Biostatistics and Epidemiology for the Toxicologist: Likelihood Ratios.

Likelihood ratios compare two values (i.e., case rates) in order to illustrate the magnitude of the difference between the two. This ratio increases the confidence one can have in a diagnostic test from a different vantage point than that of sensitivity and specificity. The calculations of likelihood ratios are presented along with a simplified approach. Likelihood ratios are another tool the toxicologist should employ in their understanding of statistics and probability.

Learning from disability studies to introduce the role of the individual to naturalistic accounts of disease.

Disability studies have been successfully focusing on individuals' lived experiences, the personalization of goals, and the constitution of the individual in defining disease and restructuring public understandings of disability. Although they had a strong influence in the policy making and medical modeling of disease, their framework has not been translated to traditional naturalistic accounts of disease. I will argue that, using new developments in evolutionary biology (Extended Evolutionary Synthesis [EES] about questions of proper function) and behavioral ecology (Niche conformance and construction about the questions of reference classes in biostatistics accounts), the main elements of the framework of disability studies can be used to represent life histories at the conceptual level of the two main "non-normative" accounts of disease. I chose these accounts since they are related to medicine in a more descriptive way. The success of the practical aspects of disability studies this way will be communicated without causing injustice to the individual since they will represent the individuality of the patient in two main naturalistic accounts of disease: the biostatistical account and the evolutionary functional account. Although most accounts criticizing the concept of disease as value-laden do not supply a positive element, disability studies can supply a good point for descriptive extension of the concept through inclusion of epistemic agency.

Tutorial on Biostatistics: Sample Size and Power Calculation for Ophthalmic Studies With Correlated Binary Eye Outcomes.

Purpose: To describe and demonstrate sample size and power calculation for ophthalmic studies with a binary outcome from one or both eyes. Methods: We describe sample size and power calculation for four commonly used eye designs: (1) one-eye design or person-design: one eye per subject or outcome is at person-level; (2) paired design: two eyes per subject and two eyes are in different treatment groups; (3) two-eye design: two eyes per subject and both eyes are in the same treatment group; and (4) mixture design: mixture of one eye and two eyes per subject. For each design, we demonstrate sample size and power calculations in real ophthalmic studies. Results: Using formulas and commercial or free statistical packages including SAS, STATA, R, and PS, we calculated sample size and power. We demonstrated that different statistical packages require different parameters and provide similar, yet not identical, results. We emphasize that studies using data from two eyes of a subject need to account for the intereye correlation for appropriate sample size and power calculations. We demonstrate the gain in efficiency in designs that include two eyes of a subject compared to one-eye designs. Conclusions: Ophthalmic studies use different eye designs that include one or both eyes in the same or different treatment groups. Appropriate sample size and power calculations depend on the eye design and should account for intereye correlation when two eyes from some or all subjects are included in a study. Calculations can be executed using formulas and commercial or free statistical packages.

Biostatistical Aspects of Whole Genome Sequencing Studies: Preprocessing and Quality Control.

Rapid advances in high-throughput DNA sequencing technologies have enabled large-scale whole genome sequencing (WGS) studies. Before performing association analysis between phenotypes and genotypes, preprocessing and quality control (QC) of the raw sequence data need to be performed. Because many biostatisticians have not been working with WGS data so far, we first sketch Illumina's short-read sequencing technology. Second, we explain the general preprocessing pipeline for WGS studies. Third, we provide an overview of important QC metrics, which are applied to WGS data: on the raw data, after mapping and alignment, after variant calling, and after multisample variant calling. Fourth, we illustrate the QC with the data from the GENEtic SequencIng Study Hamburg-Davos (GENESIS-HD), a study involving more than 9000 human whole genomes. All samples were sequenced on an Illumina NovaSeq 6000 with an average coverage of 35× using a PCR-free protocol. For QC, one genome in a bottle (GIAB) trio was sequenced in four replicates, and one GIAB sample was successfully sequenced 70 times in different runs. Fifth, we provide empirical data on the compression of raw data using the DRAGEN original read archive (ORA). The most important quality metrics in the application were genetic similarity, sample cross-contamination, deviations from the expected Het/Hom ratio, relatedness, and coverage. The compression ratio of the raw files using DRAGEN ORA was 5.6:1, and compression time was linear by genome coverage. In summary, the preprocessing, joint calling, and QC of large WGS studies are feasible within a reasonable time, and efficient QC procedures are readily available.

Fast matrix completion in epigenetic methylation studies with informative covariates.

