A modified poisson regression approach to prospective studies with binary data.

Relative risk is usually the parameter of interest in epidemiologic and medical studies. In this paper, the author proposes a modified Poisson regression approach (i.e., Poisson regression with a robust error variance) to estimate this effect measure directly. A simple 2-by-2 table is used to justify the validity of this approach. Results from a limited simulation study indicate that this approach is very reliable even with total sample sizes as small as 100. The method is illustrated with two data sets.

[1]  Markku Mikael Nurminen.  To use or not to use the odds ratio in epidemiologic analyses? , 1995, European Journal of Epidemiology.

[2]  Xiaonan Xue,et al.  Estimating the relative risk in cohort studies and clinical trials of common outcomes. , 2003, American journal of epidemiology.

[3]  T. Cook.  Advanced statistics: up with odds ratios! A case for odds ratios when outcomes are common. , 2002, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[4]  R. Bender,et al.  Calculating the "number needed to be exposed" with adjustment for confounding variables in epidemiological studies. , 2002, Journal of clinical epidemiology.

[5]  Allan Donner,et al.  Design and Analysis of Cluster Randomization Trials in Health Research , 2001 .

[6]  John M. Lachin,et al.  Biostatistical Methods: The Assessment of Relative Risks , 2000 .

[7]  D. Gens,et al.  Diaspirin Cross-Linked Hemoglobin (DCLHb) in the Treatment of Severe Traumatic Hemorrhagic Shock: A Randomized Controlled Efficacy Trial , 1999 .

[8]  Lisa M. Schwartz,et al.  Misunderstandings about the effects of race and sex on physicians' referrals for cardiac catheterization. , 1999, The New England journal of medicine.

[9]  J. Zhang,et al.  What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. , 1998, JAMA.

[10]  D Kriebel,et al.  Prevalence odds ratio or prevalence ratio in the analysis of cross sectional data: what is to be done? , 1998, Occupational and environmental medicine.

[11]  M R Petersen,et al.  Prevalence proportion ratios: estimation and hypothesis testing. , 1998, International journal of epidemiology.

[12]  S Greenland,et al.  Standardized estimates from categorical regression models. , 1995, Statistics in medicine.

[13]  D. Consonni,et al.  Estimation of prevalence rate ratios from cross-sectional data. , 1995, International journal of epidemiology.

[14]  M. Bracken,et al.  Clinically useful measures of effect in binary analyses of randomized trials. , 1994, Journal of clinical epidemiology.

[15]  N. Jewell,et al.  A geometric approach to assess bias due to omitted covariates in generalized linear models , 1993 .

[16]  S. Genuth,et al.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. , 1993, The New England journal of medicine.

[17]  S. Greenland,et al.  Estimating standardized risk differences from odds ratios. , 1991, Biometrics.

[18]  D L Sackett,et al.  An assessment of clinically useful measures of the consequences of treatment. , 1988, The New England journal of medicine.

[19]  S Wallenstein,et al.  Epidemiologic programs for computers and calculators. Inferences on odds ratios, relative risks, and risk differences based on standard regression programs. , 1987, American journal of epidemiology.

[20]  S Greenland,et al.  Interpretation and choice of effect measures in epidemiologic analyses. , 1987, American journal of epidemiology.

[21]  W D Flanders,et al.  Large sample confidence intervals for regression standardized risks, risk ratios, and risk differences. , 1987, Journal of chronic diseases.

[22]  R. Royall.  Model robust confidence intervals using maximum likelihood estimators , 1986 .

[23]  S. Zeger,et al.  Longitudinal data analysis using generalized linear models , 1986 .

[24]  S Wacholder,et al.  Binomial regression in GLIM: estimating risk ratios and risk differences. , 1986, American journal of epidemiology.

[25]  J M Robins,et al.  Estimation of a common effect parameter from sparse follow-up data. , 1985, Biometrics.

[26]  M. Gail,et al.  Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates , 1984 .

[27]  S. Greenland.  Limitations of the logistic analysis of epidemiologic data. , 1979, American journal of epidemiology.

[28]  J. Cornfield,et al.  A method of estimating comparative rates from clinical data; applications to cancer of the lung, breast, and cervix. , 1951, Journal of the National Cancer Institute.