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Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice
Abstract
Background
The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders.
Methods
PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models.
Findings
Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy.
Interpretation
To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.
Journal Article
The prodigal tongue : dispatches from the future of English
Mark Abley, author of 'Spoken Here', presents an exploration of the way that the English language as it is spoken around the world is likely to transform and be transformed during the 21st century.
Book
Predictable Financial Crises
Using historical data on postwar financial crises around the world, we show that the combination of rapid credit and asset price growth over the prior three years, whether in the nonfinancial business or the household sector, is associated with a 40% probability of entering a financial crisis within the next three years. This compares with a roughly 7% probability in normal times, when neither credit nor asset price growth is elevated. Our evidence challenges the view that financial crises are unpredictable \"bolts from the sky\" and supports the Kindleberger-Minsky view that crises are the byproduct of predictable, boom-bust credit cycles. This predictability favors policies that lean against incipient credit-market booms.
Journal Article
Anomalies and the Expected Market Return
We provide the first systematic evidence on the link between long-short anomaly portfolio returns—a cornerstone of the cross-sectional literature—and the timeseries predictability of the aggregate market excess return. Using 100 representative anomalies from the literature, we employ a variety of shrinkage techniques (including machine learning, forecast combination, and dimension reduction) to efficiently extract predictive signals in a high-dimensional setting. We find that longshort anomaly portfolio returns evince statistically and economically significant out-of-sample predictive ability for the market excess return. The predictive ability of anomaly portfolio returns appears to stem from asymmetric limits of arbitrage and overpricing correction persistence.
Journal Article
The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence
The first CT scanners in the early 1970s already used iterative reconstruction algorithms; however, lack of computational power prevented their clinical use. In fact, it took until 2009 for the first iterative reconstruction algorithms to come commercially available and replace conventional filtered back projection. Since then, this technique has caused a true hype in the field of radiology. Within a few years, all major CT vendors introduced iterative reconstruction algorithms for clinical routine, which evolved rapidly into increasingly advanced reconstruction algorithms. The complexity of algorithms ranges from hybrid-, model-based to fully iterative algorithms. As a result, the number of scientific publications on this topic has skyrocketed over the last decade. But what exactly has this technology brought us so far? And what can we expect from future hardware as well as software developments, such as photon-counting CT and artificial intelligence? This paper will try answer those questions by taking a concise look at the overall evolution of CT image reconstruction and its clinical implementations. Subsequently, we will give a prospect towards future developments in this domain.Key Points• Advanced CT reconstruction methods are indispensable in the current clinical setting.• IR is essential for photon-counting CT, phase-contrast CT, and dark-field CT.• Artificial intelligence will potentially further increase the performance of reconstruction methods.
Journal Article
Corporate Social Responsibility Report Narratives and Analyst Forecast Accuracy
Standalone corporate social responsibility (CSR) reports vary considerably in the content of information released due to their voluntary nature. In this study, we develop a disclosure score based on the tone, readability, length, and the numerical and horizon content of CSR report narratives, and examine the relationship between the CSR disclosure scores and analyst forecasts. We find that CSR reporters with high disclosure scores are associated with more accurate forecasts, whereas low score CSR reporters are not associated with more accurate forecasts than firms who do not issue CSR reports. The findings are robust to controlling for firm characteristics including CSR activity ratings and financial narratives. The findings are driven by experienced CSR reporters rather than first-time CSR reporters. Together, our findings suggest that the content of CSR reports helps to improve analyst forecast accuracy, and this relationship is more pronounced for CSR reports with more substantial content.
Journal Article
ARE FOUNDER CEOS MORE OVERCONFIDENT THAN PROFESSIONAL CEOS? EVIDENCE FROM S&P 1500 COMPANIES
Research summary: We provide evidence that founder chief executive officers (CEOs) of large S&P 1500 companies are more overconfident than their nonfounder counterparts (\"professional CEOs\"). We measure overconfidence via tone of CEO tweets, tone of CEO statements during earnings conference calls, management earnings forecasts, and CEO option-exercise behavior. Compared with professional CEOs, founder CEOs use more optimistic language on Twitter and during earnings conference calls. In addition, founder CEOs are more likely to issue earnings forecasts that are too high; they are also more likely to perceive their firms to be undervalued, as implied by their option-exercise behavior. We provide evidence that, to date, investors appear unaware of this \"overconfidence bias\" among founders. Managerial summary: This article helps to explain why firms managed by founder chief executive officers (CEOs) behave differently from those managed by professional CEOs. We study a sample of S&P 1500 firms and find strong evidence that founder CEOs are more overconfident than professional CEOs. To date, investors appear unaware of this overconfidence bias among founders. Our study should help firm stakeholders, including investors, employees, suppliers, and customers, put the statements and actions of founder CEOs in perspective. Our study should also help members of corporate boards make more informed decisions about whether to retain (or bring back) founder CEOs or hire professional CEOs.
Journal Article
What Motivates Effort? Evidence and Expert Forecasts
How much do different monetary and non-monetary motivators induce costly effort? Does the effectiveness line up with the expectations of researchers and with results in the literature? We conduct a large-scale real-effort experiment with eighteen treatment arms. We examine the effect of (1) standard incentives; (2) behavioural factors like social preferences and reference dependence; and (3) non-monetary inducements from psychology. We find that (1) monetary incentives work largely as expected, including a very low piece rate treatment which does not crowd out effort; (2) the evidence is partly consistent with standard behavioural models, including warm glow, though we do not find evidence of probability weighting; (3) the psychological motivators are effective, but less so than incentives. We then compare the results to forecasts by 208 academic experts. On average, the experts anticipate several key features, like the effectiveness of psychological motivators. A sizeable share of experts, however, expects crowd-out, probability weighting, and pure altruism, counterfactually. As a further comparison, we present a metaanalysis of similar treatments in the literature. Overall, predictions based on the literature are correlated with, but underperform, the expert forecasts.
Journal Article