On the role of Volterra integral equations in self-consistent, product-limit, inverse probability of censoring weighted, and redistribution-to-the-right estimators for the survival function.

This paper reconsiders several results of historical and current importance to nonparametric estimation of the survival distribution for failure in the presence of right-censored observation times, demonstrating in particular how Volterra integral equations help inter-connect the resulting estimators. The paper begins by considering Efron's self-consistency equation, introduced in a seminal 1967 Berkeley symposium paper. Novel insights provided in the current work include the observations that (i) the self-consistency equation leads directly to an anticipating Volterra integral equation whose solution is given by a product-limit estimator for the censoring survival function; (ii) a definition used in this argument immediately establishes the familiar product-limit estimator for the failure survival function; (iii) the usual Volterra integral equation for the product-limit estimator of the failure survival function leads to an immediate and simple proof that it can be represented as an inverse probability of censoring weighted estimator; (iv) a simple identity characterizes the relationship between natural inverse probability of censoring weighted estimators for the survival and distribution functions of failure; (v) the resulting inverse probability of censoring weighted estimators, attributed to a highly influential 1992 paper of Robins and Rotnitzky, were implicitly introduced in Efron's 1967 paper in its development of the redistribution-to-the-right algorithm.

Insertable cardiac monitor-guided early intervention to reduce atrial fibrillation burden following catheter ablation: Study design and clinical protocol (ICM-REDUCE-AF trial).

Background: Percutaneous catheter ablation (CA) to achieve pulmonary vein isolation is an effective treatment for drug-refractory paroxysmal and persistent atrial fibrillation (AF). However, recurrence rates after a single AF ablation procedure remain elevated. Conventional management after CA ablation has mostly been based on clinical AF recurrence.

Discussion on "Instrumental variable estimation of the causal hazard ratio," by Linbo Wang, Eric Tchetgen Tchetgen, Torben Martinussen, and Stijn Vansteelandt.

We propose and study an augmented variant of the estimator proposed by Wang, Tchetgen Tchetgen, Martinussen, and Vansteelandt.

Affordability of Forensic Assertive Community Treatment Programs: A Return-on-Investment Analysis.

Objective: In this study, the authors assessed return on investment (ROI) associated with a forensic assertive community treatment (FACT) program.

Methods: A retrospective secondary data analysis of a randomized controlled trial comprising 70 legal-involved patients with severe mental illness was conducted in Rochester, New York. Patients were randomly assigned to receive either FACT or outpatient psychiatric treatment including intensive case management.

Screening for chronic diseases: optimizing lead time through balancing prescribed frequency and individual adherence.

Screening for chronic diseases, such as cancer, is an important public health priority, but traditionally only the frequency or rate of screening has received attention. In this work, we study the importance of adhering to recommended screening policies and develop new methodology to better optimize screening policies when adherence is imperfect. We consider a progressive disease model with four states (healthy, undetectable preclinical, detectable preclinical, clinical), and overlay this with a stochastic screening-behavior model using the theory of renewal processes that allows us to capture imperfect adherence to screening programs in a transparent way.

Regression trees and ensembles for cumulative incidence functions.

The use of cumulative incidence functions for characterizing the risk of one type of event in the presence of others has become increasingly popular over the past two decades. The problems of modeling, estimation and inference have been treated using parametric, nonparametric and semi-parametric methods. Efforts to develop suitable extensions of machine learning methods, such as regression trees and ensemble methods, have begun comparatively recently.

Robust Q-learning.

Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to estimate certain nuisance parameters, and misspecification of these working models can result in residual confounding and/or efficiency loss. We propose a robust Q-learning approach which allows estimating such nuisance parameters using data-adaptive techniques.

Intratumoral heterogeneity of second-harmonic generation scattering from tumor collagen and its effects on metastatic risk prediction.

Background: Metastases are the leading cause of breast cancer-related deaths. The tumor microenvironment impacts cancer progression and metastatic ability. Fibrillar collagen, a major extracellular matrix component, can be studied using the light scattering phenomenon known as second-harmonic generation (SHG).

Genomics models in radiotherapy: From mechanistic to machine learning.

Machine learning (ML) provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy.

Censoring Unbiased Regression Trees and Ensembles.

This paper proposes a novel paradigm for building regression trees and ensemble learning in survival analysis. Generalizations of the CART and Random Forests algorithms for general loss functions, and in the latter case more general bootstrap procedures, are both introduced. These results, in combination with an extension of the theory of censoring unbiased transformations applicable to loss functions, underpin the development of two new classes of algorithms for constructing survival trees and survival forests: Censoring Unbiased Regression Trees and Censoring Unbiased Regression Ensembles.

Readmission Patterns During Long-Term Follow-Up After Left Ventricular Assist Device Implantation.

As more patients are supported for longer periods by a left ventricular assist device (LVAD), hospital readmission is becoming a growing problem. However, data about temporal changes in readmission rates and causes for patients with prolonged LVAD support are limited. We aimed to evaluate rates, causes, and predictors of any and long-term readmission after LVAD placement at our institution.

Modeling the rate of HIV testing from repeated binary data amidst potential never-testers.

