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The advantages of using person location estimates from the Rasch model over raw scores for the measurement of change using a common test include the linearization of scores and the automatic handling of statistical properties of repeated measurements. However, the application of the model requires that the responses to the items are statistically independent in the sense that the specific responses to the items on the first time of testing do not affect the responses at a second time. This requirement implies that the responses to the items at both times of assessment are governed only by the invariant location parameters of the items at the two times of testing and the location parameters of each person each time. A specific form of dependence that is pertinent when the same items are used is when the observed response to an item at the second time of testing is affected by the response to the same item at the first time, a form of dependence which has been referred to as response dependence. This paper presents the logic of applying the Rasch model to quantify, control and remove the effect of response dependence in the measurement of change when the same items are used on two occasions. The logic is illustrated with four sets of simulation studies with dichotomous items and with a small example of real data. It is shown that the presence of response dependence can reduce the evidence of change, a reduction which may impact interpretations at the individual, research, and policy levels. [ABSTRACT FROM AUTHOR]

In an ultra-high dimensional setting with a huge number of covariates, variable screening is useful for dimension reduction before applying a more refined method for model selection and statistical analysis. This paper proposes a new sure joint screening procedure for right-censored time-to-event data based on a sparsity-restricted semiparametric accelerated failure time model. Our method, referred to as Buckley-James assisted sure screening (BJASS), consists of an initial screening step using a sparsity-restricted least-squares estimate based on a synthetic time variable and a refinement screening step using a sparsity-restricted least-squares estimate with the Buckley-James imputed event times. The refinement step may be repeated several times to obtain more stable results. We show that with any fixed number of refinement steps, the BJASS procedure retains all important variables with probability tending to 1. Simulation results are presented to illustrate its performance in comparison with some marginal screening methods. Real data examples are provided using a diffuse large-B-cell lymphoma (DLBCL) data and a breast cancer data. We have implemented the BJASS method using Matlab and made it available to readers through Github https://github.com/yiucla/BJASS . [ABSTRACT FROM AUTHOR]

There are considerable challenges in analyzing large-scale compositional data. In this paper, we introduce the Spike-and-Slab Lasso linear regression in the presence of compositional covariates for parameter estimation and variable selection. We consider the well-known isometric log-ratio (ilr) coordinates to avoid misleading statistical inference. The separable and non-separable (adaptative) Spike-and-Slab Lasso penalties are compared to verify the advantages of each approach. The proposed method is illustrated on simulated and on real Brazilian child malnutrition data. [ABSTRACT FROM AUTHOR]

Several researchers have described two-part models with patient-specific stochastic processes for analysing longitudinal semicontinuous data. In theory, such models can offer greater flexibility than the standard two-part model with patient-specific random effects. However, in practice, the high dimensional integrations involved in the marginal likelihood (i.e. integrated over the stochastic processes) significantly complicates model fitting. Thus, non-standard computationally intensive procedures based on simulating the marginal likelihood have so far only been proposed. In this paper, we describe an efficient method of implementation by demonstrating how the high dimensional integrations involved in the marginal likelihood can be computed efficiently. Specifically, by using a property of the multivariate normal distribution and the standard marginal cumulative distribution function identity, we transform the marginal likelihood so that the high dimensional integrations are contained in the cumulative distribution function of a multivariate normal distribution, which can then be efficiently evaluated. Hence, maximum likelihood estimation can be used to obtain parameter estimates and asymptotic standard errors (from the observed information matrix) of model parameters. We describe our proposed efficient implementation procedure for the standard two-part model parameterisation and when it is of interest to directly model the overall marginal mean. The methodology is applied on a psoriatic arthritis data set concerning functional disability. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including bivariate probit model for joint spatial analysis of areal-referenced binary data, MacNab explores identifiability issues, and tackling the problem of missing data in spatial analysis.

