Search

Comparative Poisson trials of an experimental treatment versus a control typically condition on the total number of events that occur across both arms (Design A). Inference is based on the binomial distribution. Recently, an approach termed Design C to compare K experimental treatments to the same control was introduced. Under Design C without curtailment, the trial continues until a prespecified number of events occur in the control arm, leading to inference based on the negative multinomial distribution. The question remains of how advantageous it is to conduct one Design C trial comparing K experimental treatment arms to the same control arm as opposed to conducting K separate Design A trials each comparing one experimental treatment arm to a different control arm. This paper, therefore, compares the expected number of subjects to enroll for the two designs under uncurtailed and curtailed settings. The designs are evaluated when the null hypothesis and various assumptions for the alternative hypothesis hold. We simulate a variety of combinations for the Type 1 error, power, and ratio of the incidence rate of events in the experimental treatment to control arms. Design C frequently offers significant savings in terms of sample size relative to Design A. [ABSTRACT FROM AUTHOR]

This paper examines whether there are disparities in environmental enforcement. It contributes to the extant literature by examining if the political representation of Latinos influences levels of inspections for Hispanic populations. Using data from the Integrated Database for Enforcement Analysis (IDEA), the U.S. Census Bureau, and the National Association of Latino Elected and Appointed Officials (NALEO), this paper examines patterns of state enforcement of the Resource Conservation and Recovery Act (RCRA) nationwide from 2005 to 2011 to determine if there are disparities in levels of inspections. Using zero-inflated negative binomial models, results indicate minimal evidence of ethnic or class-based disparities in enforcement of the RCRA during this time period. However, there is evidence indicating weaker enforcement efforts in African American counties. [ABSTRACT FROM AUTHOR]

Longitudinal data are often collected in biomedical applications in such a way that measurements on more than one response are taken from a given subject repeatedly overtime. For some problems, these multiple profiles need to be modeled jointly to get insight on the joint evolution and/or association of these responses over time. In practice, such longitudinal outcomes may have many zeros that need to be accounted for in the analysis. For example, in dietary intake studies, as we focus on in this paper, some food components are eaten daily by almost all subjects, while others are consumed episodically, where individuals have time periods where they do not eat these components followed by periods where they do. These episodically consumed foods need to be adequately modeled to account for the many zeros that are encountered. In this paper, we propose a joint model to analyze multivariate hierarchical semicontinuous data characterized by many zeros and more than one replicate observations at each measurement occasion. This approach allows for different probability mechanisms for describing the zero behavior as compared with the mean intake given that the individual consumes the food. To deal with the potentially large number of multivariate profiles, we use a pairwise model fitting approach that was developed in the context of multivariate Gaussian random effects models with large number of multivariate components. The novelty of the proposed approach is that it incorporates: (1) multivariate, possibly correlated, response variables; (2) within subject correlation resulting from repeated measurements taken from each subject; (3) many zero observations; (4) overdispersion; and (5) replicate measurements at each visit time. [ABSTRACT FROM AUTHOR]

This paper investigates multiple imputation methods for regression models with interacting continuous and binary predictors when continuous variable may be missing. Usual implementations for parametric multiple imputation assume a multivariate normal structure for the variables, which is not satisfied for a binary variable nor its interaction with a continuous variable. To accommodate interactions, missing covariates are multiply imputed from conditional distribution in a manner consistent with the joint model. Alternative imputation methods under multivariate normal assumptions are also considered as candidate approximations and evaluated in a simulation study. The results suggest that the joint modeling procedure performs generally well across a wide range of scenarios and so do the approximation methods that incorporate interactions in the model appropriately by stratification. It is critical to include interactions in the imputation model as failure to do so may result in low coverage and bias. We apply the joint modeling approach and approximation methods in the study of childhood trauma with gender × trauma interaction. [ABSTRACT FROM AUTHOR]

Changes in cognitive function over time are of interest in ageing research. A joint model is constructed to investigate. Generally, cognitive function is measured through more than one test, and the test scores are integers. The aim is to investigate two test scores and use an extension of a bivariate binomial distribution to define a new joint model. This bivariate distribution model the correlation between the two test scores. To deal with attrition due to death, the Weibull hazard model and the Gompertz hazard model are used. A shared random-effects model is constructed, and the random effects are assumed to follow a bivariate normal distribution. It is shown how to incorporate random effects that link the bivariate longitudinal model and the survival model. The joint model is applied to the English Longitudinal Study of Ageing data. [ABSTRACT FROM AUTHOR]

