ACPM Podcast

Today, we talked about the importance of understanding which statistical test to use for your research based on your variables. Anova and the student's t test both test for means, but Anova is better for more than two populations and categories, while the student's t test is better for dichotomous or binary variables. It all depends on your research question and variables. Cross-sectional studies are a type of observational study design where a researcher collects data on different variables of interest in a sample population at one point in time. These studies provide information on the prevalence and relative prevalence of variables in a population, and can be used to infer associations between variables. The strengths of this design include its efficiency and ability to generate hypotheses, but weaknesses include potential for effect cause and limited information. Cross-sectional studies are similar to cohort studies in that they both draw from a single target.

Researchers have conducted a new study using a statistical method called "factor analysis" to analyze and interpret large amounts of data. This method can help identify patterns and relationships within data. Stay tuned for more This week's episode of the Podiatry Research Podcast discussed the case control study design. This type of study starts with a group of participants with a specific outcome (cases) and a group without the outcome (controls) and looks back at their past exposures to identify any potential associations. Case control studies are good for studying rare conditions and have a relatively low cost and fast turnaround time, but they can be biased and lack the ability to calculate prevalence or incidence.

Our fourth podcast episode talks about pros and cons of research. The hosts discuss simple linear regression and its importance in research. They explain the assumptions that need to be met in order to use this statistical method, including independence, normality, and equal variance. They also mention the information that simple linear regression can provide, such as P-value, 95% confidence interval, root mean squared error, and beta coefficients.

Logistic regression is a statistical method used to predict a binary outcome (yes or no, true or false). It calculates the odds ratio, which is the probability of the outcome occurring. This method does not require the same assumptions as linear regression. It can be used in research studies where the outcome is categorical rather than numerical, and can consider multiple variables and adjust for confounding factors. A study on prescribing habits among podiatrists is used as an example of the application of logistic regression. Cohort studies are a type of longitudinal, observational study that follow participants over time to assess the relationship between an exposure and an outcome. They are similar to randomized control trials in that they both track outcomes over time, but they differ in that cohort studies are observational and do not randomly assign participants to an exposure group. Retrospective cohort studies, also known as chart review studies, are used to examine associations between exposures and outcomes in cases where randomized control trials are not possible or ethical.

Statistical tests such as the Chi-Square test and Fisher's Exact test are commonly used in research to determine if there is a significant association or difference between two variables. They can be used to compare groups, survey populations, assess response bias, and more. Negative studies, where the p-value is not significant, are also important and should still be published. A case series is a detailed report of a group of subjects who are all experiencing the same outcome. It is typically used for rare or novel diseases and can offer insight into potential links between outcomes and exposures or interventions. Some limitations of case series include the lack of a control group and the difficulty in establishing causal relationships. However, they are inexpensive and can lead to the discovery of new diseases and treatments. Two statistical tests, Fisher's exact test and Pearson's chi squared, are often used in case studies.

The episode discusses the history of statistics and its importance in research, especially in the medical field. It also explains the different types of statistics (frequentist and Bayesian) and the importance of being able to critically appraise research papers and choose the appropriate research design and statistical analysis. The episode ends by encouraging researchers to tune in for future episodes that will cover different research designs and statistical analyses in depth. This is week one and the introductory episode. Our Hosts share their academic and professional background along with our excitement about presenting Pediatric research info that podiatrists can use to become better academics. We go on to explore the purpose, strengths, weaknesses and uses of a Case Report study design. We conclude with a brief historical discussion and relay that we plan to bring a new research design every week based on the frequentist approach to statistics.