TY - GEN
T1 - Analyzing chronic diseases with latent growth models
T2 - 42nd Annual Hawaii International Conference on System Sciences, HICSS
AU - Freeze, Ronald
AU - Campagnolo, Denise
AU - Santanam, Raghu
AU - Partovi, Shahram
AU - Vinze, Ajay
AU - Tyry, Tuula
PY - 2009
Y1 - 2009
N2 - Evidence based decision making in the context of chronic disease management requires long term tracking and analysis of patient data. This study demonstrates how disease data tracking can help in understanding underlying patterns in chronic disease progression. Latent Growth Modeling (LGM) is used as a tool to analyze the long term chronic data related to the progression of Multiple Sclerosis (MS). The survey data has been collected on a bi-annual basis by the North American Research Committee on Multiple Sclerosis (NARCOMS), a project of the Consortium of Multiple Sclerosis Centers for the purpose of clinical trial recruitment and epidemiological research. This data set allows for study of MS progression, by measuring three base models: Patient Determined Disease Steps (PDDS), Overall Health and Emotional Health. MS patient data are grouped as early, middle and late disease status. This study analyzes three temporal data points spanning three years and identifies patient traits that are both patient and physician controlled. Empirical evidence confirms many practitioner observations.
AB - Evidence based decision making in the context of chronic disease management requires long term tracking and analysis of patient data. This study demonstrates how disease data tracking can help in understanding underlying patterns in chronic disease progression. Latent Growth Modeling (LGM) is used as a tool to analyze the long term chronic data related to the progression of Multiple Sclerosis (MS). The survey data has been collected on a bi-annual basis by the North American Research Committee on Multiple Sclerosis (NARCOMS), a project of the Consortium of Multiple Sclerosis Centers for the purpose of clinical trial recruitment and epidemiological research. This data set allows for study of MS progression, by measuring three base models: Patient Determined Disease Steps (PDDS), Overall Health and Emotional Health. MS patient data are grouped as early, middle and late disease status. This study analyzes three temporal data points spanning three years and identifies patient traits that are both patient and physician controlled. Empirical evidence confirms many practitioner observations.
UR - http://www.scopus.com/inward/record.url?scp=78650760206&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650760206&partnerID=8YFLogxK
U2 - 10.1109/HICSS.2009.72
DO - 10.1109/HICSS.2009.72
M3 - Conference contribution
AN - SCOPUS:78650760206
SN - 9780769534503
T3 - Proceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS
BT - Proceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS
Y2 - 5 January 2009 through 9 January 2009
ER -