Modeling treatment effects on binary outcomes with grouped-treatment variables and individual covariates.
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Charles E McCulloch | Mark van der Laan | S Claiborne Johnston | C. McCulloch | M. J. van der Laan | S. Johnston | Tanya Henneman | T. Henneman
[1] J. Kalbfleisch,et al. Between- and within-cluster covariate effects in the analysis of clustered data. , 1998, Biometrics.
[2] A Muñoz,et al. Effectiveness of potent antiretroviral therapy on time to AIDS and death in men with known HIV infection duration. Multicenter AIDS Cohort Study Investigators. , 1998, JAMA.
[3] V. Siskind. The need for randomization in the study of intended effects. , 1985, Statistics in medicine.
[4] J. Hausman. Specification tests in econometrics , 1978 .
[5] David A. Jaeger,et al. Problems with Instrumental Variables Estimation when the Correlation between the Instruments and the Endogenous Explanatory Variable is Weak , 1995 .
[6] Peter E. Kennedy. A Guide to Econometrics , 1979 .
[7] S. Johnston,et al. Combining ecological and individual variables to reduce confounding by indication: case study--subarachnoid hemorrhage treatment. , 2000, Journal of clinical epidemiology.
[8] K McPherson,et al. The Cochrane Lecture. The best and the enemy of the good: randomised controlled trials, uncertainty, and assessing the role of patient choice in medical decision making. , 1994, Journal of epidemiology and community health.
[9] A. Hoes,et al. Confounding and indication for treatment in evaluation of drug treatment for hypertension , 1997, BMJ.
[10] K. S. Joseph. The evolution of clinical practice and time trends in drug effects. , 1994, Journal of clinical epidemiology.
[11] L. Stefanski,et al. Instrumental Variable Estimation in Binary Regression Measurement Error Models , 1995 .
[12] D. Byar. Why data bases should not replace randomized clinical trials. , 1980, Biometrics.
[13] S. Johnston,et al. Effect of endovascular services and hospital volume on cerebral aneurysm treatment outcomes. , 2000, Stroke.
[14] Joshua D. Angrist,et al. Identification of Causal Effects Using Instrumental Variables , 1993 .
[15] D P Byar,et al. Using observational data from registries to compare treatments: the fallacy of omnimetrics. , 1984, Statistics in medicine.
[16] P. Albert,et al. Models for longitudinal data: a generalized estimating equation approach. , 1988, Biometrics.
[17] Joseph P. Newhouse,et al. Does More Intensive Treatment of Acute Myocardial Infarction in the Elderly Reduce Mortality? Analysis Using Instrumental Variables , 1995 .
[18] R L Prentice,et al. Design considerations for estimation of exposure effects on disease risk, using aggregate data studies. , 1996, Statistics in medicine.
[19] M Palta,et al. Effect of omitted confounders on the analysis of correlated binary data. , 1997, Biometrics.
[20] W W Hauck,et al. Should we adjust for covariates in nonlinear regression analyses of randomized trials? , 1998, Controlled clinical trials.
[21] M. Kramer,et al. Uses of ecologic studies in the assessment of intended treatment effects. , 1999, Journal of clinical epidemiology.
[22] W W Hauck,et al. A consequence of omitted covariates when estimating odds ratios. , 1991, Journal of clinical epidemiology.