scholarly journals A demonstration of a multi-method variable selection approach for treatment selection: Recommending cognitive–behavioral versus psychodynamic therapy for mild to moderate adult depression

2019 ◽  
Vol 30 (2) ◽  
pp. 137-150 ◽  
Author(s):  
Zachary D. Cohen ◽  
Thomas T. Kim ◽  
Henricus L. Van ◽  
Jack J. M. Dekker ◽  
Ellen Driessen
2017 ◽  
Author(s):  
Zachary Daniel Cohen ◽  
Thomas Kim ◽  
Henricus Van ◽  
Jack Dekker ◽  
Ellen Driessen

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.


2019 ◽  
Author(s):  
Sierra Bainter ◽  
Thomas Granville McCauley ◽  
Tor D Wager ◽  
Elizabeth Reynolds Losin

In this paper we address the problem of selecting important predictors from some larger set of candidate predictors. Standard techniques are limited by lack of power and high false positive rates. A Bayesian variable selection approach used widely in biostatistics, stochastic search variable selection, can be used instead to combat these issues by accounting for uncertainty in the other predictors of the model. In this paper we present Bayesian variable selection to aid researchers facing this common scenario, along with an online application (https://ssvsforpsych.shinyapps.io/ssvsforpsych/) to perform the analysis and visualize the results. Using an application to predict pain ratings, we demonstrate how this approach quickly identifies reliable predictors, even when the set of possible predictors is larger than the sample size. This technique is widely applicable to research questions that may be relatively data-rich, but with limited information or theory to guide variable selection.


2019 ◽  
Vol 158 (5) ◽  
pp. 210
Author(s):  
Bo Ning ◽  
Alexander Wise ◽  
Jessi Cisewski-Kehe ◽  
Sarah Dodson-Robinson ◽  
Debra Fischer

The Analyst ◽  
2014 ◽  
Vol 139 (19) ◽  
pp. 4836 ◽  
Author(s):  
Bai-chuan Deng ◽  
Yong-huan Yun ◽  
Yi-zeng Liang ◽  
Lun-zhao Yi

2021 ◽  
Author(s):  
Katrina L Kezios

Abstract In any research study, there is an underlying research process that should begin with a clear articulation of the study’s goal. The study’s goal drives this process; it determines many study features including the estimand of interest, the analytic approaches that can be used to estimate it, and which coefficients, if any, should be interpreted. “Misalignment” can occur in this process when analytic approaches and/or interpretations do not match the study’s goal; misalignment is potentially more likely to arise when study goals are ambiguously framed. This study documented misalignment in the observational epidemiologic literature and explored how the framing of study goals contributes to its occurrence. The following misalignments were examined: 1) use of an inappropriate variable selection approach for the goal (a “goal-methods” misalignment) and 2) interpretation of coefficients of variables for which causal considerations were not made (e.g., Table 2 Fallacy, a “goal-interpretation” misalignment). A random sample of 100 articles published 2014-2018 in the top 5 general epidemiology journals were reviewed. Most reviewed studies were causal, with either explicitly stated (13/103, 13%) or associationally-framed (71/103, 69%) aims. Full alignment of goal-methods-interpretations was infrequent (9/103, 9%), although clearly causal studies (5/13, 38%) were more often fully aligned than seemingly causal ones (3/71, 4%). Goal-methods misalignments were common (34/103, 33%), but most frequently, methods were insufficiently reported to draw conclusions (47/103, 46%). Goal-interpretations misalignments occurred in 31% (32/103) of studies and occurred less often when the methods were aligned (2/103, 2%) compared with when the methods were misaligned (13/103, 13%).


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