SmoothHazard:An R package for fitting regression models to interval-censored observations of illness-death models

The irreversible illness-death model describes the pathway from an initial state to an absorbing state either directly or through an intermediate state. This model is frequently used in medical applications where the intermediate state represents illness and the absorbing state represents death. In many studies, disease onset times are not known exactly. This happens for example if the disease status of a patient can only be assessed at follow-up visits. In this situation the disease onset times are interval-censored. This article presents the SmoothHazard package for R. It implements algorithms for simultaneously fitting regression models to the three transition intensities of an illness-death model where the transition times to the intermediate state may be interval-censored and all the event times can be right-censored. The package parses the individual data structure of the subjects in a data set to find the individual contributions to the likelihood. The three baseline transition intensity functions are modelled by Weibull distributions or alternatively by M -splines in a semi-parametric approach. For a given set of covariates, the estimated transition intensities can be combined into predictions of cumulative event probabilities and life expectancies

The Validation and Assessment of Machine Learning: A Game of Prediction from High-Dimensional Data

In applied statistics, tools from machine learning are popular for analyzing complex and high-dimensional data. However, few theoretical results are available that could guide to the appropriate machine learning tool in a new application. Initial development of an overall strategy thus often implies that multiple methods are tested and compared on the same set of data. This is particularly difficult in situations that are prone to over-fitting where the number of subjects is low compared to the number of potential predictors. The article presents a game which provides some grounds for conducting a fair model comparison. Each player selects a modeling strategy for predicting individual response from potential predictors. A strictly proper scoring rule, bootstrap cross-validation, and a set of rules are used to make the results obtained with different strategies comparable. To illustrate the ideas, the game is applied to data from the Nugenob Study where the aim is to predict the fat oxidation capacity based on conventional factors and high-dimensional metabolomics data. Three players have chosen to use support vector machines, LASSO, and random forests, respectively

Principled Selection of Baseline Covariates to Account for Censoring in Randomized Trials with a Survival Endpoint

The analysis of randomized trials with time-to-event endpoints is nearly always plagued by the problem of censoring. As the censoring mechanism is usually unknown, analyses typically employ the assumption of non-informative censoring. While this assumption usually becomes more plausible as more baseline covariates are being adjusted for, such adjustment also raises concerns. Pre-specification of which covariates will be adjusted for (and how) is difficult, thus prompting the use of data-driven variable selection procedures, which may impede valid inferences to be drawn. The adjustment for covariates moreover adds concerns about model misspecification, and the fact that each change in adjustment set, also changes the censoring assumption and the treatment effect estimand. In this paper, we discuss these concerns and propose a simple variable selection strategy that aims to produce a valid test of the null in large samples. The proposal can be implemented using off-the-shelf software for (penalized) Cox regression, and is empirically found to work well in simulation studies and real data analyses

Return to the workforce following first hospitalization for heart failure: a Danish nationwide cohort study

Background: Return to work is important financially, as a marker of functional status and for self-esteem in patients developing chronic illness. We examined return to work after first heart failure (HF) hospitalization. Methods: By individual-level linkage of nationwide Danish registries, we identified 21455 patients of working age (18-60 years) with a first HF hospitalization in the period of 1997-2012. Of these 11880 (55%) were in the workforce prior to HF hospitalization and comprised the study population. We applied logistic regression to estimate odds ratios (OR) for associations between age, sex, length of hospital stay, level of education, income, comorbidity and return to work. Results: One year after first HF hospitalization, 8040 (67.7%) returned to the workforce, 2981 (25.1%) did not, 805 (6.7%) died and 54 (0.5%) emigrated. Predictors of return to work included younger age (18-30 vs. 51-60 years, OR 3.12; 95% CI 2.42-4.03), male sex (OR 1.22 [1.18-1.34]) and level of education (long-higher vs. basic school OR 2.06 [1.63-2.60]). Conversely, hospital stay >7 days (OR 0.56 [0.51-0.62]) and comorbidity including history of stroke (OR 0.55 [0.45-0.69]), chronic kidney disease (OR 0.46 [0.36-0.59]), chronic obstructive pulmonary disease (OR 0.62 [0.52-0.75]), diabetes (OR 0.76 [0.68-0.85]) and cancer (OR 0.49 [0.40-0.61]) were all significantly associated with lower chance of return to work. Conclusions: Patients in the workforce prior to HF hospitalization had low mortality but high risk of detachment from the workforce one year later. Young age, male sex, and higher level of education were predictors of return to work

