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    Biostatistical scientists

    Empirical Literature Math Research Engineer Science Social Science

    Biostatistical scientists bolster the validity of research

    More empirical rigor, precision and accuracy, and internal and external validity

    After my last post, I want to expand upon what the literature does to the person. One of my professors in graduate school said, "The literature changes you." At the time, I thought this was the dorkiest statement of all time. As a first year PhD student, I had NO IDEA what research constituted in regards to knowing the empirical literature.

    The truth is, the literature DOES change you. It led me to fight an uphill battle for 6 years in the name of isomorphism. Come hell or high water, it will be published! (Manuscript currently under review with The Clinical Supervisor)

    When I started my job as an assistant professor of biostatistics, I knew that I needed to get vested in the statistical consultation, evidence-based medicine, diagnostic testing, and epidemiology literature. One thing that really impacted me was an article that stated something to the effect of "The best biostatistical consultants are biostatistical scientists." 

    Biostatistical scientists conduct collaborative as well as their own research. They provide high quality consultation to researchers from the inception to the publication of a research study. They teach courses related to empirical and statistical reasoning to residents, fellows, faculty, physicians, and staff. Lastly, and what really struck a chord with me, was that biostatistical scientists are supposed to invent new methods for applied practice.  

    Understand something, I LOVE MATH.  And, I LOVE SCIENCE. But, mathematical notation and emerging mathematical theory are not within span of competencies. I came from a social science background where higher order mathematics are not requisite parts of the curricula. So, I knew that my conceptual and applied competencies related to math would not "cut it" in comparison to my fellow colleagues and academicians that specialize in these AWESOME FIELDS.  

    Therefore, my idea/invention/new method would have to come from my conceptual and applied background. If you are reading this post, you are looking at the result of the literature's impact on me and a lot of hard work.  

    I hope that it helps you in all of your future research endeavors.

    EH
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    Mastery of the literature

    Effect Size "gold Standard" Outcome Novel Research Question Relevant Research Question Research Design Statistical Power Analysis

    Mastery of the literature leads to relevant research questions

    Become an expert in the empirical field of endeavor

    There is nothing more important when designing and conducting research than being heavily vested in the associated knowledge base. Research questions are born and formulated out of the literature. One cannot argue for a "gap" in the literature unless he or she has put forth the time and effort to know all of the literature. The literature also makes it very easy to make hard decisions in the preliminary phases of study planning.

    Here is what the literature can do for you:

    1. Give you an evidence-based measure of effect to use in an a priori power analysis. It will show more empirical rigor on your part if you use the values from the most current and highest-quality evidence available.

    2. Help you choose the "gold standard" outcome that is most generalizable and applicable to your audience and peers. Using the best outcome measure available increases the internal validity of your study as well. If the same outcome is used in many studies, then it has more validity evidence to back it up. This, again, shows stronger empirical reasoning on your part.

    3. Allow you to ask a question that is relevant and that will generate new knowledge. You will be able to pass the "So what?" question with ease when you know the literature. You will know what new knowledge needs to be generated and how it is relevant in the context of the existing literature.

    4. Help you choose the correct research design to answer your research question. If you find that the literature only has observational evidence related to your area of interest, then you can make the informed decision to employ a more complex design to yield causal effects.   
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    McNemar's as a post hoc test for Cochran's Q

    Cochran's Q Test Kruskal-Wallis Mann-Whitney U McNemar's Test Non-parametric Statistics

    McNemar's can be used as a post hoc test

    Significant main effects for Cochran's Q need to be explained

    Non-parametric tests like chi-square, fisher's exact test, Kruskal-Wallis, Cochran's Q, and Friedman's ANOVA do not have post hoc analyses to explain significant main effects. In order to conduct these post hoc anlayses, researchers have to resort to using subsequent non-parametric tests for two groups.

    In a prior post, I explained how Mann-Whitney U tests were used in a post hoc fashion for significant main effects found with Kruskal-Wallis analyses. This is pertinent for between-subjects tests.

    If you are using a within-subjects design with three or more observations of a dichotomous categorical outcome, you utilize Cochran's Q test to assess main effects. If a significant main effect is found, then McNemar's tests have to be employed for post hoc group comparisons. Significant post hoc tests (or relative risk calculations) will provide evidence of significant differences across observations or within-subjects.

    Non-parametric statistics should be employed more often than they are in the literature. Many published studies use small sample sizes and ordinal or categorical outcomes. The statistical assumptions of more power parametric statistics can often not be met with these types of designs. Non-parametric statistics are robust to these violations and should be used accordingly. Post hoc analyses are important in non-parametric statistics, just like in parametric statistics. 
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    Bonferroni corrections

    Bonferroni Type I Error

    Correct for increased Type I error rates when testing multiple hypotheses

    Divide the alpha value by the number of tests being run

    The Bonferroni correction is a stalwart of statistical and empirical reasoning. Statistics has its flaws and its benefits. Statistics are everywhere but not always understood. Statistics are used to answer research questions...but they can sometimes be employed in an incorrect fashion or in a very BIASED fashion. Mark Twain said, "There are lies, damn lies, and statistics."

    The Bonferroni correction is used to account for increased experimentwise error rates when testing multiple hypotheses. Experimentwise error rates are used to describe the increased chances of committing a Type I error when running multiple chi-squares, t-tests, ANOVAs, and other statistics concurrently. You are simply more likely to detect statistical significance by chance with the more statistical tests that you run.  

