Pragmatic Precision Psychiatry-A New Direction for Optimizing Treatment Selection.
TL;DR: In this paper, the authors present a review of recent methodological advances for developing precision treatment rules (PTRs) that combine information across multiple prescriptive predictors, but this work has been much less successful in psychiatry than some other areas of medicine.
Abstract: Importance Clinical trials have identified numerous prescriptive predictors of mental disorder treatment response, ie, predictors of which treatments are best for which patients. However, none of these prescriptive predictors is strong enough alone to guide precision treatment planning. This has prompted growing interest in developing precision treatment rules (PTRs) that combine information across multiple prescriptive predictors, but this work has been much less successful in psychiatry than some other areas of medicine. Study designs and analysis schemes used in research on PTR development in other areas of medicine are reviewed, key challenges for implementing similar studies of mental disorders are highlighted, and recent methodological advances to address these challenges are described here. Observations Discovering prescriptive predictors requires large samples. Three approaches have been used in other areas of medicine to do this: conduct very large randomized clinical trials, pool individual-level results across multiple smaller randomized clinical trials, and develop preliminary PTRs in large observational treatment samples that are then tested in smaller randomized clinical trials. The third approach is most feasible for research on mental disorders. This approach requires working with large real-world observational electronic health record databases; carefully selecting samples to emulate trials; extracting information about prescriptive predictors from electronic health records along with other inexpensive data augmentation strategies; estimating preliminary PTRs in the observational data using appropriate methods; implementing pragmatic trials to validate the preliminary PTRs; and iterating between subsequent observational studies and quality improvement pragmatic trials to refine and expand the PTRs. New statistical methods exist to address the methodological challenges of implementing this approach. Conclusions and Relevance Advances in pragmatic precision psychiatry will require moving beyond the current focus on randomized clinical trials and adopting an iterative discovery-confirmation process that integrates observational and experimental studies in real-world clinical populations.
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TL;DR: There have been crucial advances in psychiatric diagnosis and treatment in recent decades but there is considerable need for further improvements in assessment and intervention, and improvements will likely not be achieved by any specific paradigm shifts in psychiatric practice and research, but rather by incremental progress and iterative integration.
30 citations
TL;DR: Novel information is provided about patient evaluations of treatment across diagnoses and countries varying in income level, and it is suggested that a critical issue in improving the quality of care for mental disorders should be fostering persistence in professional help‐seeking if earlier treatments are not helpful.
8 citations
TL;DR: In this article , the authors synthesize the literature pertaining to the effectiveness of ketamine in treating key domains of depressive symptomatology (i.e., cognition, anhedonia, suicidality, and psychosocial functionality).
8 citations
TL;DR: The development and evaluation of evidence-based treatments (EBTs) for mental disorders represent an enormous advance with continued progress designed to understand the techniques and increase their use in clinical practice as discussed by the authors .
6 citations
TL;DR: The emerging model of precision psychiatry has the potential to mitigate some of psychiatry's most pressing issues, including improving disease classification, lengthy treatment duration, and suboptimal treatment outcomes.
Abstract: By developing a more comprehensive understanding of the physiological underpinnings of mental illness, precision medicine has the potential to revolutionize psychiatric care. With recent breakthroughs in next-generation multi-omics technologies and data analytics, it is becoming more feasible to leverage multimodal biomarkers, from genetic variants to neuroimaging biomarkers, to objectify diagnostics and treatment decisions in psychiatry and improve patient outcomes. Ongoing work in precision psychiatry will parallel progress in precision oncology and cardiology to develop an expanded suite of blood- and neuroimaging-based diagnostic tests, empower monitoring of treatment efficacy over time, and reduce patient exposure to ineffective treatments. The emerging model of precision psychiatry has the potential to mitigate some of psychiatry's most pressing issues, including improvingdisease classification, lengthy treatment duration, and suboptimal treatment outcomes. This narrative-style review summarizes some of the emerging breakthroughs and recurring challenges in the application of precision medicine approaches to mental healthcare.
4 citations
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Book•
16 Mar 2009
TL;DR: This paper presents a case study on survival analysis: Prediction of secondary cardiovascular events and lessons from case studies on overfitting and optimism in prediction models.
