Number Needed to Treat (NNT) is an important epidemiological calculation that shows how many people have to be treated to preventing a future bad outcome. In a perfect situation, the NNT would be 1, meaning that every person that you treat will not get the bad outcome in the future.

NNT is calculated using other epidemiological calculations:

Control Event Rate (CER) - Proportion of the control group with the outcome
Experimental Event Rate (EER) - Proportion of the treatment group with the outcome
|CER - EER| is the Absolute Risk Reduction (ARR) or the effect of the treatment in comparison to a control treatment.
Then, in order to calculate NNT, just use (1/ARR).  

With smaller values of ARR, or small treatment effects, the NNT will increase meaning that more people have to be treated to prevent a future bad outcome. Higher values of ARR show a strong treatment effect, fewer people will have to be treated to prevent a bad outcome because it is so efficacious.

Case series designs yield the lowest form of observational evidence. Researchers choose a series of cases in the population that share some sort of similar characteristic and then they analyze pertinent predictor, demographic, and clinical factors associated with the outcome in the group of cases.  Case series designs are at times also called pilot studies.  

Case series designs are often employed in basic science and pre-clinical research.  They are useful for generating hypotheses, effect sizes for future power analyses, and studying extremely rare outcomes.  

Case series designs are an excellent choice for novice researchers looking to get their feet "wet" in empirical pursuits. They also prove their worth when evidence-based measures of effect do not exist in the literature.  Running a small pilot study or case series can yield important measures of effect. 

Hello everyone!

Please follow Research Engineer on Facebook and Twitter!  New content is on the way!

I am currently revising the manuscript for my dissertation on isomorphism.  After six years of an uphill battle for isomorphism, it is finally accepted with revisions!  Fingers crossed!

I'm so excited about integrating social media into Research Engineer.  My SEO friends and colleagues say it is time to get a YouTube channel and put this "mug" to work.  Hahaha, get ready world!

Thanks for the support, everyone!

Dr. H

Click on the buttons below to follow Research Engineer!

When it comes to running statistics on one sample, an a priori hypothesis is a necessity for making proper inferences. Many survey instruments lack fundamental psychometric evidence to back up the constructs and/or items it is intended to measure.  When a Likert-scale type response set or an ordinal outcome is to be assessed statistically in one sample, the one-sample median test is used.

In order to run the test, researchers must specify where along the ordinal continuum that they hypothesize that the population mean exists.  The one-sample median test then compares the observed median to the hypothesized median and the p-value is interpreted.

Due to limited precision, accuracy, and variability in ordinal outcomes, it behooves researchers to use either 5-point, 7-point, or higher level Likert scales.  With more options, more unique variance can be accounted for the in the analysis and statistical power is increased.  One-sampled tests possess more statistical power than other between-subjects statistics because there is only one group being analyzed, no other independent groups are included.

By far, the prospective cohort design is the most powerful observational design. The design can yield a measure of incidence (number of new cases in a population), longitudinal effects (etiology and disease progression), and the potential for decreased observation bias (more control on study design and data collection).

Retrospective cohort designs can yield some measures of incidence in patient populations. However, researchers are limited to the variables that have been collected in an objective fashion within homogeneous populations. Incidence is a much more valid measure when generated using a prospective cohort design. Researchers choose in an a priori fashion exactly what variables will be collected in the measured.

Incidence is a much more precise measure of association versus prevalence. Prospective and experimental designs can yield measures of incidence and establish the relative risk of developing an outcome. Researchers and clinicians also have a better understanding of incidence and relative risk versus prevalence and odds ratios.

Longitudinal data is data collected over an extended period of time. Longitudinal data is necessary for understanding the etiology and progression of disease states. Survival and time-to-event analyses produce popular measures in medicine such as 1-year, 3-year, and 5-year survival and recurrence. The primary issue with collecting longitudinal data is attrition and loss to follow-up with the prospective sample. As participants fall out of the study or are lost, the validity of the data greatly decreases.

Again, it is important to state that prospective designs give more control to researchers in regards to what data is collected. Every variable that you find pertinent for establishing causal effects between predictor and outcome variables, when controlling for all important demographic, prognostic, clinical, and confounding variables, can be chosen and collected. Observational biases associated with retrospective research do not apply with these studies because you can collect all of the data on all the variables that you chose, given that there is a theoretical, conceptual, or physiological reason for doing so.

Propensity score matching is a statistical methodology that is used in observation research designs. It also is very useful for controlling for confounding variables in multivariate models. A new page has been added describing its use in research.

I published all of the research calculators available in Research Engineer to one page for easier access. New pages are also available for internal consistency reliability and inter-rater reliability. 

If you use Research Engineer for purposes of regression analysis, then you have seen the methods analyses associated with residual analysis and meeting certain statistical assumptions like linearity, normality, and equal variances. New pages are available to give deeper insights into these important statistical assumptions.

Click on a button below to get started! Thank you!

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!

As I have been adding statistical assumptions to the website, it is safe to say that the "levee" has broke and I have A LOT more work to do to make this the best statistical and empirical website.  With this being said, check out the new assumptions pages: