Principal stratification designs to estimate input data missing due to death

Biometrics

Volumn 63, Issue 3, 2007, Pages 641-649

  Frangakis, Constantine E ^a   Rubin, Donald B ^b   An, Ming Wen ^a   MacKenzie, Ellen ^a  

^a JOHNS HOPKINS UNIVERSITY   (United States)

^b HARVARD UNIVERSITY   (United States)

Author keywords

Causal inference; Censoring by death; Missing data; Potential outcomes; Principal stratification; Quantum mechanics

Indexed keywords

DATA ACQUISITION; INPUT OUTPUT PROGRAMS;

CAUSAL INFERENCES; CENSORING BY DEATH; CONDITIONAL DISTRIBUTION; CRITICAL EVENTS; DATA MISSING; INPUT DATAS; INPUT VARIABLES; MISSING DATA; POTENTIAL OUTCOMES; PRINCIPAL STRATIFICATION;

QUANTUM THEORY;

COHORT ANALYSIS; DATA ACQUISITION; ESTIMATION METHOD; INJURY; MORTALITY; STATISTICAL DISTRIBUTION;

ARTICLE; COHORT ANALYSIS; CONCEPTUAL FRAMEWORK; CRITICAL ILLNESS; DATA COLLECTION METHOD; HUMAN; MORTALITY; OUTCOME ASSESSMENT; PREDICTION; STANDARD; STATISTICAL ANALYSIS; TIME OF DEATH; TREATMENT PLANNING;

EID: 34548421653     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2007.00847_1.x     Document Type: Article

References (28)

1. Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996). Identification of causal effects using instrumental variables (with Discussion). Journal of the American Statistical Association 91, 444 472.
2. Cohen, H. J., et al. (2002). A controlled trial of inpatient and outpatient geriatric evaluation and management. New England Journal of Medicine 346, 905 912.
3. Cornoni-Huntley, J., et al. (1993). Established populations for epidemiologic studies of the elderly: Study design and methodology. Aging (Milano) 5, 27 37.
4. Cox, D. R. (1992). Causality: Some statistical aspects. Journal of the Royal Statistical Society, Series A 155, part 2, 291 301.
5. Fairclough, D. L., Peterson, H. F., and Chang, V. (1998) Why are missing quality of life data a problem in clinical trials of cancer therapy? Statistics in Medicine 17, 667 677.
6. Frangakis, C. E. and Rubin, D. B. (2002). Principal stratification in causal inference. Biometrics, 58, 21 29.
7. Gilbert, P. B., Bosch, R. J., and Hudgens, M. G. (2003). Sensitivity analysis for the assessment of causal vaccine effects on viral load in AIDS vaccine trials. Biometrics 59, 531 541.
8. GISSI 1986). Effectiveness of intravenous thrombolytic treatment in acute myocardial infarction. Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico. The Lancet 1 (8478 397 402.
9. Imbens, G. W. and Rubin, D. B. (1997). Bayesian inference for causal effects in randomized experiments with noncompliance. Annals of Statistics 25, 305 327.
10. Kurland, B. F. and Heagerty, P. J. (2005). Directly parametrized regression conditioning on being alive: Analysis of longitudinal data truncated by deaths. Biostatistics 6, 241 258.
11. Lin, H., McCulloch, C. E., and Mayne, S. T. (2002). Maximum likelihood estimation in the joint analysis of time-to-event and multiple longitudinal variables. Statistics in Medicine 21, 2369 2382.
12. MacKenzie, E. J., Rivara, F. P., Jurkovich, G. J., Nathens, A. B., Frey, K. P., Egleston, B. L., Salkever, D. S., and Scharfstein, D. O. (2006). A national evaluation of the effect of trauma-center care of mortality. New England Journal of Medicine 354, 366 378.
13. Maclure, M. (1991). The case-crossover design: A method for studying transient effects on the risk of acute events. American Journal of Epidemiology 133, 144 153.
14. Manski, C. F. (2003). Partial Identification of Probability Distributions. New York : Springer.
15. McMahon, R. P. and Harrell, F. E. Jr. (2001). Joint testing of mortality and a non-fatal outcome in clinical trials. Statistics in Medicine 20, 1165 1172.
16. Neyman, J. (1923). On the application of probability theory to agricultural experiments: Essay on principles, section 9. Translated in Statistical Science 5, 465 480, 1990.
17. Prentice, R. L., Kalbfleisch, J. D., Peterson, A. V., Flournoy, N., Farewell, V. T., and Breslow, N. E. (1978). The analysis of failure times in the presence of competing risks. Biometrics 34, 541 554.
18. Reuben, D., Borok, G., Wolde-Tsadik, G., Ershoff, D., Fishman, L., Ambrosini, V., Liu, Y., Rubenstein, L., and Beck, J. (1995). Randomized trial of comprehensive geriatric assessment in the care of hospitalized patients. New England Journal of Medicine 332, 1345 1350.
19. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66, 688 701.
20. Rubin, D. B. (1976). Inference and missing data. Biometrika 63, 581 592.
21. Rubin, D. B. (1977). Formalizing subjective notions about the effects of nonrespondents in sample surveys. Journal of the American Statistical Association 72, 538 543.
22. Rubin, D. B. (1978). Bayesian inference for causal effects. Annals of Statistics 6, 34 58.
23. Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. New York : Wiley.
24. Rubin, D. B. (2000). Comment on "Causal inference without counterfactuals by A. P. Dawid. Journal of the American Statistical Association 95, 435 437.
25. Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association 469, 322 331.
26. Scharfstein, D. O., Rotnitzky, A., and Robins, J. M. (1999). Adjusting for nonignorable drop-out using semiparametric nonresponse models (with discussion). Journal of the American Statistical Association 94, 1096 1146.
27. Vinson, D. C., Mabe, N., Leonard, L. L., Alexander, J., Becker, J., Boyer, J., Moll, J. (1995). Alcohol and injury. A case-crossover study. Archives of Family Medicine 4, 505 511.
28. Zhang, J. L. and Rubin, D. B. (2003). Estimation of causal effects via principal stratification when some outcomes are truncated by "death." Journal of Educational and Behavioral Statistics 28, 353 368.