concrete: Targeted Estimation of Survival and Competing Risks in Continuous Time

This article introduces the R package concrete, which implements a recently developed targeted maximum likelihood estimator (TMLE) for the cause-specific absolute risks of time-to-event outcomes measured in continuous time. Cross-validated Super Learner machine learning ensembles are used to estimate propensity scores and conditional cause-specific hazards, which are then targeted to produce robust and efficient plug-in estimates of the effects of static or dynamic interventions on a binary treatment given at baseline quantified as risk differences or risk ratios. Influence curve-based asymptotic inference is provided for TMLE estimates and simultaneous confidence bands can be computed for target estimands spanning multiple multiple times or events. In this paper we review the one-step continuous-time TMLE methodology as it is situated in an overarching causal inference workflow, describe its implementation, and demonstrate the use of the package on the PBC dataset.Comment: 18 pages, 4 figures, submitted to the R Journa

Risk of out-of-hospital cardiac arrest in antidepressant drug users

Conflicting results have been reported regarding the association between antidepressant use and out‐of‐hospital cardiac arrest (OHCA) risk. We investigated whether the use of antidepressants is associated with OHCA. METHODS: We conducted a nationwide nested case–control study to assess the association of individual antidepressant drugs within drug classes with the hazard of OHCA. Cases were defined as OHCA from presumed cardiac causes. Cox regression with time‐dependent exposure and time‐dependent covariates was conducted to calculate hazard ratios (HR) and 95% confidence intervals (95% CIs) overall and in subgroups defined by established cardiac disease and cardiovascular risk factors. Also, we studied antidepressants with and without sodium channel blocking or potassium channel blocking properties separately. RESULTS: During the study period from 2001 to 2015 we observed 10 987 OHCA cases, and found increased OHCA rate for high‐dose citalopram (>20 mg) and high‐dose escitalopram (>10 mg; HR:1.46 [95% CI:1.27–1.69], HR:1.43 [95% CI:1.16–1.75], respectively) among selective serotonin reuptake inhibitors (reference drug sertraline), and for high‐dose mirtazapine (>30; HR:1.59 [95% CI:1.18–2.14]) among the serotonin–norepinephrine reuptake inhibitors or noradrenergic and specific serotonergic antidepressants (reference drug duloxetine). Among tricyclic antidepressants (reference drug amitriptyline), no drug was associated with significantly increased OHCA rate. Increased OHCA rate was found for antidepressants with known potassium channel blocking properties (HR:1.14 [95% CI:1.05–1.23]), but for not those with sodium channel blocking properties. Citalopram, although not statistically significant, and mirtazapine were associated with increased OHCA rate in patients without cardiac disease and cardiovascular risk factors. CONCLUSION: Our findings indicate that careful titration of citalopram, escitalopram and mirtazapine dose may have to be considered due to drug safety issues

Lithium in drinking water and incidence of suicide:A nationwide individual-level cohort study with 22 years of follow-up

Suicide is a major public health concern. High-dose lithium is used to stabilize mood and prevent suicide in patients with affective disorders. Lithium occurs naturally in drinking water worldwide in much lower doses, but with large geographical variation. Several studies conducted at an aggregate level have suggested an association between lithium in drinking water and a reduced risk of suicide; however, a causal relation is uncertain. Individual-level register-based data on the entire Danish adult population (3.7 million individuals) from 1991 to 2012 were linked with a moving five-year time-weighted average (TWA) lithium exposure level from drinking water hypothesizing an inverse relationship. The mean lithium level was 11.6 μg/L ranging from 0.6 to 30.7 μg/L. The suicide rate decreased from 29.7 per 100,000 person-years at risk in 1991 to 18.4 per 100,000 person-years in 2012. We found no significant indication of an association between increasing five-year TWA lithium exposure level and decreasing suicide rate. The comprehensiveness of using individual-level data and spatial analyses with 22 years of follow-up makes a pronounced contribution to previous findings. Our findings demonstrate that there does not seem to be a protective effect of exposure to lithium on the incidence of suicide with levels below 31 μg/L in drinking water