Two‐phase outcome‐dependent sampling (ODS) designs are useful when resource constraints prohibit expensive exposure ascertainment on all study subjects. One class of ODS designs for longitudinal binary data stratifies subjects into three strata according to those who experience the event at none, some, or all follow‐up times. For time‐varying covariate effects, exclusively selecting subjects with response

In longitudinal studies, repeated measures are collected over time and hence they tend to be serially correlated. These studies are commonly analyzed using linear mixed models (LMMs), and in this article we consider an extension of the skew‐normal/independent LMM, where the error term has a dependence structure, such as damped exponential correlation or autoregressive correlation of order p. The proposed

In practice, stepped wedge trials frequently include clusters of differing sizes. However, investigations into the theoretical aspects of stepped wedge designs have, until recently, typically assumed equal numbers of subjects in each cluster and in each period. The information content of the cluster‐period cells, clusters, and periods of stepped wedge designs has previously been investigated assuming

Multiple correlated traits/phenotypes are often collected in genetic association studies and they may share a common genetic mechanism. Joint analysis of correlated phenotypes has well‐known advantages over one‐at‐a‐time analysis including gain in power and better understanding of genetic etiology. However, when the phenotypes are of discordant types such as binary and continuous, the joint modeling

Optimal prevention and treatment strategies for a disease of multiple causes, such as pneumonia, must be informed by the population distribution of causes among cases, or cause‐specific case fractions (CSCFs). CSCFs may further depend on additional explanatory variables. Existing methodological literature in disease etiology research does not fully address the regression problem, particularly under

Chance imbalance in baseline characteristics is common in randomized clinical trials. Regression adjustment such as the analysis of covariance (ANCOVA) is often used to account for imbalance and increase precision of the treatment effect estimate. An objective alternative is through inverse probability weighting (IPW) of the propensity scores. Although IPW and ANCOVA are asymptotically equivalent,

In 2019 we published a pair of articles in Statistics in Medicine that describe how to calculate the minimum sample size for developing a multivariable prediction model with a continuous outcome, or with a binary or time‐to‐event outcome. As for any sample size calculation, the approach requires the user to specify anticipated values for key parameters. In particular, for a prediction model with a

The correct identification of change‐points during ongoing outbreak investigations of infectious diseases is a matter of paramount importance in epidemiology, with major implications for the management of health care resources, public health and, as the COVID‐19 pandemic has shown, social live. Onsets, peaks, and inflexion points are some of them. An onset is the moment when the epidemic starts. A

The microbiome plays an important role in human health by mediating the path from environmental exposures to health outcomes. The relative abundances of the high‐dimensional microbiome data have an unit‐sum restriction, rendering standard statistical methods in the Euclidean space invalid. To address this problem, we use the isometric log‐ratio transformations of the relative abundances as the mediator

In this article, we present a new variable selection method for regression and classification purposes, particularly for microbiome analysis. Our method, called subsampling ranking forward selection (SuRF), is based on LASSO penalized regression, subsampling and forward‐selection methods. SuRF offers major advantages over existing variable selection methods in terms of both sparsity of selected models

Medical imaging data have been widely used in modern health care, particularly in the prognosis, screening, diagnosis, and treatment of various diseases. In this study, we consider a latent factor‐on‐image (LoI) regression model that regresses a latent factor on ultrahigh dimensional imaging covariates. The latent factor is characterized by multiple manifest variables through a factor analysis model

A popular method for simultaneously modeling of correlated count response with excess zeros and time to event is by means of the joint models. In these models, the likelihood‐based methods (such as expectation‐maximization algorithm and Newton‐Raphson) are used for estimating the parameters, but in the presence of contaminations, these methods are unstable. To overcome this challenge, we extend the

A cross sectional population is defined as a population of living individuals at the sampling or observational time. Cross‐sectionally sampled data with binary disease outcome are commonly analyzed in observational studies for identifying how covariates correlate with disease occurrence. It is generally understood that cross‐sectional binary outcome is not as informative as longitudinally collected

Clinical trials studying treatments for rare diseases are challenging to design and conduct due to the limited number of patients eligible for the trial. One design used to address this challenge is the small n, sequential, multiple assignment, randomized trial (snSMART). We propose a new snSMART design that investigates the response rates of a drug tested at a low and high dose compared with placebo

