Literature DB >> 21686085

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

Stuart G Baker1, Karen S Lindeman, Barnett S Kramer.   

Abstract

The paired availability design for historical controls postulated four classes corresponding to the treatment (old or new) a participant would receive if arrival occurred during either of two time periods associated with different availabilities of treatment. These classes were later extended to other settings and called principal strata. Judea Pearl asks if principal stratification is a goal or a tool and lists four interpretations of principal stratification. In the case of the paired availability design, principal stratification is a tool that falls squarely into Pearl's interpretation of principal stratification as "an approximation to research questions concerning population averages." We describe the paired availability design and the important role played by principal stratification in estimating the effect of receipt of treatment in a population using data on changes in availability of treatment. We discuss the assumptions and their plausibility. We also introduce the extrapolated estimate to make the generalizability assumption more plausible. By showing why the assumptions are plausible we show why the paired availability design, which includes principal stratification as a key component, is useful for estimating the effect of receipt of treatment in a population. Thus, for our application, we answer Pearl's challenge to clearly demonstrate the value of principal stratification.

Entities:  

Keywords:  causal inference; paired availability design; principal stratification

Mesh:

Year:  2011        PMID: 21686085      PMCID: PMC3114955          DOI: 10.2202/1557-4679.1338

Source DB:  PubMed          Journal:  Int J Biostat        ISSN: 1557-4679            Impact factor:   0.968


  12 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.  Simultaneous-equation estimation in a clinical trial of the effect of smoking on birth weight.

Authors:  T Permutt; J R Hebel
Journal:  Biometrics       Date:  1989-06       Impact factor: 2.571

Review 3.  Principal stratification--a goal or a tool?

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

4.  Adjusting for non-compliance and contamination in randomized clinical trials.

Authors:  J Cuzick; R Edwards; N Segnan
Journal:  Stat Med       Date:  1997-05-15       Impact factor: 2.373

5.  Epidural analgesia and the incidence of cesarean section: time for another close look.

Authors:  D H Chestnut
Journal:  Anesthesiology       Date:  1997-09       Impact factor: 7.892

6.  Explanatory and pragmatic estimates of the treatment effect when deviations from allocated treatment occur.

Authors:  R G Newcombe
Journal:  Stat Med       Date:  1988-11       Impact factor: 2.373

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

Authors:  S G Baker; K S Lindeman
Journal:  Stat Med       Date:  1994-11-15       Impact factor: 2.373

8.  Estimation and inference for the causal effect of receiving treatment on a multinomial outcome: an alternative approach.

Authors:  Stuart G Baker
Journal:  Biometrics       Date:  2011-03       Impact factor: 2.571

9.  The Will Rogers phenomenon. Stage migration and new diagnostic techniques as a source of misleading statistics for survival in cancer.

Authors:  A R Feinstein; D M Sosin; C K Wells
Journal:  N Engl J Med       Date:  1985-06-20       Impact factor: 91.245

10.  The paired availability design for historical controls.

Authors:  S G Baker; K S Lindeman; B S Kramer
Journal:  BMC Med Res Methodol       Date:  2001-09-26       Impact factor: 4.615

View more
  2 in total

1.  Latent class instrumental variables: a clinical and biostatistical perspective.

Authors:  Stuart G Baker; Barnett S Kramer; Karen S Lindeman
Journal:  Stat Med       Date:  2015-08-04       Impact factor: 2.373

2.  The risky reliance on small surrogate endpoint studies when planning a large prevention trial.

Authors:  Stuart G Baker; Barnett S Kramer
Journal:  J R Stat Soc Ser A Stat Soc       Date:  2012-06-28       Impact factor: 2.483

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.