Statistical Methods for Cardiovascular Researchers.
ABSTRACT: Biostatistics continues to play an essential role in contemporary cardiovascular investigations, but successful implementation of biostatistical methods can be complex.To present the rationale behind statistical applications and to review useful tools for cardiology research.Prospective declaration of the research question, clear methodology, and study execution that adheres to the protocol together and serve as the critical foundation of a research endeavor. Both parametric and distribution-free measures of central tendency and dispersion are presented. T testing, ANOVA, and regression analyses are reviewed. Survival analysis, logistic regression, and interim monitoring are also discussed. Finally, common weaknesses in statistical analyses are considered.Biostatistics can be productively applied to cardiovascular research if investigators (1) develop and rely on a well-written protocol and analysis plan, (2) consult with a biostatistician when necessary, and (3) write results clearly, differentiating confirmatory from exploratory findings.
Project description: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.
Project description:<b>Introduction: </b>Adequate knowledge of biostatistics is essential for healthcare providers to stay up to date with medical advances and maintain an evidence-based practice. However, training in clinical research in Canadian residency programs varies considerably. Our study aimed to determine Canadian urology trainees' knowledge of biostatistics and interpretation of the scientific literature.<br><br><b>Methods: </b>We conducted a national survey of all Canadian urology residents and fellows, which assessed experiences with biostatistics, self-reported confidence with statistical questions, and knowledge of biostatistical concepts.<br><br><b>Results: </b>Out of 201 urology trainees, 74 (36.8%) responded to the survey. The majority of respondents disagreed or strongly disagreed with the fact that they had sufficient training in biostatistics in medical school (67.6%) or that their current knowledge was sufficient for the rest of their career (66.1%). If given the chance, 82.3% of respondents would like to learn more about biostatistics. About half of respondents were able to correctly identify ordinal variables (51.5%), discrete variables (52.9%), or interpret adjusted odds ratios (50.0%). Despite senior residents reporting more confidence on biostatistical questions, our results did not demonstrate significant differences in overall knowledge according to level of training or experience with biostatistics.<br><br><b>Conclusions: </b>Our results identified important knowledge gaps among current Canadian urology trainees. Most trainees do not believe they have sufficient training in biostatistics. Knowledge of basic statistical concepts was lower than expected and did not significantly differ according to level of training. Our results highlight the need for structured, dedicated training in biostatistics for urology trainees within the new Competence by Design teaching framework.
Project description:Statistical principles and methods are critical to the success of biomedical and translational research. However, it is difficult to track and evaluate the monetary value of a biostatistician to a medical school (SoM). Limited published data on this topic is available, especially comparing across SoMs. Using National Institutes of Health (NIH) awards and American Association of Medical Colleges (AAMC) faculty counts data (2010-2013), together with online information on biostatistics faculty from 119 institutions across the country, we demonstrated that the number of biostatistics faculty was significantly positively associated with the amount of NIH awards, both as a school total and on a per faculty basis, across various sizes of U.S. SoMs. Biostatisticians, as a profession, need to be proactive in communicating and advocating the value of their work and their unique contribution to the long-term success of a biomedical research enterprise.
Project description:There are 69 National Cancer Institute (NCI) designated Cancer Centers (CCs) in the United States. Biostatistical collaboration is pivotal in cancer research, and support for a cancer biostatistics shared resource facility (C-BSRF) is included in the award. Although the services and staff needed in a C-BSRF have been outlined in general terms and best practices for biostatistical consultations and collaboration in an academic health center have been agreed upon, implementing these practices in the demanding setting of cancer centers interested in pursuing or maintaining NCI designation remains challenging. We surveyed all C-BSRF websites to assess their organizational charts, governance, size, services provided, and financial models and have identified 10 essential practices for the development of a successful C-BSRF. Here, we share our success with, and barriers to, implementation of these practices. Showcasing development plans for these essential practices resulted in an NCI score of "Excellent to Outstanding" for our C-BSRF in 2015, and performance metrics in 2016-2017 demonstrated notable improvement since our original Cancer Center Support Grant (CCSG) application in 2014. We believe that the essential practices described here can be adapted and adjusted, as needed, for CCs of various sizes and with different types of cancer research programs.