In this article, we present a Bayesian framework for multivariate longitudinal data analysis with a focus on selection of important elements in the generalized autoregressive matrix. An efficient Gibbs sampling algorithm was developed for the proposed model and its implementation in a comprehensive R package called MLModelSelection is available on the comprehensive R archive network. The performance

For a continuous time‐to‐event outcome and an expensive‐to‐measure exposure, we develop a pooling design and propose a likelihood‐based approach to estimate the hazard ratios (HRs) of a Cox proportional hazards (PH) model. Our proposed approach fits a PH model based on pooled exposures with individually observed time‐to‐event outcomes. The design and estimation exploits the equivalence of the conditional

Data used to estimate the burden of diseases (BOD) are usually sparse, noisy, and heterogeneous. These data are collected from surveys, registries, and systematic reviews that have different areal units, are conducted at different times, and are reported for different age groups. In this study, we developed a Bayesian geo‐statistical model to combine aggregated sparse, noisy BOD data from different

This article concerns evaluating the effectiveness of a continuous diagnostic biomarker against a continuous gold standard that is measured with error. Extending the work of Obuchowski (2005, 2016), Wu et al (2016) suggested an accuracy index and proposed an estimator for the index with error‐prone standard when the reliability coefficient is known. Combining with additional measurements (without measurement

Statistical methods are well developed for estimating the area under the receiver operating characteristic curve (AUC) based on a random sample where the gold standard is available for every subject in the sample, or a two‐phase sample where the gold standard is ascertained only at the second phase for a subset of subjects sampled using fixed sampling probabilities. However, the methods based on a

As a future trend of healthcare, personalized medicine tailors medical treatments to individual patients. It requires to identify a subset of patients with the best response to treatment. The subset can be defined by a biomarker (eg, expression of a gene) and its cutoff value. Topics on subset identification have received massive attention. There are over two million hits by keyword searches on Google

In this article, we present a new methodology to model patient transitions and length of stay in the emergency department using a series of conditional Coxian phase‐type distributions, with covariates. We reformulate the Coxian models (standard Coxian, Coxian with multiple absorbing states, joint Coxian, and conditional Coxian) to take into account heterogeneity in patient characteristics such as arrival

A frequent problem in longitudinal studies is that data may be assessed at subject‐selected, irregularly spaced time‐points, resulting in highly unbalanced outcome data, inducing bias, especially if availability of data is directly related to outcome. Our aim was to develop a multivariate joint model in a mixed outcomes framework to minimize irregular sampling bias. We demonstrate using blood glucose

Functional data are commonly observed in many emerging biomedical fields and their analysis is an exciting developing area in statistics. Numerous statistical methods, such as principal components, analysis of variance, and linear regression, have been extended to functional data. The statistical analysis of functions can be significantly improved using nonparametric and robust estimators. New ideas

In time‐to‐event analysis, the traditional summary measures have been based on the hazard function, survival function, quantile event time, restricted mean event time, and residual lifetime. Under competing risks, furthermore, typical summary measures have been the cause‐specific hazard function and cumulative incidence function. Recently inactivity time has recaptured attention in the literature,

The inverse probability weighted Cox model is frequently used to estimate the marginal hazard ratio. Its validity requires a crucial condition that the propensity score model be correctly specified. To provide protection against misspecification of the propensity score model, we propose a weighted estimation method rooted in the empirical likelihood theory. The proposed estimator is multiply robust

The prioritized net benefit (Δ) is a measure of the benefit‐risk balance in clinical trials, based on generalized pairwise comparisons (GPC) using several prioritized outcomes. Its estimation requires the classification as Wins or Losses of all possible pairs of patients, one from the experimental treatment (E) group and one from the control treatment (C) group. In this simulation study, we assessed

We propose a latent linear mixed model to analyze multivariate longitudinal data of multiple ordinal variables, which are manifestations of fewer continuous latent variables. We focus on the latent level where the effects of observed covariates on the latent variables are of interest. We incorporate serial correlation into the variance component rather than assuming independent residuals. We show that

