High‐dimensional biomarkers such as genomics are increasingly being measured in randomized clinical trials. Consequently, there is a growing interest in developing methods that improve the power to detect biomarker‐treatment interactions. We adapt recently proposed two‐stage interaction detecting procedures in the setting of randomized clinical trials. We also propose a new stage 1 multivariate screening

The neonatal mortality rate in Rwanda remains above the United Nations Sustainable Development Goal 3 target of 12 deaths per 1,000 live births. As part of a larger effort to reduce preventable neonatal deaths in the country, we conducted a study to examine risk factors for low birthweight. The data was collected via a cost‐efficient cluster‐based outcome‐dependent sampling scheme wherein clusters

Precision medicine relies on the idea that, for a particular targeted agent, only a subpopulation of patients are sensitive to it and thus may benefit from it therapeutically. In practice, it often is assumed based on pre‐clinical data that a treatment‐sensitive subpopulation is known, and moreover that the agent is substantively efficacious in that subpopulation. Due to important differences between

Changepoint detection methods are used in many areas of science and engineering, e.g., in the analysis of copy number variation data to detect abnormalities in copy numbers along the genome. Despite the broad array of available tools, methodology for quantifying our uncertainty in the strength (or presence) of given changepoints post‐selection are lacking. Post‐selection inference offers a framework

To elucidate the molecular mechanisms underlying genetic variants identified from genome‐wide association studies (GWAS) for a variety of phenotypic traits encompassing binary, continuous, count, and survival outcomes, we propose a novel and flexible method to test for mediation that can simultaneously accommodate multiple genetic variants and different types of outcome variables. Specifically, we

Image‐on‐image regression analysis, using images to predict images, is a challenging task, due to (1) the high dimensionality and (2) the complex spatial dependence structures in image predictors and image outcomes. In this work, we propose a novel image‐on‐image regression model, by extending a spatial Bayesian latent factor model to image data, where low‐dimensional latent factors are adopted to

When a ranking of institutions such as medical centers or universities is based on a numerical measure of performance provided with a standard error, confidence intervals (CIs) should be calculated to assess the uncertainty of these ranks. We present a novel method based on Tukey's honest significant difference test to construct simultaneous CIs for the true ranks. When all the true performances are

Multivariate spatial data, where multiple responses are simultaneously recorded across spatially indexed observational units, are routinely collected in a wide variety of disciplines. For example, the Southern Ocean Continuous Plankton Recorder survey collects records of zooplankton communities in the Indian sector of the Southern Ocean, with the aim of identifying and quantifying spatial patterns

We introduce a framework for estimating causal effects of binary and continuous treatments in high dimensions. We show how posterior distributions of treatment and outcome models can be used together with doubly robust estimators. We propose an approach to uncertainty quantification for the doubly robust estimator, which utilizes posterior distributions of model parameters and (1) results in good frequentist

In biomedical practices, multiple biomarkers are often combined using a prespecified classification rule with tree structure for diagnostic decisions. The classification structure and cutoff point at each node of a tree are usually chosen on an ad hoc basis, depending on decision makers' experience. There is a lack of analytical approaches that lead to optimal prediction performance, and that guide

Inverse probability of treatment weights (IPTWs) are commonly used to control for confounding when estimating causal effects of point exposures from observational data. When planning a study that will be analyzed with IPTWs, determining the required sample size for a given level of statistical power is challenging because of the effect of weighting on the variance of the estimated causal means. This

We study the efficiency of covariate‐specific estimates of pure risk (one minus the survival function) when some covariates are only available for case‐control samples nested in a cohort. We focus on the semiparametric additive hazards model in which the hazard function equals a baseline hazard plus a linear combination of covariates with either time‐varying or time‐invariant coefficients. A published

Restricted mean survival time (RMST) is a clinically interpretable and meaningful survival metric that has gained popularity in recent years. Several methods are available for regression modeling of RMST, most based on pseudo‐observations or what is essentially an inverse‐weighted complete‐case analysis. No existing RMST regression method allows for the covariate effects to be expressed as functions

Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome. Existing confidence intervals for causal effects based on instrumental variables assume that all of the putative instrumental variables are valid; a valid instrumental variable is a variable that affects the outcome only by affecting the treatment and is not related to unmeasured confounders. However

This article presents a new method for modelling collective movement in continuous time with behavioural switching, motivated by simultaneous tracking of wild or semi‐domesticated animals. Each individual in the group is at times attracted to a unobserved leading point. However, the behavioural state of each individual can switch between ‘following’ and ‘independent’. The ‘following’ movement is modelled