Model assessment is a standard component of statistical analysis, but it has received relatively little attention within the dynamic treatment regime literature. In this paper, we focus on the dynamic-weighted ordinary least squares approach to optimal dynamic treatment regime estimation, introducing how its double-robustness property may be leveraged for model assessment, and how quasilikelihood may be used for model selection. These ideas are demonstrated through simulation studies, as well as through application to data from the sequenced treatment alternatives to relieve depression study. [ABSTRACT FROM AUTHOR]

Longitudinal zero-inflated count data arise frequently in substance use research when assessing the effects of behavioral and pharmacological interventions. Zero-inflated count models (e.g. zero-inflated Poisson or zero-inflated negative binomial) with random effects have been developed to analyze this type of data. In random effects zero-inflated count models, the random effects covariance matrix is typically assumed to be homogeneous (constant across subjects). However, in many situations this matrix may be heterogeneous (differ by measured covariates). In this paper, we extend zero-inflated count models to account for random effects heterogeneity by modeling their variance as a function of covariates. We show via simulation that ignoring intervention and covariate-specific heterogeneity can produce biased estimates of covariate and random effect estimates. Moreover, those biased estimates can be rectified by correctly modeling the random effects covariance structure. The methodological development is motivated by and applied to the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) study, the largest clinical trial of alcohol dependence performed in United States with 1383 individuals. [ABSTRACT FROM AUTHOR]

We asked three leading researchers in the area of dynamic treatment regimes to share their stories on how they became interested in this topic and their perspectives on the most important opportunities and challenges for the future. [ABSTRACT FROM AUTHOR]

Pattern-mixture model (PMM)-based controlled imputations have become a popular tool to assess the sensitivity of primary analysis inference to different post-dropout assumptions or to estimate treatment effectiveness. The methodology is well established for continuous responses but less well established for binary responses. In this study, we formulate the copy-reference and jump-to-reference PMMs for longitudinal binary data using a multivariate probit model with latent variables. We discuss the maximum likelihood, Bayesian, and multiple imputation methods for estimating the treatment effect under the specified PMM. Simulation studies are conducted to evaluate the performance of these methods. These methods are also illustrated using data from a bipolar mania study. [ABSTRACT FROM AUTHOR]

Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform-i.e. gene expression- is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects. [ABSTRACT FROM AUTHOR]

The X-linked genetic association is overlooked in most of the genetic studies because of the complexity of X-chromosome inactivation process. In fact, the biological process of the gene at the same locus can vary across different subjects. Besides, the skewness of X-chromosome inactivation is inherently subject-specific (even tissue-specific within subjects) and cannot be accurately represented by a population-level parameter. To tackle this issue, a new model is proposed to incorporate the X-linked genetic association into right-censored survival data. The novel model can present that the X-linked genes on different subjects may go through different biological processes via a mixed distribution. Further, a random effect is employed to describe the uncertainty of the biological process for every subject. The proposed method can derive the probability for the escape of X-chromosome inactivation and derive the unbiased estimates of the model parameters. The Legendre-Gauss Quadrature method is used to approximate the integration over the random effect. Finite sample performance of this method is examined via extensive simulation studies. An application is illustrated with the implementation on a cancer genetic study with right-censored survival data. [ABSTRACT FROM AUTHOR]

Many biomedical and psychosocial studies involve population mixtures, which consist of multiple latent subpopulations. Because group membership cannot be observed, standard methods do not apply when differential treatment effects need to be studied across subgroups. We consider a two-group mixture in which membership of latent subgroups is determined by structural zeroes of a zero-inflated count variable and propose a new approach to model treatment differences between latent subgroups in a longitudinal setting. It has also been incorporated with the inverse probability weighted method to address data missingness. As the approach builds on the distribution-free functional response models, it requires no parametric distribution model and thereby provides a robust inference. We illustrate the approach with both real and simulated data. [ABSTRACT FROM AUTHOR]

A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting, we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research. [ABSTRACT FROM AUTHOR]

