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1.
Pharm Stat ; 21(4): 757-763, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-35819117

RESUMO

The graphical approach by Bretz et al. is a convenient tool to construct, visualize and perform multiple test procedures that are tailored to structured families of hypotheses while controlling the familywise error rate. A critical step is to update the transition weights following a pre-specified algorithm. In their original publication, however, the authors did not provide a detailed rationale for the update formula. This paper closes the gap and provides three alternative arguments for the update of the transition weights of the graphical approach. It is a legacy of the first author, based on an unpublished technical report from 2014, and after his untimely death reconstructed by the other two authors as a tribute to Willi Maurer's collaboration with Andy Grieve and contributions to biostatistics over many years.


Assuntos
Bioestatística , Modelos Estatísticos , Algoritmos , Interpretação Estatística de Dados , Humanos
2.
PLoS One ; 17(7): e0272007, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35867721

RESUMO

Interval estimation with accurate coverage for risk difference (RD) in a correlated 2 × 2 table with structural zero is a fundamental and important problem in biostatistics. The score test-based and Bayesian tail-based confidence intervals (CIs) have good coverage performance among the existing methods. However, as approximation approaches, they have coverage probabilities lower than the nominal confidence level for finite and moderate sample sizes. In this paper, we propose three new CIs for RD based on the fiducial, inferential model (IM) and modified IM (MIM) methods. The IM interval is proven to be valid. Moreover, simulation studies show that the CIs of fiducial and MIM methods can guarantee the preset coverage rate even for small sample sizes. More importantly, in terms of coverage probability and expected length, the MIM interval outperforms other intervals. Finally, a real example illustrates the application of the proposed methods.


Assuntos
Bioestatística , Modelos Estatísticos , Teorema de Bayes , Biometria , Bioestatística/métodos , Intervalos de Confiança , Tamanho da Amostra
3.
Sheng Wu Gong Cheng Xue Bao ; 38(5): 2019-2025, 2022 May 25.
Artigo em Chinês | MEDLINE | ID: mdl-35611747

RESUMO

The current implementation of labor education in college is insufficient and does not match its importance. The main reasons lie in outdated conceptual understanding, monotonic implementing form and lack of teaching resources for labor education. This status quo does not meet the requirements for professional and creative labor works in modern society. In order to address this challenge, we propose to incorporate labor education into professional education. Such incorporation not only mutually promotes both labor and professional education, but also integrates professional knowledge and labor skills during the teaching process, thus combining the elements of traditional labor education with timely requirement for creative labor works. This article introduced a way to incorporate labor education into biostatistics courses, and analyzed the mutually beneficial effect of such approaches.


Assuntos
Bioestatística , Currículo , Humanos
4.
Stud Health Technol Inform ; 294: 302-306, 2022 May 25.
Artigo em Inglês | MEDLINE | ID: mdl-35612081

RESUMO

Suitable causal inference in biostatistics can be best achieved by knowledge representation thanks to causal diagrams or directed acyclic graphs. However, necessary and sufficient causes are not easily represented. Since existing ontologies do not fill this gap, we designed OntoBioStat in order to enable covariate selection support based on causal relation representations. OntoBioStat automatic ontological causal diagram construction and inferences are detailed in this study. OntoBioStat inferences are allowed by Semantic Web Rule Language rules and axioms. First, statements made by the users include outcome, exposure, covariate, and causal relation specification. Then, reasoning enable automatic construction using generic instances of Meta_Variable and Necessary_Variable classes. Finally, inferred classes highlighted potential bias such as confounder-like. Ontological causal diagram built with OntoBioStat was compared to a standard causal diagram (without OntoBioStat) in a theoretical study. It was found that confounding and bias were not completely identified by the standard causal diagram, and erroneous covariate sets were provided. Further research is needed in order to make OntoBioStat more usable.


