Using the outcome for imputation of missing predictor values was preferred

Journal of Clinical Epidemiology

Volumn 59, Issue 10, 2006, Pages 1092-1101

  Moons, Karel G M ^a,d   Donders, Rogier A R T ^a,b   Stijnen, Theo ^c   Harrell Jr , Frank E ^d  

^a UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

^b UTRECHT UNIVERSITY   (Netherlands)

^c ERASMUS MEDICAL CENTER   (Netherlands)

^d VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

Bias; Imputation; Missing predictors; Precision; Prediction

Indexed keywords

ACCURACY; ADULT; ANALYTIC METHOD; ANALYTICAL ERROR; ARTICLE; COMPUTER PROGRAM; EPIDEMIOLOGICAL DATA; FEMALE; HUMAN; IMPUTATION; LUNG EMBOLISM; MAJOR CLINICAL STUDY; MALE; MISSING PREDICTOR VALUE; MULTIPLE IMPUTATION; PREDICTION; PRIORITY JOURNAL; REGRESSION ANALYSIS; RISK FACTOR; SIMULATION; STANDARD;

ADULT; AGE FACTORS; AGED; BIAS (EPIDEMIOLOGY); COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; FEMALE; HUMANS; MALE; MIDDLE AGED; MODELS, BIOLOGICAL; OUTCOME ASSESSMENT (HEALTH CARE); POSTOPERATIVE COMPLICATIONS; PULMONARY EMBOLISM; RISK FACTORS; SOFTWARE;

EID: 33748709502     PISSN: 08954356     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jclinepi.2006.01.009     Document Type: Article

References (38)

