Related Experiment Video
Updated: Jul 18, 2026
11:51
Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making
Published on: March 2, 2011
Menopausal hormone therapy decisions: insights from a multi-attribute model
Marilyn M Schapira1, Mary Ann Gilligan, Timothy L McAuliffe
1Department of Medicine, Clement J Zablocki VA Medical Center and the Medical College of Wisconsin, Froedtert East Office Building, Suite 4200, Milwaukee, WI 53226, USA. mschap@mcw.edu
Patient Education and Counseling
|January 20, 2004
Summary
This study developed a decision model for menopausal hormone replacement therapy (HRT). Current HRT users valued heart disease prevention and symptom relief, while former and never users cited side effects and breast cancer concerns.
Area of Science:
- Decision analysis
- Health economics
- Women's health
Background:
- Menopausal hormone replacement therapy (HRT) decisions involve complex trade-offs.
- Understanding patient values is crucial for effective HRT utilization.
Purpose of the Study:
- To develop a multi-attribute utility (MAU) decision model for menopausal hormone replacement therapy (HRT).
- To identify key factors influencing HRT decisions and compare utility scores across user groups.
Main Methods:
- Structured interviews (n=40) to identify decision factors.
- Telephone surveys (n=97) to ascertain utility scores.
- Linear regression to identify predictive utilities for HRT use.
Main Results:
- Composite utility scores significantly differed across HRT users (0.55), former users (-0.27), and never users (-0.19).
- Current users prioritized heart disease, osteoporosis, and symptom relief.
- Former users cited side effects; never users cited breast cancer concerns.
Conclusions:
- The MAU model provides insight into personal expectations and values influencing HRT use.
- Decision factors vary significantly based on HRT usage status.
- The model can inform personalized HRT recommendations.
More Related Videos
Related Concept Videos
01:22Hormonal Regulation of the Menstrual Cycle
The ovarian cycle regulates endometrial changes throughout a single menstrual cycle via the coordinated action of gonadotrophin-releasing hormone (GnRH) and gonadotrophins.
At puberty, GnRH begins a pulsatile release pattern, which triggers the anterior pituitary gland to secrete follicle-stimulating hormone (FSH) and luteinizing hormone (LH). The frequency and amplitude of GnRH pulses vary across the menstrual cycle, with faster pulses favoring LH release and slower pulses favoring FSH release.
At puberty, GnRH begins a pulsatile release pattern, which triggers the anterior pituitary gland to secrete follicle-stimulating hormone (FSH) and luteinizing hormone (LH). The frequency and amplitude of GnRH pulses vary across the menstrual cycle, with faster pulses favoring LH release and slower pulses favoring FSH release.
01:28Menopause
Menopause, a natural biological process marking the end of a woman's fertility, typically occurs between the fifth and sixth decade of life. This phase is characterized by the exhaustion of the ovarian follicle pool, leading to less responsive ovaries despite the high levels of Follicle Stimulating Hormone (FSH) and Luteinizing Hormone (LH). The consequential decrease in estrogen production results in symptoms like hot flashes, heavy sweating, headaches, hair loss, muscle pains, vaginal...
01:15Model Approaches for Pharmacokinetic Data: Physiological Models
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
01:26Pharmacokinetic Models: Comparison and Selection Criterion
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
01:27Pharmacodynamic Models: Overview
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
01:15Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions
PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).