Number of imputations needed to stabilize estimated treatment difference in longitudinal data analysis

Statistical Methods in Medical Research

Volumn 26, Issue 2, 2017, Pages 674-690

  Lu, Kaifeng ^a  

^a Harborside Financial Center   (United States)

Author keywords

coefficient of variation; conditional variability; multiple imputation; randomized trial; repeated sampling

Indexed keywords

CONFIDENCE INTERVAL; DATA ANALYSIS; HUMAN; SAMPLING; SIMULATION; BIOSTATISTICS; CLINICAL TRIAL (TOPIC); COMPUTER SIMULATION; LONGITUDINAL STUDY; PROCEDURES; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA;

BIOSTATISTICS; CLINICAL TRIALS AS TOPIC; COMPUTER SIMULATION; CONFIDENCE INTERVALS; DATA INTERPRETATION, STATISTICAL; HUMANS; LIKELIHOOD FUNCTIONS; LONGITUDINAL STUDIES; MODELS, STATISTICAL;

EID: 85018742924     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280214554439     Document Type: Article

References (24)

1. Rubin DB,. Multiple imputations in sample surveys: a phenomenological Bayesian approach to nonresponse (with discussion). Proc Surv Res Methods Sec Am Stat Assoc 1978, pp. 20-34.
2. Rubin DB,. Multiple imputation for nonresponse in surveys, New York: Wiley, 1987.
3. Rubin DB,. Multiple imputation after 18+ years. J Am Stat Assoc 1996; 91: 473-489.
4. Schafer JL,. Multiple imputation: a primer. Stat Methods Med Res 1999; 8: 3-15.
5. Allison PD,. Multiple imputation for missing data: a cautionary tale. Soc Method Res 2000; 28: 301-309.
6. Sinharay S, Stern HS, Russell D,. The use of multiple imputation for the analysis of missing data. Psychol Methods 2001; 6: 317-329.
7. Zhang P,. Multiple imputation: theory and method. Int Stat Rev 2003; 71: 581-592.
8. Harel O, Zhou XH,. Multiple imputation: review of theory, implementation and software. Stat Med 2007; 26: 3057-3077.
9. Rubin DB, Schenker N,. Multiple imputation for interval estimation from simple random samples with ignorable nonresponse. J Am Stat Assoc 1986; 81: 366-374.
10. Li KH, Raghunathan TE, Rubin DB,. Large-sample significance levels from multiply imputed data using moment-based statistics and an F reference distribution. J Am Stat Assoc 1991; 86: 1065-1073.
11. Schafer JL,. Analysis of incomplete multivariate data, New York: Chapman & Hall, 1997.
12. von Hippel PT,. How many imputations are needed? a comment on Hershberger and Fisher (2003). Struct Equ Model 2005; 12: 334-335.
13. Molenberghs G, Kenward MG,. Missing data in clinical trials, Chichester, UK: Wiley, 2007.
14. Royston P,. Multiple imputation of missing values. Stata J 2004; 4: 227-241.
15. Harel O,. Inferences on missing information under multiple imputation and two-stage multiple imputation. Stat Methodol 2007; 4: 75-89.
16. Graham JW, Olchowski AE, Gilreath TD,. How many imputations are really needed? some practical clarifications of multiple imputation theory. Prev Sci 2007; 8: 206-213.
17. Spratt M, Carpenter J, Sterne JAC, et al. Strategies for multiple imputation in longitudinal studies. Pract Epidemiol 2010; 172: 478-487.
18. Shen CW, Chen YH,. Model selection of generalized estimating equations with multiply imputed longitudinal data. Biometrical J 2013; 55: 899-911.
19. Bodner TE,. What improves with increased missing data imputations? Struct Equ Mod 2008; 15: 651-675.
20. Little RJA, Rubin DB,. Statistical analysis with missing data, 2nd edn. New York: John Wiley & Sons, 2002.
21. Wang SK, Chow SC,. Advanced linear models: theory and applications, New York: Marcel Dekker, 1994.
22. Schepsmeier U, Stöber J,. Derivatives and Fisher information of bivariate copulas. Stat Pap 2014; 55: 525-542.
23. Muñoz A, Carey V, Schouten JP, et al. A parametric family of correlation structures for the analysis of longitudinal data. Biometrics 1992; 48: 733-742.
24. Little R, Yau L,. Intent-to-treat analysis for longitudinal studies with drop-outs. Biometrics 1996; 52: 1324-1333.