Multiple imputation for an incomplete covariate that is a ratio

Statistics in Medicine

Volumn 33, Issue 1, 2014, Pages 88-104

  Morris, Tim P ^a,b   White, Ian R ^b   Royston, Patrick ^a   Seaman, Shaun R ^b   Wood, Angela M ^c  

^a MRC CLINICAL TRIALS UNIT   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Compatibility; Missing data; Multiple imputation; Ratios

Indexed keywords

CHOLESTEROL; HEMOGLOBIN;

ANALYSIS OF COVARIANCE; ARTICLE; BAYES THEOREM; BODY MASS; CHOLESTEROL BLOOD LEVEL; COHORT ANALYSIS; CONFIDENCE INTERVAL; CORRELATION ANALYSIS; CORRELATION COEFFICIENT; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; OUTCOME ASSESSMENT; PREDICTIVE VALUE; PROPORTIONAL HAZARDS MODEL; REGRESSION ANALYSIS; SENSITIVITY ANALYSIS; SIMULATION; SYSTEMATIC ERROR;

COMPATIBILITY; MISSING DATA; MULTIPLE IMPUTATION; RATIOS;

BAYES THEOREM; BODY MASS INDEX; CD4 LYMPHOCYTE COUNT; CHOLESTEROL; COHORT STUDIES; COMPUTER SIMULATION; FEMALE; HEMOGLOBINS; HIV INFECTIONS; HUMANS; MALE; MODELS, STATISTICAL; NEOPLASMS; REGRESSION ANALYSIS; SOUTH AFRICA;

EID: 84889648504     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5935     Document Type: Article

References (34)

