Tags

  • Published on

    Research Engineer makes applied research and statistics easier

    Chi-square Database Management Diagnostic Testing Effect Size Epidemiology Evidence-based Medicine Kruskal-Wallis Kurtosis Mann-Whitney U Non-parametric Statistics Normality Odds Ratio With 95% CI Psychometrics Relative Risk Research Research Design Research Question Sample Size Skewness Statistical Assumptions Statistical Power Analysis Statistics Survey Variables Wilcoxon

    Research Engineer is designed to get you to the correct research question, research design, sample size, database, and statistical test

    Based on your decisions to the questions presented, you will get to right place

    A few words on what I'm doing on here. I am a biostatistician, methodologist, psychometrician, and counselor. Everyday, the incredibly intelligent people I work with including physicians, residents, fellows, staff, and faculty feel anxiety when it comes to statistics and research. Research has shown that statistics can induce cognitive dissonance in an individual due to limited experiences and competencies. The collective unconscious has sequestered statistics and research into a dark corner and that's scary.

    Research and statistics are the methods by which we, as scientists, analyze, synthesize, and evaluate our research findings in a manner that can be generalized to the appropriate audience. If our methods for communicating research findings causes cognitive dissonance, just because it relates to research and statistics, then how can one ever really be able to generalize the clinical literature and integrate it into clinical practice?

    After seven years of being the one to induce cognitive dissonance in others related to research and statistics, I decided to make a useful tool for students and researchers that could alleviate some of the feelings of anxiety associated with research and statistics. I built Research Engineer.

    Research Engineer is designed to get you to the correct research question, research design, sample size, database, statistical test, evidence-based medicine intervention, diagnostic calculation, epidemiological calculation, variables, surveys, psychometrics, and educational framework to answer your current question (and future questions). 

    I am trying to bring research and statistics out of the collective unconscious and into the conscious mind where it can be effectively communicated among researchers, scientists, and students by creating this decision engine. It is easy to get to the correct research or statistical component, just answer the questions that I present you in the webpages and click on the buttons with your answer in them. Also, the step-by-step methods for conducting and interpreting each statistical test in SPSS are presented on their respective webpages. 

    You can also contact me via phone, social media, and email at any time in you have questions. If you need some help conducting statistics for a research project, I have eight years of experience across thousands of individual projects and I would love to help you on your study.  We can negotiate prices if you are an undergraduate or graduate researcher. 

    In conclusion, Research Engineer makes choosing research methods and statistical tests MUCH EASIER. Just answer the questions embedded in the various decision engines and get to the correct method or test, EVERY TIME.

    Thanks for your continued support, dear friends and colleagues. And many thanks and salutations to the individuals that use Research Engineer. I am honored and humbled to have this great opportunity to create a very useful and unique website. You all are the ones that make it shine!

    ​Sincerely,

    R. Eric Heidel, Ph.D.
    Assistant Professor of Biostatistics
    ​Affiliate Professor of Biomedical Engineering
    Department of Surgery
    Office of Medical Education, Research, and Development
    University of Tennessee Graduate School of Medicine
    Owner and Operator, Scale, LLC
  • Published on

    Research Engineer is the world's first online decision tree for applied research and statistics

    Database Management Diagnostic Testing Education Epidemiology Evidence-based Medicine Psychometric Tests Research Design Research Engineer Research Question Statistical-power-test Statistics Survey Variables

    Fully automated and freely accessible to researchers around the world

    The first interactive decision tree that integrates statistical assumptions and post hoc analyses

    Research Engineer is going to be presented for the first time in a public forum next Tuesday. I'm pretty excited to let all of my colleagues know what I've been up to these past five months. I realized earlier today that Research Engineer has completely changed my life for the better. And I'm so thankful to all of those that have supported me along the way.

    And to visitors of this website, I extend my most gracious and humble thanks for your patronage. The website will continue to grow and help you in all of your future empirical endeavors.

    I have built the world's first online decision engine for research questions, research designs, statistics, statistical power, databases, evidence-based medicine, survey design, psychometrics, epidemiology, diagnostic testing, variables, and education. I look forward to the future!
  • Published on

    Adjusted odds ratios in medicine

    Adjusted Odds Ratio Evidence-based Medicine Logistic Regression Medicine Unadjusted Odds Ratio

    Logistic regression yields adjusted odds ratios

    Adjusted odds ratios are easier generalized to clinical situations

    There is a strong need in clinical medicine for adjusted odds ratios with 95% confidence intervals. Medicine, as a science, often uses categorical outcomes to research causal effects. It is important to assess clinical outcomes (measured at the dichotomous categorical level) within the context of various predictor, clinical, prognostic, demographic, and confounding variables. Logistic regression is the statistical method used to understand the associations between the aforementioned variables and dichotomous categorical outcomes.

    Logistic regression yields adjusted odds ratios with 95% confidence intervals, rather than the more prevalent unadjusted odds ratios used in 2x2 tables. The odds ratios in logistic regression are "adjusted" because their associations to the dichotomous categorical outcome are "controlled for" or "adjusted" by the other variables in the model. The 95% confidence interval is used as the primary inference with adjusted odds ratios, just like with unadjusted odds ratios. If the 95% confidence interval crosses over 1.0, then there is a non-significant association with the outcome variable.  

