Two popular approaches for relating correlated measurements of a non‐Gaussian response variable to a set of predictors are to fit a marginal model using generalized estimating equations and to fit a generalized linear mixed model (GLMM) by introducing latent random variables. The first approach is effective for parameter estimation, but leaves one without a formal model for the data with which to assess

Immunotherapy is the most promising new cancer treatment for various pediatric tumors and has resulted in an unprecedented surge in the number of novel immunotherapeutic treatments that need to be evaluated in clinical trials. Most phase I/II trial designs have been developed for evaluating only one candidate treatment at a time, and are thus not optimal for this task. To address these issues, we propose

Measurement error arises through a variety of mechanisms. A rich literature exists on the bias introduced by covariate measurement error and on methods of analysis to address this bias. By comparison, less attention has been given to errors in outcome assessment and nonclassical covariate measurement error. We consider an extension of the regression calibration method to settings with errors in a continuous

In an infectious disease cohort study, individuals who have been infected with a pathogen are often recruited for follow up. The period between infection and the onset of symptomatic disease, referred to as the incubation period, is of interest because of its importance on disease surveillance and control. However, the incubation period is often difficult to ascertain due to the uncertainty associated

Statistical models are often fitted to obtain a concise description of the association of an outcome variable with some covariates. Even if background knowledge is available to guide preselection of covariates, stepwise variable selection is commonly applied to remove irrelevant ones. This practice may introduce additional variability and selection is rarely certain. However, these issues are often

Family studies routinely employ biased sampling schemes in which individuals are randomly chosen from a disease registry and genetic and phenotypic data are obtained from their consenting relatives. We view this as a two‐phase study and propose the use of an efficient selection model for the recruitment of families to form a phase II sample subject to budgetary constraints. Simple random sampling,

We introduce a principled method for Bayesian subgroup analysis. The approach is based on casting subgroup analysis as a Bayesian decision problem. The two main innovations are: (1) the explicit consideration of a “subgroup report,” comprising multiple subpopulations; and (2) adapting an inhomogeneous Markov chain Monte Carlo simulation scheme to implement stochastic optimization. The latter makes

Multiomics or integrative omics data have been increasingly common in biomedical studies, holding a promise in better understanding human health and disease. In this article, we propose an integrative copula discrimination analysis classifier in the context of two‐class classification, which relaxes the common Gaussian assumption and gains power by borrowing information from multiple omics data types

Little has been published in terms of dose‐finding methodology in virology. Aside from a few papers focusing on HIV, the considerable progress in dose‐finding methodology of the last 25 years has focused almost entirely on oncology. While adverse reactions to cytotoxic drugs may be life threatening, for anti‐viral agents we anticipate something different: side effects that provoke the cessation of

In genetic association studies, mixed effects models have been widely used in detecting the pleiotropy effects which occur when one gene affects multiple phenotype traits. In particular, bivariate mixed effects models are useful for describing the association of a gene with a continuous trait and a binary trait. However, such models are inadequate to feature the data with response mismeasurement, a

The Fragility Index has been introduced as a complement to the P‐value to summarize the statistical strength of evidence for a trial's result. The Fragility Index (FI) is defined in trials with two equal treatment group sizes, with a dichotomous or time‐to‐event outcome, and is calculated as the minimum number of conversions from nonevent to event in the treatment group needed to shift the P‐value

Precision medicine incorporates patient‐level covariates to tailor treatment decisions, seeking to improve outcomes. In longitudinal studies with time‐varying covariates and sequential treatment decisions, precision medicine can be formalized with dynamic treatment regimes (DTRs): sequences of covariate‐dependent treatment rules. To date, the precision medicine literature has not addressed a ubiquitous

A biosimilar is a biological product that is highly similar to an existing approved reference drug and has no clinically meaningful difference from it. Biosimilars are composed of or derived from living cells or organisms. Therefore, they are often sensitive to slight variations in the manufacturing process. Consequently, in demonstrating biosimilarity, it might be inappropriate to focus solely on

Although all clinical trials are designed and monitored using more than one endpoint, methods are needed to assure that decision criteria are chosen to reflect the clinically relevant tradeoffs that assure the trial's scientific integrity. This article presents a framework for the design and monitoring clinical trials in a bivariate outcome space. The framework uses a rectangular hyperbola to define

