Estimating confidence regions of common measures of the baseline and treatment effect on dichotomous outcome of a population

References

• Austin, P. C. (2007). The performance of different propensity score method for estimating marginal odds ratio. Statistics in Medicine 26:3078–3094.
   Google Scholar
• Austin, P. C. (2010). Absolute risk reductions, relative risks, relative risk reductions, and numbers needed to treat can be obtained from a logistic regression model. Journal of Clinical Epidemiology 63:2–6.
   Google Scholar
• Austin, P. C., Grootendorst, P., Normand, S. L. T., Anderson, G. M. (2007). Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: A Monte Carlo study. Statistics in Medicine 26:754–768.
   Google Scholar
• Bieler, G. S., Brown, G. G., Williams, R. L., Brogan, D. J. (2010). Estimating model-adjusted risks, risk differences, and risk ratios from complex survey data. American Journal of Epidemiology 171:618–623.
   Google Scholar
• Carpenter, J., Bithell, J. (2000). Bootstrap confidence intervals: When, which, what? A practical guide for medical statisticians. Statistics in Medicine 19:1141–1164.
   Google Scholar
• Cheung, Y. B. (2007). A modified least-squares regression approach to the estimation of risk difference. American Journal of Epidemiology 166:1337–1344.
   Google Scholar
• Davison, A. C., Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge: Cambridge University Press.
   Google Scholar
• Gail, M. H., Wieand, S., Piantadosi, S. (1984). Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika 71:431–444.
   Google Scholar
• Gehrmann, U., Kuss, O., Wellmann, J., Bender, R. (2010). Logistic regression was preferred to estimate risk differences and numbers needed to be exposed adjusted for covariates. Journal of Clinical Epidemiology 63:1223–1231.
   Google Scholar
• Graubard, B. I., Korn, E. L. (1999). Predictive margins with survey data. Biometrics 55:652–659.
   Google Scholar
• Greenland, S. (1987). Interpretation and choice of effect measures in epidemiologic analyses. American Journal of Epidemiology 125:761–768.
   Google Scholar
• Greenland, S. (2004a). Model-based estimation of relative risk and other epidemiologic measures in studies of common outcomes and case–control studies. American Journal of Epidemiology 160:301–305.
   Google Scholar
• Greenland, S. (2004b). Interval estimation by simulation as an alternative to and extension of confidence intervals. International Journal of Epidemiology 33:1389–1397.
   Google Scholar
• Greenland, S., Robins, J. M. (1986). Identifiability, exchangeability, epidemiological confounding. International Journal of Epidemiology 15:413–419.
   Google Scholar
• Greenland, S., Robins, J. M., Pearl, J. (1999). Confounding and collapsibility in causal inference. Statistical Science 14:29–46.
   Google Scholar
• Lane, P. W., Nelder, J. A. (1982). Analysis of covariance and standardization as instance of prediction. Biometrics 36:613–621.
   Google Scholar
• Lee, Y., Nelder, J. A. (2004). Conditional and marginal models: Another view. Statistical Science 19:219–228.
   Google Scholar
• Lindsey, J. K. (1996). Parametric Statistical Inference. Oxford: Clarendon Press.
   Google Scholar
• McNutt, L. A., Wu, C., Xue, X., Hafner, J. P. (2003). Estimating the relative risk in cohort studies and clinical trials of common outcomes. American Journal of Epidemiology 157:940–943.
   Google Scholar
• Nguyen, T. V. H., Bengtsson, C., Nguyen, G. K., Hoang, T. H., Phung, D. C., Srberg, M., Granstrm, M. (2008). Evaluation of two triple-therapy regiments with metronidazole or clarithromycin for the eradication of H. pyloriinfection in Vietnamese children, a randomized double blind clinical trial. Helicobacter 13:550–556.
   Google Scholar
• Rosenbaum, P. R. (1995). Observational Studies. New York: Springer.
   Google Scholar
• Rosenbaum, P. R., Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55.
   Google Scholar
• Rothman, K. J., Greenland, S., Lash, T. L. (2008). Modern Epidemiology. 3rd ed. Philadelphia: Lippincott Williams & Wilkins.
   Google Scholar
• Rubin, D. B., Wang, X., Yin, L., Zell, E. (2009). Estimating the effect of treating hospital type on cancer survival in Sweden using principal stratification. In: O'Hagan, T., West, M., eds. The Handbook of Applied Bayesian Analysis. Oxford: Oxford University Press.
   Google Scholar
• Spiegelman, D., Hertzmark, E. (2005). Easy SAS calculations for risk or prevalence ratios and differences. American Journal of Epidemiology 162:199–200.
   Google Scholar
• Wang, X., Jin, Y., Yin, L. (2014). Measuring and estimating treatment effect on dichotomous outcome of a population. Statistical Methods in Medical Research. doi:10.1177/0962280213502146.
   Google Scholar
• Wang, X., Jin, Y., Yin, L. (2015). Point and interval estimations of marginal risk difference by logistic model. Communications in Statistics: Theory and Methods. doi:10.1080/03610926.2013.851229.
   Google Scholar
• Zhang, J., Yu, K. F. (1998). What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. Journal of the American Medical Association 280:1690–1691.
   Google Scholar
• Zou, Y. (2014). A modified poisson regression approach to prospective studies with binary data. American Journal of Epidemiology 159:702–706.
   Google Scholar