312 related articles for article (PubMed ID: 37105540)
1. Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: A simulation study.
Sisk R; Sperrin M; Peek N; van Smeden M; Martin GP
Stat Methods Med Res; 2023 Aug; 32(8):1461-1477. PubMed ID: 37105540
[No Abstract] [Full Text] [Related]
2. 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]
3. Multiple imputation with missing data indicators.
Beesley LJ; Bondarenko I; Elliot MR; Kurian AW; Katz SJ; Taylor JM
Stat Methods Med Res; 2021 Dec; 30(12):2685-2700. PubMed ID: 34643465
[TBL] [Abstract][Full Text] [Related]
4. Outcome-sensitive multiple imputation: a simulation study.
Kontopantelis E; White IR; Sperrin M; Buchan I
BMC Med Res Methodol; 2017 Jan; 17(1):2. PubMed ID: 28068910
[TBL] [Abstract][Full Text] [Related]
5. A nonparametric multiple imputation approach for missing categorical data.
Zhou M; He Y; Yu M; Hsu CH
BMC Med Res Methodol; 2017 Jun; 17(1):87. PubMed ID: 28587662
[TBL] [Abstract][Full Text] [Related]
6. Multiple imputation with missing indicators as proxies for unmeasured variables: simulation study.
Sperrin M; Martin GP
BMC Med Res Methodol; 2020 Jul; 20(1):185. PubMed ID: 32640992
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study.
Marshall A; Altman DG; Royston P; Holder RL
BMC Med Res Methodol; 2010 Jan; 10():7. PubMed ID: 20085642
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Robust imputation method with context-aware voting ensemble model for management of water-quality data.
Choi J; Lim KJ; Ji B
Water Res; 2023 Sep; 243():120369. PubMed ID: 37499538
[TBL] [Abstract][Full Text] [Related]
11. Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study.
Marshall A; Altman DG; Holder RL
BMC Med Res Methodol; 2010 Dec; 10():112. PubMed ID: 21194416
[TBL] [Abstract][Full Text] [Related]
12. A comparison of different methods to handle missing data in the context of propensity score analysis.
Choi J; Dekkers OM; le Cessie S
Eur J Epidemiol; 2019 Jan; 34(1):23-36. PubMed ID: 30341708
[TBL] [Abstract][Full Text] [Related]
13. Compatibility in Missing Data Handling Across the Prediction Model Pipeline: A Simulation Study.
Tsvetanova A; Sperrin M; Jenkins D; Peek N; Buchan I; Hyland S; Martin G
Stud Health Technol Inform; 2024 Jan; 310():1476-1477. PubMed ID: 38269704
[TBL] [Abstract][Full Text] [Related]
14. Missing data should be handled differently for prediction than for description or causal explanation.
Sperrin M; Martin GP; Sisk R; Peek N
J Clin Epidemiol; 2020 Sep; 125():183-187. PubMed ID: 32540389
[TBL] [Abstract][Full Text] [Related]
15. Neural networks based on attention architecture are robust to data missingness for early predicting hospital mortality in intensive care unit patients.
Zeng Z; Liu Y; Yao S; Liu J; Xiao B; Liu C; Gong X
Digit Health; 2023; 9():20552076231171482. PubMed ID: 37179744
[TBL] [Abstract][Full Text] [Related]
16. Prediction Model Performance With Different Imputation Strategies: A Simulation Study Using a North American ICU Registry.
Steif J; Brant R; Sreepada RS; West N; Murthy S; Görges M
Pediatr Crit Care Med; 2022 Jan; 23(1):e29-e44. PubMed ID: 34560774
[TBL] [Abstract][Full Text] [Related]
17. Evaluation of predictive model performance of an existing model in the presence of missing data.
Li P; Taylor JMG; Spratt DE; Karnes RJ; Schipper MJ
Stat Med; 2021 Jul; 40(15):3477-3498. PubMed ID: 33843085
[TBL] [Abstract][Full Text] [Related]
18. Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics.
Dekermanjian JP; Shaddox E; Nandy D; Ghosh D; Kechris K
BMC Bioinformatics; 2022 May; 23(1):179. PubMed ID: 35578165
[TBL] [Abstract][Full Text] [Related]
19. Missing data was handled inconsistently in UK prediction models: a review of method used.
Tsvetanova A; Sperrin M; Peek N; Buchan I; Hyland S; Martin GP
J Clin Epidemiol; 2021 Dec; 140():149-158. PubMed ID: 34520847
[TBL] [Abstract][Full Text] [Related]
20. Strategies for handling missing data that improve Frailty Index estimation and predictive power: lessons from the NHANES dataset.
Pridham G; Rockwood K; Rutenberg A
Geroscience; 2022 Apr; 44(2):897-923. PubMed ID: 35103915
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]