Survey of methods and statistical models used in the analysis of occupational cohort studies.

Abstract

This survey was conducted to determine the frequency with which different data analysis techniques are being used in occupational cohort studies. Of particular interest was the relative use of external and internal comparison groups, and the choice of multivariable model. Occupational cohort studies published in 1990-91 were located with Medline and Index Medicus, and the contents of several relevant journals were systematically reviewed. Each study was categorised by the methods of external or internal comparisons performed. Of 200 occupational cohort studies identified, 104 (52%) conducted only external comparisons, 46 (23%) conducted only internal, and 50 (25%) presented both. Of those that used an external referent population, about two thirds used a national standard. 40 of the studies that performed internal comparisons fitted multivariable models, with use divided about equally between proportional hazards regression, Poisson regression, and logistic regression. The finding that logistic regression is used quite commonly, even though it does not directly model time dependent data of the type frequently encountered in occupational cohort studies, was suprising. The reasons why investigators choose from among the available statistical and modelling techniques are likely to include familiarity, ease of use, restrictions in study population characteristics, especially study size, and others. Authors should be encouraged to be more explicit about the statistical methods used in the analysis of occupational cohort studies, as well as whether important assumptions about their data have been evaluated.