Accommodation of missing data in supportive and palliative care clinical trials

Current Opinion in Supportive and Palliative Care

Volumn 6, Issue 4, 2012, Pages 465-470

  Kurland, Brenda F ^a   Johnson, Laura L ^b   Diehrc, Paula H ^c  

^a Fred Hutchinson Cancer Research Center   (United States)

^b National Institutes of Health   (United States)

^c UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Clinical trials; Missing data; Truncation because of death

Indexed keywords

CLINICAL TRIAL (TOPIC); HUMAN; INFORMATION PROCESSING; METHODOLOGY; PALLIATIVE THERAPY; REVIEW; STATISTICAL ANALYSIS; STATISTICS; SURVIVAL;

CLINICAL TRIALS AS TOPIC; DATA COLLECTION; DATA INTERPRETATION, STATISTICAL; HUMANS; PALLIATIVE CARE; RESEARCH DESIGN; SURVIVAL ANALYSIS;

EID: 84871204300     PISSN: 17514258     EISSN: 17514266     Source Type: Journal    
DOI: 10.1097/SPC.0b013e328358441d     Document Type: Review

References (36)

1. O'Neill RT, Temple R. The prevention and treatment of missing data in clinical trials: an FDA perspective on the importance of dealing with it. Clin Pharmacol Ther 2012; 91:550-554.
2. Vollenweider D, Boyd CM, Puhan MA. High prevalence of potential biases threatens the interpretation of trials in patients with chronic disease. BMC Med 2011; 9:73.
3. 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: The National Academies Press. 2010. Free for download at http://www.nap.edu/catalog/12955.html.
4. Philipson PM, Ho WK, Henderson R. Comparative review of methods for handling drop-out in longitudinal studies. Stat Med 2008; 27:6276-6298.
5. Desai M, Kubo J, Esserman D, Terry MB. The handling of missing data in molecular epidemiology studies. Cancer Epidemiol Biomarkers Prev 2011; 20:1571-1579.
6. Kurland BF, Johnson LL, Egleston BL, Diehr PH. Longitudinal data with follow-up truncated by death: match the analysis method to research aims. Stat Sci 2009; 24:211-222.
7. Diehr P, Johnson LL. Accounting for missing data in end-of-life research. J Palliat Med 2005; 8 (Suppl. 1):S50-S57.
8. Egleston BL, Miller SM, Meropol NJ. The impact of misclassification due to survey response fatigue on estimation and identifiability of treatment effects. Stat Med 2011; 30:3560-3572.
9. Fleming TR. Addressing missing data in clinical trials. Ann Intern Med 2011; 154:113-117.
10. Schneider S, Junghaenel DU, Keefe FJ, et al. Individual differences inthe day-today variability of pain, fatigue, and well being in patients with rheumatic disease: associations with psychological variables. Pain 2012; 153:813-822.
11. Amtmann D, Cook KF, Jensen MP, et al. Development of a PROMIS item bank to measure pain interference. Pain 2010; 150:173-182.
12. Khanna D, Krishnan E, Dewitt EM, et al. Patient-Reported Outcomes Measurement Information System (PROMIS(R))-the future of measuring patient reported outcomes in rheumatology. Arthritis Care Res (Hoboken) 2011; 63 (S11):S486-S490.
13. Pilkonis PA, Choi SW, Reise SP, et al. Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS(R)): depression, anxiety, and anger. Assessment 2011; 18:263-283.
14. Kroenke K, Theobald D, Wu J, et al. Effect of telecare management on pain and depression in patients with cancer: a randomized trial. JAMA 2010; 304:163-171.
15. Dy SM, Roy J, Ott GE, et al. Tell us: a Web-based tool for improving communication among patients, families, and providers in hospice and palliative care through systematic data specification, collection, and use. J Pain Symptom Manage 2011; 42:526-534.
16. Gaussoin SA, Espeland MA, Absher J, et al. Ascertaining dementia-related outcomes for deceased or proxy-dependent participants: an overview of the Women's Health Initiative Memory Study supplemental case ascertainment protocol. Int J Geriatr Psychiatry 2012; 27:205-214.
