The Biostatistics and Methodological Innovation Working (BMIW) Group is one of several working groups within the CANadian Network and Centre for Trials INternationally (CANNeCTIN). This programme received funding from the Canadian Institutes of Health Research and the Canada Foundation for Innovation beginning in 2008, to enhance the infrastructure and build capacity for large Canadian-led clinical trials in cardiovascular diseases (CVD) and diabetes mellitus (DM). The overall aims of the BMIW Group's programme within CANNeCTIN, are to advance biostatistical and methodological research, and to build biostatistical capacity in CVD and DM. Our program of research and training includes: monthly videoconferences on topical biostatistical and methodological issues in CVD/DM clinical studies; providing presentations on methods issues at the annual CANNeCTIN meetings; collaborating with clinician investigators on their studies; training young statisticians in biostatistics and methods in CVD/DM trials and organizing annual symposiums on topical methodological issues. We are focused on the development of new biostatistical methods and the recruitment and training of highly qualified personnel--who will become leaders in the design and analysis of CVD/DM trials. The ultimate goal is to enhance global health by contributing to efforts to reduce the burden of CVD and DM.

Numerous studies demonstrating that statistical errors are common in basic science publications have led to calls to improve statistical training for basic scientists. In this article, we sought to evaluate statistical requirements for PhD training and to identify opportunities for improving biostatistics education in the basic sciences. We provide recommendations for improving statistics training for basic biomedical scientists, including: 1. Encouraging departments to require statistics training, 2. Tailoring coursework to the students' fields of research, and 3. Developing tools and strategies to promote education and dissemination of statistical knowledge. We also provide a list of statistical considerations that should be addressed in statistics education for basic scientists.

The efficiency of clinical procedures is based on practical and theoretical knowledge. Countless daily information is available to the orthodontist, but it is up to this professional to know how to select what really has an impact on clinical practice. Evidence-based orthodontics ends up requiring the clinician to know the basics of biostatistics to understand the results of scientific publications. Such concepts are also important for researchers, for correct data planning and analysis.

Biostatistics courses are integral to many undergraduate biology programs. Such courses have often been taught using point-and-click software, but these programs are now seldom used by researchers or professional biologists. Instead, biology professionals typically use programming languages, such as R, which are better suited to analyzing complex data sets. However, teaching biostatistics and programming simultaneously has the potential to overload the students and hinder their learning. We sought to mitigate this overload by using cognitive load theory (CLT) to develop assignments for two biostatistics courses. We evaluated the effectiveness of these assignments by comparing student cohorts who were taught R using these assignments (n = 146) with those who were taught R through example scripts or were instructed on a point-and-click software program (control, n = 181). We surveyed all cohorts and analyzed statistical and programming ability through students' lab reports or final exams. Students who learned R through our assignments rated their programming ability higher and were more likely to put the usage of R as a skill in their curricula vitae. We also found that the treatment students were more motivated, less frustrated, and less stressed when using R. These results suggest that we can use CLT to teach challenging material.

Background: The reproducibility policy at the journal  Biostatistics rewards articles with badges for data and code sharing.  This study investigates the effect of badges at increasing reproducible research. Methods:  The setting of this observational study is the  Biostatistics and  Statistics in Medicine (control journal) online research archives.  The data consisted of 240 randomly sampled articles from 2006 to 2013 (30 articles per year) per journal.  Data analyses included: plotting probability of data and code sharing by article submission date, and Bayesian logistic regression modelling. Results:  The probability of data sharing was higher at  Biostatistics than the control journal but the probability of code sharing was comparable for both journals.  The probability of data sharing increased by 3.9 times (95% credible interval: 1.5 to 8.44 times, p-value probability that sharing increased: 0.998) after badges were introduced at  Biostatistics.  On an absolute scale, this difference was only a 7.6% increase in data sharing (95% CI: 2 to 15%, p-value: 0.998).  Badges did not have an impact on code sharing at the journal (mean increase: 1 time, 95% credible interval: 0.03 to 3.58 times, p-value probability that sharing increased: 0.378).  64% of articles at Biostatistics that provide data/code had broken links, and at Statistics in Medicine, 40%; assuming these links worked only slightly changed the effect of badges on data (mean increase: 6.7%, 95% CI: 0.0% to 17.0%, p-value: 0.974) and on code (mean increase: -2%, 95% CI: -10.0 to 7.0%, p-value: 0.286). Conclusions:  The effect of badges at  Biostatistics was a 7.6% increase in the data sharing rate, 5 times less than the effect of badges at  Psychological Science.  Though badges at  Biostatistics did not impact code sharing, and had a moderate effect on data sharing, badges are an interesting step that journals are taking to incentivise and promote reproducible research.

