Literature DB >> 21105867

Separated at birth: statisticians, social scientists, and causality in health services research.

Bryan E Dowd1.   

Abstract

OBJECTIVE: Health services research is a field of study that brings together experts from a wide variety of academic disciplines. It also is a field that places a high priority on empirical analysis. Many of the questions posed by health services researchers involve the effects of treatments, patient and provider characteristics, and policy interventions on outcomes of interest. These are causal questions. Yet many health services researchers have been trained in disciplines that are reluctant to use the language of causality, and the approaches to causal questions are discipline specific, often with little overlap. How did this situation arise? This paper traces the roots of the division and some recent attempts to remedy the situation. DATA SOURCES AND SETTINGS: Existing literature. STUDY
DESIGN: Review of the literature. © Health Research and Educational Trust.

Entities:  

Mesh:

Year:  2010        PMID: 21105867      PMCID: PMC3064910          DOI: 10.1111/j.1475-6773.2010.01203.x

Source DB:  PubMed          Journal:  Health Serv Res        ISSN: 0017-9124            Impact factor:   3.402


  13 in total

1.  An introduction to instrumental variables for epidemiologists.

Authors:  S Greenland
Journal:  Int J Epidemiol       Date:  2000-08       Impact factor: 7.196

2.  Instrumental variables: application and limitations.

Authors:  Edwin P Martens; Wiebe R Pestman; Anthonius de Boer; Svetlana V Belitser; Olaf H Klungel
Journal:  Epidemiology       Date:  2006-05       Impact factor: 4.822

3.  Instruments for causal inference: an epidemiologist's dream?

Authors:  Miguel A Hernán; James M Robins
Journal:  Epidemiology       Date:  2006-07       Impact factor: 4.822

4.  On the Nature of Size Factors.

Authors:  S Wright
Journal:  Genetics       Date:  1918-07       Impact factor: 4.562

5.  Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods.

Authors:  Thérèse A Stukel; Elliott S Fisher; David E Wennberg; David A Alter; Daniel J Gottlieb; Marian J Vermeulen
Journal:  JAMA       Date:  2007-01-17       Impact factor: 56.272

6.  The science of improvement.

Authors:  Donald M Berwick
Journal:  JAMA       Date:  2008-03-12       Impact factor: 56.272

7.  Instrumental variables I: instrumental variables exploit natural variation in nonexperimental data to estimate causal relationships.

Authors:  Jeremy A Rassen; M Alan Brookhart; Robert J Glynn; Murray A Mittleman; Sebastian Schneeweiss
Journal:  J Clin Epidemiol       Date:  2009-04-08       Impact factor: 6.437

8.  Instrumental variables II: instrumental variable application-in 25 variations, the physician prescribing preference generally was strong and reduced covariate imbalance.

Authors:  Jeremy A Rassen; M Alan Brookhart; Robert J Glynn; Murray A Mittleman; Sebastian Schneeweiss
Journal:  J Clin Epidemiol       Date:  2009-04-05       Impact factor: 6.437

9.  Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables.

Authors:  M McClellan; B J McNeil; J P Newhouse
Journal:  JAMA       Date:  1994-09-21       Impact factor: 56.272

10.  Logical, epistemological and statistical aspects of nature-nurture data interpretation.

Authors:  O Kempthorne
Journal:  Biometrics       Date:  1978-03       Impact factor: 2.571

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  7 in total

1.  Falsification Testing of Instrumental Variables Methods for Comparative Effectiveness Research.

Authors:  Steven D Pizer
Journal:  Health Serv Res       Date:  2015-08-21       Impact factor: 3.402

2.  Statistics and causality: separated to reunite-commentary on Bryan Dowd's "separated at birth".

Authors:  Judea Pearl
Journal:  Health Serv Res       Date:  2011-02-09       Impact factor: 3.402

3.  Commentary on Bryan Dowd's paper "separated at birth: statisticians, social scientists, and causality in health services research".

Authors:  A James O'Malley
Journal:  Health Serv Res       Date:  2011-01-28       Impact factor: 3.402

4.  Instrumental variable specifications and assumptions for longitudinal analysis of mental health cost offsets.

Authors:  A James O'Malley
Journal:  Health Serv Outcomes Res Methodol       Date:  2012-09-25

5.  Different analyses estimate different parameters of the effect of erythropoietin stimulating agents on survival in end stage renal disease: a comparison of payment policy analysis, instrumental variables, and multiple imputation of potential outcomes.

Authors:  David D Dore; Shailender Swaminathan; Roee Gutman; Amal N Trivedi; Vincent Mor
Journal:  J Clin Epidemiol       Date:  2013-08       Impact factor: 6.437

6.  Effect identification in comparative effectiveness research.

Authors:  J Michael Oakes
Journal:  EGEMS (Wash DC)       Date:  2013-01-17

7.  When is enough, enough? Understanding and solving your sample size problems in health services research.

Authors:  Victoria Pye; Natalie Taylor; Robyn Clay-Williams; Jeffrey Braithwaite
Journal:  BMC Res Notes       Date:  2016-02-12
  7 in total

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