17. Shardell M, Hicks GE, Miller RR, et al. Pattern-mixture models for analyzing normal outcome data with proxy respondents. Stat Med 2010; 29:1522-1538.
18. Shardell M, Alley DE, Miller RR, et al. Comparing reports from hip-fracture patients and their proxies: implications on evaluating sex differences in disability and depressive symptoms. J Aging Health 2012; 24:367-383.
19. Steiner D, Munera C, Hale M, et al. Efficacy and safety of buprenorphine transdermal system (BTDS) for chronic moderate to severe low back pain: a randomized, double-blind study. J Pain 2011; 12:1163-1173.
20. Roy J, Lin X. Latent variable models for longitudinal data with multiple continuous outcomes. Biometrics 2000; 56:1047-1054.
21. Kim Y. Missing data handling in chronic pain trials. J Biopharmaceut Stat 2011; 21:311-325.
22. Diehr P, Patrick DL, McDonell MB, Fihn SD. Accounting for deaths in longitudinal studies using the SF-36: the performance of the Physical Component Scale of the Short Form 36-item health survey and the PCTD. Med Care 2003; 41:1065-1073.
23. Diehr P, Lafferty WE, Patrick DL, et al. Quality of life at the end of life. Health Qual Life Outcomes 2007; 5:51; doi:10.1186/1477-7525-5-51.
24. Bunouf P, Grouin JM, Molenberghs G. Analysis of an incomplete binary outcome derived from frequently recorded longitudinal continuous data: application to daily pain evaluation. Stat Med 2012; 31:1554-1571.
25. White IR, Kalaitzaki E, Thompson SG. Allowing for missing outcome data and incomplete uptake of randomised interventions, with application to an Internet-based alcohol trial. Stat Med 2011; 30:3192-3207.
26. Wang C, Daniels MJ, Scharfstein DO, Land S.ABayesian shrinkage model for incomplete longitudinal binary data with application to the Breast Cancer Prevention Trial. J Am Stat Assoc 2010; 105:1333-1346.
27. Sinha SK, Troxel AB, Lipsitz SR, et al. A bivariate pseudolikelihood for incomplete longitudinal binary data with nonignorable nonmonotone miss-ingness. Biometrics 2011; 67:1119-1126.
28. Forster JE, MaWhinney S, Ball EL, Fairclough D. A varying-coefficient method for analyzing longitudinal clinical trials data with nonignorable dropout. Con-temp Clin Trials 2012; 33:378-385.
29. Hardouin JB, Conroy R, Sebille V. Imputation by the mean score should be avoided when validating a Patient Reported Outcomes questionnaire by a Rasch model in presence of informative missing data. BMC Med Res Methodol 2011; 11:105.
30. Peyre H, Leplege A, Coste J. Missing data methods for dealing with missing items in quality of life questionnaires. A comparison by simulation of personal mean score, full information maximum likelihood, multiple imputation, and hot deck techniques applied to the SF-36 in the French 2003 decennial health survey. Qual Life Res 2011; 20:287-300.
31. Hays RD, Bode R, Rothrock N, et al. The impact of next and back buttons on time to complete and measurement reliability in computer-based surveys. Qual Life Res 2010; 19:1181-1184.
32. Pearl J. Principal stratification-A goal or a tool? Int J Biostat 2012; 7:20; doi: 10.2202/1557-4679.1322.
33. Kurland BF, Heagerty PJ. Directly parameterized regression conditioning on being alive: analysis of longitudinal data truncated by deaths. Biostatistics 2005; 6:241-258.
34. Shardell M, Hicks GE, Miller RR, Magaziner J. Semiparametric regression models for repeated measures of mortal cohorts with nonmonotone missing outcomes and time-dependent covariates. Stat Med 2010; 29:2282-2296.
35. Egleston BL, Scharfstein DO, Freeman EE, West SK. Causal inference for nonmortality outcomes in the presence of death. Biostatistics 2007; 8:526-545.
36. Chiba Y, Van der Weele TJ. A simple method for principal strata effects when the outcome has been truncated due to death. Am J Epidemiol 2011; 173:745-751.