Hester van Eeren

| Chapter 4 4 | 78 Imbens, G. W. (2000). The role of the propensity score in estimating dose-response functions. Biometrika, 87 , 706-710. Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. The Review of Economics and Statistics, 86 , 4-29. Kreif, N., Grieve, R., Radice, R., Sadique, Z., Ramsahai, R., & Sekhon, J. S. (2012). Methods for estimating subgroup effects in cost-effectiveness analyses that use observational data. Medical Decision Making, 32 , 750-763. Kurth, T., Walker, A. M., Glynn, R. J., Chan, K. A., Gaziano, J. M., Berger, K., & Robins, J. M. (2006). Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity- based weighting under conditions of nonuniform effect. American Journal of Epidemiology, 163 , 262-270. Liem, Y. S., Wong, J. B., Hunink, M. M., de Charro, F. T., & Winkelmayer, W. C. (2010). Propensity scores in the presence of effect modification: A case study using the comparison of mortality on hemodialysis versus peritoneal dialysis. Emerging Themes in Epidemiology, 7 , 1-8. Lunt, M., Solomon, D., Rothman, K., Glynn, R., Hyrich, K., Symmons, D. P., . . . British Society for Rheumatology Biologics Register Control Centre, C. (2009). Different methods of balancing covariates leading to different effect estimates in the presence of effect modification. American Journal of Epidemiology, 169 , 909-917. Norcross, J. C., & Wampold, B. E. (2011). What works for whom: Tailoring psychotherapy to the person. Journal of Clinical Psychology, 67 , 127-132. Radice, R., Ramsahai, R., Grieve, R., Kreif, N., Sadique, Z., & Sekhon, J. S. (2012). Evaluating treatment effectiveness in patient subgroups: A comparison of propensity score methods with an automated matching approach. International Journal of Biostatistics, 8 , 25. R Development Core Team (2010). R: A language and environment for statistical computing (Version 2.13.0). Vienna, Austria: R Foundation for Statistical Computing. Rosenbaum, P. R. (1991). Discussing hidden bias in observational studies. Annals of Internal Medicine, 115 , 901-905. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70 , 41-55. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66 , 688-701. Rubin, D. B. (1979). Using multivariate matched sampling and regression adjustment to control bias in observational studies. Journal of the American Statistical Association, 74 , 318-324. Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine, 127 , 757-763. Rubin, D. B. (2004). On principles for modeling propensity scores in medical research. Pharmacoepidemiology and Drug Safety, 13 , 855-857. Rubin, D. B., & Thomas, N. (2000). Combining propensity score matching with additional adjustments for prognostic covariates. Journal of the American Statistical Association, 95 , 573-585. Shah, B. R., Laupacis, A., Hux, J. E., & Austin, P. C. (2005). Propensity score methods gave similar results to traditional regression modeling in observational studies: A systematic review. Journal of Clinical Epidemiology, 58 , 550-559. Soeteman, D. I., Verheul, R., Meerman, A. M., Ziegler, U., Rossum, B. V., Delimon, J., . . . Kim, J. J. (2011). Cost-effectiveness of psychotherapy for cluster C personality disorders: A decision-analytic model in the Netherlands. Journal of Clinical Psychiatry, 72 , 51-59. Spreeuwenberg, M. D., Bartak, A., Croon, M. A., Hagenaars, J. A., Busschbach, J. J., Andrea, H., . . . Stijnen, T. (2010). The multiple propensity score as control for bias in the comparison of more than two treatment arms: An introduction from a case study in mental health. Medical Care, 48 , 166-174.