A placebo prognostic index (PI) as a moderator of outcomes in the treatment of adolescent depression: Could it inform risk-stratification in treatment with cognitive-behavioral therapy, fluoxetine, or their combination?

Lorenzo‐Luaces, Lorenzo; Rodríguez‐Quintana, Natalie; Riley, Tennisha N.; Weisz, John R.

doi:10.1080/10503307.2020.1747657

Cited by 14 publications

(13 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonetheless, analyses of outcomes in naturalistic settings do not suggest that substance use disorders predict treatment outcomes (Van der Lem et al, 2012). Similarly, while some trials exclude patients on the basis of having severe symptoms, the existing data suggests that deperession severity is either not a moderator of treatment outcomes or that it suggests patients can benefit more from active intervention (Bower et al, 2013;Driessen et al, 2010;Fournier et al, 2008;Lorenzo-Luaces et al, 2017;Lorenzo-Luaces, Rodriguez-Quintana, Riley, et al, 2020). Thus, we recommend that researchers be more intentional about their entry criteria, only excluding patients when they have strong reasons to believe they will not benefit from treatment as well as disinterest in the status of the entry variable as a potential moderator.…”

Section: Increasing Heterogeneity In Treatment Trialsmentioning

confidence: 99%

See 1 more Smart Citation

Personalized Medicine and Cognitive Behavioral Therapies for Depression: Small Effects, Big Problems, and Bigger Data

et al. 2020

Self Cite

View full text Add to dashboard Cite

Cognitive-behavioral therapies (CBTs) are the most widely studied form of psychotherapy for disorders like depression and anxiety. Nonetheless, there is heterogeneity in response to CBTs vs. other treatments. Researchers have become increasingly interested in using pre-treatment individual differences (i.e., moderators) to match patients to the most effective treatments for them. Several methods to combine multiple variables to create precision treatment rules (PTRs) that identify subgroups have been proposed. We review the rationale behind multivariable PTRs as well as the findings of studies that have used different PTRs. We identify conceptual and methodological issues in the literature. Multivariable treatment assignment is a promising avenue of research. Nonetheless, effect sizes appear to be small and most of the samples that have been used to study these questions have been grossly underpowered to detect small effects. We recommend researchers explore multivariable treatment selection strategies, particularly those resembling risk-stratification, in heterogeneous samples of patients undergoing low-intensity CBTs vs. realistic minimal controls.

show abstract

Section: Increasing Heterogeneity In Treatment Trialsmentioning

confidence: 99%

“…(Lopez-Gomez et al, 2019; Lorenzo-Luaces et al, 2017;Lorenzo-Luaces, Rodriguez-Quintana, Riley, et al, 2020) and others(Delgadillo & Gonzalez Salas Duhne, 2020; Delgadillo et al, 2017;Webb et al, 2020) have used regularization to explore predictors of treatment outcomes.…”

mentioning

confidence: 99%

Personalized Medicine and Cognitive Behavioral Therapies for Depression: Small Effects, Big Problems, and Bigger Data

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…To address missing data, including outcome assessments, we used a random forests imputation procedure (see Lorenzo-Luaces et al, 2019 ) using random forests with the R package missForest (Stekhoven and Bühlmann, 2012). Multivariable imputation models are preferred to completers analyses ( Janssen et al, 2009 ; Moons, Donders, Stijnen, & Harrell Jr, 2006 ; Sterne et al, 2009 ; van Kuijk, Viechtbauer, Peeters, & Smits, 2016 ) as well as last-observation-carried-forward (LOCF) imputations ( Kenward & Molenberghs, 2009 ; Lachin, 2016 ) because they are more representative of the whole sample, have more power, and are less subject to bias due to the nature of missingness ( Janssen et al, 2009 ; Moons et al, 2006 ; Sterne et al, 2009 ; van Kuijk et al, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

“…For example, in the Treatment for Adolescent Depression Study (TADS; N = 439), adolescents were randomly assigned to a placebo control, a cognitive-behavioral therapy (CBT) condition, antidepressant medications (MEDs), and their combination treatment (COMB). In a recent re-analysis of TADS, we constructed a multivariable prognostic index that predicted overall response as well as a relatively superior response to the combination of CBT and MEDs than to CBT or MEDs alone ( Lorenzo-Luaces, Rodriguez-Quintana, Riley, & Weisz, 2019 ). Although the index was composed of multiple variables, the variables added very little beyond the effects of symptom severity.…”

mentioning

confidence: 99%

Double trouble: Do symptom severity and duration interact to predicting treatment outcomes in adolescent depression?

Lorenzo‐Luaces

Rodríguez‐Quintana

Bailey

2020

Behaviour Research and Therapy

Self Cite

View full text Add to dashboard Cite

Studies suggest that depression severity and duration interact to predict outcomes in depression treatment. To our knowledge, no study has explored this question in a sample with a placebo control, two therapies, and their combination nor with adolescents. We used data from the Treatment of Adolescent Depression Study (N=439), in which adolescent were randomized to placebo (PBO), cognitive-behavioral therapy (CBT), antidepressants medications (MEDs), or their combination (COMB). We explore the interaction between depression severity, chronicity, and treatments (vs. placebo) in predicting outcomes. There was interaction between severity and chronicity when comparing COMB and CBT with PBO, but not MEDs. In non-chronic depression, the effects of CBT were inversely related to severity to the point that CBT appeared iatrogenic with more severe depression. In chronic depression, the effects of CBT did not vary by severity, but the relative effects of COMB grew, being smallest in milder, more dysthymic-like depression, and largest in chronic-severe depression. These findings support calls to classify depression by severity and chronicity as well efforts to risk stratify patients to different intensity of care according to these variables.

show abstract

Section: Increasing Heterogeneity In Treatment Trialsmentioning

confidence: 99%

Personalized medicine and cognitive-behavioral therapies for depression: Small effects, big problems, and bigger data

Lorenzo‐Luaces¹,

Peipert²,

Jesús-Romero³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Cognitive-behavioral therapies (CBTs) are the most widely studied form psychotherapy for disorders like depression and anxiety. Nonetheless, there is heterogeneity in response to CBTs vs. other treatments. Researchers have become increasingly interested in using pre-treatment individual differences (i.e., moderators) to match patients to the most effective treatments for them. Several methods to combine multiple variables to create precision treatment rules (PTRs) that identify subgroups have been proposed. We review the rationale behind multivariable PTRs as well as studies that have used different PTRs. We identify conceptual and methodological issues in the literature. Multivariable treatment assignment is a promising avenue of research. Nonetheless, effect sizes appear to be small and most of the samples that have been used to study these questions have been grossly underpowered to detect small effects. We recommend researchers explore multivariable treatment selection strategies, particularly those resembling risk-stratification, in heterogeneous samples of patients undergoing low-intensity CBTs vs. realistic minimal controls.

show abstract

A placebo prognostic index (PI) as a moderator of outcomes in the treatment of adolescent depression: Could it inform risk-stratification in treatment with cognitive-behavioral therapy, fluoxetine, or their combination?

Cited by 14 publications

References 63 publications

Personalized Medicine and Cognitive Behavioral Therapies for Depression: Small Effects, Big Problems, and Bigger Data

Personalized Medicine and Cognitive Behavioral Therapies for Depression: Small Effects, Big Problems, and Bigger Data

Double trouble: Do symptom severity and duration interact to predicting treatment outcomes in adolescent depression?

Personalized medicine and cognitive-behavioral therapies for depression: Small effects, big problems, and bigger data

Contact Info

Product

Resources

About