Assessing Residual Bias in Estimating Influenza Vaccine Effectiveness

Anne M. Butler, James Bradley Layton, Whitney S. Krueger, Abhijit Vasudeo Kshirsagar, Leah J. McGrath

Research output: Contribution to journalArticle

Abstract

Background: Estimating influenza vaccine effectiveness using an unvaccinated comparison group may result in biased effect estimates. Objectives: To explore the reduction of confounding bias in an active comparison of high-dose versus standard-dose influenza vaccines, as compared with vaccinated versus unvaccinated comparisons. Methods: Using Medicare data from the United States end-stage renal disease program (2009-2013), we compared the risk of all-cause mortality among recipients of high-dose vaccine (HDV) versus standard-dose vaccine (SDV), HDV versus no vaccine, and SDV versus no vaccine. To quantify confounding bias, analyses were restricted to the preinfluenza season, when the protective effect of vaccination should not yet be observed. We estimated the standardized mortality ratio-weighted cumulative incidence functions using Kaplan-Meier methods and calculated risk ratios (RRs) and risk differences between groups. Results: Among 350,921 eligible patients contributing 825,642 unique patient preinfluenza seasons, 0.8% received HDV, 70.5% received SDV, and 28.7% remained unvaccinated. Comparisons with unvaccinated patients yielded spurious decreases in mortality risk during the preinfluenza period, for HDV versus none [RR, 0.60; 95% confidence interval (CI), 0.51-0.70)] and SDV versus none (RR, 0.72; 95% CI, 0.70-0.75). The effect estimate was attenuated in the HDV versus SDV comparison (RR, 0.89; 95% CI, 0.77-1.03). Estimates on the absolute scale followed a similar pattern. Conclusions: The HDV versus SDV comparison yielded less-biased estimates of the all-cause mortality before influenza season compared to those with nonuser comparison groups. Vaccine effectiveness and safety researchers should consider the active comparator design to reduce bias due to differences in underlying health status between vaccinated and unvaccinated individuals.

LanguageEnglish (US)
Pages73-78
Number of pages6
JournalMedical care
Volume57
Issue number1
DOIs
StatePublished - Jan 1 2019

Fingerprint

Influenza Vaccines
Vaccines
Odds Ratio
Mortality
Confidence Intervals
Medicare
Human Influenza
Health Status
Chronic Kidney Failure

Keywords

  • active comparator
  • bias
  • confounding
  • epidemiology
  • influenza
  • negative control
  • vaccine effectiveness

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Cite this

Assessing Residual Bias in Estimating Influenza Vaccine Effectiveness. / Butler, Anne M.; Layton, James Bradley; Krueger, Whitney S.; Kshirsagar, Abhijit Vasudeo; McGrath, Leah J.

In: Medical care, Vol. 57, No. 1, 01.01.2019, p. 73-78.

Research output: Contribution to journalArticle

Butler, Anne M. ; Layton, James Bradley ; Krueger, Whitney S. ; Kshirsagar, Abhijit Vasudeo ; McGrath, Leah J. / Assessing Residual Bias in Estimating Influenza Vaccine Effectiveness. In: Medical care. 2019 ; Vol. 57, No. 1. pp. 73-78.
@article{0e560a862ccc44de89b8430dde0123c7,
title = "Assessing Residual Bias in Estimating Influenza Vaccine Effectiveness",
abstract = "Background: Estimating influenza vaccine effectiveness using an unvaccinated comparison group may result in biased effect estimates. Objectives: To explore the reduction of confounding bias in an active comparison of high-dose versus standard-dose influenza vaccines, as compared with vaccinated versus unvaccinated comparisons. Methods: Using Medicare data from the United States end-stage renal disease program (2009-2013), we compared the risk of all-cause mortality among recipients of high-dose vaccine (HDV) versus standard-dose vaccine (SDV), HDV versus no vaccine, and SDV versus no vaccine. To quantify confounding bias, analyses were restricted to the preinfluenza season, when the protective effect of vaccination should not yet be observed. We estimated the standardized mortality ratio-weighted cumulative incidence functions using Kaplan-Meier methods and calculated risk ratios (RRs) and risk differences between groups. Results: Among 350,921 eligible patients contributing 825,642 unique patient preinfluenza seasons, 0.8{\%} received HDV, 70.5{\%} received SDV, and 28.7{\%} remained unvaccinated. Comparisons with unvaccinated patients yielded spurious decreases in mortality risk during the preinfluenza period, for HDV versus none [RR, 0.60; 95{\%} confidence interval (CI), 0.51-0.70)] and SDV versus none (RR, 0.72; 95{\%} CI, 0.70-0.75). The effect estimate was attenuated in the HDV versus SDV comparison (RR, 0.89; 95{\%} CI, 0.77-1.03). Estimates on the absolute scale followed a similar pattern. Conclusions: The HDV versus SDV comparison yielded less-biased estimates of the all-cause mortality before influenza season compared to those with nonuser comparison groups. Vaccine effectiveness and safety researchers should consider the active comparator design to reduce bias due to differences in underlying health status between vaccinated and unvaccinated individuals.",
keywords = "active comparator, bias, confounding, epidemiology, influenza, negative control, vaccine effectiveness",
author = "Butler, {Anne M.} and Layton, {James Bradley} and Krueger, {Whitney S.} and Kshirsagar, {Abhijit Vasudeo} and McGrath, {Leah J.}",
year = "2019",
month = "1",
day = "1",
doi = "10.1097/MLR.0000000000001018",
language = "English (US)",
volume = "57",
pages = "73--78",
journal = "Medical Care",
issn = "0025-7079",
publisher = "Lippincott Williams and Wilkins",
number = "1",

