Literature DB >> 20838848

Using Directed Acyclic Graphs to detect limitations of traditional regression in longitudinal studies.

Erica E M Moodie1, D A Stephens.   

Abstract

INTRODUCTION: Longitudinal data are increasingly available to health researchers; these present challenges not encountered in cross-sectional data, not the least of which is the presence of time-varying confounding variables and intermediate effects.
OBJECTIVES: We review confounding and mediation in a longitudinal setting and introduce causal graphs to explain the bias that arises from conventional analyses.
CONCLUSIONS: When both time-varying confounding and mediation are present in the data, traditional regression models result in estimates of effect coefficients that are systematically incorrect, or biased. In a companion paper (Moodie and Stephens in Int J Publ Health, 2010b, this issue), we describe a class of models that yield unbiased estimates in a longitudinal setting.

Mesh:

Year:  2010        PMID: 20838848     DOI: 10.1007/s00038-010-0184-x

Source DB:  PubMed          Journal:  Int J Public Health        ISSN: 1661-8556            Impact factor:   3.380


  7 in total

1.  Estimation of dose-response functions for longitudinal data using the generalised propensity score.

Authors:  Erica E M Moodie; David A Stephens
Journal:  Stat Methods Med Res       Date:  2010-05-04       Impact factor: 3.021

2.  Marginal Structural Models: unbiased estimation for longitudinal studies.

Authors:  Erica E M Moodie; D A Stephens
Journal:  Int J Public Health       Date:  2010-10-08       Impact factor: 3.380

3.  Causal diagrams for epidemiologic research.

Authors:  S Greenland; J Pearl; J M Robins
Journal:  Epidemiology       Date:  1999-01       Impact factor: 4.822

4.  Design of the Monitored Occlusion Treatment of Amblyopia Study (MOTAS).

Authors:  C E Stewart; A R Fielder; D A Stephens; M J Moseley
Journal:  Br J Ophthalmol       Date:  2002-08       Impact factor: 4.638

5.  A longitudinal study of vaginal douching and bacterial vaginosis--a marginal structural modeling analysis.

Authors:  Rebecca M Brotman; Mark A Klebanoff; Tonja R Nansel; William W Andrews; Jane R Schwebke; Jun Zhang; Kai F Yu; Jonathan M Zenilman; Daniel O Scharfstein
Journal:  Am J Epidemiol       Date:  2008-05-23       Impact factor: 4.897

6.  Marginal structural models for analyzing causal effects of time-dependent treatments: an application in perinatal epidemiology.

Authors:  Lisa M Bodnar; Marie Davidian; Anna Maria Siega-Riz; Anastasios A Tsiatis
Journal:  Am J Epidemiol       Date:  2004-05-15       Impact factor: 4.897

7.  Treatment dose-response in amblyopia therapy: the Monitored Occlusion Treatment of Amblyopia Study (MOTAS).

Authors:  Catherine E Stewart; Merrick J Moseley; David A Stephens; Alistair R Fielder
Journal:  Invest Ophthalmol Vis Sci       Date:  2004-09       Impact factor: 4.799

  7 in total
  11 in total

1.  Marginal Structural Models: unbiased estimation for longitudinal studies.

Authors:  Erica E M Moodie; D A Stephens
Journal:  Int J Public Health       Date:  2010-10-08       Impact factor: 3.380

2.  Correcting for Measurement Error in Time-Varying Covariates in Marginal Structural Models.

Authors:  Ryan P Kyle; Erica E M Moodie; Marina B Klein; Michał Abrahamowicz
Journal:  Am J Epidemiol       Date:  2016-07-13       Impact factor: 4.897

3.  Fetal sex and race modify the predictors of fetal growth.

Authors:  Simone A Reynolds; James M Roberts; Lisa M Bodnar; Catherine L Haggerty; Ada O Youk; Janet M Catov
Journal:  Matern Child Health J       Date:  2015-04

Review 4.  Assessing the effect of hormonal contraception on HIV acquisition in observational data: challenges and recommended analytic approaches.

Authors:  Chelsea B Polis; Daniel Westreich; Jennifer E Balkus; Renee Heffron
Journal:  AIDS       Date:  2013-10       Impact factor: 4.177

5.  Emulating a Novel Clinical Trial Using Existing Observational Data. Predicting Results of the PreVent Study.

Authors:  Andrew J Admon; John P Donnelly; Jonathan D Casey; David R Janz; Derek W Russell; Aaron M Joffe; Derek J Vonderhaar; Kevin M Dischert; Susan B Stempek; James M Dargin; Todd W Rice; Theodore J Iwashyna; Matthew W Semler
Journal:  Ann Am Thorac Soc       Date:  2019-08

6.  Do changing levels of maternal exercise during pregnancy affect neonatal adiposity? Secondary analysis of the babies after SCOPE: evaluating the longitudinal impact using neurological and nutritional endpoints (BASELINE) birth cohort (Cork, Ireland).

Authors:  Tom Norris; Fergus P McCarthy; Ali S Khashan; Deidre M Murray; Mairead Kiely; Jonathan O'B Hourihane; Philip N Baker; Louise C Kenny
Journal:  BMJ Open       Date:  2017-12-01       Impact factor: 2.692

7.  Physical activity at age 11 years and chronic disabling fatigue at ages 13 and 16 years in a UK birth cohort.

Authors:  Simon M Collin; Tom Norris; Kevin C Deere; Russell Jago; Andy R Ness; Esther Crawley
Journal:  Arch Dis Child       Date:  2018-01-30       Impact factor: 3.791

8.  Childhood sleep and adolescent chronic fatigue syndrome (CFS/ME): evidence of associations in a UK birth cohort.

Authors:  Simon M Collin; Tom Norris; Paul Gringras; Peter S Blair; Kate Tilling; Esther Crawley
Journal:  Sleep Med       Date:  2018-01-31       Impact factor: 3.492

9.  Depressive symptoms at age 9-13 and chronic disabling fatigue at age 16: A longitudinal study.

Authors:  Simon M Collin; Tom Norris; Carol Joinson; Maria E Loades; Glyn Lewis; Stephen A Stansfeld; Esther Crawley
Journal:  J Adolesc       Date:  2019-08-02

10.  Factors associated with catch-up growth in early infancy in rural Pakistan: A longitudinal analysis of the women's work and nutrition study.

Authors:  Rebecca Pradeilles; Tom Norris; Elaine Ferguson; Haris Gazdar; Sidra Mazhar; Hussain Bux Mallah; Azmat Budhani; Rashid Mehmood; Saba Aslam; Alan D Dangour; Elizabeth Allen
Journal:  Matern Child Nutr       Date:  2018-11-13       Impact factor: 3.092

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