Assuntos
Biometria , Bioestatística , Viés , Causalidade
7.
Parasit Vectors ; 15(1): 35, 2022 Jan 24.
Artigo em Inglês | MEDLINE | ID: mdl-35073988

RESUMO

Dose-response relationships reflect the effects of a substance on organisms, and are widely used in broad research areas, from medicine and physiology, to vector control and pest management in agronomy. Furthermore, reporting on the response of organisms to stressors is an essential component of many public policies (e.g. public health, environment), and assessment of xenobiotic responses is an integral part of World Health Organization recommendations. Building upon an R script that we previously made available, and considering its popularity, we have now developed a software package in the R environment, BioRssay, to efficiently analyze dose-response relationships. It has more user-friendly functions and more flexibility, and proposes an easy interpretation of the results. The functions in the BioRssay package are built on robust statistical analyses to compare the dose/exposure-response of various bioassays and effectively visualize them in probit-graphs.


Assuntos
Bioensaio/estatística & dados numéricos , Bioestatística , Software , Animais , Bioestatística/instrumentação , Bioestatística/métodos , Relação Dose-Resposta a Droga , Humanos , Dose Letal Mediana , Saúde Pública/estatística & dados numéricos
8.
PLoS One ; 17(1): e0263015, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35081161

RESUMO

Problem-based learning (PBL) allows students to learn medical statistics through problem solving experience. The aim of this study was to assess the efficiency of PBL modules implemented in the blended learning courses in medical statistics through knowledge outcomes and student satisfaction. The pilot study was designed as a randomized controlled trial that included 53 medical students who had completed all course activities. The students were randomized in two groups: the group with access to PBL modules within the blended learning course (hPBL group) and the group without access to PBL modules-only blended learning course (BL group). There were no significant differences between the groups concerning socio-demographic characteristics, previous academic success and modality of access to course materials. Students from hPBL group had a significantly higher problem solving score (p = 0.012; effect size 0.69) and the total medical statistics score (p = 0,046; effect size 0.57). Multivariate regression analysis with problem solving as an outcome variable showed that problem solving was associated with being in hPBL group (p = 0.010) and having higher grade point average (p = 0.037). Multivariate regression analysis with the medical statistics score as an outcome variable showed the association between a higher score on medical statistics with access to PBL modules (p = 0.045) and a higher grade point average (p = 0.021). All students in hPBL group (100.0%) considered PBL modules useful for learning medical statistics. PBL modules can be easily implemented in the existing courses within medical statistics using the Moodle platform, they have high applicability and can complement, but not replace other forms of teaching. These modules were shown to be efficient in learning, to be well accepted among students and to be a potential missing link between teaching and learning medical statistics. The authors of this study are planning to create PBL modules for advanced courses in medical statistics and to conduct this study on other universities with a more representative study sample, with the aim to overcome the limitations of the existing study and confirm its results.


Assuntos
Bioestatística , Educação Médica , Aprendizagem Baseada em Problemas , Estudantes de Medicina , Feminino , Humanos , Masculino , Projetos Piloto
9.
Cell ; 185(2): 224-226, 2022 01 20.
Artigo em Inglês | MEDLINE | ID: mdl-35063068

RESUMO

Elle Lett is the winner of the 2021 Rising Black Scientists Award for a post-graduate scholar. For this award, we asked emerging Black scientists to tell us about the experiences that sparked their interest in the life sciences, their vision and goals, and how they want to contribute to a more inclusive scientific community. This is her story.


Assuntos
Disciplinas das Ciências Biológicas , Bioestatística , Pessoal de Laboratório Médico/psicologia , Justiça Social , Pessoas Transgênero/psicologia , Distinções e Prêmios , Educação de Pós-Graduação , Feminino , Objetivos , Humanos , Racismo/psicologia
10.
J Thorac Cardiovasc Surg ; 163(3): 1116-1124.e1, 2022 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33349448

RESUMO

OBJECTIVE: Biostatistics are frequently used in research published in the domain of cardiothoracic surgery. The objective of this study was to describe the scope of statistical techniques reported in the literature and to highlight implications for editorial review and critical appraisal. METHODS: Original research articles published between January and April 2017 in the Journal of Thoracic and Cardiovascular Surgery, Annals of Thoracic Surgery, and the European Journal of Cardio-Thoracic Surgery were examined. For each article, the statistical method(s) reported were recorded and categorized by complexity. RESULTS: We reviewed 293 articles that reported 1068 statistical methods. The mean number of different statistical methods reported per article was 3.6 ± 1.9, with variation by subspecialty and journal. The most common statistical methods were contingency tables (in 59% of articles), t tests (49%), and survival methods (49%). Only 4% of articles used descriptive statistics alone. An introductory level of statistical knowledge was deemed sufficient for understanding 16% of articles, whereas for the remainder a higher level of knowledge would be needed. CONCLUSIONS: Contemporary cardiothoracic surgery research frequently requires the use of complex statistical methods. This was evident across articles for all cardiothoracic surgical subspecialties as reported in 3 high-impact journals. Routine review of manuscript submissions by biostatisticians is needed to ensure the appropriate use and reporting of advanced statistical methods in cardiothoracic surgery research.