Commercialized multigene panel testing brings unprecedented opportunities to understand germline genetic contributions to hereditary cancers. Most genetic testing companies classify the pathogenicity of variants as pathogenic, benign, or variants of unknown significance (VUSs). The unknown pathogenicity of VUSs poses serious challenges to clinical decision‐making. This study aims to assess the frequency

Inferring the causal effect of a treatment on an outcome in an observational study requires adjusting for observed baseline confounders to avoid bias. However, adjusting for all observed baseline covariates, when only a subset are confounders of the effect of interest, is known to yield potentially inefficient and unstable estimators of the treatment effect. Furthermore, it raises the risk of finite‐sample

Medical studies that depend on electronic health records (EHR) data are often subject to measurement error, as the data are not collected to support research questions under study. These data errors, if not accounted for in study analyses, can obscure or cause spurious associations between patient exposures and disease risk. Methodology to address covariate measurement error has been well developed;

Patients' longitudinal biomarker changing patterns are crucial factors for their disease progression. In this research, we apply functional principal component analysis techniques to extract these changing patterns and use them as predictors in landmark models for dynamic prediction. The time‐varying effects of risk factors along a sequence of landmark times are smoothed by a supermodel to borrow information

In this article, we introduce the recently developed intrinsic estimator method in the age‐period‐cohort (APC) models in examining disease incidence and mortality data, further develop a likelihood ratio (L‐R) test for testing differences in temporal trends across populations, and apply the methods to examining temporal trends in the age, period or calendar time, and birth cohort of the US heart disease

When planning a Phase III clinical trial, suppose a certain subset of patients is expected to respond particularly well to the new treatment. Adaptive enrichment designs make use of interim data in selecting the target population for the remainder of the trial, either continuing with the full population or restricting recruitment to the subset of patients. We define a multiple testing procedure that

In longitudinal studies, the values of biomarkers are often informatively missing due to dropout. The conventional functional principal component analysis typically disregards the missing information and simply treats the unobserved data points as missing completely at random. As a result, the estimation of the mean function and the covariance surface might be biased, resulting in a biased estimation

In modern observational studies using electronic health records or other routinely collected data, both the outcome and covariates of interest can be error‐prone and their errors often correlated. A cost‐effective solution is the two‐phase design, under which the error‐prone outcome and covariates are observed for all subjects during the first phase and that information is used to select a validation

In the analysis of censored survival data, to avoid a biased inference of treatment effects on the hazard function of the survival time, it is important to consider the treatment heterogeneity. Without requiring any prior knowledge about the subgroup structure, we propose a data driven subgroup analysis procedure for the heterogeneous Cox model by constructing a pairwise fusion penalized partial likelihood‐based

Maximum approximate Bernstein likelihood estimates of the baseline density function and the regression coefficients in the proportional hazard regression models based on interval‐censored event time data result in smooth estimates of the survival functions which enjoys an almost n1/2‐rate of convergence faster than the n1/3‐rate for the existing estimates. The proposed method was shown by a simulation

Biomarkers of interest in urine, serum, or other biological matrices often have an assay limit of detection. When concentration levels of the biomarkers for some subjects fall below the limit, the measures for those subjects are censored. Censored data due to detection limits are very common in public health and medical research. If censored data from a single exposure group follow a normal distribution

The joinpoint regression model (JRM) is used to describe trend changes in many applications and relies on the detection of joinpoints (changepoints). However, the existing joinpoint detection methods, namely, the grid search (GS)‐based methods, are computationally demanding, and hence, the maximum number of computable joinpoints is limited. Herein, we developed a genetic algorithm‐based joinpoint (GAJP)

Statistical methods for analysis of survey data have been developed to facilitate research. More recently, Lumley and Scott (2013) developed an approach to extend the Mann‐Whitney‐Wilcoxon (MWW) rank sum test to survey data. Their approach focuses on the null of equal distribution. In many studies, the MWW test is called for when two‐sample t‐tests (with or without equal variance assumed) fail to provide

Confounding can cause substantial bias in nonexperimental studies that aim to estimate causal effects. Propensity score methods allow researchers to reduce bias from measured confounding by summarizing the distributions of many measured confounders in a single score based on the probability of receiving treatment. This score can then be used to mitigate imbalances in the distributions of these measured