DNA methylation is an important epigenetic mark that modulates gene expression through the inhibition of transcriptional proteins binding to DNA. As in many other omics experiments, the issue of missing values is an important one, and appropriate imputation techniques are important in avoiding an unnecessary sample size reduction as well as to optimally leverage the information collected. We consider the case where relatively few samples are processed via an expensive high-density whole genome bisulfite sequencing (WGBS) strategy and a larger number of samples is processed using more affordable low-density, array-based technologies. In such cases, one can impute the low-coverage (array-based) methylation data using the high-density information provided by the WGBS samples. In this paper, we propose an efficient Linear Model of Coregionalisation with informative Covariates (LMCC) to predict missing values based on observed values and covariates. Our model assumes that at each site, the methylation vector of all samples is linked to the set of fixed factors (covariates) and a set of latent factors. Furthermore, we exploit the functional nature of the data and the spatial correlation across sites by assuming some Gaussian processes on the fixed and latent coefficient vectors, respectively. Our simulations show that the use of covariates can significantly improve the accuracy of imputed values, especially in cases where missing data contain some relevant information about the explanatory variable. We also showed that our proposed model is particularly efficient when the number of columns is much greater than the number of rows-which is usually the case in methylation data analysis. Finally, we apply and compare our proposed method with alternative approaches on two real methylation datasets, showing how covariates such as cell type, tissue type or age can enhance the accuracy of imputed values.

Assessing attitudes towards biostatistics education among medical students: adaptation and preliminary evaluation of the Chinese version survey of attitudes towards statistics (SATS-36).

BACKGROUND: Despite the numerous advantages of mastering biostatistics, medical students generally perceive biostatistics as a difficult and challenging subject and even experience anxiety during the courses. Evidence for the correlation between students' academic achievements and their attitudes, indicating that attitudes at the beginning of the biostatistics course may affect cognitive competence at the end of the course and subsequently influence student academic performance. However, there are current disagreements regarding the measurement and evaluation of attitudes related to statistics. Thus, there is a need for standard instruments to assess them. This study was conducted to develop a Chinese version of the Survey of Attitudes Toward Statistics (SATS-36) in order to acquire a valid instrument to measure medical students' attitudes toward biostatistics under Chinese medical educational background. METHODS: The Chinese version SATS-36 was developed through translation and back-translation of the original scale, with subsequent revisions based on expert advice to ensure the most appropriate item content. The local adaption was performed with a cohort of 1709 Chinese-speaking medical undergraduate and graduate students enrolled in biostatistics courses. And then, the reliability, validity and discrimination of the questionnaires were evaluated through correlation coefficient calculation, factor analysis, parallel analysis and other methods. RESULTS: The Chinese version SATS-36 consisted of 36 items and loaded a five-factor structure by factor analysis, which offered an alternative similar but not equal to that original six-factor structure. The cumulative variance contribution rate was 62.20%, the Cronbach's α coefficient was 0.908, the Guttman split-half reliability coefficient was 0.905 and the test-retest reliability coefficient was 0.752. Discriminant analysis revealed small to large significant differences in the five attitude subscales. CONCLUSIONS: The Chinese version SATS-36 with good validity and reliability in this study can be used to evaluate the learning framework of Chinese medical students.

Wakefield's Harm-Based Critique of the Biostatistical Theory.

Jerome Wakefield criticizes my biostatistical analysis of the pathological-as statistically subnormal biological part-functional ability relative to species, sex, and age-for its lack of a harm clause. He first charges me with ignoring two general distinctions: biological versus medical pathology, and disease of a part versus disease of a whole organism. He then offers 10 counterexamples that, he says, are harmless dysfunctions but not medical disorders. Wakefield ends by arguing that we need a harm clause to explain American psychiatry's 1973 decision to declassify homosexuality. I reply, first, that his two distinctions are philosophic fantasies alien to medical usage, invented only to save his own harmful-dysfunction analysis (HDA) from a host of obvious counterexamples. In any case, they do not coincide with the harmless/harmful distinction. In reality, medicine admits countless chronic diseases that are, contrary to Wakefield, subclinical for most of their course, as well as many kinds of typically harmless skin pathology. As for his 10 counterexamples, no medical source he cites describes them as he does. I argue that none of his examples contradicts the biostatistical analysis: all either are not part-dysfunctions (situs inversus, incompetent sperm, normal-flora infection) or are indeed classified as medical disorders (donated kidney, Typhoid Mary's carrier status, latent tuberculosis or HIV, cherry angiomas). And if Wakefield's HDA fits psychiatry, the fact that it does not fit medicine casts doubt on psychiatry's status as a medical specialty.

