Causal inference under interference with prognostic scores for dynamic group therapy studies

Han Bing (), Paddock Susan M. and Burgette Lane
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Han Bing: Southern California Kaiser Permanente, Pasadena, CA, USA
Paddock Susan M.: NORC at the University of Chicago, Chicago, IL, USA
Burgette Lane: RAND Corporation, Pittsburgh, PA, USA

The International Journal of Biostatistics, 2022, vol. 18, issue 2, 439-453

Abstract: Group therapy is a common treatment modality for behavioral health conditions. Patients often enter and exit groups on an ongoing basis, leading to dynamic therapy groups. Examining the effect of high versus low session attendance on patient outcomes is a research question of interest. However, there are several challenges to identifying causal effects in this setting, including the lack of randomization, interference among patients, and the interrelatedness of patient participation. Dynamic therapy groups motivate a unique causal inference scenario, as the treatment statuses are completely defined by the patient attendance record for the therapy session, which is also the structure inducing interference. We adopt the Rubin causal model framework to define the causal effect of high versus low session attendance of group therapy at both the individual patient and peer levels. We propose a strategy to identify individual, peer, and total effects of high attendance versus low attendance on patient outcomes by the prognostic score stratification. We examine performance of our approach via simulation and apply it to data from a group cognitive behavioral therapy trial for treating depression among patients in a substance use disorders treatment setting.

Keywords: causal inference; interference; mental health; prognostic score (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/ijb-2019-0126

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