Randomly censored survival data are frequently encountered in biomedical or reliability applications and clinical trial analyses. Testing the significance of statistical hypotheses is crucial in such analyses to get conclusive inference but the existing likelihood‐based tests, under a fully parametric model, are extremely nonrobust against outliers in the data. Although there exists a few robust estimators

Meta‐analysis of individual patient data (IPD) is increasingly used to synthesize data from multiple trials. IPD meta‐analysis offers several advantages over meta‐analyzing aggregate data, including the capacity to individualize treatment recommendations. Trials usually collect information on many patient characteristics. Some of these covariates may strongly interact with treatment (and thus be associated

An increasing number of genome‐wide association studies (GWAS) summary statistics is made available to the scientific community. Exploiting these results from multiple phenotypes would permit identification of novel pleiotropic associations. In addition, incorporating prior biological information in GWAS such as group structure information (gene or pathway) has shown some success in classical GWAS

Dental caries (i.e., cavities) is one of the most common chronic childhood diseases and may continue to progress throughout a person's lifetime. The Iowa Fluoride Study (IFS) was designed to investigate the effects of various fluoride, dietary and nondietary factors on the progression of dental caries among a cohort of Iowa school children. We develop a mixed effects model to perform a comprehensive

Two‐way contingency tables arise in many fields, such as in medical studies, where the relation between two discrete random variables or responses is to be assessed. We propose to analyze and visualize a sample of 2 × 2 tables in the context of single‐subject repeated measurements design by means of compositional data (CoDa) methods. First, we propose to visualize the tables in a quaternary diagram

In analyzing repeated measurements from randomized controlled trials with mixed‐effects models, it is important to carefully examine the conventional normality assumption regarding the random‐effects distribution and its dependence on treatment allocation in order to avoid biased estimation and correctly interpret the estimated random‐effects distribution. In this article, we propose the use of a gradient

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,

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

The Cox proportional hazards model is typically used to analyze time‐to‐event data. If the event of interest is rare and covariates are difficult or expensive to collect, the nested case‐control (NCC) design provides consistent estimates at reduced costs with minimal impact on precision if the model is specified correctly. If our scientific goal is to conduct inference regarding an association of interest

Small sample, sequential, multiple assignment, randomized trials (snSMARTs) are multistage trials with the overall goal of determining the best treatment after a fixed amount of time. In snSMART trials, patients are first randomized to one of three treatments and a binary (e.g. response/nonresponse) outcome is measured at the end of the first stage. Responders to first stage treatment continue their

When a new treatment regimen is expected to have comparable or slightly worse efficacy to that of the control regimen but has benefits in other domains such as safety and tolerability, a noninferiority (NI) trial may be appropriate but is fraught with difficulty in justifying an acceptable NI margin that is based on both clinical and statistical input. To overcome this, we propose to utilize composite

Analyses of trials of group administered treatments require an identifier for therapy group to account for clustering by group. All patients randomized to receive the group administered treatment could be assigned an intended group identifier following randomization. Alternatively, an actual group could be based on those patients that comply with group therapy. We investigate the implications for intention‐to‐treat

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

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

Meta‐analyses of a treatment's effect compared with a control frequently calculate the meta‐effect from standardized mean differences (SMDs). SMDs are usually estimated by Cohen's d or Hedges' g. Cohen's d divides the difference between sample means of a continuous response by the pooled standard deviation, but is subject to nonnegligible bias for small sample sizes. Hedges' g removes this bias with

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

For massive survival data, we propose a subsampling algorithm to efficiently approximate the estimates of regression parameters in the additive hazards model. We establish consistency and asymptotic normality of the subsample‐based estimator given the full data. The optimal subsampling probabilities are obtained via minimizing asymptotic variance of the resulting estimator. The subsample‐based procedure

When interpreting the relative effects from a network meta‐analysis (NMA), researchers are usually aware of the potential limitations that may render the results for some comparisons less useful or meaningless. In the presence of sufficient and appropriate data, some of these limitations (eg, risk of bias, small‐study effects, publication bias) can be taken into account in the statistical analysis