datbrghtmodterms: BRIGHT BDI depressive symptom data with (G = 4) module groups divided into separate MM terms.

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. These data are differentiated from datbrghtterms in that sessions are replaced with (coarsened to) modules, each of which contains 4 sessions. Under the open-enrollment protocol, clients could start at the beginning of any module. Each module represents one of four topic areas that collect sessions focused on those topics. The data are configured to support model runs using engine functions (dpgrowmm, dpgrowmult, ddpgrow).

Usage

datbrghtmodterms

Arguments

format

A list object for 815 total observations on 299 subjects

Details

y. Client depressive symptom score responses. There areN = 815total measures forP = 299subjects. 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 lengthN(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 lengthN. 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% and 87%, respectively. \item subj.aff. A list object with each term a vector that indexesn[g]subjects linked to each ofg = 1, ..., (G = 4)CBT therapy groups. Each group is specialized to its own multiple membership (MM) term for employment of engine functiondpgrowmmult. The number of CBT clients for each group are(n[1] = 17, n[2] = 18, n[3] = 19, n[4] = 78)for a total ofPaff = 132clients that attended sessions, as compared to the 140 assigned to the CBT arm.
subj.aff_mat. A matrix object that concatenates the client identifiers insubj.affacross groups for modeling all groups, together, in a single MM term with engine functiondpgrowmm.
W.subj.aff. A list object containingG, n[g] x S[g]multiple membership weight matrices that together map theP_aff = 132affected subjects (in each element of thesubj.afflist) to their particular sessions attended within their assigned group. There are a total of 61 CBT modules allocated to theGCBT therapy groups asS[1] = 9, S[2] = 10, S[3] = 10, S[4] = 32. 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. Ann = 132 x S = 61matrix object that concatenates the matrix entries of the list object fromW.subj.affinto a block-diagonal matrix (with each group of clients and modules disjoint and non-communicating with the others) for modeling in a single MM term under functiondpgrowmm.
dosemat. Ann.tot = 299 x (S+1) = 62matrix object that maps all clients to modules for use under engine functionddpgrow. The first column is an intercept filled with 1's. The rows for column 2:62 sum to 1 for the first 132 clients (who attend CBT modules) in a multiple membership fashion. The rows for non-CBT clients are are constructed as(1,0,0,..,0)such that no attendance is the hold-out category for identification.
numdose. A numeric vector of length equal toG=4CBT open enrollment groups with each entry capturing the number of modules for that group. This vector is used to allocate modules to the set of base distributions chosen withoptionunder theddpgrowengine function; for example,option = c("car","mvn","car","ind")assigns the noted distributional choices to the corresponding blocks of modules collected innumdose.
labt. A character vector of length equal toG=4CBT open enrollment groups that provides a label for each module block under a distinct base distribution. These labels will be used in the rendered plots using accessor functions associated toddpgrow.
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 theG=4MM terms. A sub-group would collect modules that communicate with each other, but not with the modules of other sub-groups. For these data, the modules within each CBT therapy group all communicate. Then each vector ingroupcontains the single value 1 of length equal to the number of sessions in the applicable therapy group. This object is input for engine functiondpgrowmultin the case it is desired to place all of theG = 4MM 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 = 4groups, for theS = 61CBT modules for modeling under engine functiondpgrowmm.
Omega. A list object with each element containing anS[g] x S[g]CAR adjacency matrix used to model prior association among effects for each MM term under prior"mmcar", whereS[g]are the number of effects for CBT therapy groupg. One may employ the approach subset of adjacency matrices for those terms under which one desires to specify prior"mmcar".
Omega_mat. An(S = 61) x (S = 61)matrix object encoding the dependence structure among modules that concatenates the entries ofOmegainto a block diagonal structure for use indpgrowmm.

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, soon to appear in: JRSS Series A (Statistics in Society).