TY - JOUR
T1 - A demonstration of a multi-method variable selection approach for treatment selection
T2 - Recommending cognitive–behavioral versus psychodynamic therapy for mild to moderate adult depression
AU - Cohen, Zachary D.
AU - Kim, Thomas T.
AU - Van, Henricus L.
AU - Dekker, Jack J.M.
AU - Driessen, Ellen
PY - 2020/2/17
Y1 - 2020/2/17
N2 - Objective: We use a new variable selection procedure for treatment selection which generates treatment recommendations based on pre-treatment characteristics for adults with mild-to-moderate depression deciding between cognitive behavioral (CBT) versus psychodynamic therapy (PDT). Method: Data are drawn from a randomized comparison of CBT versus PDT for depression (N = 167, 71% female, mean-age = 39.6). The approach combines four different statistical techniques to identify patient characteristics associated consistently with differential treatment response. Variables are combined to generate predictions indicating each individual’s optimal-treatment. The average outcomes for patients who received their indicated treatment versus those who did not were compared retrospectively to estimate model utility. Results: Of 49 predictors examined, depression severity, anxiety sensitivity, extraversion, and psychological treatment-needs were included in the final model. The average post-treatment Hamilton-Depression-Rating-Scale score was 1.6 points lower (95%CI = [0.5:2.8]; d = 0.21) for those who received their indicated-treatment compared to non-indicated. Among the 60% of patients with the strongest treatment recommendations, that advantage grew to 2.6 (95%CI = [1.4:3.7]; d = 0.37). Conclusions: Variable selection procedures differ in their characterization of the importance of predictive variables. Attending to consistently-indicated predictors may be sensible when constructing treatment selection models. The small N and lack of separate validation sample indicate a need for prospective tests before this model is used.
AB - Objective: We use a new variable selection procedure for treatment selection which generates treatment recommendations based on pre-treatment characteristics for adults with mild-to-moderate depression deciding between cognitive behavioral (CBT) versus psychodynamic therapy (PDT). Method: Data are drawn from a randomized comparison of CBT versus PDT for depression (N = 167, 71% female, mean-age = 39.6). The approach combines four different statistical techniques to identify patient characteristics associated consistently with differential treatment response. Variables are combined to generate predictions indicating each individual’s optimal-treatment. The average outcomes for patients who received their indicated treatment versus those who did not were compared retrospectively to estimate model utility. Results: Of 49 predictors examined, depression severity, anxiety sensitivity, extraversion, and psychological treatment-needs were included in the final model. The average post-treatment Hamilton-Depression-Rating-Scale score was 1.6 points lower (95%CI = [0.5:2.8]; d = 0.21) for those who received their indicated-treatment compared to non-indicated. Among the 60% of patients with the strongest treatment recommendations, that advantage grew to 2.6 (95%CI = [1.4:3.7]; d = 0.37). Conclusions: Variable selection procedures differ in their characterization of the importance of predictive variables. Attending to consistently-indicated predictors may be sensible when constructing treatment selection models. The small N and lack of separate validation sample indicate a need for prospective tests before this model is used.
KW - cognitive behavioral therapy
KW - depression
KW - precision medicine
KW - psychodynamic therapy
KW - treatment selection
KW - variable selection
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U2 - 10.1080/10503307.2018.1563312
DO - 10.1080/10503307.2018.1563312
M3 - Article
C2 - 30632922
AN - SCOPUS:85060014240
VL - 30
SP - 137
EP - 150
JO - Psychotherapy Research
JF - Psychotherapy Research
SN - 1050-3307
IS - 2
ER -