Project description:Survival rates of patients with osteosarcoma have remained stagnant over the last thirty years. Better understanding of biology, new therapeutics, and improved biomarkers are needed. The Children's Oncology Group (COG) addressed this need by developing one of the largest osteosarcoma biorepositories ever, containing over 15,000 tumor and tissue samples from over 1,500 patients.The biology study P9851 and the banking study AOST06B1 has enrolled 1,787 patients (as of September, 2013). Clinical information was lacking on 510 patients on P9851, who were not enrolled on a concurrent therapeutic trial. The value of these specimens was diminished. The lack of statistical support available for biology projects slowed the analysis of several critical studies. The QuadW Foundation, CureSearch, and the COG formed the Childhood Sarcoma Biostatistics and Annotation Office (CSBAO) to provide the infrastructure and address these needs by linking clinically annotated patient data to archived tissue samples and to develop biostatistical support for childhood sarcoma research.Originally 5.3% of samples from the 510 patients on P9851 not enrolled on a therapeutic study had full clinical annotation. The efforts of the CSBAO have linked clinical annotation to 90.8% of those specimens and provided statistical analyses to several studies that had used COG samples. As a result, 24 biology studies in osteosarcoma have been completed and published in peer-reviewed journals.These samples and in-silico data are available to the research community for basic and translational science projects to improve the biological understanding and treatment of patients affected by osteosarcoma.
Project description:<h4>Background</h4>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.<h4>Methods</h4>Statistics journal articles published in 2010 from 35 statistical journals were reviewed by two biostatisticians. Generalized linear mixed models were used to determine which characteristics (author, article, and journal) were independently associated with citation counts (as of April 1, 2017) in other peer-reviewed articles.<h4>Results</h4>Of 722 articles reviewed, 428 were classified as new biostatistics methods. In a multivariable model, for articles that were not freely accessible on the journal's website, having code available appeared to offer no boost to the number of citations (adjusted rate ratio = 0.96, 95% CI = 0.74 to 1.24, p = 0.74); however, for articles that were freely accessible on the journal's website, having code available was associated with a 2-fold increase in the number of citations (adjusted rate ratio = 2.01, 95% CI = 1.30 to 3.10, p = 0.002). Higher citation rates were also associated with higher numbers of references, longer articles, SCImago Journal Rank indicator (SJR), and total numbers of publications among authors, with the strongest impact on citation rates coming from SJR (rate ratio = 1.21 for a 1-unit increase in SJR; 95% CI = 1.11 to 1.32).<h4>Conclusion</h4>These analyses shed new insight into factors associated with citation rates of articles on new biostatistical methods. Making computer code available to readers is a goal worth striving for that may enhance biostatistics knowledge translation.
Project description:OBJECTIVES:To assess biostatistical quality of study protocols submitted to German medical ethics committees according to personal appraisal of their statistical members. DESIGN:We conducted a web-based survey among biostatisticians who have been active as members in German medical ethics committees during the past 3 years. SETTING:The study population was identified by a comprehensive web search on websites of German medical ethics committees. PARTICIPANTS:The final list comprised 86 eligible persons. In total, 57 (66%) completed the survey. QUESTIONNAIRE:The first item checked whether the inclusion criterion was met. The last item assessed satisfaction with the survey. Four items aimed to characterise the medical ethics committee in terms of type and location, one item asked for the urgency of biostatistical training addressed to the medical investigators. The main 2×12 items reported an individual assessment of the quality of biostatistical aspects in the submitted study protocols, while distinguishing studies according to the German Medicines Act (AMG)/German Act on Medical Devices (MPG) and studies non-regulated by these laws. PRIMARY AND SECONDARY OUTCOME MEASURES:The individual assessment of the quality of biostatistical aspects corresponds to the primary objective. Thus, participants were asked to complete the sentence 'In x% of the submitted study protocols, the following problem occurs', where 12 different statistical problems were formulated. All other items assess secondary endpoints. RESULTS:For all biostatistical aspects, 45 of 49 (91.8%) participants judged the quality of AMG/MPG study protocols much better than that of 'non-regulated' studies. The latter are in median affected 20%-60%?more often by statistical problems. The highest need for training was reported for sample size calculation, missing values and multiple comparison procedures. CONCLUSIONS:Biostatisticians being active in German medical ethics committees classify the biostatistical quality of study protocols as low for 'non-regulated' studies, whereas quality is much better for AMG/MPG studies.