Subgroup identification with differential treatment effects serves as an important step towards precision medicine, as it provides evidence regarding how individuals with specific characteristics respond to a given treatment. This knowledge not only supports the tailoring of treatment strategies but also prompts the development of new treatments. This manuscript provides a brief overview of the issues associated with the methodologies aimed at identifying subgroups with differential treatment effects, and studies in depth the operational characteristics of five data-driven methods that have appeared recently in the literature. The performance of the methods under study to identify correctly the covariates affecting treatment effects is evaluated via simulation and under various conditions. Two clinical trial data sets are also used to illustrate the application of these methods. Discussion and recommendations pertaining to the use of these methods are provided, with emphasis on the relative performance of the methods under the conditions studied. [ABSTRACT FROM AUTHOR]

In discrete choice experiments, patients are presented with sets of health states described by various attributes and asked to make choices from among them. Discrete choice experiments allow health care researchers to study the preferences of individual patients by eliciting trade-offs between different aspects of health-related quality of life. However, many discrete choice experiments yield data with incomplete ranking information and sparsity due to the limited number of choice sets presented to each patient, making it challenging to estimate patient preferences. Moreover, methods to identify outliers in discrete choice data are lacking. We develop a Bayesian hierarchical random effects rank-ordered multinomial logit model for discrete choice data. Missing ranks are accounted for by marginalizing over all possible permutations of unranked alternatives to estimate individual patient preferences, which are modeled as a function of patient covariates. We provide a Bayesian version of relative attribute importance, and adapt the use of the conditional predictive ordinate to identify outlying choice sets and outlying individuals with unusual preferences compared to the population. The model is applied to data from a study using a discrete choice experiment to estimate individual patient preferences for health states related to prostate cancer treatment. [ABSTRACT FROM AUTHOR]

The joint modeling of mean and variance for longitudinal data is an active research area. This type of model has the advantage of accounting for heteroscedasticity commonly observed in between and within subject variations. Most of researches focus on improving the estimating efficiency but ignore many data features frequently encountered in practice. In this article, we develop a mixed-effects location scale joint model that concurrently accounts for longitudinal data with multiple features. Specifically, our joint model handles heterogeneity, skewness, limit of detection, measurement errors in covariates which are typically observed in the collection of longitudinal data from many studies. We employ a Bayesian approach for making inference on the joint model. The proposed model and method are applied to an AIDS study. Simulation studies are performed to assess the performance of the proposed method. Alternative models under different conditions are compared. [ABSTRACT FROM AUTHOR]

Heterogeneity in treatment efficacy is a major concern in clinical trials. Clustering may help to identify the treatment responders and the non-responders. In the context of longitudinal cluster analyses, sample size and variability of the times of measurements are the main issues with the current methods. Here, we propose a new two-step method for the Clustering of Longitudinal data by using an Extended Baseline. The first step relies on a piecewise linear mixed model for repeated measurements with a treatment-time interaction. The second step clusters the random predictions and considers several parametric (model-based) and non-parametric (partitioning, ascendant hierarchical clustering) algorithms. A simulation study compares all options of the clustering of longitudinal data by using an extended baseline method with the latent-class mixed model. The clustering of longitudinal data by using an extended baseline method with the two model-based algorithms was the more robust model. The clustering of longitudinal data by using an extended baseline method with all the non-parametric algorithms failed when there were unequal variances of treatment effect between clusters or when the subgroups had unbalanced sample sizes. The latent-class mixed model failed when the between-patients slope variability is high. Two real data sets on neurodegenerative disease and on obesity illustrate the clustering of longitudinal data by using an extended baseline method and show how clustering may help to identify the marker(s) of the treatment response. The application of the clustering of longitudinal data by using an extended baseline method in exploratory analysis as the first stage before setting up stratified designs can provide a better estimation of treatment effect in future clinical trials. [ABSTRACT FROM AUTHOR]

