Alternative sensitivity analyses for regression estimates of treatment effects to unobserved confounding in binary and survival data-Reference-Cited by-同舟云学术

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Alternative sensitivity analyses for regression estimates of treatment effects to unobserved confounding in binary and survival data

Published:2021-10-22 Issue: Volume: Page:
ISSN:1618-2510
Container-title:Statistical Methods & Applications
language:en
Short-container-title:Stat Methods Appl

Author:

Choi Byeong Yeob^ORCID,Fine Jason P.,Fernandez Roman,Brookhart M. Alan

Funder

National Cancer Institute

Publisher

Springer Science and Business Media LLC

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://link.springer.com/content/pdf/10.1007/s10260-021-00597-z.pdf

Reference27 articles.

1. Agresti A (2015) Foundations of linear and generalized linear models. Wiley, New Jersey

2. Brookhart MA, Stürmer T, Glynn RJ, Rassen J, Schneeweiss S (2010) Confounding control in healthcare database research: challenges and potential approaches. Med Care 48(6 Suppl):S114-120

3. Buzas JS, Stefanski LA (1996) Instrumental variable estimation in a probit measurement error model. J Stat Plan Inference 55:47–62

4. Carroll RJ, Spiegelman CH, Lan KKG, Bailey KT, Abbott RD (1984) On errors-in-variables for binary regression models. Biometrika 71:19–25

5. Choi BY, Fine JP, Brookhart MA (2020) Bias testing, bias correction, and confounder selection using an instrumental variable model. Stat Med 39(29):4386–4404

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