Statistical Consultation Line: (865) 742-7731
Accredited Professional Statistician For Hire
  • Contact Form

Feasible research questions are answerable

10/31/2014

0 Comments

 

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.

Scale, LLC
0 Comments

Acquiring the clinical evidence

10/30/2014

0 Comments

 

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.

Scale, LLC
0 Comments

Merging databases

10/30/2014

0 Comments

 

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.

Scale, LLC
0 Comments

Adjusted odds ratios in medicine

10/26/2014

1 Comment

 

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.  

Scale, LLC
1 Comment

Construct specification in survey research

10/25/2014

1 Comment

 

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.  

Scale, LLC
1 Comment

The Bcc line

10/24/2014

0 Comments

 

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!

Scale, LLC
0 Comments

Non-inferiority trials and the Affordable Care Act

10/21/2014

0 Comments

 

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.   

Scale, LLC

*Lesaffre, E.  Superiority, equivalence, and non-inferiority trials.  Bull NYU Hosp Jt Dis.  2008;66(2): 150-154.
0 Comments

Applying clinical evidence and journal clubs

10/19/2014

0 Comments

 

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.    

Scale, LLC

*Straus SE, Glasziou P, Richardson WS, Haynes RB.  (2011).  Evidence-based medicine:  How to practice and teach it.  Edinburgh: Churchill LIvingston.  
0 Comments

Multivariate statistical designs

10/17/2014

0 Comments

 

Multivariate statistical tests show evidence of association between predictor variables and an outcome, when controlling for demographic, confounding, and other patient data.

Multivariate statistics are more reflective of real-world medicine

We covered between-subjects and within-subjects analyses in the first Statistical Designs post. Multivariate statistics will be the focus in Statistical Designs 2.

While 90% of statistics reported in the literature fall under the guise of between-subjects and within-subjects analyses, they do not properly account for all of the variance and confounding effects that exist in reality. Multivariate statistics play an important role in empirical reasoning because they allow us to control for various demographic, confounding, clinical, or prognostic variables that mitigate, mediate, and affect the association between a predictor and outcome variable. They are also much more representative of reality and true effects that exist within human populations.

Very few if any relationships or treatment effects in physiology, psychology, education, or life in general are bivariate in nature. Relationships and treatment effects in reality ARE multivariate, diverse, and confounded by any number of characteristics. Therefore, it makes sense that researchers should be conducting multivariate statistics to truly understand human phenomena.  

With this being said, it is important to use multivariate statistics ONLY when you are asking a multivariate research question. Throwing a bunch of variables into a model without some sort of theoretical or conceptual reason for including them can yield false treatment effects and increase Type I errors. Also, these spurious variables can create "statistical noise" which detracts from a model's capability for detecting significant associations.

Choosing the correct multivariate statistic to answer your question is simple. You choose the multivariate analysis based on the outcome.

1. Categorical outcomes - Logistic regression (dichotomous), multinomial logistic regression (polychotomous), Kaplan-Meier, Cochran-Mantel-Haenszel, Cox regression (dichotomous/survival/time-to-event)

2. Ordinal outcomes - Proportional odds regression

3. Continuous outcomes - Factorial ANOVA with fixed effects, factorial ANOVA with random effects, factorial ANOVA with mixed effects, ANCOVA, multiple regression, MANOVA, MANCOVA

4. Count outcomes - Negative binomial regression (variance larger than mean) and Poisson regression (mean larger than variance)

Scale, LLC
0 Comments

Publication of Research Findings

10/12/2014

0 Comments

 

Publish the results of your study in a top-tier journal and become famous!

Be Meticulous, Scrupulous, Obsessive, and Objective

I'm trying to get my applied and methodological research published just like everyone else out there in academia. The one thing I have learned is that all journals have different methods of submission, all with different expectations of writing styles, and varying methods for structuring the paper and citing prior research.

Submitting a research manuscript is a tedious and anxiety-ridden process. It is by no means easy, user-friendly, or logical. I have to make a judicious effort to remember the email address, ID, password, and contact information for each submission at any time. You MUST do this, because editors and reviewers will NOT do it for you.  

