Abstract
| Lingua originale | English |
|---|---|
| pagine (da-a) | 243-259 |
| Numero di pagine | 17 |
| Rivista | JOURNAL OF DATA SCIENCE |
| Volume | 9 |
| Stato di pubblicazione | Published - 2011 |
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Quantifying treatment effects when flexibly modeling individual change in a nonlinear mixed effects model. / Muggeo, Vito Michele Rosario.
In: JOURNAL OF DATA SCIENCE, Vol. 9, 2011, pag. 243-259.Risultato della ricerca: Article
}
TY - JOUR
T1 - Quantifying treatment effects when flexibly modeling individual change in a nonlinear mixed effects model
AU - Muggeo, Vito Michele Rosario
PY - 2011
Y1 - 2011
N2 - A core task in analyzing randomized clinical trials based on longitudinal data is to find the best way to describe the change over time for each treatment arm. We review the implementation and estimation of a flexible piecewise Hierarchical Linear Model (HLM) to model change over time. The flexible piecewise HLM consists of two phases with differing rates of change. The breakpoints between these two phases, as well as the rates of change per phase are allowed to vary between treatment groups as well as individuals. While this approach may provide better model fit, how to quantify treatment diff erences over the longitudinal period is not clear. In this paper, we develop a procedure for summarizing the longitudinal data for the flexible piecewise HLM on the lines of Cook et al. (2004). We focus on quantifying the overall treatment efficacy using the area under the curve (AUC) of the individual flexible piecewise HLM models. Methods are illustrated through data from a placebo-controlled trial in the treatment of depression comparing psychotherapy and pharmacotherapy.
AB - A core task in analyzing randomized clinical trials based on longitudinal data is to find the best way to describe the change over time for each treatment arm. We review the implementation and estimation of a flexible piecewise Hierarchical Linear Model (HLM) to model change over time. The flexible piecewise HLM consists of two phases with differing rates of change. The breakpoints between these two phases, as well as the rates of change per phase are allowed to vary between treatment groups as well as individuals. While this approach may provide better model fit, how to quantify treatment diff erences over the longitudinal period is not clear. In this paper, we develop a procedure for summarizing the longitudinal data for the flexible piecewise HLM on the lines of Cook et al. (2004). We focus on quantifying the overall treatment efficacy using the area under the curve (AUC) of the individual flexible piecewise HLM models. Methods are illustrated through data from a placebo-controlled trial in the treatment of depression comparing psychotherapy and pharmacotherapy.
KW - Area under the curve (AUC)
KW - Hierarchical Linear model
KW - breakpoint
KW - random eff ects
KW - subject-speci fic estimates.
UR - http://hdl.handle.net/10447/54183
UR - http://www.jds-online.com/file_download/305/JDS-683.pdf
M3 - Article
VL - 9
SP - 243
EP - 259
JO - JOURNAL OF DATA SCIENCE
JF - JOURNAL OF DATA SCIENCE
SN - 1680-743X
ER -