Using Interviewer Random Effects to Remove Selection Bias From HIV Prevalence Estimates

BMC Medical Research Methodology - United Kingdom
doi 10.1186/1471-2288-15-8
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Springer Science and Business Media LLC

Related search

Response to “Selection Bias”

CMAJ
Medicine
2017English

Using Ground Radar Overlaps to Verify the Retrieval of Calibration Bias Estimates From Spaceborne Platforms

2019English

Selection Bias Effects on RAISE-ETP Results

American Journal of Psychiatry
PsychiatryMental Health
2016English

US HIV Prevalence Estimates: Why the Delay in Publication?

American Journal of Public Health
EnvironmentalPublic HealthOccupational Health
1997English

Bias Selection Using Task-Targeted Random Subspaces for Robust Application of Graph-Based Semi-Supervised Learning

2012English

On Which Variable(s) Should We Condition to Remove Confounding Bias?

British Journal of Medicine and Medical Research
2015English

Interviewer Effects in Food Acquisition Surveys

Public Health Nutrition
NutritionDieteticsPublic HealthEnvironmentalMedicineOccupational Health
2018English

United Nations Estimates of HIV Prevalence in Zambia Under Attack

BMJ
1999English

Adjustment for Misclassification Bias Is Necessary in Survey-Based Arthritis Surveillance to Produce Credible Estimates of Prevalence

Arthritis and Rheumatology
RheumatologyAllergyImmunology
2018English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy