Literature DB >> 27285260

Evaluating Public Health Interventions: 3. The Two-Stage Design for Confounding Bias Reduction-Having Your Cake and Eating It Two.

Donna Spiegelman1, Claudia L Rivera-Rodriguez1, Sebastien Haneuse1.   

Abstract

In public health evaluations, confounding bias in the estimate of the intervention effect will typically threaten the validity of the findings. It is a common misperception that the only way to avoid this bias is to measure detailed, high-quality data on potential confounders for every intervention participant, but this strategy for adjusting for confounding bias is often infeasible. Rather than ignoring confounding altogether, the two-phase design and analysis-in which detailed high-quality confounding data are obtained among a small subsample-can be considered. We describe the two-stage design and analysis approach, and illustrate its use in the evaluation of an intervention conducted in Dar es Salaam, Tanzania, of an enhanced community health worker program to improve antenatal care uptake.

Entities:  

Mesh:

Year:  2016        PMID: 27285260      PMCID: PMC4926607          DOI: 10.2105/AJPH.2016.303250

Source DB:  PubMed          Journal:  Am J Public Health        ISSN: 0090-0036            Impact factor:   9.308


  20 in total

1.  Does additional confounder information alter the estimated risk of bleeding associated with phenprocoumon use--results of a two-phase study.

Authors:  Sigrid Behr; Walter Schill; Iris Pigeot
Journal:  Pharmacoepidemiol Drug Saf       Date:  2012-02-02       Impact factor: 2.890

2.  Analytic methods for two-stage case-control studies and other stratified designs.

Authors:  W D Flanders; S Greenland
Journal:  Stat Med       Date:  1991-05       Impact factor: 2.373

3.  Evaluating Public Health Interventions: 2. Stepping Up to Routine Public Health Evaluation With the Stepped Wedge Design.

Authors:  Donna Spiegelman
Journal:  Am J Public Health       Date:  2016-03       Impact factor: 9.308

4.  Ambient air pollution and preterm birth in the environment and pregnancy outcomes study at the University of California, Los Angeles.

Authors:  Beate Ritz; Michelle Wilhelm; Katherine J Hoggatt; Jo Kay C Ghosh
Journal:  Am J Epidemiol       Date:  2007-08-04       Impact factor: 4.897

Review 5.  Methodological issues regarding confounding and exposure misclassification in epidemiological studies of occupational exposures.

Authors:  Aaron Blair; Patricia Stewart; Jay H Lubin; Francesco Forastiere
Journal:  Am J Ind Med       Date:  2007-03       Impact factor: 2.214

6.  A two stage design for the study of the relationship between a rare exposure and a rare disease.

Authors:  J E White
Journal:  Am J Epidemiol       Date:  1982-01       Impact factor: 4.897

7.  Anamorphic analysis: sampling and estimation for covariate effects when both exposure and disease are known.

Authors:  A M Walker
Journal:  Biometrics       Date:  1982-12       Impact factor: 2.571

8.  osDesign: An R Package for the Analysis, Evaluation, and Design of Two-Phase and Case-Control Studies.

Authors:  Sebastien Haneuse; Takumi Saegusa; Thomas Lumley
Journal:  J Stat Softw       Date:  2011-08       Impact factor: 6.440

9.  Statistical Design Features of the Healthy Communities Study.

Authors:  Warren J Strauss; Christopher J Sroka; Edward A Frongillo; S Sonia Arteaga; Catherine M Loria; Eric S Leifer; Colin O Wu; Heather Patrick; Howard A Fishbein; Lisa V John
Journal:  Am J Prev Med       Date:  2015-10       Impact factor: 5.043

10.  Relation of breast cancer with passive and active exposure to tobacco smoke.

Authors:  A Morabia; M Bernstein; S Héritier; N Khatchatrian
Journal:  Am J Epidemiol       Date:  1996-05-01       Impact factor: 4.897

View more
  2 in total

Review 1.  From Epidemiologic Knowledge to Improved Health: A Vision for Translational Epidemiology.

Authors:  Michael Windle; Hojoon D Lee; Sarah T Cherng; Catherine R Lesko; Colleen Hanrahan; John W Jackson; Mara McAdams-DeMarco; Stephan Ehrhardt; Stefan D Baral; Gypsyamber D'Souza; David W Dowdy
Journal:  Am J Epidemiol       Date:  2019-12-31       Impact factor: 4.897

2.  National estimates from the Youth '19 Rangatahi smart survey: A survey calibration approach.

Authors:  C Rivera-Rodriguez; T Clark; T Fleming; D Archer; S Crengle; R Peiris-John; S Lewycka
Journal:  PLoS One       Date:  2021-05-14       Impact factor: 3.240

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.