    Adjusted odds ratios are important in medicine because very few physiological or medical phenomena are bivariate in nature. Most disease states or physiological disorders are understood and detected within the context of many different factors or variables.  Therefore, to truly understand treatment effects and clinical phenomena, multivariate adjustment must occur to properly account for clinical, prognostic, demographic, and confounding variables.  
  • Published on

    Applying clinical evidence and journal clubs

    Evidence-based Medicine Systematic Review

    Journal clubs should focus strictly on RCTs and meta-analyses

    Baseline competencies are needed before applying clinical evidence

    I participate in a lot of journal clubs at my institution. As the resident "stats person," I get called by residents before they present at journal club to help them discern the statistical methods of papers. I am also asked to attend journal clubs to assist in putting statistical findings into relevant clinical contexts.

    However, a pedagogical disservice is given to learners every time they are asked, "Would this evidence change your clinical practice?" This is a rhetorical question...they must NEVER let one piece of evidence change their clinical practice! This is especially true if the journal club topic that week focuses on observational or quasi-experimental designs!

    The famous text by Straus et al.* stipulates that the individual trial or observational study is the LEAST FAVORABLE type of evidence to be sought out in applied clinical medicine. Systematic reviews, synopses of syntheses, and summaries are the most tangible and relevant pieces of clinical evidence when it comes to real-life patient populations.

    A better use of a graduate level practitioner's time would be to seek out the highest levels of evidence in journal club, all the time. Observational studies are much more feasible for busy residents and fellows to conduct for academic requirements. But when it comes to teaching residents how to apply clinical evidence to their practice in the journal club environment, the highest levels of evidence should be used by faculty to correctly model reality-based clinical practice.    
    *Straus SE, Glasziou P, Richardson WS, Haynes RB.  (2011).  Evidence-based medicine:  How to practice and teach it.  Edinburgh: Churchill LIvingston.  
  • Published on

    Evidence-based medicine and its applications

    Acquiring Clinical Evidence Applying Clinical Evidence Appraisal Of The Literature Appraising Clinical Evidence Asking Clinical Questions Assessing Clinical Practice Bloom's Taxonomy Cognitive Dissonance EBM Evidence-based Medicine PICO

    Critical appraisal of the clinical evidence

    The cart before the horse

    I'm getting ready to add an Education section to the website, I decided to go back to first principles.  Bloom's Taxonomy had a pervasive impact on my philosophy of learning, teaching, and cognitive complexity.  I used it back in February of this year for an evidence-based medicine (EBM) presentation at work.  Bloom's Taxonomy* stipulated six levels of "knowing" or cognitive complexity.  The six levels in increasing order were knowledge, comprehension, application, analysis, synthesis, and evaluation.  

    Here is the conundrum that Bloom's Taxonomy exacts upon applied EBM practice:

    There are five steps to EBM:  Asking, acquiring, appraising, applying, and assessing.  

    With asking, the EBM literature posits that clinicians experience "cognitive dissonance" when they have a knowledge gap in their clinical practice.  In order to deter the dissonance, the clinician decides to ask a clinical question to fill that gap.

    With acquiring, the clinician uses the PICO (population, intervention, comparator, outcome) mnemonic to acquire the best clinical evidence, given the resources and time available.

    Now we get to critical appraisal of the literature.  When looking at the nomenclature of the word "appraisal," it is reflective of the highest level of "knowing" or cognitive complexity in Bloom's Taxonomy, evaluation.  EBM stipulates that clinicians must be able to critically appraise the methods and statistical analyses of published studies.  This means that clinicians have to have functioning at a very high cognitive level to do this correctly.

    However, past literature has shown that researchers feel anxious and intimidated by statistics due to a lack of experience and competency.**  Also, undergraduate and graduate medical training rarely equips clinicians with the necessary competencies to conduct and effectively interpret clinical research evidence.***

    So, how can your everyday clinician with limited empirical/statistical training who feels "cognitive dissonance" a second time in the five steps of EBM critically appraise the literature?  Therein lies the conundrum, in my opinion.  

    I'm positing that we need to refocus our efforts on the lower echelons of Bloom's Taxonomy by educating physicians, residents, fellows, faculty, pharmacists, nurses, and staff to better understand (knowledge), recognize (comprehension), choose (application), examine (analysis), and design (synthesis) research studies before we can expect them to critically appraise (evaluation) the literature.  
    *Bloom, B. S.; Engelhart, M. D.; Furst, E. J.; Hill, W. H.;Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York: David McKay Company.
    **Marquardt, DW.  Criteria for evaluating the performance of statistical consultants in industry.  The American Statistician 1981; 35; 216-219.
    ***Wegwarth O.  Statistical illiteracy in residents:  What they do not learn today will hurt their patients tomorrow.  Journal of Graduate Medical Education 2013; 5; 340-341.