Identification of complier and noncomplier average causal effects in the presence of latent missing-at-random (LMAR) outcomes: a unifying view and choices of assumptions.

The study of treatment effects is often complicated by noncompliance and missing data. In the one-sided noncompliance setting where of interest are the complier and noncomplier average causal effects, we address outcome missingness of the latent missing at random type (LMAR, also known as latent ignorability). That is, conditional on covariates and treatment assigned, the missingness may depend on compliance type. Within the instrumental variable (IV) approach to noncompliance, methods have been proposed for handling LMAR outcome that additionally invoke an exclusion restriction-type assumption on missingness, but no solution has been proposed for when a non-IV approach is used. This article focuses on effect identification in the presence of LMAR outcomes, with a view to flexibly accommodate different principal identification approaches. We show that under treatment assignment ignorability and LMAR only, effect nonidentifiability boils down to a set of two connected mixture equations involving unidentified stratum-specific response probabilities and outcome means. This clarifies that (except for a special case) effect identification generally requires two additional assumptions: a specific missingness mechanism assumption and a principal identification assumption. This provides a template for identifying effects based on separate choices of these assumptions. We consider a range of specific missingness assumptions, including those that have appeared in the literature and some new ones. Incidentally, we find an issue in the existing assumptions, and propose a modification of the assumptions to avoid the issue. Results under different assumptions are illustrated using data from the Baltimore Experience Corps Trial.

Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas.

There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression parameters must be interpreted conditional on the random effects. Copulas provide a useful alternative framework for joint modeling. One advantage of using copulas is that practitioners can directly specify marginal models for the outcomes of interest. We develop a joint model using a Gaussian copula to characterize the association between multivariate longitudinal and survival outcomes. Rather than using an unstructured correlation matrix in the copula model to characterize dependence structure as is common, we propose a novel decomposition that allows practitioners to impose structure (e.g., auto-regressive) which provides efficiency gains in small to moderate sample sizes and reduces computational complexity. We develop a Markov chain Monte Carlo model fitting procedure for estimation. We illustrate the method's value using a simulation study and present a real data analysis of longitudinal quality of life and disease-free survival data from an International Breast Cancer Study Group trial.

Enhancing biostatistics education for medical students in Poland: factors influencing perception and educational recommendations.

BACKGROUND: A number of recommendations for the teaching of biostatistics have been published to date, however, student opinion on them has not yet been studied. For this reason, the aim of the manuscript was to find out the opinions of medical students at universities in Poland on two forms of teaching biostatistics, namely traditional and practical, as well as to indicate, on the basis of the results obtained, the related educational recommendations. METHODS: The study involved a group of 527 students studying at seven medical faculties in Poland, who were asked to imagine two different courses. The traditional form of teaching biostatistics was based on the standard teaching scheme of running a test from memory in a statistical package, while the practical one involved reading an article in which a particular test was applied and then applying it based on the instruction provided. Other aspects related to the teaching of the subject were assessed. RESULTS: According to the students of each course, the practical form of teaching biostatistics reduces the stress level associated with teaching and the student exam (p < 0.001), as well as contributing to an increased level of elevated knowledge (p < 0.001), while the degree of satisfaction after passing the exam is higher (p < 0.001). A greater proportion of students (p < 0.001) believe that credit for the course could be given by doing a statistical review of an article or conducting a survey, followed by the tests learned in class. More than 95% also said that the delivery of the courses should be based on the field of study they were taking, during which time they would also like to have the opportunity to take part in optional activities and hear lectures from experts. CONCLUSION: It is recommended that more emphasis be placed on practical teaching the subject of biostatistics.

Rolling the DICE (Design, Interpret, Compute, Estimate): Interactive Learning of Biostatistics With Simulations.

Despite the increasing relevance of statistics in health sciences, teaching styles in higher education are remarkably similar across disciplines: lectures covering the theory and methods, followed by application and computer exercises in given data sets. This often leads to challenges for students in comprehending fundamental statistical concepts essential for medical research. To address these challenges, we propose an engaging learning approach-DICE (design, interpret, compute, estimate)-aimed at enhancing the learning experience of statistics in public health and epidemiology. In introducing DICE, we guide readers through a practical example. Students will work in small groups to plan, generate, analyze, interpret, and communicate their own scientific investigation with simulations. With a focus on fundamental statistical concepts such as sampling variability, error probabilities, and the construction of statistical models, DICE offers a promising approach to learning how to combine substantive medical knowledge and statistical concepts. The materials in this paper, including the computer code, can be readily used as a hands-on tool for both teachers and students.

Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy.

Determining causes of deaths (CODs) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or "domains") necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this article, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a prespecified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. The posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation dataset. The article concludes with a discussion of limitations and future directions.

Projection-based two-sample inference for sparsely observed multivariate functional data.

Modern longitudinal studies collect multiple outcomes as the primary endpoints to understand the complex dynamics of the diseases. Oftentimes, especially in clinical trials, the joint variation among the multidimensional responses plays a significant role in assessing the differential characteristics between two or more groups, rather than drawing inferences based on a single outcome. We develop a projection-based two-sample significance test to identify the population-level difference between the multivariate profiles observed under a sparse longitudinal design. The methodology is built upon widely adopted multivariate functional principal component analysis to reduce the dimension of the infinite-dimensional multi-modal functions while preserving the dynamic correlation between the components. The test applies to a wide class of (non-stationary) covariance structures of the response, and it detects a significant group difference based on a single p-value, thereby overcoming the issue of adjusting for multiple p-values that arise due to comparing the means in each of components separately. Finite-sample numerical studies demonstrate that the test maintains the type-I error, and is powerful to detect significant group differences, compared to the state-of-the-art testing procedures. The test is carried out on two significant longitudinal studies for Alzheimer's disease and Parkinson's disease (PD) patients, namely, TOMMORROW study of individuals at high risk of mild cognitive impairment to detect differences in the cognitive test scores between the pioglitazone and the placebo groups, and Azillect study to assess the efficacy of rasagiline as a potential treatment to slow down the progression of PD.

Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation.

We present the motivation, experience, and learnings from a data challenge conducted at a large pharmaceutical corporation on the topic of subgroup identification. The data challenge aimed at exploring approaches to subgroup identification for future clinical trials. To mimic a realistic setting, participants had access to 4 Phase III clinical trials to derive a subgroup and predict its treatment effect on a future study not accessible to challenge participants. A total of 30 teams registered for the challenge with around 100 participants, primarily from Biostatistics organization. We outline the motivation for running the challenge, the challenge rules, and logistics. Finally, we present the results of the challenge, the participant feedback as well as the learnings. We also present our view on the implications of the results on exploratory analyses related to treatment effect heterogeneity.

Poor statistical reporting: do we have a reason for concern? A narrative review and recommendations.

PURPOSE OF REVIEW: The aim of the review conducted was to present recent articles indicating the need to implement statistical recommendations in the daily work of biomedical journals. RECENT FINDINGS: The most recent literature shows an unchanged percentage of journals using specialized statistical review over 20 years. The problems of finding statistical reviewers, the impractical way in which biostatistics is taught and the nonimplementation of published statistical recommendations contribute to the fact that a small percentage of accepted manuscripts contain correctly performed analysis. The statistical recommendations published for authors and editorial board members in recent years contain important advice, but more emphasis should be placed on their practical and rigorous implementation. If this is not the case, we will additionally continue to experience low reproducibility of the research. SUMMARY: There is a low level of statistical reporting these days. Recommendations related to the statistical review of submitted manuscripts should be followed more rigorously.

Marshall Joffe's Contributions to Causal Inference, Biostatistics, and Epidemiology.

We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall's research and their significance for causal inference but also offer a portrayal of Marshall's personal accomplishments and character. Our discussion of Marshall's research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall's unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall's research. While Marshall's impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones.

PGS-Depot: a comprehensive resource for polygenic scores constructed by summary statistics based methods.

Polygenic score (PGS) is an important tool for the genetic prediction of complex traits. However, there are currently no resources providing comprehensive PGSs computed from published summary statistics, and it is difficult to implement and run different PGS methods due to the complexity of their pipelines and parameter settings. To address these issues, we introduce a new resource called PGS-Depot containing the most comprehensive set of publicly available disease-related GWAS summary statistics. PGS-Depot includes 5585 high quality summary statistics (1933 quantitative and 3652 binary trait statistics) curated from 1564 traits in European and East Asian populations. A standardized best-practice pipeline is used to implement 11 summary statistics-based PGS methods, each with different model assumptions and estimation procedures. The prediction performance of each method can be compared for both in- and cross-ancestry populations, and users can also submit their own summary statistics to obtain custom PGS with the available methods. Other features include searching for PGSs by trait name, publication, cohort information, population, or the MeSH ontology tree and searching for trait descriptions with the experimental factor ontology (EFO). All scores, SNP effect sizes and summary statistics can be downloaded via FTP. PGS-Depot is freely available at http://www.pgsdepot.net.