Mesothelioma is a rare cancer caused by exposure to asbestos. Belgium has a known long history of asbestos production, resulting in one of the highest mesothelioma mortality rates worldwide. While the production of asbestos has stopped completely, the long latency period of mesothelioma, which can fluctuate between 20 and 40 years after exposure, causes incidences still to be frequent. Mesothelioma's

The relationship between association and surrogacy has been the focus of much debate in the surrogate marker literature. Recently, the individual causal association (ICA) has been introduced as a metric of surrogacy in the causal inference framework, when both the surrogate and the true endpoint are normally distributed and when both are binary. Earlier work on the normal case has demonstrated that

Restricted mean survival time (RMST) evaluates the mean event‐free survival time up to a prespecified time point. It has been used as an alternative measure of treatment effect owing to its model‐free structure and clinically meaningful interpretation of treatment benefit for right‐censored data. In clinical trials, another type of censoring called interval censoring may occur if subjects are examined

The extraordinary advancements in neuroscientific technology for brain recordings over the last decades have led to increasingly complex spatiotemporal data sets. To reduce oversimplifications, new models have been developed to be able to identify meaningful patterns and new insights within a highly demanding data environment. To this extent, we propose a new model called parameter clustering functional

This paper provides guidance for researchers with some mathematical background on the conduct of time‐to‐event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may

Treatment evaluation in advanced cancer mainly relies on overall survival and tumor size dynamics. Both markers and their association can be simultaneously analyzed by using joint models, and these approaches are supported by many softwares or packages. However, these approaches are essentially limited to linear models for the longitudinal part, which limit their biological interpretation. More biological

Many observational studies estimate causal effects using methods based on matching on the propensity score. Full matching on the propensity score is an effective and flexible method for utilizing all available data and for creating well‐balanced treatment and control groups. An important component of the full matching algorithm is the decision about whether to impose a restriction on the maximum ratio

Substantial advances in Bayesian methods for causal inference have been made in recent years. We provide an introduction to Bayesian inference for causal effects for practicing statisticians who have some familiarity with Bayesian models and would like an overview of what it can add to causal estimation in practical settings. In the paper, we demonstrate how priors can induce shrinkage and sparsity

Observational studies often benefit from an abundance of observational units. This can lead to studies that—while challenged by issues of internal validity—have inferences derived from sample sizes substantially larger than randomized controlled trials. But is the information provided by an observational unit best used in the analysis phase? We propose the use of a “pilot design,” in which observations

Two‐phase designs involve measuring extra variables on a subset of the cohort where some variables are already measured. The goal of two‐phase designs is to choose a subsample of individuals from the cohort and analyse that subsample efficiently. It is of interest to obtain an optimal design that gives the most efficient estimates of regression parameters. In this article, we propose a multiwave sampling

This article addresses the analysis of crossover designs with nonignorable dropout. We study nonreplicated crossover designs and replicated designs separately. With a primary objective of comparing the treatment mean effects, we jointly model the longitudinal measures and discrete time to dropout. We propose shared‐parameter models and mixed‐effects selection models. We adapt a linear‐mixed effects

In this article, we develop a so‐called profile likelihood ratio test (PLRT) based on the estimated error density for the multiple linear regression model. Unlike the existing likelihood ratio test (LRT), our proposed PLRT does not require any specification on the error distribution. The asymptotic properties are developed and the Wilks phenomenon is studied. Simulation studies are conducted to examine

Standard network meta‐analysis and indirect comparisons combine aggregate data from multiple studies on treatments of interest, assuming that any factors that interact with treatment effects (effect modifiers) are balanced across populations. Population adjustment methods such as multilevel network meta‐regression (ML‐NMR), matching‐adjusted indirect comparison (MAIC), and simulated treatment comparison

Research in oncology has changed the focus from histological properties of tumors in a specific organ to a specific genomic aberration potentially shared by multiple cancer types. This motivates the basket trial, which assesses the efficacy of treatment simultaneously on multiple cancer types that have a common aberration. Although the assumption of homogeneous treatment effects seems reasonable given

Multivariate count data are common in many disciplines. The variables in such data often exhibit complex positive or negative dependency structures. We propose three Bayesian approaches to modeling bivariate count data by simultaneously considering covariate‐dependent means and correlation. A direct approach utilizes a bivariate negative binomial probability mass function developed in Famoye (2010

