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Activity Number: 271
Type: Contributed
Date/Time: Monday, August 10, 2015 : 3:05 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #317770
Title: A Population-Based Approach to Analyzing Pulses in Time Series of Hormone Data
Author(s): Kenneth Horton*
Companies:
Keywords: hierarchical models ; simulation ; deconvolution ; birth-death MCMC ; Bayesian
Abstract:

Various health conditions have an underlying pulsatile hormonal component. To study pulsatile hormones, blood is rapidly sampled for an extended period of time and assayed for hormone concentration levels. The goal is to obtain estimates of secretion features such as pulse mass and decay rate from the observed time series of hormone concentration. Currently, pulse secretion estimates are obtained using separate models for each subject. The estimates from these single-subject models are then combined in a second stage to compare pulse features between groups. Such two-stage approaches result in compounding bias as errors from the first stage are carried over to the second stage. Thus, we developed a population model framework that allows multiple subjects to be modeled simultaneously. Estimation was conducted using a birth-death Markov chain Monte Carlo (BDMCMC) algorithm. Extensive simulations were conducted to compare the population approach to a single-subject BDMCMC approach. The model was also applied to experimental luteinizing hormone data in sheep and humans. Using the population model resulted in substantial improvements in characterizing pulse hormone secretion.


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