Assessing the heterogeneity of treatment effects via potential outcomes of individual patients

Journal of the Royal Statistical Society. Series C: Applied Statistics

Volumn 62, Issue 5, 2013, Pages 687-704

  Zhang, Zhiwei ^a   Wang, Chenguang ^b   Nie, Lei ^a   Soon, Guoxing ^a  

^a CENTER FOR DRUG EVALUATION AND RESEARCH   (United States)

^b JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

Causal inference; Conditional independence; Copula; Counterfactual; Random effect; Sensitivity analysis

Indexed keywords

EID: 84885424877     PISSN: 00359254     EISSN: 14679876     Source Type: Journal    
DOI: 10.1111/rssc.12012     Document Type: Article

References (23)

1. Berger, R. L. and Boos, D. D. (1994) P values maximised over a confidence set for the nuisance parameter. J. Am. Statist. Ass., 89, 1012-1016.
2. Carroll, R. J., Spiegelman, C. H., Lan, K. K. G., Kent, K. T. and Abbott, R. D. (1984) On error-in-variables for binary regression models. Biometrika, 71, 19-25.
3. Fatkenheuer, G., Nelson, M., Lazzarin, A., Konourina, I., Hoepelman, A. I., Lampiris, H., Hirschel, B., Tebas, P., Raffi, F., Trottier, B., Bellos, N., Saag, M., Cooper, D. A., Westby, M., Tawadrous, M., Sullivan, J. F., Ridgway, C., Dunne, M. W., Felstead, S., Mayer, H.and van der Ryst, E. for the MOTIVATE Study Teams (2008) Subgroup analyses of maraviroc in previously treated R5 HIV-1 infection. New Engl. J. Med., 359, 1442-1455.
4. Gadbury, G. L. and Iyer, H. K. (2000) Unit-treatment interaction and its practical consequences. Biometrics, 56, 882-885.
5. Gadbury, G. L., Iyer, H. K. and Albert, J. M. (2004) Individual treatment effects in randomized trials with binary outcomes. J. Statist. Planng Inf., 121, 163-174.
6. Gadbury, G. L., Iyer, H. K. and Allison, D. B. (2001) Evaluating subject-treatment interaction when comparing two treatments. J. Biopharm. Statist., 11, 313-333.
7. Gail, M. and Simon, R. (1985) Testing for qualitative interactions between treatment effects and patient subsets. Biometrics, 41, 361-372.
8. Gulick, R. M., Lalezari, J., Goodrich, J., Clumeck, N., DeJesus, E., Horban, A., Nadler, J., Clotet, B., Karlsson, A., Wohlfeiler, M., Montana, J. B., McHale, M., Sullivan, J., Ridgway, C., Felstead, S., Dunne, M. W., van der Ryst, E. and Mayer, H.for the MOTIVATE Study Teams (2008) maraviroc for previously treated patients with R5 HIV-1 infection. New Engl. J. Med., 359, 1429-1441.
9. Holland, P. W. (1986) Statistics and causal inference (with discussion). J. Am. Statist. Ass., 81, 945-970.
10. Huang, Y., Gilbert, P. B. and Janes, H. (2012) Assessing treatment-selection markers using a potential outcomes framework. Biometrics, 68, 687-696.
11. Johnson, N. L. and Kotz, S. (1970) Distributions in Statistics, vol. . Boston: Houghton-Mifflin.
12. Liang, K.-Y. and Liu, X. H. (1991) Estimating equations in generalized linear models with measurement error. In Estimating Functions (ed. A. P. Godambe ). Oxford: Clarendon.
13. Nelsen, R. B. (1999) An Introduction to Copulas. New York: Springer.
14. Peto, R. (1982) Statistical Aspects of Cancer Trials. London: Chapman and Hall.
15. Pocock, S. J., Assmann, S. E., Enos, L. E. and Kasten, L. E. (2002) Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Statist. Med., 21, 2917-2930.
16. Poulson, R. S., Gadbury, G. L. and Allison, D. B. (2012) Treatment heterogeneity and individual qualitative interaction. Am. Statistn, 66, 16-24.
17. Rosenbaum, P. R. and Rubin, D. B. (1983) The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55.
18. Rubin, D. B. (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol., 66, 688-701.
19. Rubin, D. B. (1976) Inference and missing data. Biometrika, 63, 581-592.
20. Russek-Cohen, E. and Simon, R. M. (1997) Evaluating treatments when a gender by treatment interaction may exist. Statist. Med., 16, 455-464.
21. Stefanski, L. A. and Boos, D. D. (2002) The calculus of M-estimation. Am. Statistn, 56, 29-38.
22. van der Vaart, A. W. (1998) Asymptotic Statistics. Cambridge: Cambridge University Press.
23. Zeger, S. L., Liang, K.-Y. and Albert, P. S. (1988) Models for longitudinal data: a generalized estimating equation approach. Biometrics, 44, 1049-1060.