Assuntos
Pesquisa Biomédica/estatística & dados numéricos , Bioestatística , Procedimentos Cirúrgicos Cardíacos/estatística & dados numéricos , Modelos Estatísticos , Publicações Periódicas como Assunto/estatística & dados numéricos , Bibliometria , Interpretação Estatística de Dados , Humanos , Fator de Impacto de Revistas
12.
Elife ; 102021 10 06.
Artigo em Inglês | MEDLINE | ID: mdl-34612811

RESUMO

Within neuroscience, psychology, and neuroimaging, the most frequently used statistical approach is null hypothesis significance testing (NHST) of the population mean. An alternative approach is to perform NHST within individual participants and then infer, from the proportion of participants showing an effect, the prevalence of that effect in the population. We propose a novel Bayesian method to estimate such population prevalence that offers several advantages over population mean NHST. This method provides a population-level inference that is currently missing from study designs with small participant numbers, such as in traditional psychophysics and in precision imaging. Bayesian prevalence delivers a quantitative population estimate with associated uncertainty instead of reducing an experiment to a binary inference. Bayesian prevalence is widely applicable to a broad range of studies in neuroscience, psychology, and neuroimaging. Its emphasis on detecting effects within individual participants can also help address replicability issues in these fields.


Scientists use statistical tools to evaluate observations or measurements from carefully designed experiments. In psychology and neuroscience, these experiments involve studying a randomly selected group of people, looking for patterns in their behaviour or brain activity, to infer things about the population at large. The usual method for evaluating the results of these experiments is to carry out null hypothesis statistical testing (NHST) on the population mean ­ that is, the average effect in the population that the study participants were selected from. The test asks whether the observed results in the group studied differ from what might be expected if the average effect in the population was zero. However, in psychology and neuroscience studies, people's brain activity and performance on cognitive tasks can differ a lot. This means important effects in individuals can be lost in the overall population average. Ince et al. propose that this shortcoming of NHST can be overcome by shifting the statistical analysis away from the population mean, and instead focusing on effects in individual participants. This led them to create a new statistical approach named Bayesian prevalence. The method looks at effects within each individual in the study and asks how likely it would be to see the same result if the experiment was repeated with a new person chosen from the wider population at random. Using this approach, it is possible to quantify how typical or uncommon an observed effect is in the population, and the uncertainty around this estimate. This differs from NHST which only provides a binary 'yes or no' answer to the question, 'does this experiment provide sufficient evidence that the average effect in the population is not zero?' Another benefit of Bayesian prevalence is that it can be applied to studies with small numbers of participants which cannot be analysed using other statistical methods. Ince et al. show that the Bayesian prevalence can be applied to a range of psychology and neuroimaging experiments, from brain imaging to electrophysiology studies. Using this alternative statistical method could help address issues of replication in these fields where NHST results are sometimes not the same when studies are repeated.


Assuntos
Bioestatística , Neurociências/estatística & dados numéricos , Psicologia/estatística & dados numéricos , Teorema de Bayes , Interpretação Estatística de Dados , Humanos , Projetos de Pesquisa
13.
Trials ; 22(1): 478, 2021 Jul 22.
Artigo em Inglês | MEDLINE | ID: mdl-34294129

