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    Merging databases

    Database Management Merging Databases

    Grouping variables assist in merging databases

    Database management is essential when conducting research

    I work with brilliant people in academic and clinical medicine.  These individuals are dedicated and professional people that work really hard to serve patient populations. Database management is not an everyday concern for most medical professionals. It IS an everyday concern for medical professionals that analyze data.

    When working with a single research database, multiple databases, or secondary analysis of existing datasets, it is highly important to have some method for identifying individual participants with unique identifiers. Also, there must be some sort of primary "grouping" variable that distinguishes groups, datasets, or strata in more complex databases.

    The unique identifiers serve the purpose of meeting the assumption of independence of observations. The "grouping" variable serves as a means for sensitivity and subgroup analyses. It further helps data analysts work with secondary, tertiary, and ancillary research questions.

    The efforts you make as a researcher to have an objective and consistent method for data entry, maintenance, and analysis pays great dividends in the analysis phase of a research study. It is also the difference between it taking your statistician FIVE MINUTES to analyze your data versus FIVE MONTHS, or a happy statistical day versus a nightmare scenario.
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    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.  
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    Construct specification in survey research

    Construct Specification Isomorphism Operationalization Survey Survey Construct Specification Survey Methods

    Construct specifications help operationalize phenomena

    Construct specifications should be completed for all surveys

    Coming from a social science background, I understand that social scientists can spend the vast majority of their time just trying to measure for the construct or behavior they are interested in. I spent a year of my life constructing a survey instrument to measure for the construct of isomorphism in clinical supervision. It is an exciting and yet daunting task to create something from nothing, and I commend social scientists that try to capture variance in human beings.

    Surveys can be used to answer "unique" research questions. And by unique, things like isomorphism that exist at a very abstract or unconscious level are perceived in any number of ways to any number of people. Also remember, these types of "unique" constructs often exact a reaction of "cognitive dissonance" in your peers because they are "unknown," "different," or "weird."

    All of that being said, the VERY FIRST thing you should do when conducting a survey research study is create a construct specification related to the construct you are measuring for in the proposed survey.

    Remember, the survey should be written to represent just ONE construct. It is important to give an operational definition to the ONE construct. Define it in objective and measurable terms if at all possible, and use that definition as the basis for building subsequent components, content areas, and "factors." The construct specification serves as a springboard for showing how your construct exists or is theorized to exist in the context of the empirical literature. You are essentially making an argument, based on the literature in the area, that the construct can be, should be, or has not been properly assessed.

    Creating a construct specification also constitutes seeking out existing survey instruments that measure something theoretically, conceptually, or empirically linked to your construct of interest. Find the "gold standard" survey instruments with the most validity evidence and seek out permission for their use in your study (if needed).  

    Explicitly describe the population of interest associated with your survey. What are the inclusion and exclusion criteria for being a potential participant in your survey study? How will you go about recruiting participants? Will you use incentives?  How will you administer the survey?  Will you be able to meet sample size requirements of 150-300 for a pilot study and 300-1,000 for a validation study?

    The next section of a construct specification operationalizes the content areas of your construct. Each content area should have an operational definition. Then, each component (or item) that makes up the content area should be defined and described in regards to its relevance to the construct. Lastly, give a citation from the empirical literature area to back up the argument for relevance. Do this for each component (or item) for each content area of the constuct. This can be a tedious process for more "abstract" constructs, but it is essential to provide an empirical framework/argument so that your audience can the proper frame of reference for perceiving the construct.

    The last section of the construct specification is the "Table of specifications" where you given numerical designations of the percentage of the survey allocated to each content area of the construct. The number of items and content areas and their coverage within the survey must be equivalent to the make-up of the second section of the construct specification. If your construct is theorized to be composed of three content areas and one of the content areas represents 60% of the literature, then that content area should represent 60% of the items in your survey.

    Going through this process is an excellent opportunity to become vested in the empirical literature and become an expert in the field. It is a time-consuming process to build a strong construct specification, but it provides a much higher quality end product.  
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    The Bcc line

    Mass Emails In Survey Research Survey

    The Bcc line of an email can be used to send mass emails

    A survey researcher's best friend

    I designed and tested a survey instrument for purposes of my dissertation. I published the survey to an online survey administration site.

    Then, I went to every website for every Counselor Education graduate program in the United States and Canada and got as many emails of students and faculty that I could find. All in all, I spent about two months of my life putting together a list of over 3.200 emails.  

    After seeking out the help of the IT department, I learned how to send out mass emails to potential participants WITHOUT the emails arriving as junk or spam. Here are the steps:

    1. Type every email address in ONE column of an Excel database.

    2. Open up a "New Message" email and put your own email address into the To: box.

    3. Click on the Cc: button to open up the menu.

    4. Highlight the column of email addresses, right click your mouse, click Copy.

    5. Paste the emails into the Bcc: box in the email heading.

    6. Type your email out (with informed consent) and embed the link to the online version of your survey into the email.

    7.  lick Send.

    It's that easy!
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    Non-inferiority trials and the Affordable Care Act

    Affordable Care Act Clinical Pathways Non-inferiority

    Non-inferiority trials will be popular with ACA

    Analysis of cost savings and quality care

    With the dissemination of the Affordable Care Act into the healthcare system, clinical pathways and efficiency in treatment will be mainstays within medicine.  Hospitals and institutions will have to find ways to provide quality care in an economically viable fashion both for patients and the "bottom line."

    Healthcare professionals are always looking at ways to cut costs.  One way that researchers will be able to assist in these endeavors is to promote the use of non-inferiority designs.  By providing a cheaper treatment that is JUST AS GOOD as the current "gold standard" treatment, costs can be reduced dramatically as it relates to clinical treatment regimens.  

    There must be an empirically and clinically validated margin of non-inferiority.  Sample sizes will increase as relevant margins decrease.  These trials are most relevant when 1) secondary endpoints are better, 2) treatments are cheaper or easier to administer, 3) randomized controlled trials are not feasible or ethical, and 4) when compliance to treatment will be higher in comparison to the "gold standard."*

    Better secondary endpoints, cheaper and more patient-friendly treatments, and higher rates of compliance will all be necessary as the Affordable Care Act promulgates into the healthcare marketplace.   
    *Lesaffre, E.  Superiority, equivalence, and non-inferiority trials.  Bull NYU Hosp Jt Dis.  2008;66(2): 150-154.
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    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.