Method comparison studies are concerned with estimating relationship between two clinical measurement methods. The methods often exhibit a structural change in the relationship over the measurement range. Ignoring this change would lead to an inaccurate estimate of the relationship. Motivated by a study of two digoxin assays where such a change occurs, this article develops a statistical methodology

Dynamic treatment regimes operationalize precision medicine as a sequence of decision rules, one per stage of clinical intervention, that map up‐to‐date patient information to a recommended intervention. An optimal treatment regime maximizes the mean utility when applied to the population of interest. Methods for estimating an optimal treatment regime assume the data to be fully observed, which rarely

An emulator is a fast‐to‐evaluate statistical approximation of a detailed mathematical model (simulator). When used in lieu of simulators, emulators can expedite tasks that require many repeated evaluations, such as sensitivity analyses, policy optimization, model calibration, and value‐of‐information analyses. Emulators are developed using the output of simulators at specific input values (design

In many statistical regression and prediction problems, it is reasonable to assume monotone relationships between certain predictor variables and the outcome. Genomic effects on phenotypes are, for instance, often assumed to be monotone. However, in some settings, it may be reasonable to assume a partially linear model, where some of the covariates can be assumed to have a linear effect. One example

The Cancer Registry of Norway has been administrating a national cervical cancer screening program since 1992 by coordinating triennial cytology exam screenings for the female population between 25 and 69 years of age. Up to 80% of cancers are prevented through mass screening, but this comes at the expense of considerable screening activity and leads to overtreatment of clinically asymptomatic precancers

Cluster randomization trials randomize groups (called clusters) of subjects (called subunits) between intervention arms, and observations are collected from each subject. In this case, subunits within each cluster share common frailties, so that the observations from subunits of each cluster tend to be correlated. Oftentimes, the outcome of a cluster randomization trial is a time‐to‐event endpoint

In many studies on the spatial risk of disease, investigators use geographic locations at the time of disease diagnosis in spatial models to search for individual areas of elevated risk. However, these studies often fail to find a significant spatial signal. This may be due to the misspecification of the timing and location of pertinent exposures. Environmental exposures related to cancer risk vary

Data augmentation has been commonly utilized to analyze correlated binary data using multivariate probit models in Bayesian analysis. However, the identification issue in the multivariate probit models necessitates a rigorous Metropolis‐Hastings algorithm for sampling a correlation matrix, which may cause slow convergence and inefficiency of Markov chains. It is well‐known that the parameter‐expanded

While the traditional clinical trial design lays emphasis on testing the treatment effect between randomly assigned groups, it ignores the role of patient preference for a particular treatment in the trial. Yet, for healthcare providers who seek to optimize the patient‐centered treatment strategy, the evaluation of a patient's psychology toward each treatment could be a key consideration. The two‐stage

We study explained variation under the additive hazards regression model for right‐censored data. We consider different approaches for developing such a measure, and focus on one that estimates the proportion of variation in the failure time explained by the covariates. We study the properties of the measure both analytically, and through extensive simulations. We apply the measure to a well‐known

We develop model‐assisted estimators for complex survey data for the proportion of a population that experienced some event by a specified time t. Theory for the new estimators uses time‐to‐event models as the underlying framework but have both good model‐based and design‐based properties. The estimators are compared in a simulation to traditional survey estimation methods and are also applied to a

Informative and accurate survival prediction with individualized dynamic risk profiles over time is critical for personalized disease prevention and clinical management. The massive genetic data, such as SNPs from genome‐wide association studies (GWAS), together with well‐characterized time‐to‐event phenotypes provide unprecedented opportunities for developing effective survival prediction models.

The probability of agreement has been used as an effective strategy for quantifying the similarity between the reliability of two populations. By contrast to hypothesis testing approaches based on P‐values, the probability of agreement provides a more realistic assessment of similarity by emphasizing practically important differences. In this article, we propose the use of the probability of agreement

This article proposes a novel adaptive design algorithm that can be used to find optimal treatment allocations in N‐of‐1 clinical trials. This new methodology uses two Laplace approximations to provide a computationally efficient estimate of population and individual random effects within a repeated measures, adaptive design framework. Given the efficiency of this approach, it is also adopted for treatment

Graphical modeling represents an established methodology for identifying complex dependencies in biological networks, as exemplified in the study of co‐expression, gene regulatory, and protein interaction networks. The available observations often exhibit an intrinsic heterogeneity, which impacts on the network structure through the modification of specific pathways for distinct groups, such as disease

