Literature DB >> 7846425

The paired availability design: a proposal for evaluating epidural analgesia during labor.

S G Baker1, K S Lindeman.   

Abstract

The paired availability design (PAD) can reduce selection bias when it is not possible to randomize subjects. PAD consists of independent pairs of experimental and control groups. Within each pair, the intervention is the availability of treatment not its receipt. In the experimental group, the new treatment is made available to all subjects although some may not receive it. In the control group, the experimental treatment is generally not available to subjects although some may receive it in special circumstances. We present a statistic to test a null hypothesis that the receipt of intervention will increase response by a specified non-zero amount delta. We propose this design for use in a study of the effect of epidural analgesia on the rate of Caesarean section.

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Year:  1994        PMID: 7846425     DOI: 10.1002/sim.4780132108

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  29 in total

1.  Principal stratification in causal inference.

Authors:  Constantine E Frangakis; Donald B Rubin
Journal:  Biometrics       Date:  2002-03       Impact factor: 2.571

2.  Principal stratification and attribution prohibition: good ideas taken too far.

Authors:  Marshall Joffe
Journal:  Int J Biostat       Date:  2011-09-14       Impact factor: 0.968

3.  Predicting treatment effect from surrogate endpoints and historical trials: an extrapolation involving probabilities of a binary outcome or survival to a specific time.

Authors:  Stuart G Baker; Daniel J Sargent; Marc Buyse; Tomasz Burzykowski
Journal:  Biometrics       Date:  2011-08-13       Impact factor: 2.571

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

Authors:  Wen Yu; Kani Chen; Michael E Sobel; Zhiliang Ying
Journal:  J R Stat Soc Series B Stat Methodol       Date:  2015-03-01       Impact factor: 4.488

Review 5.  Principal stratification--a goal or a tool?

Authors:  Judea Pearl
Journal:  Int J Biostat       Date:  2011-03-30       Impact factor: 0.968

6.  Clarifying the role of principal stratification in the paired availability design.

Authors:  Stuart G Baker; Karen S Lindeman; Barnett S Kramer
Journal:  Int J Biostat       Date:  2011-05-20       Impact factor: 0.968

7.  Additional thoughts on causal inference, probability theory, and graphical insights.

Authors:  Stuart G Baker
Journal:  Stat Med       Date:  2013-11-10       Impact factor: 2.373

8.  Principal stratification: All-or-none versus partial compliance.

Authors:  Stuart G Baker
Journal:  Clin Trials       Date:  2014-06       Impact factor: 2.486

9.  Early reporting for cancer screening trials.

Authors:  Stuart G Baker; Barnett S Kramer; Philip C Prorok
Journal:  J Med Screen       Date:  2008       Impact factor: 2.136

10.  Improving the biomarker pipeline to develop and evaluate cancer screening tests.

Authors:  Stuart G Baker
Journal:  J Natl Cancer Inst       Date:  2009-07-02       Impact factor: 13.506

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