A central but challenging problem in genetic studies is to test for (usually weak) associations between a complex trait (e.g., a disease status) and sets of multiple genetic variants. Due to the lack of a uniformly most powerful test, data‐adaptive tests, such as the adaptive sum of powered score (aSPU) test, are advantageous in maintaining high power against a wide range of alternatives. However,

Imputation is a popular technique for handling item nonresponse. Parametric imputation is based on a parametric model for imputation and is not robust against the failure of the imputation model. Nonparametric imputation is fully robust but is not applicable when the dimension of covariates is large due to the curse of dimensionality. Semiparametric imputation is another robust imputation based on

Marginal structural models (MSMs) with inverse probability weighted estimators (IPWEs) are widely used to estimate causal effects of treatment sequences on longitudinal outcomes in the presence of time‐varying confounding and dependent censoring. However, IPWEs for MSMs can be inefficient and unstable if weights are estimated by maximum likelihood. To improve the performance of IPWEs, covariate balancing

Electronic health records (EHRs) have become a platform for data‐driven granular‐level surveillance in recent years. In this paper, we make use of EHRs for early prevention of childhood obesity. The proposed method simultaneously provides smooth disease mapping and outlier information for obesity prevalence that are useful for raising public awareness and facilitating targeted intervention. More precisely

Comparing areas under the ROC curve (AUCs) is a popular approach to compare prognostic biomarkers. The aim of this paper is to present an efficient method to control the family‐wise error rate when multiple comparisons are performed. We suggest to combine the max‐t test and the closed testing procedures. We build on previous work on asymptotic results for ROC curves and on general multiple testing

We anticipate that unmanned aerial vehicles will become popular wildlife survey platforms. Because detecting animals from the air is imperfect, we develop a mark‐recapture line transect method using two digital cameras, possibly mounted on one aircraft, which cover the same area with a short time delay between them. Animal movement between the passage of the cameras introduces uncertainty in individual

Optimal individualized treatment rules (ITRs) provide customized treatment recommendations based on subject characteristics to maximize clinical benefit in accordance with the objectives in precision medicine. As a result, there is growing interest in developing statistical tools for estimating optimal ITRs in evidence‐based research. In health economic perspectives, policy makers consider the tradeoff

In a regression setting, it is often of interest to quantify the importance of various features in predicting the response. Commonly, the variable importance measure used is determined by the regression technique employed. For this reason, practitioners often only resort to one of a few regression techniques for which a variable importance measure is naturally defined. Unfortunately, these regression

With multiple possible mediators on the causal pathway from a treatment to an outcome, we consider the problem of decomposing the effects along multiple possible causal path(s) through each distinct mediator. Under a path‐specific effects framework, such fine‐grained decompositions necessitate stringent assumptions, such as correctly specifying the causal structure among the mediators, and no unobserved

Health research using electronic health records (EHR) has gained popularity, but misclassification of EHR‐derived disease status and lack of representativeness of the study sample can result in substantial bias in effect estimates and can impact power and type I error. In this paper, we develop new strategies for handling disease status misclassification and selection bias in EHR‐based association

Given the heterogeneous responses to therapy and the high cost of treatments, there is an increasing interest in identifying pretreatment predictors of therapeutic effect. Clearly, the success of such an endeavor will depend on the amount of information that the patient‐specific variables convey about the individual causal treatment effect on the response of interest. In the present work, using causal

A flexible class of semiparametric partly linear frailty transformation models is considered for analyzing clustered interval‐censored data, which arise naturally in complex diseases and dental research. This class of models features two nonparametric components, resulting in a nonparametric baseline survival function and a potential nonlinear effect of a continuous covariate. The dependence among

Left truncation commonly occurs in many areas, and many methods have been proposed in the literature for the analysis of various types of left‐truncated failure time data. For the situation, a common approach is to conduct the analysis conditional on truncation times, and the method is relatively simple but may not be efficient. In this paper, we discuss regression analysis of such data arising from

We propose new tests for assessing whether covariates in a treatment group and matched control group are balanced in observational studies. The tests exhibit high power under a wide range of multivariate alternatives, some of which existing tests have little power for. The asymptotic permutation null distributions of the proposed tests are studied and the P‐values calculated through the asymptotic

This paper presents a model‐based method for clustering multivariate binary observations that incorporates constraints consistent with the scientific context. The approach is motivated by the precision medicine problem of identifying autoimmune disease patient subsets or classes who may require different treatments. We start with a family of restricted latent class models or RLCMs. However, in the

