265 related articles for article (PubMed ID: 37415114)
1. A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis.
Jahangiri M; Kazemnejad A; Goldfeld KS; Daneshpour MS; Mostafaei S; Khalili D; Moghadas MR; Akbarzadeh M
BMC Med Res Methodol; 2023 Jul; 23(1):161. PubMed ID: 37415114
[TBL] [Abstract][Full Text] [Related]
2. A comparison of multiple imputation methods for missing data in longitudinal studies.
Huque MH; Carlin JB; Simpson JA; Lee KJ
BMC Med Res Methodol; 2018 Dec; 18(1):168. PubMed ID: 30541455
[TBL] [Abstract][Full Text] [Related]
3. A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: a simulation study.
De Silva AP; Moreno-Betancur M; De Livera AM; Lee KJ; Simpson JA
BMC Med Res Methodol; 2017 Jul; 17(1):114. PubMed ID: 28743256
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Evaluation of approaches for multiple imputation of three-level data.
Wijesuriya R; Moreno-Betancur M; Carlin JB; Lee KJ
BMC Med Res Methodol; 2020 Aug; 20(1):207. PubMed ID: 32787781
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. Logistic regression vs. predictive mean matching for imputing binary covariates.
Austin PC; van Buuren S
Stat Methods Med Res; 2023 Nov; 32(11):2172-2183. PubMed ID: 37750213
[TBL] [Abstract][Full Text] [Related]
9. Evaluating Methods for Imputing Missing Data from Longitudinal Monitoring of Athlete Workload.
Benson LC; Stilling C; Owoeye OBA; Emery CA
J Sports Sci Med; 2021 Jun; 20(2):188-196. PubMed ID: 33948096
[TBL] [Abstract][Full Text] [Related]
10. Comparison of Imputation Strategies for Incomplete Longitudinal Data in Life-Course Epidemiology.
Shaw C; Wu Y; Zimmerman SC; Hayes-Larson E; Belin TR; Power MC; Glymour MM; Mayeda ER
Am J Epidemiol; 2023 Nov; 192(12):2075-2084. PubMed ID: 37338987
[TBL] [Abstract][Full Text] [Related]
11. A real data-driven simulation strategy to select an imputation method for mixed-type trait data.
May JA; Feng Z; Adamowicz SJ
PLoS Comput Biol; 2023 Mar; 19(3):e1010154. PubMed ID: 36947561
[TBL] [Abstract][Full Text] [Related]
12. Review and evaluation of imputation methods for multivariate longitudinal data with mixed-type incomplete variables.
Cao Y; Allore H; Vander Wyk B; Gutman R
Stat Med; 2022 Dec; 41(30):5844-5876. PubMed ID: 36220138
[TBL] [Abstract][Full Text] [Related]
13. Dealing with missing delirium assessments in prospective clinical studies of the critically ill: a simulation study and reanalysis of two delirium studies.
Raman R; Chen W; Harhay MO; Thompson JL; Ely EW; Pandharipande PP; Patel MB
BMC Med Res Methodol; 2021 May; 21(1):97. PubMed ID: 33952189
[TBL] [Abstract][Full Text] [Related]
14. Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: a simulation study.
De Silva AP; Moreno-Betancur M; De Livera AM; Lee KJ; Simpson JA
BMC Med Res Methodol; 2019 Jan; 19(1):14. PubMed ID: 30630434
[TBL] [Abstract][Full Text] [Related]
15. Multiple imputation with sequential penalized regression.
Zahid FM; Heumann C
Stat Methods Med Res; 2019 May; 28(5):1311-1327. PubMed ID: 29451087
[TBL] [Abstract][Full Text] [Related]
16. Missing data strategies for time-varying confounders in comparative effectiveness studies of non-missing time-varying exposures and right-censored outcomes.
Desai M; Montez-Rath ME; Kapphahn K; Joyce VR; Mathur MB; Garcia A; Purington N; Owens DK
Stat Med; 2019 Jul; 38(17):3204-3220. PubMed ID: 31099433
[TBL] [Abstract][Full Text] [Related]
17. Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model.
Huque MH; Moreno-Betancur M; Quartagno M; Simpson JA; Carlin JB; Lee KJ
Biom J; 2020 Mar; 62(2):444-466. PubMed ID: 31919921
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. The handling of missing data in trial-based economic evaluations: should data be multiply imputed prior to longitudinal linear mixed-model analyses?
Ben ÂJ; van Dongen JM; Alili ME; Heymans MW; Twisk JWR; MacNeil-Vroomen JL; de Wit M; van Dijk SEM; Oosterhuis T; Bosmans JE
Eur J Health Econ; 2023 Aug; 24(6):951-965. PubMed ID: 36161553
[TBL] [Abstract][Full Text] [Related]
20. Evaluation of multiple imputation approaches for handling missing covariate information in a case-cohort study with a binary outcome.
Middleton M; Nguyen C; Moreno-Betancur M; Carlin JB; Lee KJ
BMC Med Res Methodol; 2022 Apr; 22(1):87. PubMed ID: 35369860
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]