Biostatistics is a key but challenging subject in medical curricula that is usually delivered via a didactic approach in China. However, whether it is the best teaching approach to improve the learner's competency, especially for medical postgraduates is yet to be proved. Therefore, a block curriculum design was initially developed to provide selective education to the postgraduates towards the professional career of their interest. A questionnaire was designed to assess the students' perceptions toward biostatistics as these affective factors might impact the learning process. Thus, the present study aimed to detect whether the new block curriculum design could promote the students' positive perceptions and further improve the course achievement.

As statisticians develop new methodological approaches, there are many factors that influence whether others will utilize their work. This paper is a bibliometric study that identifies and quantifies associations between characteristics of new biostatistics methods and their citation counts. Of primary interest was the association between numbers of citations and whether software code was available to the reader.

ABCG2, member of ATP-binding cassette (ABC) transporter family, is known as crucial regulator related to multi-drug resistance in human tumors and has recently been putatively studied as human carcinoma cell biomarker. While, effects of ABCG2 on human gastric cancer (GC) has not been illustrated thoroughly. In this study, by applying biostatistics mining methods, we observed that ABCG2 is frequently aberrantly expressed in GC patients through exploring dataset of GSE19826 in NCBI GEO database. Contemporary, extreme up-regulation of ABCG2 was discovered in both GC specimens and cell lines of our center, from which we observed high level of ABCG2 associated with GC clinicopathologic features and poor outcomes. Depletion of ABCG2 in MKN-45 GC cells, the cell proliferation was significantly impacted along with cell cycle arrest, and cell apoptosis was induced. Interestingly, combined with data mining of NCBI database, CRKL, a pivotal GC promoter, presents a significant positive correlation with ABCG2. And the expression of CRKL in GC cells was obviously affected through ABCG2 depletion. Simultaneously, over-expression of CRKL in MKN-45 cells significantly rescued most of the phenotypes induced by ABCG2 depletion. Thus, we suggest that ABCG2 is a potential biomarker and target upstream CRKL, which could be further studied for GC diagnosis and therapeutic treatment.

Previous studies have reported that the aberrantly expressed AKR1B10 is associated with many cancer development, however the functional roles of AKR1B10 and its regulatory mechanisms in hepatocellular carcinoma (HCC) have been limited studied. In this project, we identified AKR1B10 functional as an oncogene in HCC through tumor/normal human tissue comparison from both GEO microarray and TCGA RNAseq dataset. Further experimental validations from three HCC cell lines (SMMC-7721, HePG2 and HeP3B) also suggested the ontogenetic functions of AKR1B10 in HCC tumor growth. By knocking down AKR1B10 through shRNA in HCC HeP3B cells, we showed it significantly induced cell cycle arrest and inhibited cell growth. Interestingly, integrative analysis of TCGA RNAseq data and miRNA-seq data predicted that miR-383-5p, a novel post-transcriptional tumor suppressor, is negatively associated with AKR1B10 expression. To further investigate the role of miR-383-5p in regulating AKR1B10 in HCC, we performed Dual-luciferase reporter assay experiments. Results showed that miR-383-5p is an upstream modulator targeting AKR1B10 in the post-transcriptional stage. Thus, we report AKR1B10 modulated regulated by miR-383-5p, promotes HCC tumor progress, and could be potentially a therapeutic target for precision medicine in HCC.

Longitudinal studies are helpful in understanding how subtle associations between factors of interest change over time. Our goal is to apply statistical methods which are appropriate for analyzing longitudinal data to a repeated measures epidemiological study as a tutorial in the appropriate use and interpretation of random effects models. To motivate their use, we study the association of alcohol consumption on markers of HIV disease progression in an observational cohort. To make valid inferences, the association among measurements correlated within a subject must be taken into account. We describe a linear mixed effects regression framework that accounts for the clustering of longitudinal data and that can be fit using standard statistical software. We apply the linear mixed effects model to a previously published dataset of HIV infected individuals with a history of alcohol problems who are receiving HAART (n = 197). The researchers were interested in determining the effect of alcohol use on HIV disease progression over time. Fitting a linear mixed effects multiple regression model with a random intercept and random slope for each subject accounts for the association of observations within subjects and yields parameters interpretable as in ordinary multiple regression. A significant interaction between alcohol use and adherence to HAART is found: subjects who use alcohol and are not fully adherent to their HIV medications had higher log RNA (ribonucleic acid) viral load levels than fully adherent non-drinkers, fully adherent alcohol users, and non-drinkers who were not fully adherent. Longitudinal studies are increasingly common in epidemiological research. Software routines that account for correlation between repeated measures using linear mixed effects methods are now generally available and straightforward to utilize. These models allow the relaxation of assumptions needed for approaches such as repeated measures ANOVA, and should be routinely incorporated into the analysis of cohort studies.