RESUMO

BACKGROUND: Sub-Saharan Africa continues to carry a high burden of communicable diseases such as TB and HIV and non-communicable diseases such as hypertension and other cardiovascular conditions. Although investment in research has led to advances in improvements in outcomes, a lot still remains to be done to build research capacity in health. Like many other regions in the world, Sub-Saharan Africa suffers from a critical shortage of biostatisticians and clinical trial methodologists. METHODS: Funded through a Fogarty Global Health Training Program grant, the Faculty of Medicine and Health Sciences at Stellenbosch University in South Africa established a new Masters Program in Biostatistics which was launched in January 2017. In this paper, we describe the development of a biostatistical and clinical trials collaboration Module, adapted from a similar course offered in the Health Research Methodology program at McMaster University. DISCUSSION: Guided by three core principles (experiential learning; multi-/inter-disciplinary approach; and formal mentorship), the Module aims to advance biostatistical collaboration skills of the trainees by facilitating learning in how to systematically apply fundamental statistical and trial methodological knowledge in practice while strengthening some soft skills which are necessary for effective collaborations with other healthcare researchers to solve health problems. We also share some preliminary findings from the first four cohorts that took the Module in January-November 2018 to 2021. We expect that this Module can provide an example of how to improve biostatistical and clinical trial collaborations and accelerate research capacity building in low-resource settings. FUNDING SOURCE: Fogarty International Center of the National Institutes of Health.


Assuntos
Bioestatística , Universidades , Fortalecimento Institucional , Humanos , Pesquisadores , África do Sul
14.
Int J Epidemiol ; 50(4): 1384-1393, 2021 08 30.
Artigo em Inglês | MEDLINE | ID: mdl-34113988

RESUMO

A primary goal of longitudinal studies is to examine trends over time. Reported results from these studies often depend on strong, unverifiable assumptions about the missing data. Whereas the risk of substantial bias from missing data is widely known, analyses exploring missing-data influences are commonly done either ad hoc or not at all. This article outlines one of the three primary recognized approaches for examining missing-data effects that could be more widely used, i.e. the shared-parameter model (SPM), and explains its purpose, use, limitations and extensions. We additionally provide synthetic data and reproducible research code for running SPMs in SAS, Stata and R programming languages to facilitate their use in practice and for teaching purposes in epidemiology, biostatistics, data science and related fields. Our goals are to increase understanding and use of these methods by providing introductions to the concepts and access to helpful tools.


Assuntos
Biometria , Modelos Estatísticos , Viés , Bioestatística , Humanos , Estudos Longitudinais
19.
Brief Bioinform ; 22(5)2021 09 02.
Artigo em Inglês | MEDLINE | ID: mdl-33822893

RESUMO

A major task in the analysis of microbiome data is to identify microbes associated with differing biological conditions. Before conducting analysis, raw data must first be adjusted so that counts from different samples are comparable. A typical approach is to estimate normalization factors by which all counts in a sample are multiplied or divided. However, the inherent variation associated with estimation of normalization factors are often not accounted for in subsequent analysis, leading to a loss of precision. Rank normalization is a nonparametric alternative to the estimation of normalization factors in which each count for a microbial feature is replaced by its intrasample rank. Although rank normalization has been successfully applied to microarray analysis in the past, it has yet to be explored for microbiome data, which is characterized by high frequencies of 0s, strongly correlated features and compositionality. We propose to use rank normalization as an alternative to the estimation of normalization factors and examine its performance when paired with a two-sample t-test. On a rigorous 3rd-party benchmarking simulation, it is shown to offer strong control over the false discovery rate, and at sample sizes greater than 50 per treatment group, to offer an improvement in performance over commonly used normalization factors paired with t-tests, Wilcoxon rank-sum tests and methodologies implemented by R packages. On two real datasets, it yielded valid and reproducible results that were strongly in agreement with the original findings and the existing literature, further demonstrating its robustness and future potential. Availability: The data underlying this article are available online along with R code and supplementary materials at https://github.com/matthewlouisdavisBioStat/Rank-Normalization-Empowers-a-T-Test.


Assuntos
Bactérias/genética , Infecções Bacterianas/diagnóstico , Bioestatística/métodos , Neoplasias Colorretais/microbiologia , Doença de Crohn/microbiologia , Microbioma Gastrointestinal/genética , Metagenoma , Infecções Bacterianas/microbiologia , Benchmarking , Estudos de Casos e Controles , Criança , Estudos de Coortes , Simulação por Computador , Feminino , Humanos , Masculino , Computação Matemática , Metagenômica/métodos , RNA Ribossômico 16S/genética , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Estatísticas não Paramétricas
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