}

TY - JOUR

T1 - Assessing Residual Bias in Estimating Influenza Vaccine Effectiveness

AU - Butler, Anne M.

AU - Layton, James Bradley

AU - Krueger, Whitney S.

AU - Kshirsagar, Abhijit Vasudeo

AU - McGrath, Leah J.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Background: Estimating influenza vaccine effectiveness using an unvaccinated comparison group may result in biased effect estimates. Objectives: To explore the reduction of confounding bias in an active comparison of high-dose versus standard-dose influenza vaccines, as compared with vaccinated versus unvaccinated comparisons. Methods: Using Medicare data from the United States end-stage renal disease program (2009-2013), we compared the risk of all-cause mortality among recipients of high-dose vaccine (HDV) versus standard-dose vaccine (SDV), HDV versus no vaccine, and SDV versus no vaccine. To quantify confounding bias, analyses were restricted to the preinfluenza season, when the protective effect of vaccination should not yet be observed. We estimated the standardized mortality ratio-weighted cumulative incidence functions using Kaplan-Meier methods and calculated risk ratios (RRs) and risk differences between groups. Results: Among 350,921 eligible patients contributing 825,642 unique patient preinfluenza seasons, 0.8% received HDV, 70.5% received SDV, and 28.7% remained unvaccinated. Comparisons with unvaccinated patients yielded spurious decreases in mortality risk during the preinfluenza period, for HDV versus none [RR, 0.60; 95% confidence interval (CI), 0.51-0.70)] and SDV versus none (RR, 0.72; 95% CI, 0.70-0.75). The effect estimate was attenuated in the HDV versus SDV comparison (RR, 0.89; 95% CI, 0.77-1.03). Estimates on the absolute scale followed a similar pattern. Conclusions: The HDV versus SDV comparison yielded less-biased estimates of the all-cause mortality before influenza season compared to those with nonuser comparison groups. Vaccine effectiveness and safety researchers should consider the active comparator design to reduce bias due to differences in underlying health status between vaccinated and unvaccinated individuals.

AB - Background: Estimating influenza vaccine effectiveness using an unvaccinated comparison group may result in biased effect estimates. Objectives: To explore the reduction of confounding bias in an active comparison of high-dose versus standard-dose influenza vaccines, as compared with vaccinated versus unvaccinated comparisons. Methods: Using Medicare data from the United States end-stage renal disease program (2009-2013), we compared the risk of all-cause mortality among recipients of high-dose vaccine (HDV) versus standard-dose vaccine (SDV), HDV versus no vaccine, and SDV versus no vaccine. To quantify confounding bias, analyses were restricted to the preinfluenza season, when the protective effect of vaccination should not yet be observed. We estimated the standardized mortality ratio-weighted cumulative incidence functions using Kaplan-Meier methods and calculated risk ratios (RRs) and risk differences between groups. Results: Among 350,921 eligible patients contributing 825,642 unique patient preinfluenza seasons, 0.8% received HDV, 70.5% received SDV, and 28.7% remained unvaccinated. Comparisons with unvaccinated patients yielded spurious decreases in mortality risk during the preinfluenza period, for HDV versus none [RR, 0.60; 95% confidence interval (CI), 0.51-0.70)] and SDV versus none (RR, 0.72; 95% CI, 0.70-0.75). The effect estimate was attenuated in the HDV versus SDV comparison (RR, 0.89; 95% CI, 0.77-1.03). Estimates on the absolute scale followed a similar pattern. Conclusions: The HDV versus SDV comparison yielded less-biased estimates of the all-cause mortality before influenza season compared to those with nonuser comparison groups. Vaccine effectiveness and safety researchers should consider the active comparator design to reduce bias due to differences in underlying health status between vaccinated and unvaccinated individuals.

KW - active comparator

KW - bias

KW - confounding

KW - epidemiology

KW - influenza

KW - negative control

KW - vaccine effectiveness

UR - http://www.scopus.com/inward/record.url?scp=85058537273&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058537273&partnerID=8YFLogxK

U2 - 10.1097/MLR.0000000000001018

DO - 10.1097/MLR.0000000000001018

M3 - Article

VL - 57

SP - 73

EP - 78

JO - Medical Care

T2 - Medical Care

JF - Medical Care

SN - 0025-7079

IS - 1

ER -