Improving health in Africa is a high priority internationally. Inadequate research capacity to produce local, relevant research has been identified as a limitation to improved population health. Increasing attention is being paid to the higher education sector in Africa as a method of addressing this; evidence that such investment is having the desired impact is required. A 1998 3-year investment by the Special Programme for Research and Training in Tropical Diseases (TDR) in research training at the School of Public Health, University of the Witwatersrand, South Africa was reviewed to assess its' impact.

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.

The emphasis on team science in clinical and translational research increases the importance of collaborative biostatisticians (CBs) in healthcare. Adequate training and development of CBs ensure appropriate conduct of robust and meaningful research and, therefore, should be considered as a high-priority focus for biostatistics groups. Comprehensive training enhances clinical and translational research by facilitating more productive and efficient collaborations. While many graduate programs in Biostatistics and Epidemiology include training in research collaboration, it is often limited in scope and duration. Therefore, additional training is often required once a CB is hired into a full-time position. This article presents a comprehensive CB training strategy that can be adapted to any collaborative biostatistics group. This strategy follows a roadmap of the biostatistics collaboration process, which is also presented. A TIE approach (Teach the necessary skills, monitor the Implementation of these skills, and Evaluate the proficiency of these skills) was developed to support the adoption of key principles. The training strategy also incorporates a "train the trainer" approach to enable CBs who have successfully completed training to train new staff or faculty.

In the quest for deciphering disease-associated biomarkers, high-performing tools for multiplexed protein expression profiling of crude clinical samples will be crucial. Affinity proteomics, mainly represented by antibody-based microarrays, have during recent years been established as a proteomic tool providing unique opportunities for parallelized protein expression profiling. But despite the progress, several main technical features and assay procedures remains to be (fully) resolved. Among these issues, the handling of protein microarray data, i.e. the biostatistics parts, is one of the key features to solve. In this study, we have therefore further optimized, validated, and standardized our in-house designed recombinant antibody microarray technology platform. To this end, we addressed the main remaining technical issues (e.g. antibody quality, array production, sample labelling, and selected assay conditions) and most importantly key biostatistics subjects (e.g. array data pre-processing and biomarker panel condensation). This represents one of the first antibody array studies in which these key biostatistics subjects have been studied in detail. Here, we thus present the next generation of the recombinant antibody microarray technology platform designed for clinical immunoproteomics.

Teaching epidemiology and biostatistics is a challenge for medical teachers. Formative evaluation has been shown to improve the learning outcomes in various medical subjects. However, the effectiveness of formative evaluation in the subject of epidemiology has yet to be clearly demonstrated.

Clinical progression of colorectal cancers (CRC) may occur in parallel with distinctive signaling alterations. We designed multidirectional analyses integrating microarray-based data with biostatistics and bioinformatics to elucidate the signaling and metabolic alterations underlying CRC development in the adenoma-carcinoma sequence.

Innovative clinical trial designs for glioblastoma (GBM) are needed to expedite drug discovery. Phase 0, window of opportunity, and adaptive designs have been proposed, but their advanced methodologies and underlying biostatistics are not widely known. This review summarizes phase 0, window of opportunity, and adaptive phase I-III clinical trial designs in GBM tailored to physicians.

Electronic medical records are emerging as a major source of data for clinical and translational research studies, although phenotypes of interest need to be accurately defined first. This article provides an overview of how to develop a phenotype algorithm from electronic medical records, incorporating modern informatics and biostatistics methods.

We assessed the safety, tolerability, pharmacokinetics, preliminary antitumour activity and pharmacodynamics of danvatirsen, an antisense oligonucleotide targeting signal transducer and activator of transcription 3 (STAT3), monotherapy and danvatirsen plus durvalumab, an antiprogrammed cell death ligand 1 monoclonal antibody, in patients with advanced solid malignancies.

To model the relationship of an area-based measure of a breast cancer screening and geographic area deprivation on the incidence of later stage breast cancer (LSBC) across a diverse region of Appalachia.