Literature DB >> 3344751

Indirect corrections for confounding under multiplicative and additive risk models.

M H Gail1, S Wacholder, J H Lubin.   

Abstract

We define a multiplicative model and an additive model for the hazards associated jointly with exposure and with the presence of a confounder like smoking. Under the multiplicative model, the crude relative risk may be adjusted indirectly, by means of a factor proposed by Axelson [1978], and implicitly by Cornfield et al. [1959] and Schlesselman [1978]. We present corresponding indirect correction formulas under the additive risk model for the risk difference and for the excess relative risk. Conditions are established under which these corrections may be applied to age-adjusted rates from composite study populations. We demonstrate that indirect corrections may be no better than crude measures of risk if one assumes the wrong model for the joint action of the exposure and confounding factors. These results are illustrated on an example of occupational exposure to vermiculite. The limitations of the techniques are discussed.

Mesh:

Year:  1988        PMID: 3344751     DOI: 10.1002/ajim.4700130108

Source DB:  PubMed          Journal:  Am J Ind Med        ISSN: 0271-3586            Impact factor:   2.214


  10 in total

Review 1.  Developments in post-marketing comparative effectiveness research.

Authors:  S Schneeweiss
Journal:  Clin Pharmacol Ther       Date:  2007-06-06       Impact factor: 6.875

2.  On quantifying the magnitude of confounding.

Authors:  Holly Janes; Francesca Dominici; Scott Zeger
Journal:  Biostatistics       Date:  2010-03-04       Impact factor: 5.899

3.  Assessment and indirect adjustment for confounding by smoking in cohort studies using relative hazards models.

Authors:  David B Richardson; Dominique Laurier; Mary K Schubauer-Berigan; Eric Tchetgen Tchetgen; Stephen R Cole
Journal:  Am J Epidemiol       Date:  2014-09-21       Impact factor: 4.897

4.  Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders.

Authors:  Tyler J Vanderweele; Onyebuchi A Arah
Journal:  Epidemiology       Date:  2011-01       Impact factor: 4.822

5.  An examination of male and female odds ratios by BMI, cigarette smoking, and alcohol consumption for cancers of the oral cavity, pharynx, and larynx in pooled data from 15 case-control studies.

Authors:  Jay H Lubin; Joshua Muscat; Mia M Gaudet; Andrew F Olshan; Maria Paula Curado; Luigino Dal Maso; Victor Wünsch-Filho; Erich M Sturgis; Neonilia Szeszenia-Dabrowska; Xavier Castellsague; Zuo-Feng Zhang; Elaine Smith; Leticia Fernandez; Elena Matos; Silvia Franceschi; Eleonora Fabianova; Peter Rudnai; Mark P Purdue; Dana Mates; Qingyi Wei; Rolando Herrero; Karl Kelsey; Hal Morgenstern; Oxana Shangina; Sergio Koifman; Jolanta Lissowska; Fabio Levi; Alexander W Daudt; Jose Eluf Neto; Chu Chen; Philip Lazarus; Deborah M Winn; Stephen M Schwartz; Paolo Boffetta; Paul Brennan; Ana Menezes; Carlo La Vecchia; Michael McClean; Renato Talamini; Thangarajan Rajkumar; Richard B Hayes; Mia Hashibe
Journal:  Cancer Causes Control       Date:  2011-07-09       Impact factor: 2.506

6.  Bespoke Instruments: A new tool for addressing unmeasured confounders.

Authors:  David B Richardson; Eric J Tchetgen Tchetgen
Journal:  Am J Epidemiol       Date:  2022-03-24       Impact factor: 5.363

7.  EVALUATING COSTS WITH UNMEASURED CONFOUNDING: A SENSITIVITY ANALYSIS FOR THE TREATMENT EFFECT.

Authors:  Elizabeth A Handorf; Justin E Bekelman; Daniel F Heitjan; Nandita Mitra
Journal:  Ann Appl Stat       Date:  2013       Impact factor: 2.083

8.  Causal inference, probability theory, and graphical insights.

Authors:  Stuart G Baker
Journal:  Stat Med       Date:  2013-05-10       Impact factor: 2.373

9.  Adjustment for tobacco smoking and alcohol consumption by simultaneous analysis of several types of cancer.

Authors:  Tor Haldorsen; Jan Ivar Martinsen; Kristina Kjærheim; Tom K Grimsrud
Journal:  Cancer Causes Control       Date:  2017-02-02       Impact factor: 2.506

10.  The Importance of Making Assumptions in Bias Analysis.

Authors:  Richard F MacLehose; Thomas P Ahern; Timothy L Lash; Charles Poole; Sander Greenland
Journal:  Epidemiology       Date:  2021-09-01       Impact factor: 4.860

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.