Many longitudinal studies with a binary outcome measure involve a fraction of subjects with a homogeneous response profile. In our motivating data set, a study on the rate of human immunodeficiency virus (HIV) self-testing in a population of men who have sex with men (MSM), a substantial proportion of the subjects did not self-test during the follow-up study. The observed data in this context consist of a binary sequence for each subject indicating whether or not that subject experienced any events between consecutive observation time points, so subjects who never self-tested were observed to have a response vector consisting entirely of zeros.

Regularity of a renewal process estimated from binary data.

Assessment of the regularity of a sequence of events over time is important for clinical decision-making as well as informing public health policy. Our motivating example involves determining the effect of an intervention on the regularity of HIV self-testing behavior among high-risk individuals when exact self-testing times are not recorded. Assuming that these unobserved testing times follow a renewal process, the goals of this work are to develop suitable methods for estimating its distributional parameters when only the presence or absence of at least one event per subject in each of several observation windows is recorded.

A Randomized Controlled Trial of the Rochester Forensic Assertive Community Treatment Model.

Objective: Forensic assertive community treatment (FACT) is an adaptation of the assertive community treatment model and is designed to serve justice-involved adults with serious mental illness. This study compared the effectiveness of a standardized FACT model and enhanced treatment as usual in reducing jail and hospital use and in promoting engagement in outpatient mental health services.

Methods: Seventy adults with psychotic disorders who were arrested for misdemeanor crimes and who were eligible for conditional discharge were recruited from the Monroe County, New York, court system.

Estimation in the semiparametric accelerated failure time model with missing covariates: improving efficiency through augmentation.

This paper considers linear regression with missing covariates and a right censored outcome. We first consider a general two-phase outcome sampling design, where full covariate information is only ascertained for subjects in phase two and sampling occurs under an independent Bernoulli sampling scheme with known subject-specific sampling probabilities that depend on phase one information (e.g.

Ethnic Disparities in Medicare Part D Satisfaction and Intention to Switch Plans.

Medicare Part D has been successful in providing affordable prescription drug coverage with relatively high levels of beneficiary reported satisfaction. We use nationally representative survey data to examine whether racial/ethnic disparities exist in reported Part D satisfaction and plan evaluations. Compared to non-Hispanic White Medicare beneficiaries, Hispanic beneficiaries are considerably more likely to report to switch to a new plan in the next year and, among beneficiaries auto-enrolled in a Part D plan, are less likely to be very satisfied with the currently enrolled plan.

Effect of Gender on the Risk of Neurologic Events and Subsequent Outcomes in Patients With Left Ventricular Assist Devices.

Previous studies have shown that women with continuous-flow left ventricular assist devices (LVADs) are at greater risk of neurologic events. However, the relation between neurologic events and subsequent outcomes by gender is not well understood. We aimed to identify gender differences in the risk of neurologic events in patients with LVAD and the impact of time-dependent neurologic event on all-cause mortality by gender.

Doubly robust survival trees.

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.

The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter.

Non-small-cell lung cancer after breast cancer: a population-based study of clinicopathologic characteristics and survival outcomes in 3529 women.

Introduction: Annually, 1.4 million women worldwide are diagnosed with breast cancer (BC) and are at risk for another common malignancy: non-small-cell lung cancer (NSCLC). No large population-based study has examined subsequent survival.

Implementation of a mortality prediction rule for real-time decision making: feasibility and validity.

Background: A previously published, retrospectively derived prediction rule for death within 30 days of hospital admission has the potential to launch parallel interdisciplinary team activities. Whether or not patient care improves will depend on the validity of prospectively generated predictions, and the feasibility of generating them on demand for a critical proportion of inpatients.

Objective: To determine the feasibility of generating mortality predictions on admission and to validate their accuracy using the scoring weights of the retrospective rule.

The Care Span: Lower Hispanic participation in Medicare Part D may reflect program barriers.

Despite the successes of Medicare's Part D prescription drug program, an estimated 12.5 percent of Americans ages sixty-five and older do not have prescription drug coverage. It is possible that some who remain without coverage do so for rational economic reasons.

Continuity of care and the risk of preventable hospitalization in older adults.

Importance: Preventable hospitalizations are common among older adults for reasons that are not well understood.

Objective: To determine whether Medicare patients with ambulatory visit patterns indicating higher continuity of care have a lower risk of preventable hospitalization.

Design: Retrospective cohort study.

Randomized controlled effectiveness trial of reciprocal peer support in heart failure.

Background: Although disease management programs for patients hospitalized with heart failure (HF) are effective, they are, however, often resource intensive, limiting their uptake. Peer support programs have led to improved outcomes among patients with other chronic conditions and may result in similar improvements for patients with HF.

Methods And Results: In this randomized controlled trial, reciprocal peer support (RPS) arm patients participated in a HF nurse practitioner-led goal setting group session, received brief training in peer communication skills, and were paired with another participant in their cohort with whom they were encouraged to talk weekly using a telephone platform.

Mortality predictions on admission as a context for organizing care activities.

Background: Favorable health outcomes are more likely to occur when the clinical team recognizes patients at risk and intervenes in consort. Prediction rules can identify high-risk subsets, but the availability of multiple rules for various conditions present implementation and assimilation challenges.

Methods: A prediction rule for 30-day mortality at the beginning of the hospitalization was derived in a retrospective cohort of adult inpatients from a community hospital in the Midwestern United States from 2008 to 2009, using clinical laboratory values, past medical history, and diagnoses present on admission.