UNASSIGNED: To exchange the type of subjective Bayesian prior selection for assumptions more directly related to statistical decision making in clinician studies and trials, the decreasingly informative prior (DIP) is considered. We expand standard Bayesian early termination methods in one-parameter statistical models for Phase II clinical trials to include decreasingly informative priors (DIP). These priors are designed to reduce the chance of erroneously adapting trials too early by parameterize skepticism in an amount always equal to the unobserved sample size.
UNASSIGNED: We show how to parameterize these priors based on effective prior sample size and provide examples for common single-parameter models, include Bernoulli, Poisson, and Gaussian distributions. We use a simulation study to search through possible values of total sample sizes and termination thresholds to find the smallest total sample size (N) under admissible designs, which we define as having at least 80% power and no greater than 5% type I error rate.
UNASSIGNED: For Bernoulli, Poisson, and Gaussian distributions, the DIP approach requires fewer patients when admissible designs are achieved. In situations where type I error or power are not admissible, the DIP approach yields similar power and better-controlled type I error with comparable or fewer patients than other Bayesian priors by Thall and Simon.
UNASSIGNED: The DIP helps control type I error rates with comparable or fewer patients, especially for those instances when increased type I error rates arise from erroneous termination early in a trial.

The high cost and time for developing a new drug or repositioning a partially-developed drug has fueled interest in \"repurposing\" drugs. Drug repurposing is particularly of interest for Alzheimer\'s disease (AD) or AD-related dementias (ADRD) because there are no unrestricted disease-modifying treatments for ADRD. We have designed and pilot tested a 3-Step Medication-Wide Association Study Plus (MWAS+) approach to rigorously accelerate the identification of drugs with a high potential to be repurposed for delaying and preventing AD/ADRD: Step 1 is a hypothesis-free exploration; Step 2 is mechanistic filtering; And Step 3 is hypothesis testing using observational data and prospective cohort design. Our results demonstrated the feasibility of the MWAS+ approach. The Step 1 analysis identified potential candidate drugs including atorvastatin and GLP1. The literature search in Step 2 found evidence supporting the mechanistic plausibility of the statin-ADRD association. Finally, Step 3 confirmed our hypothesis that statin may lower the risk of incident ADRD, which was statistically significant using a target trial design that emulated randomized controlled trials.

BACKGROUND: Network meta-analysis (NMA) is a statistical method used to combine results from several clinical trials and simultaneously compare multiple treatments using direct and indirect evidence. Statistical heterogeneity is a characteristic describing the variability in the intervention effects being evaluated in the different studies in network meta-analysis. One approach to dealing with statistical heterogeneity is to perform a random effects network meta-analysis that incorporates a between-study variance into the statistical model. A common assumption in the random effects model for network meta-analysis is the homogeneity of between-study variance across all interventions. However, there are applications of NMA where the single between-study assumption is potentially incorrect and instead the model should incorporate more than one between-study variances.
METHODS: In this paper, we develop an approach to testing the homogeneity of between-study variance assumption based on a likelihood ratio test. A simulation study was conducted to assess the type I error and power of the proposed test. This method is then applied to a network meta-analysis of antibiotic treatments for Bovine respiratory disease (BRD).
RESULTS: The type I error rate was well controlled in the Monte Carlo simulation. We found statistical evidence (p value = 0.052) against the homogeneous between-study variance assumption in the network meta-analysis BRD. The point estimate and confidence interval of relative effect sizes are strongly influenced by this assumption.
CONCLUSIONS: Since homogeneous between-study variance assumption is a strong assumption, it is crucial to test the validity of this assumption before conducting a network meta-analysis. Here we propose and validate a method for testing this single between-study variance assumption which is widely used for many NMA.

BACKGROUND: The human microbiome is inherently dynamic and its dynamic nature plays a critical role in maintaining health and driving disease. With an increasing number of longitudinal microbiome studies, scientists are eager to learn the comprehensive characterization of microbial dynamics and their implications to the health and disease-related phenotypes. However, due to the challenging structure of longitudinal microbiome data, few analytic methods are available to characterize the microbial dynamics over time.
RESULTS: We propose a microbial trend analysis (MTA) framework for the high-dimensional and phylogenetically-based longitudinal microbiome data. In particular, MTA can perform three tasks: 1) capture the common microbial dynamic trends for a group of subjects at the community level and identify the dominant taxa; 2) examine whether or not the microbial overall dynamic trends are significantly different between groups; 3) classify an individual subject based on its longitudinal microbial profiling. Our extensive simulations demonstrate that the proposed MTA framework is robust and powerful in hypothesis testing, taxon identification, and subject classification. Our real data analyses further illustrate the utility of MTA through a longitudinal study in mice.
CONCLUSIONS: The proposed MTA framework is an attractive and effective tool in investigating dynamic microbial pattern from longitudinal microbiome studies.

