Literature DB >> 25660080

Risk.

Stephen R Cole, Michael G Hudgens, M Alan Brookhart, Daniel Westreich.   

Abstract

The epidemiologist primarily studies transitions between states of health and disease. The purpose of the present article is to define a foundational parameter for such studies, namely risk. We begin simply and build to the setting in which there is more than 1 event type (i.e., competing risks or competing events), as well as more than 1 treatment or exposure level of interest. In the presence of competing events, the risks are a set of counterfactual cumulative incidence functions for each treatment. These risks can be depicted visually and summarized numerically. We use an example from the study of human immunodeficiency virus to illustrate concepts.
© The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  causal inference; cohort study; semi-Bayes method; semiparametric inference; survival analysis

Mesh:

Substances:

Year:  2015        PMID: 25660080      PMCID: PMC4325680          DOI: 10.1093/aje/kwv001

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  37 in total

1.  Semi-automated sensitivity analysis to assess systematic errors in observational data.

Authors:  Timothy L Lash; Aliza K Fink
Journal:  Epidemiology       Date:  2003-07       Impact factor: 4.822

2.  Sensitivity analyses for unmeasured confounding assuming a marginal structural model for repeated measures.

Authors:  Babette A Brumback; Miguel A Hernán; Sebastien J P A Haneuse; James M Robins
Journal:  Stat Med       Date:  2004-03-15       Impact factor: 2.373

3.  Cause-specific cumulative incidence estimation and the fine and gray model under both left truncation and right censoring.

Authors:  Ronald B Geskus
Journal:  Biometrics       Date:  2011-03       Impact factor: 2.571

4.  Bayesian perspectives for epidemiological research: I. Foundations and basic methods.

Authors:  Sander Greenland
Journal:  Int J Epidemiol       Date:  2006-01-30       Impact factor: 7.196

5.  Maximum likelihood, profile likelihood, and penalized likelihood: a primer.

Authors:  Stephen R Cole; Haitao Chu; Sander Greenland
Journal:  Am J Epidemiol       Date:  2013-10-29       Impact factor: 4.897

6.  Confounding of incidence density ratio in case-control studies.

Authors:  Sander Greenland
Journal:  Epidemiology       Date:  2013-07       Impact factor: 4.822

7.  Invited Commentary: Causal diagrams and measurement bias.

Authors:  Miguel A Hernán; Stephen R Cole
Journal:  Am J Epidemiol       Date:  2009-09-15       Impact factor: 4.897

8.  The parametric g-formula to estimate the effect of highly active antiretroviral therapy on incident AIDS or death.

Authors:  Daniel Westreich; Stephen R Cole; Jessica G Young; Frank Palella; Phyllis C Tien; Lawrence Kingsley; Stephen J Gange; Miguel A Hernán
Journal:  Stat Med       Date:  2012-04-11       Impact factor: 2.373

9.  The hazards of hazard ratios.

Authors:  Miguel A Hernán
Journal:  Epidemiology       Date:  2010-01       Impact factor: 4.822

10.  Competing risk regression models for epidemiologic data.

Authors:  Bryan Lau; Stephen R Cole; Stephen J Gange
Journal:  Am J Epidemiol       Date:  2009-06-03       Impact factor: 4.897

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  36 in total

1.  Counterpoint: the treatment decision design.

Authors:  M Alan Brookhart
Journal:  Am J Epidemiol       Date:  2015-10-26       Impact factor: 4.897

2.  Counterpoint: epidemiology to guide decision-making: moving away from practice-free research.

Authors:  Miguel A Hernán
Journal:  Am J Epidemiol       Date:  2015-10-26       Impact factor: 4.897

3.  Trends in Antibiotic Use by Birth Season and Birth Year.

Authors:  Alan C Kinlaw; Til Stürmer; Jennifer L Lund; Lars Pedersen; Michael D Kappelman; Julie L Daniels; Trine Frøslev; Christina D Mack; Henrik Toft Sørensen
Journal:  Pediatrics       Date:  2017-08-14       Impact factor: 7.124

4.  Using Animations of Risk Functions to Visualize Trends in US All-Cause and Cause-Specific Mortality, 1968-2016.

Authors:  Jacqueline E Rudolph; Stephen R Cole; Jessie K Edwards; Eric A Whitsel; Marc L Serre; David B Richardson
Journal:  Am J Public Health       Date:  2019-01-24       Impact factor: 9.308

5.  Nonparametric Bounds for the Risk Function.

Authors:  Stephen R Cole; Michael G Hudgens; Jessie K Edwards; M Alan Brookhart; David B Richardson; Daniel Westreich; Adaora A Adimora
Journal:  Am J Epidemiol       Date:  2019-04-01       Impact factor: 4.897

6.  Birth Order and Injury-Related Infant Mortality in the U.S.

Authors:  Katherine A Ahrens; Lauren M Rossen; Marie E Thoma; Margaret Warner; Alan E Simon
Journal:  Am J Prev Med       Date:  2017-06-27       Impact factor: 5.043

7.  An Illustration of Inverse Probability Weighting to Estimate Policy-Relevant Causal Effects.

Authors:  Jessie K Edwards; Stephen R Cole; Catherine R Lesko; W Christopher Mathews; Richard D Moore; Michael J Mugavero; Daniel Westreich
Journal:  Am J Epidemiol       Date:  2016-07-28       Impact factor: 4.897

8.  Bias Due to Confounders for the Exposure-Competing Risk Relationship.

Authors:  Catherine R Lesko; Bryan Lau
Journal:  Epidemiology       Date:  2017-01       Impact factor: 4.822

9.  Methodologic Issues When Estimating Risks in Pharmacoepidemiology.

Authors:  Jessie K Edwards; Laura L Hester; Mugdha Gokhale; Catherine R Lesko
Journal:  Curr Epidemiol Rep       Date:  2016-09-13

10.  Sensitivity Analyses for Misclassification of Cause of Death in the Parametric G-Formula.

Authors:  Jessie K Edwards; Stephen R Cole; Richard D Moore; W Christopher Mathews; Mari Kitahata; Joseph J Eron
Journal:  Am J Epidemiol       Date:  2018-08-01       Impact factor: 4.897

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