    The Bonferroni correction keeps researchers HONEST in regards to reporting significant main effects of clinical merit. It further deters researchers from making erroneous conclusions based on large sample sizes and implausible effect sizes.

    In order to calculate the Bonferroni-corrected alpha value to achieve statistical significance when testing multiple hypotheses concurrently, divide the alpha value of .05 by the number of hypotheses you are testing. So, if I was assessing the differences between men and women on four (4) different outcomes, (.05 / 4) = .013. This means that the inferential statistic for any of our four outcomes would have to be less than .013 to be statistically significant (rather than just being lower that the normal .05).  

    Publications have caught on to the utility and relevance of the Bonferroni correction. Some journals specify its use in the author guidelines and will reject manuscripts automatically if the correction is not used for multiple hypotheses.  

    In conclusion, use the Bonferroni!
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    Feasible research questions are answerable

    Effect Size Feasible Research Questions Research Design Research Question Sample Size Statistical Power Analysis Statistical-power-test

    Feasible research in terms of scope, time, resources, and expertise

    Changing the face of medicine versus completing a research study

    I have conducted thousands of statistical consultations over the years and have worked with many novice resident researchers over that time. One cannot help but admire the spirit, energy, and motivation of young people wanting to make an impact on medicine through research. I enjoy the zeal and drive of bright people wanting to be physicians and researchers. This is a good thing!

    That being said, I spend a lot of my time with novice researchers using deductive reasoning to hone down their research questions into something tangible and feasible. They come into the office with an idea that will change medicine forever and we will be cruising around the Caribbean in a year! This has never been researched before!  No one has ever done this before! Trust me, I want all of these proclamations to be true and I also want to change the face of medicine. Yet, most times it just not feasible to do so given the time, resources, participants, competencies and environment associated with the study.

    I focus on a few primary areas when it comes to feasible research questions with my consultees:

    1. Participant pool - Are there enough participants available in the immediate clinical or empirical environment to achieve adequate statistical power for inferential analyses? How will you recruit the participants? What are your inclusion and exclusion criteria? Inclusion and exclusion criteria may need to be modified to increase sample size.

    2. Effect size - Small effect sizes require large sample sizes.    

    3. Research design - Retrospective designs are always more feasible because the data already exists.

    4. Communication - Research never occurs in isolation. Researchers should communicate and collaborate with their peers regarding their research projects. Attendings and academic physicians can give you ideas on how to feasibly conduct your research.

    5. Time - What is the time frame for the study from inception to publication? How much time do you have to set aside for the research study? Does the completion of your research coincide with abstract deadlines of interest?

    6. Power analysis - Conduct an a priori power anlaysis based on an evidence-based measure of effect to see if the study is feasible in regards to sample size needed to achieve power.
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    Acquiring the clinical evidence

    Acquiring Clinical Evidence Boolean Operators Search Query Specificity In Literature Searching

    Specificity in literature searching

    Boolean phrases help acquire the correct literature

    I became highly vested in the EBM literature during my second year as a professor for purposes of assessing resident/fellow/faculty/physician perceptions of EBM-based practice. I wanted to know how "knowledge gaps" were really experienced, could they be experienced, and what they did about it.  

    However, I was most interested in how they accessed clinical evidence at the point of care. Some said it was readily available at bed side and others said they had their assistants run their searches for them. Others said that they read at home or had new evidence emailed to them by predetermined groups of professionals packaging evidence.

    Chances are, you have many options available to you at your institution of higher learning or applied clinical practice. But most people just go straight to Google, Bing, or Yahoo to get some quick information. If this is your chosen method, then try to use Boolean operators in your quick searches to improve the specificity (finding quality evidence) of your search queries.

    1. When you put quotations, " " around words or a phrase, then only those words inside the quotations will be searched. And, because there are so many nebulous words in statistics, just type the word with parentheses. Ex: "Logarithmic transformation"

    When typing out a phrase or series of words in quotations, the search will follow the words in the exact order you typed them into the search engine. "how to string a guitar," or "nearest pizza place" are good examples. The search would yield specific sites and information on those two queries due to the quotations.

    2. The word, OR, requires that both terms in the search query appear in the webpage or document. Using OR broadens the search yield. It can also be used to link isomorphic, similar, and interdependent concepts.

    The search "statistics" OR "precision" OR "measurement" could lead to a vast number of resources linking the three constructs and can lead to new understanding of how the three interact. If you are researching an abstract construct or phenomena, the OR statement can pay vast dividends as you search the literature.

    3. The word, AND, is the default of the Boolean system and is used to separate other Boolean operators. With more use of AND, the search yield will decrease. It is used to amalgamate the different "parts" of the search query together.  
    The search "hotel" AND "arena" AND "paid parking," will give you a very specific search result related to close hotels with valet services that are close to the local sport arena.  

    4. The words, AND NOT, will exclude anything following it in the search query. It is a good phrase to use after you have performed a few searches and have seen the same redundant sites or information pop-up. Doing this eliminates the possibility for any of the query after AND NOT from being searched.

    5. Parentheses must be used with OR statements when there is another Boolean term in the search query.  

    For example: "hotel" AND "arena" AND (Marriott OR Hilton)

    This would find you a hotel close to the arena that was either Marriott or Hilton.  

    5. The truncation, *, is a powerful search tool that will include all forms of the parent word.

    For example: Isomorph* - isomorph, isomorphs, isomorphic, isomorphism, isomorphisms

    These simple Boolean operators, OR, AND, AND NOT, parentheses, and truncations can yield much more specific (identifying the correct information needed from search) search finding.