Modern data collection often entails longitudinal repeated measurements that assume values on a Riemannian manifold. Analyzing such longitudinal Riemannian data is challenging, because of both the sparsity of the observations and the nonlinear manifold constraint. Addressing this challenge, we propose an intrinsic functional principal component analysis for longitudinal Riemannian data. Information

This paper discusses variable selection in the context of joint analysis of longitudinal data and failure time data. A large literature has been developed for either variable selection or the joint analysis but there exists only limited literature for variable selection in the context of the joint analysis when failure time data are right censored. Corresponding to this, we will consider the situation

Electroencephalography (EEG) is a noninvasive neuroimaging modality that captures electrical brain activity many times per second. We seek to estimate power spectra from EEG data that ware gathered for 557 adolescent twin pairs through the Minnesota Twin Family Study (MTFS). Typically, spectral analysis methods treat time series from each subject separately, and independent spectral densities are fit

In this paper, we study the problem of computing a principal component analysis of data affected by Poisson noise. We assume samples are drawn from independent Poisson distributions. We want to estimate principal components of a fixed transformation of the latent Poisson means. Our motivating example is microbiome data, though the methods apply to many other situations. We develop a semiparametric

The identification of valid surrogate markers of disease or disease progression has the potential to decrease the length and costs of future studies. Most available methods that assess the value of a surrogate marker ignore the fact that surrogates are often measured with error. Failing to adjust for measurement error can erroneously identify a useful surrogate marker as not useful or vice versa. We

Time is of the essence in evaluating potential drugs and biologics for the treatment and prevention of COVID‐19. There are currently 876 randomized clinical trials (phase 2 and 3) of treatments for COVID‐19 registered on clinicaltrials.gov. Covariate adjustment is a statistical analysis method with potential to improve precision and reduce the required sample size for a substantial number of these

The United Nations' Sustainable Development Goal 3.2 aims to reduce under‐five child mortality to 25 deaths per 1000 live births by 2030. Child mortality tends to be concentrated in developing regions where information needed to assess achievement of this goal often comes from surveys and censuses. In both, women are asked about their birth histories, but with varying degrees of detail. Full birth

Successful deployment of machine learning algorithms in healthcare requires careful assessments of their performance and safety. To date, the FDA approves locked algorithms prior to marketing and requires future updates to undergo separate premarket reviews. However, this negates a key feature of machine learning—the ability to learn from a growing dataset and improve over time. This paper frames the

We thank the discussants for sharing their unique perspectives on the problem of designing automatic algorithm change protocols (aACPs) for machine learning‐based software as a medical device. Both Pennello et al. and Rose highlighted a number of challenges that arise in real‐world settings, and we whole‐heartedly agree that substantial extensions of our work are needed to understand if and how aACPs

The win ratio is gaining traction as a simple and intuitive approach to analysis of prioritized composite endpoints in clinical trials. To extend it from two‐sample comparison to regression, we propose a novel class of semiparametric models that includes as special cases both the two‐sample win ratio and the traditional Cox proportional hazards model on time to the first event. Under the assumption

We consider the problem of jointly modeling multiple covariance matrices by partial common principal component analysis (PCPCA), which assumes a proportion of eigenvectors to be shared across covariance matrices and the rest to be individual‐specific. This paper proposes consistent estimators of the shared eigenvectors in the PCPCA as the number of matrices or the number of samples to estimate each

Cluster analysis is an unsupervised learning strategy that is exceptionally useful for identifying homogeneous subgroups of observations in data sets of unknown structure. However, it is challenging to determine if the identified clusters represent truly distinct subgroups rather than noise. Existing approaches for addressing this problem tend to define clusters based on distributional assumptions

Multiple imputation by chained equations (MICE) has emerged as a popular approach for handling missing data. A central challenge for applying MICE is determining how to incorporate outcome information into covariate imputation models, particularly for complicated outcomes. Often, we have a particular analysis model in mind, and we would like to ensure congeniality between the imputation and analysis

The advent and subsequent widespread availability of preventive vaccines has altered the course of public health over the past century. Despite this success, effective vaccines to prevent many high‐burden diseases, including human immunodeficiency virus (HIV), have been slow to develop. Vaccine development can be aided by the identification of immune response markers that serve as effective surrogates

Matching is a common approach to covariate adjustment in estimating causal effects in observational studies. It is important to assess covariate balance of the matched samples. This is usually done informally, in ways that have a number of limitations. First, there are many diagnostics, even if covariates are assessed one at a time, which raises multiplicity issues. In addition, joint distributions

