An overview of practical approaches for handling missing data in clinical trials

Journal of Biopharmaceutical Statistics

Volumn 19, Issue 6, 2009, Pages 1055-1073

  DeSouza, Cynthia M ^a   Legedza, Anna T R ^a   Sankoh, Abdul J ^a  

^a VERTEX PHARMACEUTICALS   (United States)

Author keywords

Dropout; GEE; LOCF; Longitudinal trial; MI GEE; MMRM; Weighted GEE

Indexed keywords

ANALYSIS OF COVARIANCE; CASE REPORT; CLINICAL STUDY; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; DATA ANALYSIS; HUMAN; INFORMATION PROCESSING; MONTE CARLO METHOD; PRIORITY JOURNAL; RANDOMIZED CONTROLLED TRIAL; REVIEW; SIMULATION; STATISTICAL ANALYSIS;

ARTHRITIS, RHEUMATOID; CLINICAL TRIALS AS TOPIC; COMPUTER SIMULATION; DATA COLLECTION; DATA INTERPRETATION, STATISTICAL; HUMANS; LONGITUDINAL STUDIES; PATIENT DROPOUTS;

EID: 75749133269     PISSN: 10543406     EISSN: 15205711     Source Type: Journal    
DOI: 10.1080/10543400903242795     Document Type: Review

References (18)

1. Beunckens, C., Sotto, C., Molenberghs, G. (2008). A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data. Comput. Stat. Data Anal. 52:1533-1548.
2. Diggle, P. J., Kenward, M. G. (1994). Informative dropout in longitudinal data analysis (with discussion). Appl. Stat. 43:49-93.
3. Dmitrienko, A., Molenberghs, G., Chuang-Stein, C., Offen, W. (2005). Analysis of Clinical Trials Using SAS: A Practical Guide. Cary, NC: SAS Institute, Inc.
4. ICH E6. (1996). Good Clinical Practice: Consolidated Guidance.
5. ICH E9. (1998). Statistical Principles for Clinical Trials. Available online at www.fda.gov/ downloads/regulatory/information/guidances/UCM129515.pdf
6. Laird, N. M., Ware, J. H. (1982). Random effects models for longitudinal data. Biometrics 38:963-974
7. Lachin, J. M. (2000). Statistical considerations in the intent-to-treat principle. Controlled Clin. Trials 21:167-189.
8. Liang, K. Y., Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73:13-22.
9. Little, R. J. A., Rubin, D. B. (1987). Statistical Analysis with Missing Data. New York: John Wiley.
10. Little, R. J. A. (1993). Pattern mixture models for multivariate incomplete data. J. Am. Stat. Assoc. 88:125-134.
11. Little, R. J. A. (1995). Modeling the dropout mechanism in repeated measures studies. J. Am. Stat. Assoc. 90:1112-1121.
12. Mallinckrodt, C. H., Lane, P. W., Schnell, D., Peng, Y., Mancusa, J. P. (2008). Recommendations for the primary analysis of continuous endpoints in longitudinal clinical trials. Drug Inform. J. 42:303-319.
13. Robins, J. M., Rotnitzky, A., Zhao, L. P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. J. Am. Stat. Assoc. 90:106-121.
14. Rochon, J. (1999). Issues in adjusting for covariates arising post randomization in clinical trials. Drug Inform. J. 33:1219-1228.
15. Rubin, D. B. (1976). Inference and missing data. Biometrika 63:581-592.
16. Rubin, D. B. (1987). Multiple Imputation for Non Response in Surveys. New York: Wiley.
17. Westfall, P., Tsai, K., Ogenstad, S., Tomoiaga, A, Moseley, S., Lu, Y. (2008). Clinical trial simulation: a statistical approach. J. Biopharm. Stat. 18:611-630.
18. Wu, M. C., Carroll, R. J. (1988). Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrika 44:175-188.