counts, binary variables, and statistical power
Measuring morbidity: Disease counts, binary variables, and statistical power
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DOI：
10.1093/geronb/55.3.s173
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发表时间：
2000-05-01
影响因子：
6.2
通讯作者：
Wilmoth, JM
中科院分区：
文献类型：
--
作者：
Ferraro, KF;Wilmoth, JM
目标。本研究将二元疾病变量的使用与自评健康模型中相同条件的计数进行比较，以更好地了解每种方法的优缺点。在特定的。该分析旨在确定二元变量方法的统计能力是否足够。在两项大型全国性调查中，对成年人的发病率进行了横断面和纵向分析。尽管两种方法之间的差异不大，但二元变量方法提供了更大的解释力和略高的R-2值。尽管有这些优势，但在某些情况下，特别是对于相对罕见和/或在结果变量上表现出适度差异的情况，统计能力不足。当使用二元变量方法时，统计能力估计是可取的，特别是如果疾病和健康状况的列表很广泛。虽然对疾病的简单计数在某些研究应用中可能有用，但在许多研究项目中，对严重和非严重疾病的单独计数应该更有用，同时避免统计能力不足的风险。
Objectives. This study compares the use of the binary disease variables with counts of the same conditions in models of Self-rated health to better understand the advantages and disadvantages of each approach. In particular. the analysis seeks to determine if statistical power is adequate fur the binary variable approach.Methods. Morbidity measures from adults in 2 large national surveys were used in both cross-sectional and longitudinal analyses.Results. Although differences across the approaches are modest, the binary variable approach offers greater explanatory power and slightly higher R-2 values, Despite these advantages, statistical power is insufficient in some cases, especially for conditions that are relatively rare and/or that manifest modest differences on the outcome variable.Discussion. Statistical power estimates are advisable when using the binary variable approach, especially if the list of diseases and health conditions is extensive. Although a simple count or diseases may be useful in some research applications, separate counts for serious and nonserious conditions should be more useful in many research projects while avoiding the risk of inadequate statistical power.