Count data are often subject to underreporting, especially in infectious disease surveillance. We propose an approximate maximum likelihood method to fit count time series models from the endemic‐epidemic class to underreported data. The approach is based on marginal moment matching where underreported processes are approximated through completely observed processes from the same class. Moreover, the

One central task in precision medicine is to establish individualized treatment rules (ITRs) for patients with heterogeneous responses to different therapies. Motivated from a randomized clinical trial for Type 2 diabetic patients on a comparison of two drugs, that is, pioglitazone and gliclazide, we consider a problem: utilizing promising candidate biomarkers to improve an existing ITR. This calls

The utilization of surrogate markers offers the opportunity to reduce the length of required follow‐up time and/or costs of a randomized trial examining the effectiveness of an intervention or treatment. There are many available methods for evaluating the utility of a single surrogate marker including both parametric and nonparametric approaches. However, as the dimension of the surrogate marker increases

The outcome in a randomized experiment is sometimes nonnegative with a clump of observations at zero and continuously distributed positive values. One widely used model for a nonnegative outcome with a clump at zero is the Tobit model, which assumes that the treatment has a shift effect on the distribution of a normally distributed latent variable and the observed outcome is the maximum of the latent

It is increasingly common clinically for cancer specimens to be examined using techniques that identify somatic mutations. In principle, these mutational profiles can be used to diagnose the tissue of origin, a critical task for the 3% to 5% of tumors that have an unknown primary site. Diagnosis of primary site is also critical for screening tests that employ circulating DNA. However, most mutations

Latent trajectory class analysis is a powerful technique to elucidate the structure underlying population heterogeneity. The standard approach relies on fully parametric modeling and is computationally impractical when the data include a large collection of non‐Gaussian longitudinal features. We introduce a new approach, the first based on artificial likelihood concepts, that avoids undue modeling

Constructing a confidence interval for the ratio of bivariate normal means is a classical problem in statistics. Several methods have been proposed in the literature. The Fieller method is known as an exact method, but can produce an unbounded confidence interval if the denominator of the ratio is not significantly deviated from 0; while the delta and some numeric methods are all bounded, they are

Cure models have been widely developed to estimate the cure fraction when some subjects never experience the event of interest. However, these models were rarely focused on the estimation of the time‐to‐cure, that is, the delay elapsed between the diagnosis and “the time from which cure is reached,” an important indicator, for instance, to address the question of access to insurance or loans for subjects

Cluster‐randomized trials (CRTs) of infectious disease preventions often yield correlated, interval‐censored data: dependencies may exist between observations from the same cluster, and event occurrence may be assessed only at intermittent study visits. This data structure must be accounted for when conducting interim monitoring and futility assessment for CRTs. In this article, we propose a flexible

Many problems that appear in biomedical decision‐making, such as diagnosing disease and predicting response to treatment, can be expressed as binary classification problems. The support vector machine (SVM) is a popular classification technique that is robust to model misspecification and effectively handles high‐dimensional data. The relative costs of false positives and false negatives can vary across

With the rapid growth of neuroimaging technologies, a great effort has been dedicated recently to investigate the dynamic changes in brain activity. Examples include time course calcium imaging and dynamic brain functional connectivity. In this paper, we propose a novel nonparametric matrix response regression model to characterize the nonlinear association between 2D image outcomes and predictors

We consider a novel problem, bi‐level graphical modeling, in which multiple individual graphical models can be considered as variants of a common group‐level graphical model and inference of both the group‐ and individual‐level graphical models is of interest. Such a problem arises from many applications, including multi‐subject neuro‐imaging and genomics data analysis. We propose a novel and efficient

In the development of cancer treatment vaccines, phase II clinical studies are conducted to examine the efficacy of a vaccine in order to screen out vaccines with minimal activity. Immune responses are commonly used as the primary endpoint for assessing vaccine efficacy. With respect to study design, Simon's two‐stage design is a popular format for phase II cancer clinical studies because of its simplicity

We propose a new highly flexible and tractable Bayesian approach to undertake variable selection in non‐Gaussian regression models. It uses a copula decomposition for the joint distribution of observations on the dependent variable. This allows the marginal distribution of the dependent variable to be calibrated accurately using a nonparametric or other estimator. The family of copulas employed are

Brain functional connectivity reveals the synchronization of brain systems through correlations in neurophysiological measures of brain activities. Growing evidence now suggests that the brain connectivity network experiences alterations with the presence of numerous neurological disorders, thus differential brain network analysis may provide new insights into disease pathologies. The data from neurophysiological