Upon rejection (and believe me, it is coming), you then have to completely reformat your manuscript with font and boldface changes and writing styles to meet the requisite needs of the new journal. You do this numerous times at the expense of the Impact Factor just to get published...pretty soon, what you ended with is nothing like what you started with, some months or years ago. It's no longer an original thought, but a mish-mash of editorial comments.  What is one to do?!

1. First and foremost, follow EXACTLY what the "Guideline for Authors" section tells you to do. Because top-tier journals receive many manuscripts, the author guidelines are an easy way to "weed out" manuscripts. ONE simple mistake in a citation or subheading, and they will reject it or send it back for revisions. You may have to COMPLETELY revamp the structure and writing style of the paper, but at least you have the body of the manuscript put together!

2. Also, the editors may have good ideas on how to make your paper better. If you get a rejection, integrate any pertinent changes into your manuscript. However, if you can tell that the reviewer barely read the manuscript (if at all) and gave you superfluous remarks, then you do not want to submit to that publication anyways.

3. DO NOT GIVE UP! Keep submitting your work. Do not every give up on your manuscript. If it is rejected ten times, make changes and revisions, and then send it in an eleventh time. Check the "Information for Authors" section of each publication and make sure that the journal is focused on the correct audience.  

4. Feel free to give the editors of the publication of interest an email or call. Ask if they would be interested in the study you are going to submit and if not, do they have any ideas for other potential publications? And go ahead and feel free to email them when you do not hear back from them, it is their job to get back to you.   

Scale, LLC
0 Comments
<<Previous

    Archives

    March 2016
    January 2016
    November 2015
    October 2015
    September 2015
    August 2015
    July 2015
    May 2015
    April 2015
    March 2015
    February 2015
    January 2015
    December 2014
    November 2014
    October 2014
    September 2014

    Author

    Eric Heidel, Ph.D. is Owner and Operator of Scalë, LLC.

