Prevalence odds ratio or prevalence ratio in the analysis of cross sectional data: what is to be done!

被引:392
作者
Thompson, ML
Myers, JE
Kriebel, D
机构
[1] Univ Washington, Dept Biostat, Seattle, WA 98195 USA
[2] Univ Cape Town, Sch Med, Dept Community Hlth, ZA-7700 Rondebosch, South Africa
[3] Univ Massachusetts, Dept Work Environm, Lowell, MA 01854 USA
关键词
prevalence; cross sectional study; logistic regression;
D O I
10.1136/oem.55.4.272
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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 FOR, 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 FOR is difficult to interpret without making restrictive assumptions and the FOR 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 FOR is always greater than or equal to IRR, but in a dynamic cohort with sufficient underlying follow up the FOR may overestimate or underestimate the IRR, depending on the duration of follow up. Logistic regression models provide point and interval estimates of the PR land FOR) 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 FOR. Multivariate estimation of the PR should be executed by means of generalised Linear models or, conservatively, by proportional hazards regression.
引用
收藏
页码:272 / 277
页数:6
相关论文
共 26 条
[1]   ON PREVALENCE, INCIDENCE, AND DURATION IN GENERAL STABLE-POPULATIONS [J].
ALHO, JM .
BIOMETRICS, 1992, 48 (02) :587-592
[2]   USE OF THE PREVALENCE RATIO V THE PREVALENCE ODDS RATIO IN VIEW OF CONFOUNDING IN CROSS-SECTIONAL STUDIES [J].
AXELSON, O ;
FREDRIKSSON, M ;
EKBERG, K .
OCCUPATIONAL AND ENVIRONMENTAL MEDICINE, 1995, 52 (07) :494-494
[3]   USE OF THE PREVALENCE RATIO-UPSILON THE PREVALENCE ODDS RATIO AS A MEASURE OF RISK IN CROSS-SECTIONAL STUDIES [J].
AXELSON, O ;
FREDRIKSSON, M ;
EKBERG, K .
OCCUPATIONAL AND ENVIRONMENTAL MEDICINE, 1994, 51 (08) :574-574
[4]  
AXELSON O, 1994, SCAND J WORK ENV HEA, V20, P9
[5]   COVARIANCE ANALYSIS OF CENSORED SURVIVAL DATA [J].
BRESLOW, N .
BIOMETRICS, 1974, 30 (01) :89-99
[6]  
COX DR, 1972, J R STAT SOC B, V34, P187
[7]  
Eisen E A, 1995, Med Lav, V86, P125
[8]   INTERPRETATION AND CHOICE OF EFFECT MEASURES IN EPIDEMIOLOGIC ANALYSES [J].
GREENLAND, S .
AMERICAN JOURNAL OF EPIDEMIOLOGY, 1987, 125 (05) :761-768
[9]   ODDS RATIOS IN CROSS-SECTIONAL STUDIES [J].
HUGHES, K .
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 1995, 24 (02) :463-464