137 related articles for article (PubMed ID: 29206922)
1. PIE: A prior knowledge guided integrated likelihood estimation method for bias reduction in association studies using electronic health records data.
Huang J; Duan R; Hubbard RA; Wu Y; Moore JH; Xu H; Chen Y
J Am Med Inform Assoc; 2018 Mar; 25(3):345-352. PubMed ID: 29206922
[TBL] [Abstract][Full Text] [Related]
2. An augmented estimation procedure for EHR-based association studies accounting for differential misclassification.
Tong J; Huang J; Chubak J; Wang X; Moore JH; Hubbard RA; Chen Y
J Am Med Inform Assoc; 2020 Feb; 27(2):244-253. PubMed ID: 31617899
[TBL] [Abstract][Full Text] [Related]
3. Inflation of type I error rates due to differential misclassification in EHR-derived outcomes: Empirical illustration using breast cancer recurrence.
Chen Y; Wang J; Chubak J; Hubbard RA
Pharmacoepidemiol Drug Saf; 2019 Feb; 28(2):264-268. PubMed ID: 30375122
[TBL] [Abstract][Full Text] [Related]
4. A cost-effective chart review sampling design to account for phenotyping error in electronic health records (EHR) data.
Yin Z; Tong J; Chen Y; Hubbard RA; Tang CY
J Am Med Inform Assoc; 2021 Dec; 29(1):52-61. PubMed ID: 34718618
[TBL] [Abstract][Full Text] [Related]
5. A maximum likelihood approach to electronic health record phenotyping using positive and unlabeled patients.
Zhang L; Ding X; Ma Y; Muthu N; Ajmal I; Moore JH; Herman DS; Chen J
J Am Med Inform Assoc; 2020 Jan; 27(1):119-126. PubMed ID: 31722396
[TBL] [Abstract][Full Text] [Related]
6. Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassification.
Beesley LJ; Mukherjee B
Biometrics; 2022 Mar; 78(1):214-226. PubMed ID: 33179768
[TBL] [Abstract][Full Text] [Related]
7. Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification.
Beesley LJ; Mukherjee B
Stat Med; 2022 Dec; 41(28):5501-5516. PubMed ID: 36131394
[TBL] [Abstract][Full Text] [Related]
8. A Quantitative Bias Analysis Approach to Informative Presence Bias in Electronic Health Records.
Zhang H; Clark AS; Hubbard RA
Epidemiology; 2024 May; 35(3):349-358. PubMed ID: 38630509
[TBL] [Abstract][Full Text] [Related]
9. Reducing Bias Due to Outcome Misclassification for Epidemiologic Studies Using EHR-derived Probabilistic Phenotypes.
Hubbard RA; Tong J; Duan R; Chen Y
Epidemiology; 2020 Jul; 31(4):542-550. PubMed ID: 32282406
[TBL] [Abstract][Full Text] [Related]
10. Prevalence estimation by joint use of big data and health survey: a demonstration study using electronic health records in New York city.
Kim RS; Shankar V
BMC Med Res Methodol; 2020 Apr; 20(1):77. PubMed ID: 32252642
[TBL] [Abstract][Full Text] [Related]
11. Bias reduction and inference for electronic health record data under selection and phenotype misclassification: three case studies.
Beesley LJ; Mukherjee B
medRxiv; 2020 Dec; ():. PubMed ID: 33398299
[TBL] [Abstract][Full Text] [Related]
12. Can the Use of Bayesian Analysis Methods Correct for Incompleteness in Electronic Health Records Diagnosis Data? Development of a Novel Method Using Simulated and Real-Life Clinical Data.
Ford E; Rooney P; Hurley P; Oliver S; Bremner S; Cassell J
Front Public Health; 2020; 8():54. PubMed ID: 32211363
[No Abstract] [Full Text] [Related]
13. SAT: a Surrogate-Assisted Two-wave case boosting sampling method, with application to EHR-based association studies.
Liu X; Chubak J; Hubbard RA; Chen Y
J Am Med Inform Assoc; 2022 Apr; 29(5):918-927. PubMed ID: 34962283
[TBL] [Abstract][Full Text] [Related]
14. Estimating screening test utilization using electronic health records data.
Hubbard R; Chubak J; Rutter C
EGEMS (Wash DC); 2014 Nov; 2(1):14. PubMed ID: 25741527
[TBL] [Abstract][Full Text] [Related]
15. Data Quality in Electronic Health Record Research: An Approach for Validation and Quantitative Bias Analysis for Imperfectly Ascertained Health Outcomes Via Diagnostic Codes.
Goldstein ND; Kahal D; Testa K; Gracely EJ; Burstyn I
Harv Data Sci Rev; 2022; 4(2):. PubMed ID: 36324333
[TBL] [Abstract][Full Text] [Related]
16. Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study.
Anderson AE; Kerr WT; Thames A; Li T; Xiao J; Cohen MS
J Biomed Inform; 2016 Apr; 60():162-8. PubMed ID: 26707455
[TBL] [Abstract][Full Text] [Related]
17. Case contamination in electronic health records based case-control studies.
Wang L; Schnall J; Small A; Hubbard RA; Moore JH; Damrauer SM; Chen J
Biometrics; 2021 Mar; 77(1):67-77. PubMed ID: 32246839
[TBL] [Abstract][Full Text] [Related]
18. Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm.
Duan R; Boland MR; Liu Z; Liu Y; Chang HH; Xu H; Chu H; Schmid CH; Forrest CB; Holmes JH; Schuemie MJ; Berlin JA; Moore JH; Chen Y
J Am Med Inform Assoc; 2020 Mar; 27(3):376-385. PubMed ID: 31816040
[TBL] [Abstract][Full Text] [Related]
19. Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping.
Zhang Y; Liu M; Neykov M; Cai T
J Mach Learn Res; 2022; 23():. PubMed ID: 37974910
[TBL] [Abstract][Full Text] [Related]
20. The empirical likelihood prior applied to bias reduction of general estimating equations.
Vexler A; Zou L; Hutson AD
Comput Stat Data Anal; 2019 Oct; 138():96-106. PubMed ID: 31031458
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
