Literature DB >> 685974

Assessing effects of confounding variables.

J J Schlesselman.   

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Year:  1978        PMID： 685974

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


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  35 in total

1.  Residual confounding after adjustment for age: a minor issue in breast cancer screening effectiveness.

Authors:  Guido van Schoor; Ellen Paap; Mireille J M Broeders; André L M Verbeek
Journal:  Eur J Epidemiol       Date:  2011-04-26       Impact factor: 8.082

Review 2.  Bias in occupational epidemiology studies.

Authors:  Neil Pearce; Harvey Checkoway; David Kriebel
Journal:  Occup Environ Med       Date:  2006-10-19       Impact factor: 4.402

3.  Nonparametric Bounds and Sensitivity Analysis of Treatment Effects.

Authors:  Amy Richardson; Michael G Hudgens; Peter B Gilbert; Jason P Fine
Journal:  Stat Sci       Date:  2014-11       Impact factor: 2.901

Review 4.  Developments in post-marketing comparative effectiveness research.

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

5.  Propensity score-based sensitivity analysis method for uncontrolled confounding.

Authors:  Lingling Li; Changyu Shen; Ann C Wu; Xiaochun Li
Journal:  Am J Epidemiol       Date:  2011-06-09       Impact factor: 4.897

6.  On the role of marginal confounder prevalence - implications for the high-dimensional propensity score algorithm.

Authors:  Tibor Schuster; Menglan Pang; Robert W Platt
Journal:  Pharmacoepidemiol Drug Saf       Date:  2015-04-10       Impact factor: 2.890

Review 7.  2,4-dichlorophenoxyacetic acid (2,4-D) and risk of non-Hodgkin lymphoma: a meta-analysis accounting for exposure levels.

Authors:  Adam M Smith; Martyn T Smith; Michele A La Merrill; Jane Liaw; Craig Steinmaus
Journal:  Ann Epidemiol       Date:  2017-03-31       Impact factor: 3.797

8.  Perspective: Are Large, Simple Trials the Solution for Nutrition Research?

Authors:  Ambika Satija; Meir J Stampfer; Eric B Rimm; Walter Willett; Frank B Hu
Journal:  Adv Nutr       Date:  2018-07-01       Impact factor: 8.701

9.  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

10.  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
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