Newborn readmissions and virtual primary care delivery: a population-based case-control study.

Background: Early post-discharge assessments for newborns are recommended. Virtual care has become more prevalent during the pandemic, providing an opportunity to better understand its impact on the quality of post-discharge newborn care. The objective of this study was to understand whether primary care visit modality (in-person vs.

A novel performance scoring quantification framework for stress test set-ups.

Stress tests, e.g., the cardiac stress test, are standard clinical screening tools aimed to unmask clinical pathology.

On the use of historical estimates.

The use of historical, i.e., already existing, estimates in current studies is common in a wide variety of application areas.

Testing for inequality constraints in singular models by trimming or winsorizing the variance matrix.

There are many applications in which a statistic follows, at least asymptotically, a normal distribution with a singular or nearly singular variance matrix. A classic example occurs in linear regression models under multicollinearity but there are many more such examples. There is well-developed theory for testing linear equality constraints when the alternative is two-sided and the variance matrix is either singular or non-singular.

Analysis of Microbiome Data in the Presence of Excess Zeros.

An important feature of microbiome count data is the presence of a large number of zeros. A common strategy to handle these excess zeros is to add a small number called pseudo-count (e.g.

Structural zeros in high-dimensional data with applications to microbiome studies.

This paper is motivated by the recent interest in the analysis of high-dimensional microbiome data. A key feature of these data is the presence of "structural zeros" which are microbes missing from an observation vector due to an underlying biological process and not due to error in measurement. Typical notions of missingness are unable to model these structural zeros.

Testing for the multivariate stochastic order among ordered experimental groups with application to dose-response studies.

The comparison of two or more ordered experimental groups based on multivariate data is common in a variety of applications such as toxicology, clinical trials and drug development, to name just a few. In this article, we develop a nonparametric methodology for analyzing such data. In particular we propose a global K sample nonparametric test for order among vector valued outcomes.

The linear stochastic order and directed inference for multivariate ordered distributions.

Researchers are often interested in drawing inferences regarding the order between two experimental groups on the basis of multivariate response data. Since standard multivariate methods are designed for two sided alternatives they may not be ideal for testing for order between two groups. In this article we introduce the notion of the linear stochastic order and investigate its properties.

Order-restricted inference for multivariate longitudinal data with applications to the natural history of hearing loss.

Multivariate outcomes are often measured longitudinally. For example, in hearing loss studies, hearing thresholds for each subject are measured repeatedly over time at several frequencies. Thus, each patient is associated with a multivariate longitudinal outcome.

Order restricted inference for multivariate binary data with application to toxicology.

In many applications researchers collect multivariate binary response data under two or more, naturally ordered, experimental conditions. In such situations one is often interested in using all binary outcomes simultaneously to detect an ordering among the experimental conditions. To make such comparisons we develop a general methodology for testing for the multivariate stochastic order between K ≥ 2 multivariate binary distributions.

Constrained inference in mixed-effects models for longitudinal data with application to hearing loss.

In medical studies, endpoints are often measured for each patient longitudinally. The mixed-effects model has been a useful tool for the analysis of such data. There are situations in which the parameters of the model are subject to some restrictions or constraints.

Analysis of Correlated Gene Expression Data on Ordered Categories.

A bootstrap based methodology is introduced for analyzing repeated measures/longitudinal microarray gene expression data over ordered categories. The proposed non-parametric procedure uses order-restricted inference to compare gene expressions among ordered experimental conditions. The null distribution for determining significance is derived by suitably bootstrapping the residuals.

Minimum-norm estimation for binormal receiver operating characteristic (ROC) curves.

The receiver operating characteristic (ROC) curve is often used to assess the usefulness of a diagnostic test. We present a new method to estimate the parameters of a popular semi-parametric ROC model, called the binormal model. Our method is based on minimization of the functional distance between two estimators of an unknown transformation postulated by the model, and has a simple, closed-form solution.

Order-restricted semiparametric inference for the power bias model.

The power bias model, a generalization of length-biased sampling, is introduced and investigated in detail. In particular, attention is focused on order-restricted inference. We show that the power bias model is an example of the density ratio model, or in other words, it is a semiparametric model that is specified by assuming that the ratio of several unknown probability density functions has a parametric form.

Dropout rates in randomized clinical trials of antipsychotics: a meta-analysis comparing first- and second-generation drugs and an examination of the role of trial design features.

Dropout is often used as an outcome measure in clinical trials of antipsychotic medication. Previous research is inconclusive regarding (a) differences in dropout rates between first- and second-generation antipsychotic medications and (b) how trial design features reduce dropout. Meta-analysis of randomized controlled trials (RCTs) of antipsychotic medication was conducted to compare dropout rates for first- and second-generation antipsychotic drugs and to examine how a broad range of design features effect dropout.

The association of dropout and outcome in trials of antipsychotic medication and its implications for dealing with missing data.

Objective: The extent to which noncompletion of a clinical trial relates to outcomes has implications for choosing the most appropriate method for contending with missing data due to dropout. We examined whether dropout relates to outcome in clinical trials of antipsychotic medication.

Methods: Data from 5 large clinical trials of schizophrenia (n=3483) were examined separately.

A composite approach that includes dropout rates when analyzing efficacy data in clinical trials of antipsychotic medications.

Background: Often, outcomes in clinical trials of antipsychotic medications are examined using last observation carried forward (LOCF). One limitation of LOCF and other common approaches is that they overlook the meaning underpinning trial completion and noncompletion. Noncompletion often relates to lack of drug tolerability.

Overdiagnosis in early detection programs.

Overdiagnosis refers to the situation where a screening exam detects a disease that would have otherwise been undetected in a person's lifetime. The disease would have not have been diagnosed because the individual would have died of other causes prior to its clinical onset. Although the probability of overdiagnosis is an important quantity for understanding early detection programs it has not been rigorously studied.

The analysis of the influence of birth order and other factors in multiple birth data.

We compare three methods which can be used to analyse the influence of birth order and other factors on health outcomes in multiple birth data. We consider marginal models based on generalized estimating equations (GEE) and two kinds of conditional models; conditional logistic regression (CLR) and mixed effects models (MEM). Although the models may be written similarly, there are differences in both the interpretation and the numerical values assigned to the parameters.

Combined association, segregation and aggregation analysis on case-control family data.

Recently genetic epidemiologists have begun using case-control family study designs to investigate the role of genetic and environmental risk factors in disease etiology. The objective of these studies is to assess the association of environmental factors with the disease trait; to characterize the disease genes using segregation analysis; and to quantify the residual familial aggregation after controlling for environmental and genetic factors. Typically these objectives are achieved by conducting separate studies and analysis.

The theory of case-control studies for early detection programs.

Although case-control studies are widely used for evaluating the benefit of early detection programs, the theoretical basis underlying this application has not been well developed. In this paper the properties of chronic disease case-control studies for evaluating early detection programs are investigated. An idealized case-control study is analyzed and the theoretical expression for the odds ratio associated with the benefit of screening is derived.