    Categories

    All
    95% Confidence Interval
    Absolute Risk Reduction
    Accuracy
    Acquiring Clinical Evidence
    Adjusted Odds Ratio
    Affordable Care Act
    Alpha Value
    ANCOVA Test
    ANOVA Test
    Applying Clinical Evidence
    Appraisal Of The Literature
    Appraising Clinical Evidence
    A Priori
    Area Under The Curve
    Asking Clinical Questions
    Assessing Clinical Practice
    AUC
    Basic Science
    Beta Value
    Between-subjects
    Biserial
    Blinding
    Bloom's Taxonomy
    Bonferroni
    Boolean Operators
    Calculator
    Case-control Design
    Case Series
    Categorical
    Causal Effects
    Chi-square
    Chi-square Assumption
    Chi-square Goodness-of-fit
    Classical Test Theory
    Clinical Pathways
    Clustered Random Sampling
    Cochran-Mantel-Haenszel
    Cochran's Q Test
    Coefficient Of Determination
    Cognitive Dissonance
    Cohort
    Comparative Effectiveness Research
    Comparator
    Concurrent Validity
    Confidence Interval
    Confirmatory Factor Analysis
    Construct Specification
    Construct Validity
    Continuous
    Control Event Rate
    Convenience Sampling Method
    Convergent Validity
    Copyright
    Correlations
    Count Variables
    Cox Regression
    Cronbach's Alpha
    Cross-sectional
    Curriculum Vitae
    Database Management
    Diagnostic Testing
    EBM
    Education
    Effect Size
    Empirical Literature
    Epidemiology
    Equivalency Trial
    Eric Heidel
    Evidence-based Medicine
    Exclusion Criteria
    Experimental Designs
    Experimental Event Rate
    Facebook
    Factorial ANOVA
    Feasible Research Questions
    FINER
    Fisher's Exact Tests
    Friedman's ANOVA
    Generalized Estimating Equations (GEE)
    "gold Standard" Outcome
    G*Power
    Guidelines For Authors
    Hazard Ratio
    Hierarchical Regression
    Homogeneity Of Variance
    Hypothesis Testing
    ICC
    Incidence
    Inclusion Criteria
    Independence Of Observations Assumption
    Independent Samples T-test
    Intention-to-treat
    Internal Consistency Reliability
    Interquartile Range
    Inter-rater Reliability
    Interval Variables
    Intervention
    Intraclass Correlation Coefficient
    Isomorphism
    Item Response Theory
    Kaplan-Meier Curve
    Kappa Statistic
    KR-20
    Kruskal-Wallis
    Kurtosis
    Levene's Test
    Likert Scales
    Linearity
    Listwise Deletion
    Logarithmic Transformations
    Logistic Regression
    Log-Rank Test
    Longitudinal Data
    MANCOVA
    Mann-Whitney U
    MANOVA
    Mass Emails In Survey Research
    Math
    Mauchly's Test
    McNemar's Test
    Mean
    Measurement
    Median
    Medicine
    Merging Databases
    Missing Data
    Mode
    Multinomial Logistic Regression
    Multiple Regression
    Multivariate Statistics
    Negative Binomial Regression
    Negative Predictive Value
    Nominal Variables
    Nonequivalent Control Group Design
    Non-inferiority
    Non-inferiority Trial
    Non-parametric Statistics
    Non-probability Sampling
    Normality
    Normality Of Difference Scores
    Normal Probability Plot
    Novel Research Question
    Number Needed To Treat
    Observational Research
    Odds Ratio With 95% CI
    One-sample Median Tests
    One-sample T-test
    One-sided Hypothesis
    One-Way Random
    Operationalization
    Ordinal
    Outcome
    Outliers
    Parametric Statistics
    Pearson's R
    Ph.D.
    Phi Coefficient
    PICO
    Pilot Study
    Point Biserial
    Poisson Regression
    Population
    Positive Predictive Value
    Post Hoc
    Post-positivism
    PPACA
    PPV
    Precision
    Predictive Validity
    Prevalence
    Principal Components Analysis
    Probability Sampling
    Propensity Score Matching
    Proportion
    Proportional Odds Regression
    Prospective Cohort
    Psychometrics
    Psychometric Tests
    Publication
    Publication Bias
    Purposive Sampling
    P-value
    Random Assignment
    Randomized Controlled Trial
    Random Selection
    Rank Biserial
    Ratio Variables
    Receiver Operator Characteristic
    Regression
    Regression Analysis
    Relative Risk
    Relevant Research Question
    Reliability
    Repeated-measures ANOVA
    Repeated-measures T-test
    Research
    Research Design
    Research Engineer
    Research Journal
    Research Question
    Residual Analysis
    Retrospective Cohort
    ROC Curve
    Sample Size
    Sampling
    Sampling Error
    Sampling Method
    Scales Of Measurement
    Science
    Search Engine
    Search Query
    Sensitivity
    Simple Random Sampling
    Sitemap
    Skewness
    Social Science
    Spearman-Brown
    Spearman's Rho
    Specificity
    Specificity In Literature Searching
    Sphericity Assumption
    Split-half Reliability
    SPSS
    Standard Deviation
    Standards Of Care
    Statistical Analysis
    Statistical Assumptions
    Statistical Consultation
    Statistical Power
    Statistical Power Analysis
    Statistical-power-test
    Statistician
    Statistics
    Stratified Random Sampling
    Survey
    Survey Construct Specification
    Survey Methods
    Systematic Review
    Test-Retest Reliability
    Twitter
    Two-sided Hypothesis
    Two-Way Mixed
    Two-Way Random
    Type I Error
    Type II Error
    Unadjusted Odds Ratio
    Validity
    Variables
    Variance
    Wilcoxon
    Within-subjects
    YouTube


    Contact Form

Contact Dr. Eric Heidel
consultation@scalelive.com
(865) 742-7731

Copyright © 2022 Scalë. All Rights Reserved. Patent Pending.