Prevalence odds ratio or prevalence ratio in the analysis of cross sectional data: what is to be done?
Tóm tắt
OBJECTIVES: To review the appropriateness of the prevalence odds ratio (POR) and the prevalence ratio (PR) as effect measures in the analysis of cross sectional data and to evaluate different models for the multivariate estimation of the PR. METHODS: A system of linear differential equations corresponding to a dynamic model of a cohort with a chronic disease was developed. At any point in time, a cross sectional analysis of the people then in the cohort provided a prevalence based measure of the effect of exposure on disease. This formed the basis for exploring the relations between the POR, the PR, and the incidence rate ratio (IRR). Examples illustrate relations for various IRRs, prevalences, and differential exodus rates. Multivariate point and interval estimation of the PR by logistic regression is illustrated and compared with the results from proportional hazards regression (PH) and generalised linear modelling (GLM). RESULTS: The POR is difficult to interpret without making restrictive assumptions and the POR and PR may lead to different conclusions with regard to confounding and effect modification. The PR is always conservative relative to the IRR and, if PR > 1, the POR is always > PR. In a fixed cohort and with an adverse exposure, the POR is always > or = IRR, but in a dynamic cohort with sufficient underlying follow up the POR may overestimate or underestimate the IRR, depending on the duration of follow up. Logistic regression models provide point and interval estimates of the PR (and POR) but may be intractable in the presence of many covariates. Proportional hazards and generalised linear models provide statistical methods directed specifically at the PR, but the interval estimation in the case of PH is conservative and the GLM procedure may require constrained estimation. CONCLUSIONS: The PR is conservative, consistent, and interpretable relative to the IRR and should be used in preference to the POR. Multivariate estimation of the PR should be executed by means of generalised linear models or, conservatively, by proportional hazards regression.
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Tài liệu tham khảo
Keiding N. Age-specific incidence and prevalence: a statistical perspective. J R Statist Soc A 1991;154:371–412.
Alho JM. On prevalence, incidence and duration in general stable populations. Biometrics 1992;48:587–92.
Lee J, Chia KS. Estimation of prevalence rate ratios for cross-sectional data: an example in occupational epidemiology. Br J Ind Med 1993;50:861–2.
Axelson O. Some recent developments in occupational epidemiology. Scand J Work Environ Health 1994;20:9–18.
Axelson O, Fredriksson M, Ekberg K. Use of the prevalence ratio v the prevalence odds ratio as a measure of risk in cross sectional studies. Occup Environ Med 1994;51:574.
Lee J, Chia KS. Use of the prevalence ratio v the prevalence odds ratio as a measure of risk in cross sectional studies. Occup Environ Med 1994;51:841.
Osborn J, Cattaruzza MS. Odds ratio and relative risk for cross-sectional data. Int J Epidemiol 1995;24:464–5.
Stromberg U. Prevalence odds ratio v prevalence ratio some further comments. Occup Environ Med 1995;42:143.
Axelson O, Fredriksson M, Ekberg K. Use of the prevalence ratio v the prevalence odds ratio in view of confounding in cross sectional studies. Occup Environ Med 1995;52:494–6.
Lee J, Chia KS. Prevalence odds ratio v prevalence ratio—a response. Occup Environ Med 1995;52:781–4.
Zocchetti C, Consonni D, Bertazzi PA. Estimation of prevalence rate ratios from cross-sectional data. Int J Epidemiol 1995;24:1064–5.
Lee J. Estimation of prevalence rate ratios from cross sectional data: a reply. Int J Epidemiol 1995;24:1066–7.
Nurminen M To use or not to use the odds ratio in epidemiologic analysis? Eur J Epidemiol 1995;11:365–71.
Zocchetti C, Consonni D, Bertazzi PA. Relationship between prevalence rate ratios and odds ratios in cross-sectional studies. Int J Epidemiol 1997;26:220–3.
Greenland S. Interpretation and choice of eVect measures in epidemiologic analysis. Am J Epidemiol 1987;125:761–8.
Rothman KJ. Modern epidemiology. Boston: Little, Brown, 1986.
Eisen E. Healthy worker eVect in morbidity studies. Med Lav 1995;86:125–38.
Steineck G, Ahlbom A. A definition of bias founded on the concept of the study base. Epidemiology 1992;3:477–82.
Nurminen M. On the epidemiologic notion of confounding and confounder identification. Scand J Work Environ Health 1997;23:64–71.
Cox DR. Regression models and life-tables (with discussion). Journal of the Royal Statististical Society B 1972;34: 187–220.
SAS Institute. SAS/STAT User’s guide, version 6.09, 4th ed. Cary, NC: SAS Institute, 1989.
SAS Institute. The GENMOD procedure. Release 6.09.Cary NC: SAS Institute, 1993. (SAS technical report P-243.)