Abstract: Introduction.- Applications of prediction models.- Study design for prediction models.- Statistical models for prediction.- Overfitting and optimism in prediction models.- Choosing between alternative statistical models.- Dealing with missing values.- Case study on dealing with missing values.- Coding of categorical and continuous predictors.- Restrictions on candidate predictors.- Selection of main effects.- Assumptions in regression models: Additivity and linearity.- Modern estimation methods.- Estimation with external methods.- Evaluation of performance.- Clinical usefulness.- Validation of prediction models.- Presentation formats.- Patterns of external validity.- Updating for a new setting.- Updating for a multiple settings.- Prediction of a binary outcome: 30-day mortality after acute myocardial infarction.- Case study on survival analysis: Prediction of secondary cardiovascular events.- Lessons from case studies.
2,771 citations
TL;DR: This work outlines a framework for comparative effectiveness research using big data that makes the target trial explicit and channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational studies, and helps avoid common methodologic pitfalls.
Abstract: Ideally, questions about comparative effectiveness or safety would be answered using an appropriately designed and conducted randomized experiment. When we cannot conduct a randomized experiment, we analyze observational data. Causal inference from large observational databases (big data) can be viewed as an attempt to emulate a randomized experiment-the target experiment or target trial-that would answer the question of interest. When the goal is to guide decisions among several strategies, causal analyses of observational data need to be evaluated with respect to how well they emulate a particular target trial. We outline a framework for comparative effectiveness research using big data that makes the target trial explicit. This framework channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational studies, and helps avoid common methodologic pitfalls.
1,175 citations
TL;DR: Improvements in methodology and reporting are needed for studies that compare modeling algorithms for clinical prediction modeling in the literature and found no evidence of superior performance of ML over LR.
885 citations
Book•
01 Jan 2011
TL;DR: This work focuses on TMLE in Adaptive Group Sequential Covariate Adjusted RCTs, which involves cross-Validated Targeted Minimum-Loss-Based Estimation and targeted Bayesian Learning.
Abstract: Models, Inference, and Truth.- The Open Problem.- Defining the Model and Parameter.- Super Learning.- Introduction to TMLE.- Understanding TMLE.- Why TMLE?.- Bounded Continuous Outcomes.- Direct Effects and Effect Among the Treated.- Marginal Structural Models.- Positivity.- Robust Analysis of RCTs Using Generalized Linear Models.- Targeted ANCOVA Estimator in RCTs.- Independent Case-Control Studies.- Why Match? Matched Case-Control Studies.- Nested Case-Control Risk Score Prediction.- Super Learning for Right-Censored Data.- RCTs with Time-to-Event Outcomes.- RCTs with Time-to-Event Outcomes and Effect Modification Parameters.- C-TMLE of an Additive Point Treatment Effect.- C-TMLE for Time-to-Event Outcomes.- Propensity-Score-Based Estimators and C-TMLE.- Targeted Methods for Biomarker Discovery.- Finding Quantitative Trait Loci Genes.- Case Study: Longitudinal HIV Cohort Data.- Probability of Success of an In Vitro Fertilization Program.- Individualized Antiretroviral Initiation Rules.- Cross-Validated Targeted Minimum-Loss-Based Estimation.- Targeted Bayesian Learning.- TMLE in Adaptive Group Sequential Covariate Adjusted RCTs.- Foundations of TMLE.- Introduction to R Code Implementation.
859 citations
TL;DR: Among the treatment modalities represented in this review, therapist adherence and competence play little role in determining symptom change, and, given the significant heterogeneity observed across findings, mean effect sizes must be interpreted with caution.
Abstract: Objective: The authors conducted a meta-analytic review of adherence–outcome and competence– outcome findings, and examined plausible moderators of these relations. Method: A computerized search of the PsycINFO database was conducted. In addition, the reference sections of all obtained studies were examined for any additional relevant articles or review chapters. The literature search identified 36 studies that met the inclusion criteria. Results: R-type effect size estimates were derived from 32 adherence–outcome and 17 competence–outcome findings. Neither the mean weighted adherence– outcome (r .02) nor competence–outcome (r .07) effect size estimates were found to be significantly different from zero. Significant heterogeneity was observed across both the adherence–outcome and competence–outcome effect size estimates, suggesting that the individual studies were not all drawn from the same population. Moderator analyses revealed that larger competence–outcome effect size estimates were associated with studies that either targeted depression or did not control for the influence of the therapeutic alliance. Conclusions: One explanation for these results is that, among the treatment modalities represented in this review, therapist adherence and competence play little role in determining symptom change. However, given the significant heterogeneity observed across findings, mean effect sizes must be interpreted with caution. Factors that may account for the nonsignificant adherence– outcome and competence–outcome findings reported within many of the studies reviewed are addressed. Finally, the implication of these results and directions for future process research are discussed.
569 citations