datbrghtterms: BRIGHT BDI depressive symptom data with (G = 4) session...

In growcurves: Bayesian Semi and Nonparametric Growth Curve Models that Additionally Include Multiple Membership Random Effects

Description

The Beck Depression Inventory - II scores for the set of de-identified clients who participated in the Building Recovery by Improving Goals, Habits and Thoughts (BRIGHT) study, a community-based effectiveness trial of group cognitive behavioral therapy intervention for treating residential substance abuse treatment clients experiencing depressive symptoms. These data include scores for three measurement waves; the first at baseline enrollment to the study, followed by two post-treatment measurements with the aim to test whether clients receiving BRIGHT intervention would experience sustained improvement. There 299 participating clients, divided between 159 assigned to the usual care arm, and 140 assigned to CBT. The data are configured to support model runs using engine function dpgrowmm.

Usage

datbrghtterms

Format

A list object for 815 total observations on 299 subjects

Details

• y. Client depressive symptom score responses. There are N = 815 total measures for P = 299 subjects. Each entry contains a composite Beck Depression Inventory-II (BDI-II) score. The BDI-II score is a sum acoss 21 four-level items (each scored 0 - 3) with a higher score signifying a greater level of depressive symptoms.

• subject. BRIGHT study client identifier (1,2,...,299. Note: Participating clients are de-identified in this dataset.

• trt. Treatment arm identifier of length N (e.g. (0,0,0,...,1,1,1,...) , either {0,1} with usual care (UC) = 0 and group cognitive behavioral therapy (CBT) = 1. There are 140 clients with CBT = 1 (even though 132 of these actually attend sessions) in order to facilitate an intent-to-treat comparison.

• time. Meaurement times in months for each repeated subject measure of length N. There are 3 distinct time points or measurement waves. e.g. (0,3,6,0,3,6,0,0,3,,,,). The first measure is at baseline when clients enrolled to BRIGHT and at two post-treatment follow-ups at 3 and 6 months with response rates of 86

• subj.aff. A list object with each term a vector that indexes n[g] subjects linked to each of g = 1, ..., (G = 4) CBT therapy groups. Each group is specialized to its own multiple membership (MM) term for employment of engine function dpgrowmmult. The number of CBT clients for each group are (n[1] = 17, n[2] = 18, n[3] = 19, n[4] = 78) for a total of Paff = 132 clients that attended sessions, as compared to the 140 assigned to the CBT arm.

• subj.aff_mat. A matrix object that concatenates the client identifiers in subj.aff across groups for modeling all groups, together, in a single MM term with engine function dpgrowmm.

• W.subj.aff. A list object containing G, n[g] x S[g] multiple membership weight matrices that together map the P_aff = 132 affected subjects (in each element of the subj.aff list) to their particular sessions attended within their assigned group. There are a total of 245 CBT sessions allocated to the G CBT therapy groups as S[1] = 36, S[2] = 40, S[3] = 40, S[4] = 129. The study was designed for clients to attend 16 sessions organized into 4 modules of 4 sessions each. The 4 modules were offered on an open enrollment or rotating basis. Additional make-up sessions added within 2 modules resulted in the possibility for some clients to attend up to 20 sessions.

• W.subj.aff_mat. An n = 132 x S = 245 matrix object that concatenates the matrix entries of the list object from W.subj.aff into a block-diagonal matrix (with each group of clients and sessions disjoint and non-communicating with the others) for modeling in a single MM term under function dpgrowmm.

• group. A list object of length equal to the number of MM terms under the "mmcar" or "mmigrp" prior formulations for session effects. Each item contains a vector that specifies sub-group membership for each block of sessions within the G=4 MM terms. A sub-group would collect sessions that communicate with each other, but not with the sessions of other sub-groups. For these data, the sessions within each CBT therapy group all communicate. Then each vector in group contains the single value 1 of length equal to the number of sessions in the applicable therapy group. This object is input for engine function dpgrowmult in the case it is desired to place all of the G = 4 MM terms under prior "mmcar". One may employ the appropriate subset of list entries for those terms under which it is desired to employ prior "mmcar".

• group_mat. A matrix object denotes the group memberships, from 1 - G = 4 groups, for the S = 245 CBT sessions for modeling under engine function dpgrowmm.

• Omega. A list object with each element containing an S[g] x S[g] CAR adjacency matrix used to model prior association among effects for each MM term under prior "mmcar", where S[g] are the number of effects for CBT therapy group g. One may employ the approach subset of adjacency matrices for those terms under which one desires to specify prior "mmcar".

• Omega_mat. An S x S matrix object encoding the dependence structure among sessions that concatenates the entries of Omega into a block diagonal structure for use in dpgrowmm.

References

K. E. Watkins, S. B. Hunter, K. A. Hepner, S. M. Paddock, E. de la Cruz, A. J. Zhou and J. Gilmore (2011) An effectiveness trial of group cognitive behavioral therapy for patients with persistent depressive symptoms in substance abuse treatment, Archives of General Psychiatry, 68(6), 1- 8.

S. M. Paddock and T. D. Savitsky (2012) Bayesian Hierarchical Semiparametric Modeling of Longitudinal Post-treatment Outcomes from Open-enrollment Therapy Groups, invited re-submission to: JRSS Series A (Statistics in Society).

growcurves documentation built on May 2, 2019, 7:03 a.m.