Category: Administration
Keywords: confounding factors, epidemiologic (PubMed Search)
Posted: 8/21/2024 by Mike Witting, MD
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“I’m not going to the hospital, my father died in a hospital.”
In planning a study it’s a good practice to consider what confounding variables you may need to look out for.
Confounding variables are associated with the predictor (independent) and outcome (dependent) variables, but they are not in the causal chain. In the above example, disease is likely the predictor variable, death is the outcome variable, and going to the hospital is a confounder. Of course, this assumes the death was not iatrogenic; then the hospital would be in the causal chain.
Patients may be selected for interventions based on severity of disease, functional status, education level, and other factors, and these may be confounders.
Confounding can be addressed at the design stage, by:
It can be addressed in the analysis stage by:
Adapted from Hulley SB, Cummings SR. Designing clinical research, 4th edition, Lippincott, Williams and Wilkins, 2013.
Category: Misc
Keywords: sample size calculation, biostatistics (PubMed Search)
Posted: 7/25/2024 by Mike Witting, MD
(Updated: 12/13/2024)
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Free Biostatistics Software Options
EpiCalc 2000 – available for Windows. Capabilities include sample size calculation, inferential statistics (p values, 95% CI), simple stratified analysis, paired and independent analyses. Right clicking allows you to do many things. (http://www.brixtonhealth.com/epicalc.html). I've had the most experience with this one.
Epi Info – supported by CDC. Available for Windows and for hand-held. (https://www.cdc.gov/epiinfo/index.html)
P/S – Power and Sample Size Calculation – available for Mac and Window. Supported by Vanderbilt biostatistics. (https://biostat.app.vumc.org/wiki/Main/PowerSampleSize)
Epicalc: http://www.brixtonhealth.com/epicalc.html
Epi Info: https://www.cdc.gov/epiinfo/index.html
Category: Misc
Posted: 6/29/2024 by Mike Witting, MD
(Updated: 12/13/2024)
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Needed for sample size determination
Power – (1-beta), where beta is the risk of a type 2 error – rejecting the accepting the null hypothesis when it is true – this is usually selected to be 0.8 or 0.9.
Significance (alpha), the chance of making a type 1 error – accepting the alternate hypothesis when the null hypothesis is true. This is usually selected to be 0.05.
One-tailed or two-tailed – is the null hypothesis one of no difference (experimental arm not better or worse) or one-sided (experimental arm not better)?
Effect Size. This is the challenging part. This is the size of the difference in outcomes you’re looking for.
For continuous outcomes (example – difference in pain scores). You’ll need an estimate for the variation in the scores between presentations, or the standard deviation. You can get this from a literature estimate or a from small local measurement, say of 10 patients or so.
For a dichotomous outcome (example – percentage of successes), you can usually estimate the percentage in one group and choose the difference you are looking for.
The effect size has a big effect on the sample size. Generally, cutting the effect size in half increases the sample size by fourfold.
Statistical software - next pearl.
Category: Misc
Keywords: Research Question (PubMed Search)
Posted: 5/20/2024 by Mike Witting, MD
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Starting a study? Frame your research question in the PICO format:
Patients (consider severity of presentation, setting, demographics)
Intervention (either something you propose or something in use)
Comparison (another group, the same group without intervention, or a benchmark)
Outcome (a measurement)
This classic format has been used to evaluate studies, as in Journal Club (by our esteemed Dr. Wilkerson), as a literature search tool, or by the Cochrane review.
Starting with a PICO research question can help you narrow your focus and maintain it.
Category: Misc
Posted: 3/12/2024 by Mike Witting, MD
(Updated: 3/28/2024)
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Considering starting a research project? Apply the FINER criteria:
Feasible
Do you have the resources to study this? Enough patients? Support?
Interesting
Does it interest you enough to devote your time to it? Does it interest colleagues?
Novel
Would it provide new findings, or confirm, refute, or extend prior findings?
Ethical
Can you think of a way to ethically study it?
Relevant
Consider possible outcomes of your research. Could the study advance care or policy?
Adapted from Hulley SB, Cummings SR. Designing clinical research, 4th edition, Lippincott, Williams and Wilkins, 2013.