Clinical trials often evaluate multiple outcome variables to form a comprehensive picture of the effects of a new treatment. The resulting multidimensional insight contributes to clinically relevant and efficient decision-making about treatment superiority. Common statistical procedures to make these superiority decisions with multiple outcomes have two important shortcomings, however: (1) Outcome variables are often modeled individually, and consequently fail to consider the relation between outcomes; and (2) superiority is often defined as a relevant difference on a single, on any, or on all outcome(s); and lacks a compensatory mechanism that allows large positive effects on one or multiple outcome(s) to outweigh small negative effects on other outcomes. To address these shortcomings, this paper proposes (1) a Bayesian model for the analysis of correlated binary outcomes based on the multivariate Bernoulli distribution; and (2) a flexible decision criterion with a compensatory mechanism that captures the relative importance of the outcomes. A simulation study demonstrates that efficient and unbiased decisions can be made while Type I error rates are properly controlled. The performance of the framework is illustrated for (1) fixed, group sequential, and adaptive designs; and (2) non-informative and informative prior distributions. [ABSTRACT FROM AUTHOR]

When establishing an effective treatment with binary data in a two-stage design, one-sided tests for a proportion p are employed. Researchers use the parameter configuration at the boundary of the null hypothesis space to determine a rejection region and an optimal design. However, it is unclear whether the (maximum) Type I error rate is achieved at the boundary especially when the sample size in stage 2 varies. In this paper, we first prove that this is true for a large family of tests in adaptive two-stage designs by showing that any test in the family has a nondecreasing power in p and then derive optimal designs. Second, similar results are established for m-stage designs with m > 2. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

In this paper, a new discrete statistical model for ordered categorical data is proposed via fixed-point discretization of a beta latent variable. The resulting discretized beta distribution has a highly flexible shape and it can be either over-dispersed or under-dispersed with respect to the binomial distribution. It has only two parameters, which may therefore parsimoniously depend on covariates and on random effects, providing new tools for the analysis of structured, clustered or longitudinal ordinal data. Practical examples and advices are given and an application of the new model to subjective evaluations of a gastrointestinal disease is shown. [ABSTRACT FROM AUTHOR]

To detect outbreaks of diseases in public health, several control charts have been proposed in the literature. In this context, the usual generalized linear model may be fitted for counts under a Negative Binomial distribution with a logarithm link function and the population size included as offset to model hospitalization rates. Different statistics are used to build CUSUM control charts to monitor daily hospitalizations and their performances are compared in simulation studies. The main contribution of the current paper is to consider different statistics based on transformations and the deviance residual to build control charts to monitor counts with seasonality effects and evaluate all the assumptions of the monitored statistics. The monitoring of daily number of hospital admissions due to respiratory diseases for people aged over 65 years in the city São Paulo-Brazil is considered as an illustration of the current proposal. [ABSTRACT FROM AUTHOR]

Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, "one-stage" random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally distributed. A second benefit of using this model is that it may be applied, with only minor modification, in a range of meta-analytic scenarios, including meta-regression, network meta-analyses and meta-analyses of diagnostic test accuracy. This single model can potentially replace the variety of often complex methods used in these areas. This paper considers, with a range of meta-analysis examples, how random-effects logistic regression models may be used in a number of different types of meta-analyses. This one-stage approach is compared with widely used meta-analysis methods including Bayesian network meta-analysis and the bivariate and hierarchical summary receiver operating characteristic (ROC) models for meta-analyses of diagnostic test accuracy. [ABSTRACT FROM AUTHOR]

Pediatric cardiac surgery may lead to poor outcomes such as acute kidney injury (AKI) and prolonged hospital length of stay (LOS). Plasma and urine biomarkers may help with early identification and prediction of these adverse clinical outcomes. In a recent multi-center study, 311 children undergoing cardiac surgery were enrolled to evaluate multiple biomarkers for diagnosis and prognosis of AKI and other clinical outcomes. LOS is often analyzed as count data, thus Poisson regression and negative binomial (NB) regression are common choices for developing predictive models. With many correlated prognostic factors and biomarkers, variable selection is an important step. The present paper proposes new variable selection methods for Poisson and NB regression. We evaluated regularized regression through penalized likelihood function. We first extend the elastic net (Enet) Poisson to two penalized Poisson regression: Mnet, a combination of minimax concave and ridge penalties; and Snet, a combination of smoothly clipped absolute deviation (SCAD) and ridge penalties. Furthermore, we extend the above methods to the penalized NB regression. For the Enet, Mnet, and Snet penalties (EMSnet), we develop a unified algorithm to estimate the parameters and conduct variable selection simultaneously. Simulation studies show that the proposed methods have advantages with highly correlated predictors, against some of the competing methods. Applying the proposed methods to the aforementioned data, it is discovered that early postoperative urine biomarkers including NGAL, IL18, and KIM-1 independently predict LOS, after adjusting for risk and biomarker variables. [ABSTRACT FROM AUTHOR]

