Multiple imputation methods for treatment noncompliance and nonresponse in randomized clinical trials

Biometrics

Volumn 65, Issue 1, 2009, Pages 88-95

  Taylor, L ^a   Zhou, X H ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Causal inference; Complier average causal effect; Missing data; Multiple imputation; Noncompliance; Nonresponse; Principal stratification

Indexed keywords

MEDICAL APPLICATIONS;

CAUSAL INFERENCES; COMPLIER AVERAGE CAUSAL EFFECT; IMPUTATION METHODS; MISSING DATA; MULTIPLE IMPUTATION; NONCOMPLIANCE; NONRESPONSE; PRINCIPAL STRATIFICATION; RANDOMIZED CLINICAL TRIALS; TREATMENT EFFECTS;

SAMPLING;

INFLUENZA VACCINE;

DATA INTERPRETATION; INFLUENZA; SIMULATION; VACCINE;

ARTICLE; CAUSAL MODELING; CONCEPTUAL FRAMEWORK; DATA ANALYSIS; INFLUENZA VACCINATION; METHODOLOGY; OUTCOME ASSESSMENT; PARAMETER; RANDOMIZED CONTROLLED TRIAL; SIMULATION; STATISTICAL ANALYSIS; TREATMENT REFUSAL;

EID: 62749129394     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2008.01023.x     Document Type: Article

References (30)

1. Barnard, J., Du, J., Hill, J., and Rubin, D. B. (1988). A broader template for analyzing broken randomized experiments. Sociological Methods and Research 27, 285 317.
2. Fay, R. E. (1996). Alternative paradigms for the analysis of imputed survey data. Journal of the American Statistical Association 91, 490 498.
3. Frangakis, C. E. and Rubin, D. B. (1999). Addressing complications of intention-to-treat analysis in the presence of all-or-none treatment- noncompliance and subsequent missing outcomes. Biometrika 8, 365 379.
4. Frangakis, C. E. and Rubin, D. B. (2002). Principal stratification in causal inference. Biometrics 58, 21 29.
5. Frangakis, C. E., Rubin, D. B., and Zhou, X. H. (2002). Clustered encouragement design with individual noncompliance: Bayesian inference and application to advance directive forms. Biostatistics 3, 147 164.
6. Halloran M. E. and Struchiner, C. J. (1995). Causal inference in infectious diseases. Epidemiology 6 (2 142 151.
7. Heitjan, D. F. (1999). Ignorability and bias in clinical trials. Statistics in Medicine 18, 2421 2434.
8. Hirano, K., Rubin, D. B., and Zhou, X. H. (2000). Assessing the effect of an influenza vaccine in an encouragement design. Biostatistics 1 (1 69 88.
9. Holland, P. (1986). Statistics and causal inference. Journal of the American Statistical Association 81, 945 960.
10. Imbens, G. and Rubin, D. B. (1997). Bayesian inference for causal effects in randomized experiments with noncompliance. Annals of Statistics 25 (1 305 327.
11. Levy, D. E., O'Malley, A. J., and Normand, S.-LT. (2004). Covariate adjustment in clinical trials with non-ignorable missing data and non-compliance. Statistics in Medicine 23, 2319 2339.
12. Little R. J. (2006). Calibrated Bayes: A Bayes/frequentist roadmap. The American Statistician 60, 213 233.
13. McDonald, C. J., Hui, S. L., and Tierney, W. M. (1992). Effect of computer reminders for influenza vaccination on Morbidity During Influenza Epidemics. M.D. Computing 9, 304 312.
14. Meng, X.-L. (1994). Multiple imputation inferences with uncongenial sources of input. Statistical Science 9, 538 573.
15. Neyman, J. (1923). On the application of probability theory to agricultural experiments: Essay on principles, section 9. (Translated in 1990.) Statistical Science 5, 465 480.
16. O'Malley, A. J. and Normand, S. L. (2005). Likelihood methods for treatment noncompliance and subsequent nonresponse in randomized trials. Biometrics 61, 325 334.
17. Rao, J. N. K. (1996). On variance estimation with imputed data. JASA 91, 499 506.
18. Robins, J. M. and Wang, N. (2000). Inference for imputation estimators. Biometrika 87, 113 124.
19. Robins, J. M., Greenland, S., and Hu, F.-C. (1999). Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated binary outcome. Journal of the American Statistical Association 94, 687 700.
20. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and non-randomized studies. Journal of Education and Psychology 66, 688 701.
21. Rubin, D. B. (1978). Bayesian inference for causal effects. Annals of Statistics 6, 34 58.
22. Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. New York : John Wiley & Sons.
23. Rubin, D. B. (1996). Multiple imputation after 18+ years. Journal of the American Statistical Association 91, 473 489.
24. Rubin, D. B. and Schenker, N. (1986). Multiple imputation for interval estimation from simple random samples with ignorable nonresponse. Journal of the American Statistical Association 81, 366 374.
25. Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. London : Chapman & Hall.
26. Schafer, J. L. and Olsen, M. K. (1998). Multiple imputation for multivariate missing-data problems: A data analyst's perspective. Multivariate Behavioral Research 33, 545 571.
27. Schenker, N. and Welsh, A. H. (1988). Asymptotic results for multiple imputation. Annals of Statistics 16, 1550 1566.
28. Tanner, M. A. and Wong, W. H. (1987). The calculation of posterior distributions by data augmentation (with discussion). JASA 82, 528 550.
29. Taylor, L. and Zhou, X. H. (2008). Relaxing latent ignorability in the ITT analysis of randomized studies with missing data and noncompliance. UW Biostatistics Working Paper Series. Working Paper 257. To appear in Statistica Sinica.
30. Zhou, X. H. and Li, S. (2005). ITT analysis of randomized encouragement design studies with missing data. Statistics in Medicine 23, 1991 2003.