1. Rubin DB. Inference and missing data. Biometrika 1976; 63:581-592.
2. Rubin DB. Multiple imputations in sample surveys: a phenomenological Bayesian approach to nonresponse. Proceedings of the Survey Research Methods Section, American Statistical Association, 1978; 20-28.
3. Rubin DB. Multiple Imputation for Nonresponse in Surveys. John Wiley and Sons: New York, 1987.
4. Russell E, Charalambous S, Pemba L, Churchyard G, Grant A, Fielding K. Low haemoglobin predicts early mortality among adults starting antiretroviral therapy in an HIV care programme in South Africa: a cohort study. BMC Public Health 2010; 10(1):433+. DOI: 10.1186/1471-2458-10-433. http://dx.doi.org/10.1186/1471-2458-10-433.
5. Yusuf S, Hawken S, Ôunpuu S, Bautista L, Franzosi MG, Commerford P, Lang CC, Rumboldt Z, Onen CL, Lisheng L. Obesity and the risk of myocardial infarction in 27, 000 participants from 52 countries: a case-control study. The Lancet 2005; 366(9497):1640-1649. DOI: 10.1016/s0140-6736(05)67663-5. http://dx.doi.org/10.1016/s0140-6736(05)67663-5.
6. Wang TJ, Gona P, Larson MG, Tofler GH, Levy D, Newton-Cheh C, Jacques PF, Rifai N, Selhub J, Robins SJ, Benjamin EJ, D'Agostino RB, Vasan RS. Multiple biomarkers for the prediction of first major cardiovascular events and death. New England Journal of Medicine 2006; 355(25):2631-2639. DOI: 10.1056/nejmoa055373. http://dx.doi.org/10.1056/nejmoa055373.
7. Arsenault BJ, Rana JS, Stroes ESG, Despres JP, Shah PK, Kastelein JJP, Wareham NJ, Boekholdt SM, Khaw KT. Beyond low-density lipoprotein cholesterol: respective contributions of non-high-density lipoprotein cholesterol levels, triglycerides, and the total cholesterol/high-density lipoprotein cholesterol ratio to coronary heart disease risk in apparently healthy men and women. Journal of the American College of Cardiology 2010; 55(1):35-41. DOI: 10.1016/j.jacc.2009.07.057. http://dx.doi.org/10.1016/j.jacc.2009.07.057.
8. Allison DB, Paultre F, Goran MI, Poehlman ET, Heymsfield SB. Statistical considerations regarding the use of ratios to adjust data. International journal of obesity and related metabolic disorders: journal of the International Association for the Study of Obesity 1995; 19(9):644-652. http://view.ncbi.nlm.nih.gov/pubmed/8574275.
9. Senn S, Julious S. Measurement in clinical trials: a neglected issue for statisticians? Statistics in Medicine 2009; 28(26):3189-3209. DOI: 10.1002/sim.3603. http://dx.doi.org/10.1002/sim.3603.
10. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Statistics in Medicine 2011; 30(4):377-399. DOI: 10.1002/sim.4067. http://dx.doi.org/10.1002/sim.4067.
11. Bartlett JW, Seaman SR, White IR, Carpenter JR. Multiple imputation of covariates by fully conditional specification: accommodating the substantive model, 2013. http://arxiv.org/abs/1210.6799.
12. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ 2007; 335(7611):136+. DOI: 10.1136/bmj.39261.471806.55. http://dx.doi.org/10.1136/bmj.39261.471806.55.
13. Moons K, Donders R, Stijnen T, Harrel F. Using the outcome for imputation of missing predictor values was preferred. Journal of clinical epidemiology 2006; 59(10):1092-1101. DOI: 10.1016/j.jclinepi.2006.01.009. http://dx.doi.org/10.1016/j.jclinepi.2006.01.009.
14. White IR, Royston P. Imputing missing covariate values for the Cox model. Statistics in Medicine 2009; 28(15):1982-1998. DOI: 10.1002/sim.3618. http://dx.doi.org/10.1002/sim.3618.
15. Schafer JL. Multiple imputation in multivariate problems when the imputation and analysis models differ. Statistica Neerlandica 2003; 57(1):19-35. DOI: 10.1111/1467-9574.00218. http://dx.doi.org/10.1111/1467-9574.00218.
16. Schafer JL. Multiple imputation: a primer. Statistical Methods in Medical Research 1999; 8(1):3-15. DOI: 10.1177/096228029900800102. http://dx.doi.org/10.1177/096228029900800102.
17. Andridge RR. Quantifying the impact of fixed effects modeling of clusters in multiple imputation for cluster randomized trials. Biometrical journal. Biometrische Zeitschrift 2011; 53(1):57-74. DOI: 10.1002/bimj.201000140. http://dx.doi.org/10.1002/bimj.201000140.
18. Meng XL. Multiple-imputation inferences with uncongenial sources of input. Statistical Science 1994; 9:538-558.
19. Rubin DB. Multiple imputation after 18+ years. Journal of the American Statistical Association 1996; 91:473-489.
20. Collins LM, Schafer JL, Kam CM. A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological methods 2001; 6(4):330-351. DOI: 10.1037//1082-989X.6.4.330-351. http://dx.doi.org/10.1037//1082-989X.6.4.330-351.
21. Liu J, Gelman A, Hill J, Su YS. On the Stationary Distribution of Iterative Imputations, 2012. http://arxiv.org/abs/1012.2902.
22. Von Hippel PT. How to impute squares, interactions, and other transformed variables. Sociological Methodology 2009; 39:265-291. DOI: 10.1111/j.1467-9531.2009.01215.x. http://dx.doi.org/10.1111/j.1467-9531.2009.01215.x.
23. Seaman SR, Bartlett JW, White IR. Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods. BMC medical research methodology 2012; 12(1):46+. DOI: 10.1186/1471-2288-12-46. http://dx.doi.org/10.1186/1471-2288-12-46.
24. Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychological Methods 2002; 7(2):147-177. DOI: 10.1037//1082-989x.7.2.147. http://dx.doi.org/10.1037//1082-989x.7.2.147.
25. StataCorp. Stata Statistical Software: Release 12. Stata Press: College Station, TX, 2011.
26. Stata multiple-imputation reference manual release 12. Stata Press, StataCorp LP, 4905 Lakeway Drive, College Station, Texas 77845, 2011.
27. Little RJA. Models for nonresponse in sample surveys. Journal of the American Statistical Association 1982; 77:237-250.
28. Schenker N, Taylor JMG. Partially parametric techniques for multiple imputation. Computational Statistics & Data Analysis 1996; 22(4):425-446. DOI: 10.1016/0167-9473(95)00057-7. http://dx.doi.org/10.1016/0167-9473(95)00057-7.
29. White IR. simsum: analyses of simulation studies including Monte Carlo error. Stata Journal 2010; 10(3):369-385.
30. Little RJA. Missing-data adjustments in large surveys. Journal of Business & Economic Statistics 1988; 6:287-296.
31. Copas JB. Regression, prediction and shrinkage. Journal of the Royal Statistical Society. Series B (Methodological) 1983; 45(3):311-354. DOI: 10.2307/2345402. http://dx.doi.org/10.2307/2345402.
32. Arnold BC, Castillo E, Sarabia JM. Conditionally specified distributions: an introduction. Statistical Science 2001; 16(3):249-265. http://www.jstor.org/stable/2676688.
33. Lunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS - a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 2000; 10(4):325-337. DOI: 10.1023/a:1008929526011. http://dx.doi.org/10.1023/a:1008929526011.
34. Clayton D. Bayesian analysis of frailty models. Technical Report, MRC Biostatistics Unit, Cambridge, 1994.