Reporting of treatment effects from randomized trials: A plea for multivariable risk ratios

Contemporary Clinical Trials

Volumn 32, Issue 3, 2011, Pages 399-402

  Groenwold, Rolf H H ^a   Moons, Karel G M ^a   Peelen, Linda M ^a   Knol, Mirjam J ^a   Hoes, Arno W ^a  

^a UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

Author keywords

Covariate adjustment; Non collapsibility; Odds ratio; Randomized controlled trials; Risk ratio

Indexed keywords

FIBRINOLYTIC AGENT; INSULIN; STREPTOKINASE; TISSUE PLASMINOGEN ACTIVATOR;

ARTICLE; CLINICAL RESEARCH; DIABETES MELLITUS; FIBRINOLYTIC THERAPY; HEART INFARCTION; HUMAN; INSULIN TREATMENT; OUTCOMES RESEARCH; RANDOMIZED CONTROLLED TRIAL (TOPIC); RISK; STATISTICAL ANALYSIS; TREATMENT OUTCOME; VERY LOW BIRTH WEIGHT;

FEMALE; HUMANS; MALE; ODDS RATIO; RANDOMIZED CONTROLLED TRIALS AS TOPIC; TREATMENT OUTCOME;

EID: 79953064836     PISSN: 15517144     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cct.2010.12.011     Document Type: Article

References (20)

1. Berger V.W. Quantifying the magnitude of baseline covariate imbalance resulting from selection bias in randomized trials. Biom J 2005, 47:119-127.
2. Roberts C., Torgerson D.J. Understanding controlled trials: baseline imbalance in randomized controlled trials. BMJ 1999, 319:185.
3. Senn S. Testing for baseline balance in clinical trials. Stat Med 1994, 13:1715-1726.
4. ICH Harmonised Tripartite Guideline Statistical principles for clinical trials. Stat Med 1999, 18:1905-1942.
5. Pocock S.J., Assmann S.E., Enos L.E., Kasten L.E. Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Stat Med 2002, 21:2917-2930.
6. Steyerberg E.W., Bossuyt P.M., Lee K.L. Clinical trials in acute myocardial infarction: should we adjust for baseline characteristics?. Am Heart J 2000, 139:745-751.
7. Hernandez A.V., Steyerberg E.W., Habbema J.D. Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements. J Clin Epidemiol 2004, 57:454-460.
8. Beardsall K., Vanhaesebrouck S., Ogilvy-Stuart A.L., et al. Early insulin therapy in very-low-birth-weight infants. N Engl J Med 2008, 359:1873-1884.
9. Gail M.H., Wieand S., Piantadosi S. Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika 1984, 71:431-444.
10. Petersen M.R., Deddens J.A. Effects of omitting a covariate in Poisson models when data are balanced. Can J Stat 2000, 28:439-445.
11. Greenland S., Robins J.M., Pearl J. Confounding and collapsibility in causal inference. Stat Sci 1999, 14:29-46.
12. Newman S.C. Biostatistical methods in epidemiology 2001, John Wiley & Sons, New York. 1st ed.
13. Bailey K.R. Clinical trials in acute myocardial infarction: when should we adjust for baseline characteristics?. Am Heart J 2000, 139:761-763.
14. McNutt L.A., Wu C., Xue X., Hafner J.P. Estimating a relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol 2003, 157:940-943.
15. Barros A.J., Hirakata V.N. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate prevalence ratios. BMC Med Res Methodol 2003, 3:21.
16. Petersen M.R., Deddens J.A. A comparison of two methods for estimating prevalence ratios. BMC Med Res Methodol 2008, 8:9.
17. Deddens J.A., Petersen M.R. Approaches for estimating prevalence ratios. Occup Environ Med 2008, 65:501-506.
18. Lumley T, Kronmal R, Ma S. Relative Risk Regression in Medical Research: Models, Contrasts, Estimators, and Algorithms. UW Biostatistics Working Paper Series 2006;Working Paper 293.
19. Kleinman L.C., Norton E.C. What's the risk? A simple approach for estimating adjusted risk measures from nonlinear models including logistic regression. Health Serv Res 2009, 44:288-302.
20. Janes H., Dominici F., Zeger S. On quantifying the magnitude of confounding. Biostatistics 2010, 11:572-582.