Intention-to-treat analysis with treatment discontinuation and missing data in clinical trials

Statistics in Medicine

Volumn 34, Issue 16, 2015, Pages 2381-2390

  Little, Roderick ^a   Kang, Shan ^a  

^a UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Clinical trials; Dropouts; Incomplete data; Intention to treat analysis; Missing data; Treatment discontinuation

Indexed keywords

HEMOGLOBIN A1C; INSULIN GLARGINE; ANTIDIABETIC AGENT; INSULIN;

ALGORITHM; ANALYSIS OF COVARIANCE; ARTICLE; CONTROLLED STUDY; FOLLOW UP; HUMAN; INTENTION TO TREAT ANALYSIS; MAJOR CLINICAL STUDY; MATHEMATICAL COMPUTING; MEDICATION COMPLIANCE; NON INSULIN DEPENDENT DIABETES MELLITUS; OUTCOME ASSESSMENT; RANDOMIZATION; STATISTICAL PARAMETERS; TREATMENT DURATION; TREATMENT WITHDRAWAL; BIOSTATISTICS; DIABETES MELLITUS, TYPE 2; INHALATIONAL DRUG ADMINISTRATION; INJECTION; PATIENT COMPLIANCE; PATIENT DROPOUT; STATISTICAL ANALYSIS; STATISTICS AND NUMERICAL DATA;

ADMINISTRATION, INHALATION; BIOSTATISTICS; DATA INTERPRETATION, STATISTICAL; DIABETES MELLITUS, TYPE 2; HUMANS; HYPOGLYCEMIC AGENTS; INJECTIONS; INSULIN; INSULIN GLARGINE; INTENTION TO TREAT ANALYSIS; PATIENT COMPLIANCE; PATIENT DROPOUTS;

EID: 84930393282     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6352     Document Type: Article

References (18)

1. Meinert CL. Toward more definitive clinical trials. Controlled Clinical Trials 1980; 1:249-261.
2. Rubin DB. Bayesian inference for causal effects: the role of randomization. The Annals of Statistics 1978; 6:34-58.
3. Holland PW. Statistics and causal inference. Journal of the American Statistical Association 1986; 81:945-970.
4. Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables (with discussion and rejoinder). Journal of the American Statistical Association 1996; 91:444-472.
5. Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics 2002; 58:21-29.
6. White IR. Uses and limitations of randomization-based efficacy estimators. Statistical Methods in Medical Research 2005; 14:327-347.
7. Little RJ, Long Q, Lin X . A comparison of methods for estimating the causal effect of a treatment in randomized clinical trials subject to noncompliance. Biometrics 2009; 65(2):640-649.
8. Frangakis CE, Rubin DB. Addressing complications of intent-to-treat analysis in the combined presence of all-or-none treatment-noncompliance and subsequent missing outcomes. Biometrika 1999; 86:365-379.
9. National Research Council . The Prevention and Treatment of Missing Data in Clinical Trials. The National Academies Press: Washington, DC, 2010.
10. nd edition, Wiley: New York, 2002.
11. Rubin DB. Inference and missing data. Biometrika 1976; 63:581-592.
12. Little RJ, Yau L. Statistical techniques for analyzing data from prevention trials: treatment of no-shows using Rubin's causal model. Psychological Methods 1998; 3:147-159.
13. Peng Y, Little RJ, Raghunathan T. An extended general location model for causal inferences from data subject to non-compliance and missing values. Biometrics 2004; 60:598-608.
14. Food and Drug Administration. Guidance for Industry, Vol.E9. Statistical Principles for Clinical Trials, 1998. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm073137.pdf.
15. White IR, Carpenter J, Horton NJ. Including all individuals is not enough: lessons for intention-to-treat analysis. Clinical Trials 2012; 9:396-407.
16. Carpenter JR, Roger JH, Kenward MG. Analysis of longitudinal trials with protocol deviation: a framework for relevant, accessible assumptions, and inference via multiple imputation. Journal of Biopharmaceutical Statistics 2013; (236):1352-1371.
17. Little RJ, Yau L. Intent-to-treat analysis in longitudinal studies with drop-outs. Biometrics 1996; 52:1324-1333.
18. Ratitch B, O'Kelly M, Tosiello R. Missing data in clinical trials: from clinical assumptions to statistical analysis using pattern mixture models. Pharmaceutical Statistics 2013; 12(6):337-347.