Project description:OBJECTIVES:The overall purposes of this first US national pilot study were to (1) test the feasibility of online administration of the Bioethical Issues in Biostatistical Consulting (BIBC) Questionnaire to a random sample of American Statistical Association (ASA) members; (2) determine the prevalence and relative severity of a broad array of bioethical violations requests that are presented to biostatisticians by investigators seeking biostatistical consultations; and (3) establish the sample size needed for a full-size phase II study. DESIGN:A descriptive survey as approved and endorsed by the ASA. PARTICIPANTS:Administered to a randomly drawn sample of 112 professional biostatisticians who were ASA members. PRIMARY AND SECONDARY OUTCOME MEASURES:The 18 bioethical violations were first ranked by perceived severity scores, then categorised into three perceived severity subcategories in order to identify seven 'top tier concern violations' and seven 'second tier concern violations'. RESULTS:Methodologically, this phase I pilot study demonstrated that the BIBC Questionnaire, as administered online to a random sample of ASA members, served to identify bioethical violations that occurred during biostatistical consultations, and provided data needed to establish the sample size needed for a full-scale phase II study. The No. 1 top tier concern was 'remove or alter some data records in order to better support the research hypothesis'. The No. 2 top tier concern was 'interpret the statistical findings based on expectation, not based on actual results'. In total, 14 of the 18 BIBC Questionnaire items, as judged by a combination of 'severity of violation' and 'frequency of occurrence over past 5 years', were rated by biostatisticians as 'top tier' or 'second tier' bioethical concerns. CONCLUSION:This pilot study gives clear evidence that researchers make requests of their biostatistical consultants that are not only rated as severe violations, but further that these requests occur quite frequently.
Project description: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.
Project description:Purpose: While the intellectual and scientific rationale for research collaboration has been articulated, a paucity of information is available on a strategic approach to facilitate the collaboration within a research network designed to reduce health disparities. This study aimed to (1) develop a conceptual model to facilitate collaboration among biostatisticians in a research network; (2) describe collaborative engagement performed by the Network's Data Coordinating Center (DCC); and (3) discuss potential challenges and opportunities in engaging the collaboration. Methods: Key components of the strategic approach will be developed through a systematic literature review. The Network's initiatives for the biostatistical collaboration will be described in the areas of infrastructure, expertise and knowledge management and experiential lessons will be discussed. Results: Components of the strategic approach model included three Ps (people, processes and programs) which were integrated into expert management, infrastructure management and knowledge management, respectively. Ongoing initiatives for collaboration with non-DCC biostatisticians included both web-based and face-to-face interaction approaches: Network's biostatistical capacities and needs assessment, webinar statistical seminars, mobile statistical workshop and clinics, adjunct appointment program, one-on-one consulting, and on-site workshop. The outreach program, as a face-to-face interaction approach, especially resulted in a useful tool for expertise management and needs assessment as well as knowledge exchange. Conclusions: Although fostering a partnered research culture, sustaining senior management commitment and ongoing monitoring are a challenge for this collaborative engagement, the proposed strategies centrally performed by the DCC may be useful in accelerating the pace and enhancing the quality of the scientific outcomes within a multidisciplinary clinical and translational research network.