Using predictors of hormone therapy use to model the healthy user bias:  how does healthy user status influence cognitive effects of hormone therapy?
OBJECTIVE
This study investigated the phenomenon known as the healthy user bias by equating hormone therapy (HT) use (past or current) with healthy user status.


METHODS
Data from the Survey of Midlife in the United States were used to identify the predictors of HT use. The unique Survey of Midlife in the United States data include psychological, demographic, health-related, and behavioral variables as well as history of HT use. Predictors of HT use were combined to derive propensity scores, describing the likelihood that a woman was an HT user, based on her psychological, demographic, physical, and behavioral profile (ie, likelihood of being a healthy user) as opposed to her actual use of HT. Finally, cognitive performance on an executive function test was examined in women stratified by propensity score.


RESULTS
Using a multiple logistic regression model, nine variables emerged as predictors of HT use. The nine variables were used to estimate the propensity or conditional probability of using HT for each subject; resultant propensity scores were ranked and divided into tertiles. Women in the highest tertile demonstrated shorter median response latencies on a test of executive function than did women who did not use HT.


CONCLUSIONS
From an array of psychological, medical, and behavioral variables, nine emerged as predictors of HT use. If validated, these features may serve as a means of estimating the phenomenon known as healthy user bias. Moreover, these data suggest that the degree to which a woman fits a model of a healthy user may influence cognitive response to HT.
Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, WI, USA. ceg@medicine.wisc.edu
Carey E
CE
Gleason
N Maritza
NM
Dowling
Elliot
E
Friedman
Whitney
W
Wharton
Sanjay
S
Asthana
D000339:Affect; D015982:Bias; D016009:Chi-Square Distribution; D003071:Cognition / Q000187:drug effects*; D015914:Estrogen Replacement Therapy / Q000523:psychology*; D056344:Executive Function; D005260:Female; D005544:Forecasting; D015438:Health Behavior; D006304:Health Status; D006306:Health Surveys; D006801:Humans; D016015:Logistic Models; D017698:Postmenopause; D012649:Self Concept; D012959:Socioeconomic Factors; D011795:Surveys and Questionnaires