These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
996 related articles for article (PubMed ID: 30153796)
1. Multiple imputation for patient reported outcome measures in randomised controlled trials: advantages and disadvantages of imputing at the item, subscale or composite score level. Rombach I; Gray AM; Jenkinson C; Murray DW; Rivero-Arias O BMC Med Res Methodol; 2018 Aug; 18(1):87. PubMed ID: 30153796 [TBL] [Abstract][Full Text] [Related]
2. Multiple imputation to deal with missing EQ-5D-3L data: Should we impute individual domains or the actual index? Simons CL; Rivero-Arias O; Yu LM; Simon J Qual Life Res; 2015 Apr; 24(4):805-15. PubMed ID: 25471286 [TBL] [Abstract][Full Text] [Related]
3. The current practice of handling and reporting missing outcome data in eight widely used PROMs in RCT publications: a review of the current literature. Rombach I; Rivero-Arias O; Gray AM; Jenkinson C; Burke Ó Qual Life Res; 2016 Jul; 25(7):1613-23. PubMed ID: 26821918 [TBL] [Abstract][Full Text] [Related]
4. PROMIS Global Health item nonresponse: is it better to impute missing item responses before computing T-scores? Thompson NR; Katzan IL; Honomichl RD; Lapin BR Qual Life Res; 2020 Feb; 29(2):537-546. PubMed ID: 31630291 [TBL] [Abstract][Full Text] [Related]
5. Comparison of statistical approaches for analyzing incomplete longitudinal patient-reported outcome data in randomized controlled trials. Rombach I; Jenkinson C; Gray AM; Murray DW; Rivero-Arias O Patient Relat Outcome Meas; 2018; 9():197-209. PubMed ID: 29950913 [TBL] [Abstract][Full Text] [Related]
6. Missing data in a multi-item instrument were best handled by multiple imputation at the item score level. Eekhout I; de Vet HC; Twisk JW; Brand JP; de Boer MR; Heymans MW J Clin Epidemiol; 2014 Mar; 67(3):335-42. PubMed ID: 24291505 [TBL] [Abstract][Full Text] [Related]
7. Missing data on the Center for Epidemiologic Studies Depression Scale: a comparison of 4 imputation techniques. Bono C; Ried LD; Kimberlin C; Vogel B Res Social Adm Pharm; 2007 Mar; 3(1):1-27. PubMed ID: 17350555 [TBL] [Abstract][Full Text] [Related]
8. Passive imputation and parcel summaries are both valid to handle missing items in studies with many multi-item scales. Eekhout I; de Vet HC; de Boer MR; Twisk JW; Heymans MW Stat Methods Med Res; 2018 Apr; 27(4):1128-1140. PubMed ID: 27334917 [TBL] [Abstract][Full Text] [Related]
9. Imputation strategies when a continuous outcome is to be dichotomized for responder analysis: a simulation study. Floden L; Bell ML BMC Med Res Methodol; 2019 Jul; 19(1):161. PubMed ID: 31345166 [TBL] [Abstract][Full Text] [Related]
10. Is using multiple imputation better than complete case analysis for estimating a prevalence (risk) difference in randomized controlled trials when binary outcome observations are missing? Mukaka M; White SA; Terlouw DJ; Mwapasa V; Kalilani-Phiri L; Faragher EB Trials; 2016 Jul; 17():341. PubMed ID: 27450066 [TBL] [Abstract][Full Text] [Related]
11. Dealing with missing data in a multi-question depression scale: a comparison of imputation methods. Shrive FM; Stuart H; Quan H; Ghali WA BMC Med Res Methodol; 2006 Dec; 6():57. PubMed ID: 17166270 [TBL] [Abstract][Full Text] [Related]
12. A systematic review of randomised controlled trials in rheumatoid arthritis: the reporting and handling of missing data in composite outcomes. Ibrahim F; Tom BD; Scott DL; Prevost AT Trials; 2016 Jun; 17(1):272. PubMed ID: 27255212 [TBL] [Abstract][Full Text] [Related]
13. What difference does multiple imputation make in longitudinal modeling of EQ-5D-5L data? Empirical analyses of simulated and observed missing data patterns. Rösel I; Serna-Higuita LM; Al Sayah F; Buchholz M; Buchholz I; Kohlmann T; Martus P; Feng YS Qual Life Res; 2022 May; 31(5):1521-1532. PubMed ID: 34797507 [TBL] [Abstract][Full Text] [Related]
14. Consequences of handling missing data for treatment response in osteoarthritis: a simulation study. Olsen IC; Kvien TK; Uhlig T Osteoarthritis Cartilage; 2012 Aug; 20(8):822-8. PubMed ID: 22441031 [TBL] [Abstract][Full Text] [Related]
15. A comparison of multiple imputation strategies for handling missing data in multi-item scales: Guidance for longitudinal studies. Mainzer R; Apajee J; Nguyen CD; Carlin JB; Lee KJ Stat Med; 2021 Sep; 40(21):4660-4674. PubMed ID: 34102709 [TBL] [Abstract][Full Text] [Related]
17. Handling of Missing Outcome Data in Acute Stroke Trials: Advantages of Multiple Imputation Using Baseline and Postbaseline Variables. Young-Saver DF; Gornbein J; Starkman S; Saver JL J Stroke Cerebrovasc Dis; 2018 Dec; 27(12):3662-3669. PubMed ID: 30297167 [TBL] [Abstract][Full Text] [Related]
18. Is the whole larger than the sum of its parts? Impact of missing data imputation in economic evaluation conducted alongside randomized controlled trials. Michalowsky B; Hoffmann W; Kennedy K; Xie F Eur J Health Econ; 2020 Jul; 21(5):717-728. PubMed ID: 32108274 [TBL] [Abstract][Full Text] [Related]
19. A review of the use of controlled multiple imputation in randomised controlled trials with missing outcome data. Tan PT; Cro S; Van Vogt E; Szigeti M; Cornelius VR BMC Med Res Methodol; 2021 Apr; 21(1):72. PubMed ID: 33858355 [TBL] [Abstract][Full Text] [Related]
20. Imputation strategies for missing binary outcomes in cluster randomized trials. Ma J; Akhtar-Danesh N; Dolovich L; Thabane L; BMC Med Res Methodol; 2011 Feb; 11():18. PubMed ID: 21324148 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]