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Reducing classification errors in cohort studies: The approach and a practical application

 

作者: Kenneth F. Schulz,   Willard Cates,   David A. Grimes,   Richard M. Selik,   Carl W. Tyler,  

 

期刊: Statistics in Medicine  (WILEY Available online 1983)
卷期: Volume 2, issue 1  

页码: 25-31

 

ISSN:0277-6715

 

年代: 1983

 

DOI:10.1002/sim.4780020104

 

出版商: Wiley Subscription Services, Inc., A Wiley Company

 

关键词: Bias;Misclassification;Statistical power;Sample size

 

数据来源: WILEY

 

摘要:

AbstractClassification errors of dependent variables can distort the results of observational studies. To reduce misclassification from our multicentre observational study of abortion complications, we extended the methodology of Lawrence and Greenwald9for use in situations of unequal sample sizes and implemented both an office review and a field review. We reabstracted 424 reported complications and a random sample of 364 reported non‐serious cases from 12 institutions participating in our study. In total, 30 per cent of the reported serious complications turned out to be misclassified: the office review detected 74 per cent of the total number of misclassifications with the remainder found in the field review. Because, with our particular data base, we estimated expending only 15 per cent of the total resources with our office effort, this represented the most cost‐efficient approach to reducing classification errors. By eliminating the false positives from our study, we forced the specificity to 1·00 which produced both an unbiased estimate of the relative risk and an increase of 4 per cent to 63 per cent in the power of our s

 

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