Semiparametric transformation models for causal inference in time-to-event studies with all-or-nothing compliance-Reference-Cited by-同舟云学术

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Semiparametric transformation models for causal inference in time-to-event studies with all-or-nothing compliance

Published:2014-05-30 Issue:2 Volume:77 Page:397-415
ISSN:1369-7412
Container-title:Journal of the Royal Statistical Society: Series B (Statistical Methodology)
language:en
Short-container-title:J. R. Stat. Soc. B

Author:

Yu Wen¹,Chen Kani²,Sobel Michael E.³,Ying Zhiliang³

Affiliation:

1. Fudan University; Shanghai China

2. Hong Kong University of Science and Technology; Hong Kong

3. Columbia University; New York USA

Funder

National Institutes of Health

National Natural Science Foundation of China

National Science Foundation

Hong Kong RGC General Research Funds

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/rssb.12072/fullpdf

Reference36 articles.

1. Identification of causal effects using instrumental variables;Angrist;J. Am. Statist. Ass.,1996

2. Analysis of survival data from randomized trial with all-or-none compliance: estimating the cost-effectiveness of a cancer screening program;Baker;J. Am. Statist. Ass.,1998

3. Estimation and inference for the causal effect of receiving treatment on a multinomial outcome: an alternative approach;Baker;Biometrics,2011

4. The paired availability design: a proposal for evaluating epidural analgesia during labor;Baker;Statist. Med.,1994

5. Accounting for no-shows in experimental evaluation designs;Bloom;Evaln Rev.,1984

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