# Characterization of salivary hormones in freely cycling smokers and the relationship between dynamic patterns in hormone levels and smoking behavior

> **NIH NIH R03** · MEDICAL UNIVERSITY OF SOUTH CAROLINA · 2020 · $74,750

## Abstract

Abstract
This proposal seeks to establish characteristics of the mathematical models describing the progressions of
salivary progesterone and estradiol hormone levels through the course of a woman's menstrual cycle.
Specifically, time and magnitude of peak hormone velocities will be estimated using derivatives of the
mathematical models developed. Large sample variances of these estimates will be obtained. Using these
characteristics, Monte-Carlo simulations will be performed to determine the capacity of our models in
estimating time and magnitude of the peak hormone rates. Simulations will include several realistic designs
such as data from complete cycle (28 days), missing 10% at random and a truncated set of 14 contiguous
days of measurements. The methods developed will be applied to data collected from a previous study with a
primary focus of testing hypotheses regarding the impact of the characteristics of the models mentioned above,
on reactivity to stressful and smoking related cues and daily smoking behavior.

## Key facts

- **NIH application ID:** 9975169
- **Project number:** 5R03DA048227-02
- **Recipient organization:** MEDICAL UNIVERSITY OF SOUTH CAROLINA
- **Principal Investigator:** Nathaniel Lee Baker
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $74,750
- **Award type:** 5
- **Project period:** 2019-07-15 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9975169

## Citation

> US National Institutes of Health, RePORTER application 9975169, Characterization of salivary hormones in freely cycling smokers and the relationship between dynamic patterns in hormone levels and smoking behavior (5R03DA048227-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9975169. Licensed CC0.

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