Literature DB >> 30555772

A review of time scale fundamentals in the g-formula and insidious selection bias.

Alexander P Keil1, Jessie K Edwards1.   

Abstract

PURPOSE OF REVIEW: We review recent examples of data analysis with the g-formula, a powerful tool for analyzing longitudinal data and survival analysis. Specifically, we focus on the common choices of time scale and review inferential issues that may arise. RECENT
FINDINGS: Researchers are increasingly engaged with questions that require time scales subject to left-truncation and right-censoring. The assumptions necessary for allowing right-censoring are well defined in the literature, whereas similar assumptions for left-truncation are not well defined. Policy and biologic considerations sometimes dictate that observational data must be analyzed on time scales that are subject to left-truncation, such as age.
SUMMARY: Further consideration of left-truncation is needed, especially when biologic or policy considerations dictate that age is the relevant time scale of interest. Methodologic development is needed to reduce potential for bias when left-truncation may occur.

Entities:  

Keywords:  causal inference; g-computation; longitudinal; survival analysis; time scale

Year:  2018        PMID: 30555772      PMCID: PMC6289285     

Source DB:  PubMed          Journal:  Curr Epidemiol Rep


  75 in total

1.  Assessment of structured socioeconomic effects on health.

Authors:  J S Kaufman; S Kaufman
Journal:  Epidemiology       Date:  2001-03       Impact factor: 4.822

2.  Properties of 2 counterfactual effect definitions of a point exposure.

Authors:  W Dana Flanders; Mitchel Klein
Journal:  Epidemiology       Date:  2007-07       Impact factor: 4.822

3.  Intervening on risk factors for coronary heart disease: an application of the parametric g-formula.

Authors:  Sarah L Taubman; James M Robins; Murray A Mittleman; Miguel A Hernán
Journal:  Int J Epidemiol       Date:  2009-04-23       Impact factor: 7.196

4.  Does obesity shorten life? The importance of well-defined interventions to answer causal questions.

Authors:  M A Hernán; S L Taubman
Journal:  Int J Obes (Lond)       Date:  2008-08       Impact factor: 5.095

5.  Estimating the potential impacts of intervention from observational data: methods for estimating causal attributable risk in a cross-sectional analysis of depressive symptoms in Latin America.

Authors:  N L Fleischer; L C H Fernald; A E Hubbard
Journal:  J Epidemiol Community Health       Date:  2010-01       Impact factor: 3.710

6.  Time scale and adjusted survival curves for marginal structural cox models.

Authors:  Daniel Westreich; Stephen R Cole; Phyllis C Tien; Joan S Chmiel; Lawrence Kingsley; Michele Jonsson Funk; Kathryn Anastos; Lisa P Jacobson
Journal:  Am J Epidemiol       Date:  2010-02-05       Impact factor: 4.897

7.  A simple G-computation algorithm to quantify the causal effect of a secondary illness on the progression of a chronic disease.

Authors:  W M van der Wal; M Prins; B Lumbreras; R B Geskus
Journal:  Stat Med       Date:  2009-08-15       Impact factor: 2.373

8.  Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease.

Authors:  Miguel A Hernán; Alvaro Alonso; Roger Logan; Francine Grodstein; Karin B Michels; Walter C Willett; Joann E Manson; James M Robins
Journal:  Epidemiology       Date:  2008-11       Impact factor: 4.822

9.  Estimation of average treatment effect with incompletely observed longitudinal data: application to a smoking cessation study.

Authors:  Hua Yun Chen; Shasha Gao
Journal:  Stat Med       Date:  2009-08-30       Impact factor: 2.373

10.  Evaluating medication effects outside of clinical trials: new-user designs.

Authors:  Wayne A Ray
Journal:  Am J Epidemiol       Date:  2003-11-01       Impact factor: 4.897

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