The biomarker-adaptive threshold design (BATD) allows researchers to simultaneously study the efficacy of treatment in the overall group and to investigate the relationship between a hypothesized predictive biomarker and the treatment effect on the primary outcome. It was originally developed for survival outcomes for Phase III clinical trials where the biomarker of interest is measured on a continuous scale. In this paper, generalizations of the BATD to accommodate count biomarkers and outcomes are developed and then studied in the multiple sclerosis (MS) context where the number of relapses is a commonly used outcome. Through simulation studies, we find that the BATD has increased power compared with a traditional fixed procedure under varying scenarios for which there exists a sensitive patient subgroup. As an illustration, we apply the procedure for two hypothesized markers, baseline enhancing lesion count and disease duration at baseline, using data from a previously completed trial. MS duration appears to be a predictive marker relationship for this dataset, and the procedure indicates that the treatment effect is strongest for patients who have had MS for less than 7.8 years. The procedure holds promise of enhanced statistical power when the treatment effect is greatest in a sensitive patient subgroup. [ABSTRACT FROM AUTHOR]

Evaluation of the Feasibility, Diagnostic Yield, and Clinical Utility of Rapid Genome Sequencing in Infantile Epilepsy (Gene-STEPS): An International, Multicentre, Pilot Cohort Study D'Gama AM, Mulhern S, Sheidley BR, Boodhoo F, Buts S, Chandler NJ, Cobb J, Curtis M, Higginbotham EJ, Holland J, Khan T, Koh J, Liang NSY, McRae L, Nesbitt SE, Oby BT, Paternoster B, Patton A, Rose G, Scotchman E, Valentine R, Wiltrout KN; Gene-STEPS Study Group; IPCHiP Executive Committee; Hayeems RZ, Jain P, Lunke S, Marshall CR, Rockowitz S, Sebire NJ, Stark Z, White SM, Chitty LS, Cross JH, Scheffer IE, Chau V, Costain G, Poduri A, Howell KB, McTague A. Lancet Neurol. 2023;22(9):812-825. doi:10.1016/S1474-4422(23)00246-6 Background: Most neonatal and infantile-onset epilepsies have presumed genetic aetiologies, and early genetic diagnoses have the potential to inform clinical management and improve outcomes. We therefore aimed to determine the feasibility, diagnostic yield, and clinical utility of rapid genome sequencing in this population. Methods: We conducted an international, multicentre, cohort study (Gene-STEPS), which is a pilot study of the International Precision Child Health Partnership (IPCHiP). IPCHiP is a consortium of four paediatric centres with tertiary-level subspecialty services in Australia, Canada, the UK, and the USA. We recruited infants with new-onset epilepsy or complex febrile seizures from IPCHiP centres, who were younger than 12 months at seizure onset. We excluded infants with simple febrile seizures, acute provoked seizures, known acquired cause, or known genetic cause. Blood samples were collected from probands and available biological parents. Clinical data were collected from medical records, treating clinicians, and parents. Trio genome sequencing was done when both parents were available, and duo or singleton genome sequencing was done when one or neither parent was available. Site-specific protocols were used for DNA extraction and library preparation. Rapid genome sequencing and analysis was done at clinically accredited laboratories, and results were returned to families. We analysed summary statistics for cohort demographic and clinical characteristics and the timing, diagnostic yield, and clinical impact of rapid genome sequencing. Findings: Between Sept 1, 2021, and Aug 31, 2022, we enrolled 100 infants with new-onset epilepsy, of whom 41 (41%) were girls and 59 (59%) were boys. Median age of seizure onset was 128 days (IQR 46-192). For 43 (43% [binomial distribution 95% CI 33-53]) of 100 infants, we identified genetic diagnoses, with a median time from seizure onset to rapid genome sequencing result of 37 days (IQR 25-59). Genetic diagnosis was associated with neonatal seizure onset versus infantile seizure onset (14 [74%] of 19 vs 29 [36%] of 81; p = 0.0027), referral setting (12 [71%] of 17 for intensive care, 19 [44%] of 43 non-intensive care inpatient, and 12 [28%] of 40 outpatient; p = 0.0178), and epilepsy syndrome (13 [87%] of 15 for self-limited epilepsies, 18 [35%] of 51 for developmental and epileptic encephalopathies, 12 [35%] of 34 for other syndromes; p = 0.001). Rapid genome sequencing revealed genetic heterogeneity, with 34 unique genes or genomic regions implicated. Genetic diagnoses had immediate clinical utility, informing treatment (24 [56%] of 43), additional evaluation (28 [65%]), prognosis (37 [86%]), and recurrence risk counselling (all cases). Interpretation: Our findings support the feasibility of implementation of rapid genome sequencing in the clinical care of infants with new-onset epilepsy. Longitudinal follow-up is needed to further assess the role of rapid genetic diagnosis in improving clinical, quality-of-life, and economic outcomes. [ABSTRACT FROM AUTHOR]