The International Journal of Biostatistics Relative Risk Estimation in Randomized Controlled Trials : A Comparison of Methods for Independent Observations

The relative risk is a clinically important measure of the effect of treatment on binary outcomes in randomized controlled trials (RCTs). An adjusted relative risk can be estimated using log binomial regression; however, convergence problems are common with this model. While alternative methods have been proposed for estimating relative risks, comparisons between methods have been limited, particularly in the context of RCTs. We compare ten different methods for estimating relative risks under a variety of scenarios relevant to RCTs with independent observations. Results of a large simulation study show that some methods may fail to overcome the convergence problems of log binomial regression, while others may substantially overestimate the treatment effect or produce inaccurate confidence intervals. Further, conclusions about the effectiveness of treatment may differ depending on the method used. We give recommendations for choosing a method for estimating relative risks in the context of RCTs with independent observations.

[1]  J Carpenter,et al.  Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. , 2000, Statistics in medicine.

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

[3]  E. Norton,et al.  What's the Risk? A simple approach for estimating adjusted risk measures from nonlinear models including logistic regression. , 2009, Health services research.

[4]  M. Smrkovsky,et al.  Bayley Scales of Infant Development - Second Edition (BSID-II)) , 2003 .

[5]  J Lee,et al.  Odds ratio or relative risk for cross-sectional data? , 1994, International journal of epidemiology.

[6]  Jesse A Berlin,et al.  Relative risks and confidence intervals were easily computed indirectly from multivariable logistic regression. , 2007, Journal of clinical epidemiology.

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

[8]  S. Weitzen,et al.  A Randomized Trial of the Fischer Cone Biopsy Excisor and Loop Electrosurgical Excision Procedure , 2004, Obstetrics and gynecology.

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

[10]  S. Lipsitz,et al.  Quasi-likelihood estimation for relative risk regression models. , 2005, Biostatistics.

[11]  S D Walter,et al.  Choice of effect measure for epidemiological data. , 2000, Journal of clinical epidemiology.

[12]  Donna Spiegelman,et al.  Easy SAS calculations for risk or prevalence ratios and differences. , 2005, American journal of epidemiology.

[13]  W. J. Elliott,et al.  Effectiveness of Home Blood Pressure Monitoring, Web Communication, and Pharmacist Care on Hypertension Control: A Randomized Controlled Trial , 2009 .

[14]  A. Penman,et al.  Complementary Log–Log Regression for the Estimation of Covariate‐Adjusted Prevalence Ratios in the Analysis of Data from Cross‐Sectional Studies , 2009, Biometrical journal. Biometrische Zeitschrift.

[15]  Farah Ahmad,et al.  Computer-Assisted Screening for Intimate Partner Violence and Control , 2009, Annals of Internal Medicine.

[16]  P. Cummings.  Methods for Estimating Adjusted Risk Ratios , 2009 .

[17]  Neurodevelopmental Outcomes of Preterm Infants Fed High-Dose Docosahexaenoic Acid , 2009 .

[18]  Binbing Yu,et al.  Estimating relative risks for common outcome using PROC NLP , 2008, Comput. Methods Programs Biomed..

[19]  J. Deddens,et al.  A comparison of two methods for estimating prevalence ratios , 2008, BMC medical research methodology.

[20]  Shuangge Ma,et al.  Relative Risk Regression in Medical Research: Models, Contrasts, Estimators, and Algorithms , 2006 .

[21]  H C Van Houwelingen,et al.  Risk ratio and rate ratio estimation in case-cohort designs: hypertension and cardiovascular mortality. , 1993, Statistics in medicine.

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

[23]  R. Kay.  Statistical Principles for Clinical Trials , 1998, The Journal of international medical research.

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

[25]  J. Deddens.  Estimation of prevalence ratios when PROC GENMOD does not converge , 2002 .

[26]  D W Hosmer,et al.  Parameter Estimation and Goodness‐of‐Fit in Log Binomial Regression , 2006, Biometrical journal. Biometrische Zeitschrift.

[27]  A. Barros,et al.  Bmc Medical Research Methodology Open Access Alternatives for Logistic Regression in Cross-sectional Studies: an Empirical Comparison of Models That Directly Estimate the Prevalence Ratio Cox Regressioncross-sectional Studieslogistic Regressionodds Ratiopoisson Regressionprevalence Ratiorobust Varia , 2003 .

[28]  Arthur S Slutsky,et al.  Ventilation strategy using low tidal volumes, recruitment maneuvers, and high positive end-expiratory pressure for acute lung injury and acute respiratory distress syndrome: a randomized controlled trial. , 2008, JAMA.

[29]  Edna Schechtman,et al.  Odds ratio, relative risk, absolute risk reduction, and the number needed to treat--which of these should we use? , 2002, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[30]  G. Zou,et al.  A modified poisson regression approach to prospective studies with binary data. , 2004, American journal of epidemiology.

[31]  Peter J Sterk,et al.  Internet-Based Self-management Plus Education Compared With Usual Care in Asthma , 2009, Annals of Internal Medicine.

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