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Items: 1 to 20 of 101

1.

Health administrative data enrichment using cohort information: Comparative evaluation of methods by simulation and application to real data.

Silenou BC, Avalos M, Helmer C, Berr C, Pariente A, Jacqmin-Gadda H.

PLoS One. 2019 Jan 31;14(1):e0211118. doi: 10.1371/journal.pone.0211118. eCollection 2019.

2.

Validation sampling can reduce bias in health care database studies: an illustration using influenza vaccination effectiveness.

Nelson JC, Marsh T, Lumley T, Larson EB, Jackson LA, Jackson ML; Vaccine Safety Datalink Team.

J Clin Epidemiol. 2013 Aug;66(8 Suppl):S110-21. doi: 10.1016/j.jclinepi.2013.01.015.

3.
4.

Adjustments for unmeasured confounders in pharmacoepidemiologic database studies using external information.

Stürmer T, Glynn RJ, Rothman KJ, Avorn J, Schneeweiss S.

Med Care. 2007 Oct;45(10 Supl 2):S158-65. Review.

5.

Adjustment for time-dependent unmeasured confounders in marginal structural Cox models using validation sample data.

Burne RM, Abrahamowicz M.

Stat Methods Med Res. 2017 Jan 1:962280217726800. doi: 10.1177/0962280217726800. [Epub ahead of print]

PMID:
28835193
6.

Guided Bayesian imputation to adjust for confounding when combining heterogeneous data sources in comparative effectiveness research.

Antonelli J, Zigler C, Dominici F.

Biostatistics. 2017 Jul 1;18(3):553-568. doi: 10.1093/biostatistics/kxx003.

7.

Response to letter to the editor from Dr Rahman Shiri: The challenging topic of suicide across occupational groups.

Niedhammer I, Milner A, Witt K, Klingelschmidt J, Khireddine-Medouni I, Alexopoulos EC, Toivanen S, Chastang JF, LaMontagne AD.

Scand J Work Environ Health. 2018 Jan 1;44(1):108-110. doi: 10.5271/sjweh.3698. Epub 2017 Dec 8.

PMID:
29218357
8.

Martingale residual-based method to control for confounders measured only in a validation sample in time-to-event analysis.

Burne RM, Abrahamowicz M.

Stat Med. 2016 Nov 10;35(25):4588-4606. doi: 10.1002/sim.7012. Epub 2016 Jun 16.

PMID:
27306611
9.

[Volume and health outcomes: evidence from systematic reviews and from evaluation of Italian hospital data].

Amato L, Colais P, Davoli M, Ferroni E, Fusco D, Minozzi S, Moirano F, Sciattella P, Vecchi S, Ventura M, Perucci CA.

Epidemiol Prev. 2013 Mar-Jun;37(2-3 Suppl 2):1-100. Review. Italian.

10.

Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation.

Wahl S, Boulesteix AL, Zierer A, Thorand B, van de Wiel MA.

BMC Med Res Methodol. 2016 Oct 26;16(1):144. Erratum in: BMC Med Res Methodol. 2016 Dec 5;16(1):170.

11.

Association between pacifier use and breast-feeding, sudden infant death syndrome, infection and dental malocclusion.

Callaghan A, Kendall G, Lock C, Mahony A, Payne J, Verrier L.

JBI Libr Syst Rev. 2005;3(6):1-33.

PMID:
27819973
12.

Simulations showed that validation of database-derived diagnostic criteria based on a small subsample reduced bias.

Abrahamowicz M, Xiao Y, Ionescu-Ittu R, Lacaille D.

J Clin Epidemiol. 2007 Jun;60(6):600-9. Epub 2007 Mar 27.

PMID:
17493519
13.

Methods to account for attrition in longitudinal data: do they work? A simulation study.

Kristman VL, Manno M, Côté P.

Eur J Epidemiol. 2005;20(8):657-62.

PMID:
16151878
14.

Correcting hazard ratio estimates for outcome misclassification using multiple imputation with internal validation data.

Ni J, Leong A, Dasgupta K, Rahme E.

Pharmacoepidemiol Drug Saf. 2017 Aug;26(8):925-934. doi: 10.1002/pds.4223. Epub 2017 May 15.

PMID:
28503870
15.

Using linked educational attainment data to reduce bias due to missing outcome data in estimates of the association between the duration of breastfeeding and IQ at 15 years.

Cornish RP, Tilling K, Boyd A, Davies A, Macleod J.

Int J Epidemiol. 2015 Jun;44(3):937-45. doi: 10.1093/ije/dyv035. Epub 2015 Apr 8.

16.

Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study.

Shah AD, Bartlett JW, Carpenter J, Nicholas O, Hemingway H.

Am J Epidemiol. 2014 Mar 15;179(6):764-74. doi: 10.1093/aje/kwt312. Epub 2014 Jan 12.

17.

Attrition Bias Related to Missing Outcome Data: A Longitudinal Simulation Study.

Lewin A, Brondeel R, Benmarhnia T, Thomas F, Chaix B.

Epidemiology. 2018 Jan;29(1):87-95. doi: 10.1097/EDE.0000000000000755.

PMID:
28926372
18.

Evaluating the impact of unmeasured confounding with internal validation data: an example cost evaluation in type 2 diabetes.

Faries D, Peng X, Pawaskar M, Price K, Stamey JD, Seaman JW Jr.

Value Health. 2013 Mar-Apr;16(2):259-66. doi: 10.1016/j.jval.2012.10.012. Epub 2013 Jan 23.

19.

Propensity score analysis with partially observed covariates: How should multiple imputation be used?

Leyrat C, Seaman SR, White IR, Douglas I, Smeeth L, Kim J, Resche-Rigon M, Carpenter JR, Williamson EJ.

Stat Methods Med Res. 2019 Jan;28(1):3-19. doi: 10.1177/0962280217713032. Epub 2017 Jun 2.

20.

Analyzing partially missing confounder information in comparative effectiveness and safety research of therapeutics.

Toh S, García Rodríguez LA, Hernán MA.

Pharmacoepidemiol Drug Saf. 2012 May;21 Suppl 2:13-20. doi: 10.1002/pds.3248.

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