Under covariate adaptive randomization, the covariate is tied to both randomization and analysis. Misclassification of such covariate will impact the intended treatment assignment; further, it is unclear what the appropriate analysis strategy should be. We explore the impact of such misclassification on the trial’s statistical operating characteristics. Simulation scenarios were created based on the misclassification rate and the covariate effect on the outcome. Models including unadjusted, adjusted for the misclassified, or adjusted for the corrected covariate were compared using logistic regression for a binary outcome and Poisson regression for a count outcome. For the binary outcome using logistic regression, type I error can be maintained in the adjusted model, but the test is conservative using an unadjusted model. Power decreased with both increasing covariate effect on the outcome as well as the misclassification rate. Treatment effect estimates were biased towards the null for both the misclassified and unadjusted models. For the count outcome using a Poisson model, covariate misclassification led to inflated type I error probabilities and reduced power in the misclassified and the unadjusted model. The impact of covariate misclassification under covariate–adaptive randomization differs depending on the underlying distribution of the outcome. [ABSTRACT FROM AUTHOR]

We introduce an automated method of record linkage that has two key features, automated selection of match field interactions to include in the model for estimation and automated threshold determination for classifying record pairs to matches or non-matches. We applied our method to two real-world examples. The first example demonstrated results consistent with our earlier work: When data quality is adequate and the match field discriminating power is high, matching algorithms exhibit similar performance. The second example demonstrated that our method yields a lower false positive rate and higher positive predictive value than the Fellegi-Sunter model in the face of low data quality. When compared to the Fellegi-Sunter model, simulation studies suggest that our method exhibits better overall performance as indicated by higher area under the curve, and less biased estimates for both the match prevalence rate and the m- and u-probabilities over a range of data scenarios, especially when the match prevalence is extreme. Computationally, our method is as efficient as the Fellegi-Sunter model. We recommend this method in situations that an unsupervised linking algorithm is needed. [ABSTRACT FROM AUTHOR]

In most medical research, treatment effectiveness is assessed using the average treatment effect or some version of subgroup analysis. The practice of individualized or precision medicine, however, requires new approaches that predict how an individual will respond to treatment, rather than relying on aggregate measures of effect. In this study, we present a conceptual framework for estimating individual treatment effects, referred to as predicted individual treatment effects. We first apply the predicted individual treatment effect approach to a randomized controlled trial designed to improve behavioral and physical symptoms. Despite trivial average effects of the intervention, we show substantial heterogeneity in predicted individual treatment response using the predicted individual treatment effect approach. The predicted individual treatment effects can be used to predict individuals for whom the intervention may be most effective (or harmful). Next, we conduct a Monte Carlo simulation study to evaluate the accuracy of predicted individual treatment effects. We compare the performance of two methods used to obtain predictions: multiple imputation and non-parametric random decision trees. Results showed that, on average, both predictive methods produced accurate estimates at the individual level; however, the random decision trees tended to underestimate the predicted individual treatment effect for people at the extreme and showed more variability in predictions across repetitions compared to the imputation approach. Limitations and future directions are discussed. [ABSTRACT FROM AUTHOR]

The analysis of complex longitudinal data is challenging due to several inherent features: (i) more than one series of responses are repeatedly collected on each subject at irregularly occasions over a period of time; (ii) censorship due to limits of quantification of responses arises left- and/or right- censoring effects; (iii) outliers or heavy-tailed noises are possibly embodied within multiple response variables. This article formulates the multivariate- t linear mixed model with censored responses (MtLMMC), which allows the analysts to model such data in the presence of the above described features simultaneously. An efficient expectation conditional maximization either (ECME) algorithm is developed to carry out maximum likelihood estimation of model parameters. The implementation of the E-step relies on the mean and covariance matrix of truncated multivariate- t distributions. To enhance the computational efficiency, two auxiliary permutation matrices are incorporated into the procedure to determine the observed and censored parts of each subject. The proposed methodology is demonstrated via a simulation study and a real application on HIV/AIDS data. [ABSTRACT FROM AUTHOR]

