Abstract
The analysis of walking behavior in a physical activity intervention is considered. A Bayesian latent structure modeling approach is proposed whereby the ability and willingness of participants is modeled via latent effects. The dropout process is jointly modeled via a linked survival model. Computational issues are addressed via posterior sampling and a simulated evaluation of the longitudinal model's ability to recover latent structure and predictor effects is considered. We evaluate the effect of a variety of socio-psychological and spatial neighborhood predictors on the propensity to walk and the estimation of latent ability and willingness in the full study.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2634-2649 |
| Number of pages | 16 |
| Journal | Statistical Methods in Medical Research |
| Volume | 25 |
| Issue number | 6 |
| DOIs | |
| State | Published - Dec 1 2016 |
| Externally published | Yes |
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Keywords
- Bayesian
- intervention
- joint model
- Latent structure
- longitudinal data
- physical activity
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
- Health Information Management
Cite this
Bayesian latent structure modeling of walking behavior in a physical activity intervention. / Lawson, Andrew B.; Ellerbe, Caitlyn; Carroll, Rachel; Alia, Kassandra; Coulon, Sandra; Wilson, Dawn K.; Vanhorn, M. Lee; StGeorge, Sara.
In: Statistical Methods in Medical Research, Vol. 25, No. 6, 01.12.2016, p. 2634-2649.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Bayesian latent structure modeling of walking behavior in a physical activity intervention
AU - Lawson, Andrew B.
AU - Ellerbe, Caitlyn
AU - Carroll, Rachel
AU - Alia, Kassandra
AU - Coulon, Sandra
AU - Wilson, Dawn K.
AU - Vanhorn, M. Lee
AU - StGeorge, Sara
PY - 2016/12/1
Y1 - 2016/12/1
N2 - The analysis of walking behavior in a physical activity intervention is considered. A Bayesian latent structure modeling approach is proposed whereby the ability and willingness of participants is modeled via latent effects. The dropout process is jointly modeled via a linked survival model. Computational issues are addressed via posterior sampling and a simulated evaluation of the longitudinal model's ability to recover latent structure and predictor effects is considered. We evaluate the effect of a variety of socio-psychological and spatial neighborhood predictors on the propensity to walk and the estimation of latent ability and willingness in the full study.
AB - The analysis of walking behavior in a physical activity intervention is considered. A Bayesian latent structure modeling approach is proposed whereby the ability and willingness of participants is modeled via latent effects. The dropout process is jointly modeled via a linked survival model. Computational issues are addressed via posterior sampling and a simulated evaluation of the longitudinal model's ability to recover latent structure and predictor effects is considered. We evaluate the effect of a variety of socio-psychological and spatial neighborhood predictors on the propensity to walk and the estimation of latent ability and willingness in the full study.
KW - Bayesian
KW - intervention
KW - joint model
KW - Latent structure
KW - longitudinal data
KW - physical activity
UR - http://www.scopus.com/inward/record.url?scp=84995809770&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84995809770&partnerID=8YFLogxK
U2 - 10.1177/0962280214529932
DO - 10.1177/0962280214529932
M3 - Article
C2 - 24741000
AN - SCOPUS:84995809770
VL - 25
SP - 2634
EP - 2649
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
SN - 0962-2802
IS - 6
ER -