Prediction under interventions: evaluation of counterfactual performance using longitudinal observational data

Predictions under interventions are estimates of what a person's risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision making. However, evaluating predictive performance of interventional predictions is challenging. Standard ways of evaluating predictive performance do not apply when using observational data, because prediction under interventions involves obtaining predictions of the outcome under conditions that are different to those that are observed for a subset of individuals in the validation dataset. This work describes methods for evaluating counterfactual performance of predictions under interventions for time-to-event outcomes. This means we aim to assess how well predictions would match the validation data if all individuals had followed the treatment strategy under which predictions are made. We focus on counterfactual performance evaluation using longitudinal observational data, and under treatment strategies that involve sustaining a particular treatment regime over time. We introduce an estimation approach using artificial censoring and inverse probability weighting which involves creating a validation dataset that mimics the treatment strategy under which predictions are made. We extend measures of calibration, discrimination (c-index and cumulative/dynamic AUCt) and overall prediction error (Brier score) to allow assessment of counterfactual performance. The methods are evaluated using a simulation study, including scenarios in which the methods should detect poor performance. Applying our methods in the context of liver transplantation shows that our procedure allows quantification of the performance of predictions supporting crucial decisions on organ allocation

A comparison of the beta-geometric model with landmarking for dynamic prediction of time to pregnancy

Peer reviewedPublisher PD

Seasonal variation of serotonin turnover in human cerebrospinal fluid, depressive symptoms and the role of the 5-HTTLPR.

Studying monoaminergic seasonality is likely to improve our understanding of neurobiological mechanisms underlying season-associated physiological and pathophysiological behavior. Studies of monoaminergic seasonality and the influence of the serotonin-transporter-linked polymorphic region (5-HTTLPR) on serotonin seasonality have yielded conflicting results, possibly due to lack of power and absence of multi-year analyses. We aimed to assess the extent of seasonal monoamine turnover and examined the possible involvement of the 5-HTTLPR. To determine the influence of seasonality on monoamine turnover, 5-hydroxyindoleacetic acid (5-HIAA) and homovanillic acid (HVA) were measured in the cerebrospinal fluid of 479 human subjects collected during a 3-year period. Cosine and non-parametric seasonal modeling were applied to both metabolites. We computed serotonin (5-HT) seasonality values and performed an association analysis with the s/l alleles of the 5-HTTLPR. Depressive symptomatology was assessed using the Beck Depression Inventory-II. Circannual variation in 5-HIAA fitted a spring-peak cosine model that was significantly associated with sampling month (P=0.0074). Season of sampling explained 5.4% (P=1.57 × 10(-7)) of the variance in 5-HIAA concentrations. The 5-HTTLPR s-allele was associated with increased 5-HIAA seasonality (standardized regression coefficient=0.12, P=0.020, N=393). 5-HIAA seasonality correlated with depressive symptoms (Spearman's rho=0.13, P=0.018, N=345). In conclusion, we highlight a dose-dependent association of the 5-HTTLPR with 5-HIAA seasonality and a positive correlation between 5-HIAA seasonality and depressive symptomatology. The presented data set the stage for follow-up in clinical populations with a role for seasonality, such as affective disorders

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Development and application of statistical models for medical scientific researc