Suppose we have a binary treatment used to influence an outcome. Given data from an observational or controlled study, we wish to determine whether or not there exists some subset of observed covariates in which the treatment is more effective than the standard practice of no treatment. Furthermore, we wish to quantify the improvement in population mean outcome that will be seen if this subgroup receives treatment and the rest of the population remains untreated. We show that this problem is surprisingly challenging given how often it is an (at least implicit) study objective. Blindly applying standard techniques fails to yield any apparent asymptotic results, while using existing techniques to confront the non-regularity does not necessarily help at distributions where there is no treatment effect. Here, we describe an approach to estimate the impact of treating the subgroup which benefits from treatment that is valid in a nonparametric model and is able to deal with the case where there is no treatment effect. The approach is a slight modification of an approach that recently appeared in the individualized medicine literature. [ABSTRACT FROM AUTHOR]

A dynamic treatment regime is a set of decision rules for how to treat a patient at multiple time points. At each time point, a treatment decision is made depending on the patient's medical history up to that point. We consider the infinite-horizon setting in which the number of decision points is very large. Specifically, we consider long trajectories of patients' measurements recorded over time. At each time point, the decision whether to intervene or not is conditional on whether or not there was a change in the patient's trajectory. We present change-point detection tools and show how to use them in defining dynamic treatment regimes. The performance of these regimes is assessed using an extensive simulation study. We demonstrate the utility of the proposed change-point detection approach using two case studies: detection of sepsis in preterm infants in the intensive care unit and detection of a change in glucose levels of a diabetic patient. [ABSTRACT FROM AUTHOR]

In comparative effectiveness studies of multicomponent, sequential interventions like blood product transfusion (plasma, platelets, red blood cells) for trauma and critical care patients, the timing and dynamics of treatment relative to the fragility of a patient's condition is often overlooked and underappreciated. While many hospitals have established massive transfusion protocols to ensure that physiologically optimal combinations of blood products are rapidly available, the period of time required to achieve a specified massive transfusion standard (e.g. a 1:1 or 1:2 ratio of plasma or platelets:red blood cells) has been ignored. To account for the time-varying characteristics of transfusions, we use semiparametric rate models for multivariate recurrent events to estimate blood product ratios. We use latent variables to account for multiple sources of informative censoring (early surgical or endovascular hemorrhage control procedures or death). The major advantage is that the distributions of latent variables and the dependence structure between the multivariate recurrent events and informative censoring need not be specified. Thus, our approach is robust to complex model assumptions. We establish asymptotic properties and evaluate finite sample performance through simulations, and apply the method to data from the PRospective Observational Multicenter Major Trauma Transfusion study. [ABSTRACT FROM AUTHOR]

One purpose of a longitudinal study is to gain a better understanding of how an outcome of interest changes among a given population over time. In what follows, a trajectory will be taken to mean the series of measurements of the outcome variable for an individual. Group-based trajectory modelling methods seek to identify subgroups of trajectories within a population, such that trajectories that are grouped together are more similar to each other than to trajectories in distinct groups. Group-based trajectory models generally assume a certain structure in the covariances between measurements, for example conditional independence, homogeneous variance between groups or stationary variance over time. Violations of these assumptions could be expected to result in poor model performance. We used simulation to investigate the effect of covariance misspecification on misclassification of trajectories in commonly used models under a range of scenarios. To do this we defined a measure of performance relative to the ideal Bayesian correct classification rate. We found that the more complex models generally performed better over a range of scenarios. In particular, incorrectly specified covariance matrices could significantly bias the results but using models with a correct but more complicated than necessary covariance matrix incurred little cost. [ABSTRACT FROM AUTHOR]