1. Little R.A., and Rubin D.B. Statistical analysis with missing data (1987), Wiley, New York
2. Little R.A. Regression with missing X's; a review. J Am Stat Assoc 87 (1992) 1227-1237
3. Laird N.M. Missing data in longitudinal studies. Stat Med 7 (1988) 305-315
4. Schafer J.L. Analysis of incomplete multivariate data. Vol. 1 (1997), Chapman & Hill, London
5. Vach W. Logistic regression with missing values in the covariates. 1st ed. (1994), Springer, New York
6. Weinberg C.R., Moledor E.S., Umbach D.M., and Sandler D.P. Imputation for exposure histories with gaps, under an excess relative risk model. Epidemiology 7 (1996) 490-497
7. Meyer K., and Windeler J. A new suggestion for the classification of missing values in the outcome of clinical trials. Clini Res Regul Affairs 15 (1998) 17-24
8. Unnebrink K., and Windeler J. Intention-to-treat: methods for delaing with missing values in clinical trials of progressively deteriorating diseases. Stat Med 20 (2001) 3931-3946
9. Faris P.D., Ghali W.A., Brant R., Norris C.M., Galbraith P.D., and Knudtson M.L. Multiple imputation versus data enhancement for dealing with missing data in observational health care outcome analyses. J Clin Epidemiol 55 (2002) 184-191
10. Schafer J.L., and Graham J.W. Missing data: our view of the state of the art. Psychol Methods 7 (2002) 147-177
11. Clark T.G., and Altman D.G. Developing a prognostic model in the presence of missing data. an ovarian cancer case study. J Clin Epidemiol 56 (2003) 28-37
12. Rubin D.B. Multiple imputation for non respons in surveys (1987), Wiley, New York
13. Donner A. The relative effectiveness of procedures commonly used in multiple regression analyses for dealing with missing values. Am Stat 36 (1982) 378-381
14. Vach W., and Blettner M. Biased estimates of the odds ratio in case-control studies due to the use of ad hoc methods of correcting for missing values for confounding variables. Am J Epidemiol 134 (1991) 895-907
15. Robins J.M., Rotnitzky A., and Zhao L.P. Estimation of regression coefficients when some regressors are not always observed. J Am Stat Assoc 89 (1994) 846-866
16. Vach W. Some issues in estimating the effect of prognostic factors from incomplete covariate data. Stat Med 16 (1997) 57-72
17. Crawford S.L., Tennstedt S.L., and McKinlay K.B. A comparison of analytic methods for non-random missingness of outcome data. J Clin Epidemiol 48 (1995) 209-219
18. Greenland S., and Finkle W.D. A critical look at methods for handling missing covariates in epidemiologic regression analyses. Am J Epidemiol 142 (1995) 1255-1264
19. Schafer J.L. Multiple imputation: a primer. Stat Methods Med Res 8 (1999) 3-15
20. Oostenbrink R., Moons K.G.M., Bleeker S.E., Moll H.A., and Grobbee D.E. Diagnostic research on routine care data: prospects and problems. J Clin Epidemiol 56 (2003) 501-506
21. Oostenbrink R., Moons K.G., Donders A.R., Grobbee D.E., and Moll H.A. Prediction of bacterial meningitis in children with meningeal signs: reduction of lumbar punctures. Acta Paediatr 90 (2001) 611-617
22. Ibrahim J.G. Incomplete data in generalized models. J Am Stat Assoc 85 (1990) 765-769
23. Herring A.H., and Ibrahim J.G. Likelihood-based methods for missing covariates in the Cox proportional hazards model. J Am Stat Assoc 96 (2001) 292-302
24. Barnard J., and Meng X.L. Application of multiple imputation in medical studies: from AIDS to NHANES. Stat Methods Med Res 8 (1999) 17-36
25. Rubin D.B., and Schenker N. Multiple imputation in health-care data bases: an overview and some applications. Stat Med 10 (1991) 585-598
26. Rubin D.B. Multiple imputation after 18+ years. J Am Stat Assoc 91 (1996) 473-489
27. Harrell F.E. Regression modeling strategies. 1st ed. (2001), Springer-Verlag, New York
28. Heitjan D.F., and Rubin D.B. Inference form coarse data via multiple imputation with application to age heaping. J Am Stat Assoc 85 (1990) 304-314
29. Ezzati-Rice T.M., Johnson W., Khare M., Little R.J.A., and Rubin D.B. A simulation study to evaluate the performance of multiple imputations in NCHS Health Examination Survey. Proc Bureau Census Eleventh Annu Res Conf (1995) 257-266
30. Unnebrink K., and Windeler J. Sensitivity analyses by worst and best case assessment: is it really sensitive?. Drug Inform J 33 (1999) 835-839
31. van Beek E.J.R., Tiel-van Buul M.M.C., Büller H.R., van Royen E.A., and ten Cate J.W. The value of lung scintigraphy in the diagnosis of pulmonary embolism. Eur J Nucl Med 20 (1993) 173-181
32. van Beek E.J.R., Kuyer P.M.M., Schenk B.E., Brandjes D.P.M., ten Cate J.W., and Büller H.R. A normal perfusion lung scan in patients with clinically suspected pulmonary embolism: frequency and clinical validity. Chest 108 (1995) 170-173
33. Turkstra F., Kuijer P.M.M., van Beek E.J.R., Brandjes D.P.M., ten Cate J.W., and Buller H.R. Diagnostic utility of ultrasonography of leg veins in patients suspected of having pulmonary embolism. Ann Intern Med 126 (1997) 775-781
34. Moons K.G., van Es G.A., Michel B.C., Buller H.R., Habbema J.D., and Grobbee D.E. Redundancy of single diagnostic test evaluation. Epidemiology 10 (1999) 276-281
35. Horton N.J., and Lipsitz S.R. Multiple imputation in practice: comparison of software packages for regression models with missing variables. Am Stat 55 (2001) 244-254
36. van Buuren S, Oudshoorn K. Flexible multivariate imputation by mice. Technical report. Leiden, The Netherlands: TNO prevention and Health; 1999. http://web.inter.nl.net/users/S.vanBuuren/mi/hmtl/mice.htm. Accessed 2005 February 1.
37. Harrell FE. The Hmisc library, 2002. http://hesweb1.med.virginia.edu/biostat/s/Hmisc.html. Accessed 2005 February 1.
38. Rubin D.B. Inferences and missing data. Biometrika 63 (1976) 581-590