Literature DB >> 16287216

ITT analysis of randomized encouragement design studies with missing data.

Xiao-Hua Zhou1, Sierra M Li.   

Abstract

In this paper, we considered a missing outcome problem in causal inferences for a randomized encouragement design study. We proposed both moment and maximum likelihood estimators for the marginal distributions of potential outcomes and the local complier average causal effect (CACE) parameter. We illustrated our methods in a randomized encouragement design study on the effectiveness of flu shots. Copyright (c) 2005 John Wiley & Sons, Ltd.

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Year:  2006        PMID： 16287216     DOI： 10.1002/sim.2388

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


  10 in total

1.  Rationale, design, and baseline characteristics of a community-based comparative effectiveness trial to prevent type 2 diabetes in economically disadvantaged adults: the RAPID Study.

Authors:  Ronald T Ackermann; Emily A Finch; Karen K Schmidt; Helena M Hoen; Laura M Hays; David G Marrero; Chandan Saha
Journal:  Contemp Clin Trials       Date:  2013-10-29       Impact factor: 2.226

2.  One-year effects of a group-based lifestyle intervention in adults with type 2 diabetes: A randomized encouragement trial.

Authors:  David T Liss; Emily A Finch; Andrew Cooper; Avani Sheth; Ashantí D Tejuosho; Nicola Lancki; Ronald T Ackermann
Journal:  Diabetes Res Clin Pract       Date:  2018-03-27       Impact factor: 5.602

3.  A Randomized Comparative Effectiveness Trial for Preventing Type 2 Diabetes.

Authors:  Ronald T Ackermann; David T Liss; Emily A Finch; Karen K Schmidt; Laura M Hays; David G Marrero; Chandan Saha
Journal:  Am J Public Health       Date:  2015-09-17       Impact factor: 9.308

4.  Using an instrumental variable to test for unmeasured confounding.

Authors:  Zijian Guo; Jing Cheng; Scott A Lorch; Dylan S Small
Journal:  Stat Med       Date:  2014-06-15       Impact factor: 2.373

5.  A field experiment on the impact of physician-level performance data on consumers' choice of physician.

Authors:  Steven C Martino; David E Kanouse; Marc N Elliott; Stephanie S Teleki; Ron D Hays
Journal:  Med Care       Date:  2012-11       Impact factor: 2.983

6.  Design and participant characteristics for a randomized effectiveness trial of an intensive lifestyle intervention to reduce cardiovascular risk in adults with type 2 diabetes: The I-D-HEALTH study.

Authors:  David T Liss; Emily A Finch; Dyanna L Gregory; Andrew Cooper; Ronald T Ackermann
Journal:  Contemp Clin Trials       Date:  2015-12-02       Impact factor: 2.226

7.  Identifiability and estimation of causal effects in randomized trials with noncompliance and completely nonignorable missing data.

Authors:  Hua Chen; Zhi Geng; Xiao-Hua Zhou
Journal:  Biometrics       Date:  2008-08-28       Impact factor: 2.571

8.  Calculating sample size for studies with expected all-or-none nonadherence and selection bias.

Authors:  Michelle D Shardell; Samer S El-Kamary
Journal:  Biometrics       Date:  2009-06       Impact factor: 2.571

9.  Designing a natural experiment to evaluate a national health care-community partnership to prevent type 2 diabetes.

Authors:  Ronald T Ackermann; Ann M Holmes; Chandan Saha
Journal:  Prev Chronic Dis       Date:  2013       Impact factor: 2.830

10.  Patient-centered primary care for adults at high risk for AUDs: the Choosing Healthier Drinking Options In primary CarE (CHOICE) trial.

Authors:  Katharine A Bradley; Evette Joy Ludman; Laura J Chavez; Jennifer F Bobb; Susan J Ruedebusch; Carol E Achtmeyer; Joseph O Merrill; Andrew J Saxon; Ryan M Caldeiro; Diane M Greenberg; Amy K Lee; Julie E Richards; Rachel M Thomas; Theresa E Matson; Emily C Williams; Eric Hawkins; Gwen Lapham; Daniel R Kivlahan
Journal:  Addict Sci Clin Pract       Date:  2017-05-17
  10 in total

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