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INTRODUCTION. Previous research has predominately relied on person-level or single characteristics of drinking episodes to characterise patterns of drinking that may confer risk. This research often relies on self-report measures. Advancements in wearable alcohol biosensors provide a multi-faceted objective measure of drinking. The current study aimed to characterise drinking episodes using data derived from a wearable alcohol biosensor. METHODS. Participants (n = 45) were adult heavy drinkers who wore the Secure Continuous Remote Alcohol Monitoring (SCRAM) bracelet and reported on their drinking behaviours. Cluster analysis was used to evaluate unique combinations of alcohol episode characteristics. Associations between clusters and self-reported person and event-level factors were also examined in univariable and multivariable models. RESULTS. Results suggested three unique clusters: Cluster 1 (most common, slowest rate of rise to and decline from peak), Cluster 2 (highest peak transdermal alcohol concentration and area under the curve) and Cluster 3 (fastest rate of decline from peak). Univariable analyses distinguished Cluster 1 as having fewer self-reported drinks and fewer episodes that occurred on weekends relative to Cluster 2. The effect for number of drinks remained in multivariable analyses. DISCUSSION AND CONCLUSIONS. This is the first study to characterise drinking patterns at the event-level using objective data. Results suggest that it is possible to distinguish drinking episodes based on several characteristics derived from wearable alcohol biosensors. This examination lays the groundwork for future studies to characterise patterns of drinking and their association with consequences of drinking behaviour.

When treatment effect modifiers influence the decision to participate in a randomized trial, the average treatment effect in the population represented by the randomized individuals will differ from the effect in other populations. In this tutorial, we consider methods for extending causal inferences about time-fixed treatments from a trial to a new target population of nonparticipants, using data from a completed randomized trial and baseline covariate data from a sample from the target population. We examine methods based on modeling the expectation of the outcome, the probability of participation, or both (doubly robust). We compare the methods in a simulation study and show how they can be implemented in software. We apply the methods to a randomized trial nested within a cohort of trial-eligible patients to compare coronary artery surgery plus medical therapy versus medical therapy alone for patients with chronic coronary artery disease. We conclude by discussing issues that arise when using the methods in applied analyses.

Introduction Our goal was to determine if features of surgical patients, easily obtained from the medical chart or brief interview, could be used to predict those likely to experience more rapid cognitive decline following surgery. Methods We analyzed data from an observational study of 560 older adults (≥70 years) without dementia undergoing major elective non‐cardiac surgery. Cognitive decline was measured using change in a global composite over 2 to 36 months following surgery. Predictive features were identified as variables readily obtained from chart review or a brief patient assessment. We developed predictive models for cognitive decline (slope) and predicting dichotomized cognitive decline at a clinically determined cut. Results In a hold‐out testing set, the regularized regression predictive model achieved a root mean squared error (RMSE) of 0.146 and a model r‐square (R2) of .31. Prediction of “rapid” decliners as a group achieved an area under the curve (AUC) of .75. Conclusion Some of our models could predict persons with increased risk for accelerated cognitive decline with greater accuracy than relying upon chance, and this result might be useful for stratification of surgical patients for inclusion in future clinical trials.

RESEARCH OBJECTIVE: In cluster‐randomized trials, participating clusters are not necessarily representative of the target population of trial‐eligible clusters where the experimental treatments might be applied. To the extent that participating and nonparticipating clusters differ in terms of covariates that are modifiers of the treatment effect, the average treatment effect in the trial may not apply to the entire target population. We describe analyses that extend (generalize or transport) causal inferences from cluster‐randomized trials to a target population of clusters, under a general nonparametric model that allows for arbitrary within‐cluster dependence. STUDY DESIGN: We consider study designs where a subset of a cohort of clusters are invited and agree to participate in a randomized trial with cluster‐level treatment assignment (ie, nested trial designs). Treatment and outcome data need only be collected from clusters participating in the trial, but data on baseline covariates must be collected from the entire cohort. We propose doubly robust estimators of potential outcome means in the target population that exploit individual‐level data on covariates and outcomes to improve efficiency and are appropriate for use with machine learning methods. POPULATION STUDIED: We illustrate the methods using a 2 × 2 factorial cluster‐randomized trial of influenza vaccination strategies conducted in 818 nursing homes nested in a cohort of 4,475 trial‐eligible Medicare‐certified nursing homes. To be eligible for the trial, nursing homes had to be free‐standing facilities (ie, not hospital based) within 50 miles of a CDC‐reporting city, have a minimum of 50 long‐stay residents, and at least 80% of residents aged 65 years or older. PRINCIPAL FINDINGS: We applied the methods to estimate the potential outcome means for the four vaccination strategies in the target population of trial‐eligible nursing homes. We estimated the conditional probability of trial participation (nursing home level) and the conditional probability of death from any cause (individual level, separately for each treatment group) using different methods to illustrate that our approach can easily accommodate them. One set of analyses used logistic regression models estimated by maximum likelihood methods. For comparison, we also estimated probabilities using various machine learning methods. Overall, the doubly robust estimator produced similar point estimates for the potential outcome mean under each treatment across all approaches for estimating the conditional probabilities of trial participation and death. Point estimates for generalizability analyses were largely similar to those from the randomized trial, suggesting that the trial‐only estimates were applicable to the broader population of trial‐eligible nursing homes for the outcome of all‐cause death. CONCLUSIONS: Recent work on extending causal inferences from individually randomized trials to a target population can be extended to the cluster trial setting, under a general causal model that allows for arbitrary within‐cluster dependence. IMPLICATIONS FOR POLICY OR PRACTICE: When cluster‐randomized trials are embedded in large health care systems, investigators can use the methods we describe to extend causal inferences from the trials to the broader health care system. PRIMARY FUNDING SOURCE: Patient‐Centered Outcomes Research Institute.

Estimating a patient's mortality risk is important in making treatment decisions. Survival trees are a useful tool and employ recursive partitioning to separate patients into different risk groups. Existing 'loss based' recursive partitioning procedures that would be used in the absence of censoring have previously been extended to the setting of right censored outcomes using inverse probability censoring weighted estimators of loss functions. In this paper, we propose new 'doubly robust' extensions of these loss estimators motivated by semiparametric efficiency theory for missing data that better utilize available data. Simulations and a data analysis demonstrate strong performance of the doubly robust survival trees compared with previously used methods. Copyright © 2016 John Wiley & Sons, Ltd.

Increasing evidence suggests that persistence of Listeria monocytogenes in food processing plants has been the underlying cause of a number of human listeriosis outbreaks. This study extracts criteria used by food safety experts in determining bacterial persistence in the environment, using retail delicatessen operations as a model. Using the Delphi method, we conducted an expert elicitation with 10 food safety experts from academia, industry, and government to classify L. monocytogenes persistence based on environmental sampling results collected over six months for 30 retail delicatessen stores. The results were modeled using variations of random forest, support vector machine, logistic regression, and linear regression; variable importance values of random forest and support vector machine models were consolidated to rank important variables in the experts’ classifications. The duration of subtype isolation ranked most important across all expert categories. Sampling site category also ranked high in importance and validation errors doubled when this covariate was removed. Support vector machine and random forest models successfully classified the data with average validation errors of 3.1% and 2.2% (n = 144), respectively. Our findings indicate that (i) the frequency of isolations over time and sampling site information are critical factors for experts determining subtype persistence, (ii) food safety experts from different sectors may not use the same criteria in determining persistence, and (iii) machine learning models have potential for future use in environmental surveillance and risk management programs. Future work is necessary to validate the accuracy of expert and machine classification against biological measurement of L. monocytogenes persistence.