A multiple-imputation-based approach to sensitivity analyses and effectiveness assessments in longitudinal clinical trials

Journal of Biopharmaceutical Statistics

Volumn 24, Issue 2, 2014, Pages 211-228

  Ayele, Birhanu Teshome ^a   Lipkovich, Ilya ^b   Molenberghs, Geert ^a,c   Mallinckrodt, Craig H ^d  

^a HASSELT UNIVERSITY   (Belgium)

^b IQVIA   (United States)

^c KU LEUVEN   (Belgium)

^d ELI LILLY AND COMPANY   (United States)

Author keywords

Longitudinal analyses; Missing data; Multiple imputation

Indexed keywords

PLACEBO; DRUG;

ANALYTIC METHOD; ARTICLE; CLINICAL ASSESSMENT; CLINICAL EFFECTIVENESS; CONTROLLED STUDY; DRUG EFFECT; DRUG EFFICACY; HUMAN; PHASE 3 CLINICAL TRIAL (TOPIC); PLACEBO MULTIPLE IMPUTATION; PRIORITY JOURNAL; RANDOMIZED CONTROLLED TRIAL (TOPIC); SENSITIVITY ANALYSIS; BIOASSAY; LONGITUDINAL STUDY; PATIENT DROPOUTS; PROBABILITY; PROCEDURES; STANDARDS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TREATMENT OUTCOME;

CLINICAL TRIALS, PHASE III AS TOPIC; ENDPOINT DETERMINATION; HUMANS; LONGITUDINAL STUDIES; MODELS, STATISTICAL; PATIENT DROPOUTS; PHARMACEUTICAL PREPARATIONS; PROBABILITY; RANDOMIZED CONTROLLED TRIALS AS TOPIC; TREATMENT OUTCOME;

EID: 84896791790     PISSN: 10543406     EISSN: 15205711     Source Type: Journal    
DOI: 10.1080/10543406.2013.859148     Document Type: Article

References (15)

1. Carpenter, J. R., Roger, J. H., Kenward, M. G. (2013). Analysis of longitudinal trials with protocol deviation: A framework for relevant, accessible assumptions, and inference via multiple imputation. 23:1352-1371.
2. Detke, M. J., Lu, Y., Goldstein, D. J., McNamara, R. K., Demitrack, M. A. (2002). Duloxetine 60 mg once daily dosing versus placebo in the acute treatment of major depression. Journal of Psychiatric Research 36:383-390. (Pubitemid 35254112)
3. Fleming, T. R. (2011). Addressing missing data in clinical trials. Annals of Internal Medicine 154:113-117.
4. Kim, Y. (2011) Missing data handling in chronic pain trials. Journal of Biopharmaceutical Statistics 21(2):311-325.
5. Little, R. J. A., Yau, L. (1996). Intent-to-Treat Analysis in Longitudinal Studies with Drop-Outs. Biometrics 52:1324-1333. (Pubitemid 26425283)
6. Liu G., Gould, A. L. (2002). Comparison of alternative strategies for analysis of longitudinal trials with dropouts. Journal of Biopharmaceutical Statistics 12(2):207-226. (Pubitemid 35205022)
7. Mallinckrodt, C. H., Kenward, M. G. (2009). Conceptual considerations regarding choice of endpoints, hypotheses, and analyses in longitudinal clinical trials. Drug Information Journal 43:449-458.
8. Molenberghs, G., Kenward, M. G. (2007). Missing Data in Clinical Studies. Chichester, UK: John Wiley & Sons.
9. National Academy of Sciences. (2010). National Research Council: The Prevention and Treatment of Missing Data in Clinical Trials. Panel on Handling Missing Data in Clinical Trials. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: National Academies Press
10. Permutt, T., Pinheiro, J. (2009). Dealing with the missing data challenge in clinical trials. Drug Information Journal 43(4):403-408.
11. Robins, J. M., Wang, N. (2000). Inference for imputation estimators, Biometrika 87:113-124.
12. Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. New York, NY: John Wiley & Sons.
13. SAS Institute, Inc. (2003). SAS/STAT User's Guide, Version 9.1. Cary, NC: SAS Institute, Inc.
14. Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. London, UK: Chapman & Hall.
15. Wang, N., Robins, J. M. (1998). Large-sample theory for parametric multiple imputation procedures. Biometrika 85:935-948.