The well‐known agreement interval by Bland and Altman is extensively applied in method comparison studies. Two clinical measurement methods are considered interchangeable if their differences are not clinically significant. The agreement interval is commonly applied to assess the spread of the differences. However, this interval is approximate (too narrow) and several authors propose calculating a

Although review papers on causal inference methods are now available, there is a lack of introductory overviews on what they can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline (“point exposure”) and the target outcome arises at a later time point. We first phrase relevant

Radiomics is an emerging field of medical image analysis research where quantitative measurements are obtained from radiological images that can be utilized to predict patient outcomes and inform treatment decisions. Cancer patients routinely undergo radiological evaluations when images of various modalities including computed tomography, positron emission tomography, and magnetic resonance images

In Section 3.2 of our paper 'Adjusted restricted mean survival times in observational studies', our notation for the pseudo‐observation model of the restricted mean survival time (RMST) was incorrect.1 We incorrectly indexed the pseudo‐observation on exposure group k, when we actually calculated the pseudo‐observations in the overall sample. The correct notation for the pseudo‐observation for individual

Randomized clinical trials are often designed to assess whether a test treatment prolongs survival relative to a control treatment. Increased patient heterogeneity, while desirable for generalizability of results, can weaken the ability of common statistical approaches to detect treatment differences, potentially hampering the regulatory approval of safe and efficacious therapies. A novel solution

Recently developed accelerometer devices have been used in large epidemiological studies for continuous and objective monitoring of physical activities. Typically, physical movements are summarized as minutes in light, moderate, and vigorous physical activities in each wearing day. Because of preponderance of zeros, zero‐inflated distributions have been used for modeling the daily moderate or higher

In medical diagnostic studies, verification of the true disease status might be partially missing based on results of diagnostic tests and other characteristics of subjects. Because estimates of area under the ROC curve (AUC) based on partially validated subjects are usually biased, it is usually necessary to estimate AUC from a bias‐corrected ROC curve. In this article, various direct estimation methods

A typical challenge facing the design and analysis of immuno‐oncology (IO) trials is the prevalence of nonproportional hazards (NPH) patterns manifested in Kaplan‐Meier curves under time‐to‐event endpoints. The NPH patterns would violate the proportional hazards assumption, and yet conventional design and analysis strategies often ignore such a violation, resulting in underpowered or even falsely negative

It has long been noticed that the efficacy observed in small early phase studies is generally better than that observed in later larger studies. Historically, the inflation of the efficacy results from early proof‐of‐concept studies is either ignored, or adjusted empirically using a frequentist or Bayesian approach. In this article, we systematically explained the underlying reason for the inflation

Statistical methods for identifying harmful chemicals in a correlated mixture often assume linearity in exposure‐response relationships. Nonmonotonic relationships are increasingly recognized (eg, for endocrine‐disrupting chemicals); however, the impact of nonmonotonicity on exposure selection has not been evaluated. In a simulation study, we assessed the performance of Bayesian kernel machine regression

Early detection of clinical outcomes such as cancer may be predicted using longitudinal biomarker measurements. Tracking longitudinal biomarkers as a way to identify early disease onset may help to reduce mortality from diseases like ovarian cancer that are more treatable if detected early. Two disease risk prediction frameworks, the shared random effects model (SREM) and the pattern mixture model

The continual reassessment method (CRM) is an adaptive design for Phase I trials whose operating characteristics, including appropriate sample size, probability of correctly identifying the maximum tolerated dose, and the expected proportion of participants assigned to each dose, can only be determined via simulation. The actual time to determine a final “best” design can take several hours or days

This article concerns with conditionally formulated multivariate Gaussian Markov random fields (MGMRF) for modeling multivariate local dependencies with unknown dependence parameters subject to positivity constraint. In the context of Bayesian hierarchical modeling of lattice data in general and Bayesian disease mapping in particular, analytic and simulation studies provide new insights into various

For meta‐analysis studies and systematic reviews, it is important to pool the data from a set of similar clinical trials. To pool the data, one needs to know their SD. Many trial reports, however, contain only the median, the minimum and maximum values, and the sample size. It is therefore important to be able to estimate the SD S from the sample size n and range r. For small n ≤ 100, we improve existing