173 related articles for article (PubMed ID: 33307188)
1. Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small.
Riley RD; Snell KIE; Martin GP; Whittle R; Archer L; Sperrin M; Collins GS
J Clin Epidemiol; 2021 Apr; 132():88-96. PubMed ID: 33307188
[TBL] [Abstract][Full Text] [Related]
2. Developing clinical prediction models when adhering to minimum sample size recommendations: The importance of quantifying bootstrap variability in tuning parameters and predictive performance.
Martin GP; Riley RD; Collins GS; Sperrin M
Stat Methods Med Res; 2021 Dec; 30(12):2545-2561. PubMed ID: 34623193
[TBL] [Abstract][Full Text] [Related]
3. Regression shrinkage methods for clinical prediction models do not guarantee improved performance: Simulation study.
Van Calster B; van Smeden M; De Cock B; Steyerberg EW
Stat Methods Med Res; 2020 Nov; 29(11):3166-3178. PubMed ID: 32401702
[TBL] [Abstract][Full Text] [Related]
4. Selecting Shrinkage Parameters for Effect Estimation: The Multi-Ethnic Study of Atherosclerosis.
Keller JP; Rice KM
Am J Epidemiol; 2018 Feb; 187(2):358-365. PubMed ID: 28992037
[TBL] [Abstract][Full Text] [Related]
5. Stability of clinical prediction models developed using statistical or machine learning methods.
Riley RD; Collins GS
Biom J; 2023 Dec; 65(8):e2200302. PubMed ID: 37466257
[TBL] [Abstract][Full Text] [Related]
6. Comparison of likelihood penalization and variance decomposition approaches for clinical prediction models: A simulation study.
Lohmann A; Groenwold RHH; van Smeden M
Biom J; 2024 Jan; 66(1):e2200108. PubMed ID: 37199142
[TBL] [Abstract][Full Text] [Related]
7. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes.
Riley RD; Snell KI; Ensor J; Burke DL; Harrell FE; Moons KG; Collins GS
Stat Med; 2019 Mar; 38(7):1276-1296. PubMed ID: 30357870
[TBL] [Abstract][Full Text] [Related]
8. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P
Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557
[TBL] [Abstract][Full Text] [Related]
9. Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes.
Riley RD; Snell KIE; Ensor J; Burke DL; Harrell FE; Moons KGM; Collins GS
Stat Med; 2019 Mar; 38(7):1262-1275. PubMed ID: 30347470
[TBL] [Abstract][Full Text] [Related]
10. Minimum sample size for external validation of a clinical prediction model with a binary outcome.
Riley RD; Debray TPA; Collins GS; Archer L; Ensor J; van Smeden M; Snell KIE
Stat Med; 2021 Aug; 40(19):4230-4251. PubMed ID: 34031906
[TBL] [Abstract][Full Text] [Related]
11. Minimum sample size for external validation of a clinical prediction model with a continuous outcome.
Archer L; Snell KIE; Ensor J; Hudda MT; Collins GS; Riley RD
Stat Med; 2021 Jan; 40(1):133-146. PubMed ID: 33150684
[TBL] [Abstract][Full Text] [Related]
12. Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary data.
Rahman MS; Sultana M
BMC Med Res Methodol; 2017 Feb; 17(1):33. PubMed ID: 28231767
[TBL] [Abstract][Full Text] [Related]
13. Developing risk models for multicenter data using standard logistic regression produced suboptimal predictions: A simulation study.
Falconieri N; Van Calster B; Timmerman D; Wynants L
Biom J; 2020 Jul; 62(4):932-944. PubMed ID: 31957077
[TBL] [Abstract][Full Text] [Related]
14. Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data.
Becker N; Toedt G; Lichter P; Benner A
BMC Bioinformatics; 2011 May; 12():138. PubMed ID: 21554689
[TBL] [Abstract][Full Text] [Related]
15. Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events.
Pavlou M; Ambler G; Seaman S; De Iorio M; Omar RZ
Stat Med; 2016 Mar; 35(7):1159-77. PubMed ID: 26514699
[TBL] [Abstract][Full Text] [Related]
16. External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb.
Snell KIE; Archer L; Ensor J; Bonnett LJ; Debray TPA; Phillips B; Collins GS; Riley RD
J Clin Epidemiol; 2021 Jul; 135():79-89. PubMed ID: 33596458
[TBL] [Abstract][Full Text] [Related]
17. A comparison of hyperparameter tuning procedures for clinical prediction models: A simulation study.
Dunias ZS; Van Calster B; Timmerman D; Boulesteix AL; van Smeden M
Stat Med; 2024 Mar; 43(6):1119-1134. PubMed ID: 38189632
[TBL] [Abstract][Full Text] [Related]
18. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models.
Babyak MA
Psychosom Med; 2004; 66(3):411-21. PubMed ID: 15184705
[TBL] [Abstract][Full Text] [Related]
19. Penalized maximum likelihood estimation to directly adjust diagnostic and prognostic prediction models for overoptimism: a clinical example.
Moons KG; Donders AR; Steyerberg EW; Harrell FE
J Clin Epidemiol; 2004 Dec; 57(12):1262-70. PubMed ID: 15617952
[TBL] [Abstract][Full Text] [Related]
20. Conducting EQ-5D Valuation Studies in Resource-Constrained Countries: The Potential Use of Shrinkage Estimators to Reduce Sample Size.
Chan KKW; Xie F; Willan AR; Pullenayegum EM
Med Decis Making; 2018 Jan; 38(1):26-33. PubMed ID: 28823185
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]