In health services research, it is common to encounter semicontinuous data, characterized by a point mass at zero followed by a right-skewed continuous distribution with positive support. Examples include health expenditures, in which the zeros represent a subpopulation of patients who do not use health services, while the continuous distribution describes the level of expenditures among health services users. Longitudinal semicontinuous data are typically analyzed using two-part random-effect mixtures with one component that models the probability of health services use, and a second component that models the distribution of log-scale positive expenditures among users. However, because the second part conditions on a non-zero response, obtaining interpretable effects of covariates on the combined population of health services users and non-users is not straightforward, even though this is often of greatest interest to investigators. Here, we propose a marginalized two-part model for longitudinal data that allows investigators to obtain the effect of covariates on the overall population mean. The model additionally provides estimates of the overall population mean on the original, untransformed scale, and many covariates take a dual population average and subject-specific interpretation. Using a Bayesian estimation approach, this model maintains the flexibility to include complex random-effect structures and easily estimate functions of the overall mean. We illustrate this approach by evaluating the effect of a copayment increase on health care expenditures in the Veterans Affairs health care system over a four-year period. [ABSTRACT FROM AUTHOR]

Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry. [ABSTRACT FROM AUTHOR]

Hierarchical models such as the bivariate and hierarchical summary receiver operating characteristic (HSROC) models are recommended for meta-analysis of test accuracy studies. These models are challenging to fit when there are few studies and/or sparse data (for example zero cells in contingency tables due to studies reporting 100% sensitivity or specificity); the models may not converge, or give unreliable parameter estimates. Using simulation, we investigated the performance of seven hierarchical models incorporating increasing simplifications in scenarios designed to replicate realistic situations for meta-analysis of test accuracy studies. Performance of the models was assessed in terms of estimability (percentage of meta-analyses that successfully converged and percentage where the between study correlation was estimable), bias, mean square error and coverage of the 95% confidence intervals. Our results indicate that simpler hierarchical models are valid in situations with few studies or sparse data. For synthesis of sensitivity and specificity, univariate random effects logistic regression models are appropriate when a bivariate model cannot be fitted. Alternatively, an HSROC model that assumes a symmetric SROC curve (by excluding the shape parameter) can be used if the HSROC model is the chosen meta-analytic approach. In the absence of heterogeneity, fixed effect equivalent of the models can be applied. [ABSTRACT FROM AUTHOR]

We propose a cause-specific quantile residual life regression where the cause-specific quantile residual life, defined as the inverse of the cumulative incidence function of the residual life distribution of a specific type of events of interest conditional on a fixed time point, is log-linear in observable covariates. The proposed test statistic for the effects of prognostic factors does not involve estimation of the improper probability density function of the cause-specific residual life distribution under competing risks. The asymptotic distribution of the test statistic is derived. Simulation studies are performed to assess the finite sample properties of the proposed estimating equation and the test statistic. The proposed method is illustrated with a real dataset from a clinical trial on breast cancer. [ABSTRACT FROM AUTHOR]

Outlier detection covers the wide range of methods aiming at identifying observations that are considered unusual. Novelty detection, on the other hand, seeks observations among newly generated test data that are exceptional compared with previously observed training data. In many applications, the general existence of novelty is of more interest than identifying the individual novel observations. For instance, in high-throughput cancer treatment screening experiments, it is meaningful to test whether any new treatment effects are seen compared with existing compounds. Here, we present hypothesis tests for such global level novelty. The problem is approached through a set of very general assumptions, making it innovative in relation to the current literature. We introduce test statistics capable of detecting novelty. They operate on local neighborhoods and their null distribution is obtained by the permutation principle. We show that they are valid and able to find different types of novelty, e.g. location and scale alternatives. The performance of the methods is assessed with simulations and with applications to real data sets. [ABSTRACT FROM AUTHOR]

Although covariate measurement error is likely the norm rather than the exception, methods for handling covariate measurement error in propensity score methods have not been widely investigated. We consider a multiple imputation-based approach that uses an external calibration sample with information on the true and mismeasured covariates, multiple imputation for external calibration, to correct for the measurement error, and investigate its performance using simulation studies. As expected, using the covariate measured with error leads to bias in the treatment effect estimate. In contrast, the multiple imputation for external calibration method can eliminate almost all the bias. We confirm that the outcome must be used in the imputation process to obtain good results, a finding related to the idea of congenial imputation and analysis in the broader multiple imputation literature. We illustrate the multiple imputation for external calibration approach using a motivating example estimating the effects of living in a disadvantaged neighborhood on mental health and substance use outcomes among adolescents. These results show that estimating the propensity score using covariates measured with error leads to biased estimates of treatment effects, but when a calibration data set is available, multiple imputation for external calibration can be used to help correct for such bias. [ABSTRACT FROM AUTHOR]