The Adolescent Brain Cognitive Development (ABCD) Study is the largest single-cohort prospective longitudinal study of neurodevelopment and children\'s health in the United States. A cohort of n = 11,880 children aged 9-10 years (and their parents/guardians) were recruited across 22 sites and are being followed with in-person visits on an annual basis for at least 10 years. The study approximates the US population on several key sociodemographic variables, including sex, race, ethnicity, household income, and parental education. Data collected include assessments of health, mental health, substance use, culture and environment and neurocognition, as well as geocoded exposures, structural and functional magnetic resonance imaging (MRI), and whole-genome genotyping. Here, we describe the ABCD Study aims and design, as well as issues surrounding estimation of meaningful associations using its data, including population inferences, hypothesis testing, power and precision, control of covariates, interpretation of associations, and recommended best practices for reproducible research, analytical procedures and reporting of results.

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

Preclinical studies using animals to study the potential of a therapeutic drug or strategy are important steps before translation to clinical trials. However, evidence has shown that poor quality in the design and conduct of these studies has not only impeded clinical translation but also led to significant waste of valuable research resources. It is clear that experimental biases are related to the poor quality seen with preclinical studies. In this chapter, we will focus on hypothesis testing type of preclinical studies and explain general concepts and principles in relation to the design of in vivo experiments, provide definitions of experimental biases and how to avoid them, and discuss major sources contributing to experimental biases and how to mitigate these sources. We will also explore the differences between confirmatory and exploratory studies, and discuss available guidelines on preclinical studies and how to use them. This chapter, together with relevant information in other chapters in the handbook, provides a powerful tool to enhance scientific rigour for preclinical studies without restricting creativity.

This work is motivated by a study of a population of multiple sclerosis (MS) patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to identify active brain lesions. At each visit, a contrast agent is administered intravenously to a subject and a series of images are acquired to reveal the location and activity of MS lesions within the brain. Our goal is to identify the enhancing lesion locations at the subject level and lesion enhancement patterns at the population level. We analyze a total of 20 subjects scanned at 63 visits (∼30Gb), the largest population of such clinical brain images. After addressing the computational challenges, we propose possible solutions to the difficult problem of transforming a qualitative scientific null hypothesis, such as \"this voxel does not enhance,\" to a well-defined and numerically testable null hypothesis based on the existing data. We call such procedure \"soft null\" hypothesis testing as opposed to the standard \"hard null\" hypothesis testing. This problem is fundamentally different from: (1) finding testing statistics when a quantitative null hypothesis is given; (2) clustering using a mixture distribution; or (3) setting a reasonable threshold with a parametric null assumption. Supplementary materials are available online.

The aim of the present study was to test if incidences of ischaemic heart disease (IHD) and usage of antihypertensive drugs are independent of weekly working hours (WWH) among full-time employees in Denmark.
Data on WWH from participants of the Danish labour force surveys, 1999-2013, were linked on an individual level to national registers with data on socioeconomic status (SES), industry, emigrations, redeemed prescriptions, hospital contacts and deaths. Participants were followed until the end of 2014 (on average 7.7 years). Poisson regression was used to model incidence rates as a function of WWH. The analyses were controlled for calendar time, time passed since start of follow-up, employment in the healthcare industry, age, sex, SES and night work.
In total, we found 3635 cases of IHD and 20 648 cases of antihypertensive drug usage. The rate ratio of IHD was 0.95 (95% CI 0.85 to 1.06) for 41-48 compared with 32-40 WWH and 1.07 (0.94 to 1.21) for >48 compared with 32-40 WWH. The corresponding rate ratios for antihypertensive drug usage were 0.99 (0.95 to 1.04) and 1.02 (0.97 to 1.08). No statistically significant interactions between WWH and sex, SES and night work, respectively, were found.
In this Danish sample, we did not find any statistically significant association between WWH and IHD or antihypertensive drug usage.

Results of industry-sponsored Phase III trials registered at clinicaltrials.gov include a rich amount of information on the efficacy of medical interventions. We propose that these results can be used to inform hypothesis testing of a new intervention through the Bayes principle. The posterior probability of positive efficacy offers an accessible interpretation of the uncertainty of efficacy and a convenient metric for global false-positive control.