Overdispersion is a common problem in count data. It can occur due to extra population-heterogeneity, omission of key predictors, and outliers. Unless properly handled, this can lead to invalid inference. Our goal is to assess the differential performance of methods for dealing with overdispersion from several sources. We considered six different approaches: unadjusted Poisson regression (Poisson), deviance-scale-adjusted Poisson regression (DS-Poisson), Pearson-scale-adjusted Poisson regression (PS-Poisson), negative-binomial regression (NB), and two generalized linear mixed models (GLMM) with random intercept, log-link and Poisson (Poisson-GLMM) and negative-binomial (NB-GLMM) distributions. To rank order the preference of the models, we used Akaike's information criteria/Bayesian information criteria values, standard error, and 95% confidence-interval coverage of the parameter values. To compare these methods, we used simulated count data with overdispersion of different magnitude from three different sources. Mean of the count response was associated with three predictors. Data from two real-case studies are also analyzed. The simulation results showed that NB and NB-GLMM were preferred for dealing with overdispersion resulting from any of the sources we considered. Poisson and DS-Poisson often produced smaller standard-error estimates than expected, while PS-Poisson conversely produced larger standard-error estimates. Thus, it is good practice to compare several model options to determine the best method of modeling count data. [ABSTRACT FROM AUTHOR]

Dropout is a common problem in longitudinal cohort studies and clinical trials, often raising concerns of nonignorable dropout. Selection, frailty, and mixture models have been proposed to account for potentially nonignorable missingness by relating the longitudinal outcome to time of dropout. In addition, many longitudinal studies encounter multiple types of missing data or reasons for dropout, such as loss to follow-up, disease progression, treatment modifications and death. When clinically distinct dropout reasons are present, it may be preferable to control for both dropout reason and time to gain additional clinical insights. This may be especially interesting when the dropout reason and dropout times differ by the primary exposure variable. We extend a semi-parametric varying-coefficient method for nonignorable dropout to accommodate dropout reason. We apply our method to untreated HIV-infected subjects recruited to the Acute Infection and Early Disease Research Program HIV cohort and compare longitudinal CD4+ T cell count in injection drug users to nonusers with two dropout reasons: anti-retroviral treatment initiation and loss to follow-up. [ABSTRACT FROM AUTHOR]

An alternative to the standard mixture model is proposed for modeling data containing cured elements or a cure fraction. This approach is based on the use of defective distributions to estimate the cure fraction as a function of the estimated parameters. In the literature there are just two of these distributions: the Gompertz and the inverse Gaussian. Here, we propose two new defective distributions: the Kumaraswamy Gompertz and Kumaraswamy inverse Gaussian distributions, extensions of the Gompertz and inverse Gaussian distributions under the Kumaraswamy family of distributions. We show in fact that if a distribution is defective, then its extension under the Kumaraswamy family is defective too. We consider maximum likelihood estimation of the extensions and check its finite sample performance. We use three real cancer data sets to show that the new defective distributions offer better fits than baseline distributions. [ABSTRACT FROM AUTHOR]

Generalized linear mixed models for longitudinal data assume that responses at different occasions are conditionally independent, given the random effects and covariates. Although this assumption is pivotal for consistent estimation, violation due to serial dependence is hard to assess by model elaboration. We therefore propose a targeted diagnostic test for serial dependence, called the transition model test (TMT), that is straightforward and computationally efficient to implement in standard software. The TMT is shown to have larger power than general misspecification tests. We also propose the targeted root mean squared error of approximation (TRSMEA) as a measure of the population misfit due to serial dependence. [ABSTRACT FROM AUTHOR]