This is the last episode of the podcast, in which the hosts talk about ways to get involved in research. This includes writing grant proposals and submitting them, and getting a mentor. The hosts also discuss their own experiences with research, with one focusing on grant proposals and the other on manuscript writing. They encourage listeners to explore and learn more about research and funding sources. 1. Research is important for informing and improving medical practice. 2. Research can seem intimidating, but it is not as mysterious as it may seem. 3. Most research is flawed in some way, but it does not mean it is worthless. 4. Researchers may collaborate with statisticians who are experts in statistical analysis. 5. It is important to approach research with a healthy skepticism. 6. To get involved in research, reach out to professors, program directors, or the speakers for advice.

This episode of the podcast discusses the Kruskal-Wallis test, which is a non-parametric alternative to the one-way ANOVA test. It is used to compare multiple independent groups when the data does not meet the assumptions of the ANOVA. The test is appropriate for ordinal or non-normal data, and it compares the ranks of the data instead The podcast talks about before and after studies which is a type of pre-post design in quasi-experimental research. It is used when randomization is not possible or when there is no control group. The purpose is to measure the effects of an intervention or exposure on a population. The quality of the study can be assessed by looking at sample selection, confounders, regression to the mean, attrition bias, and outcome variables. The Mann-Whitney U test is a nonparametric test.

Meta-analysis is a research design that is a systematic review with formal statistics. It involves identifying a specific research question, assessing the quality of methods used, and synthesizing the results with statistical analysis. Meta-analysis provides increased power by combining multiple studies to show statistical significance. Possible biases include publication bias and outcome reporting bias. Meta-Analysis is a research method that combines and analyzes data from multiple studies. It is a powerful tool that can increase the statistical power of research and make important findings more apparent. However, like any research method, it has limitations and potential biases that should be considered. Negative predictive value and positive predictive value are two important measures used in diagnostic testing, and they are influenced by the prevalence of the disease being studied. Diagnostic tests are a good starting point for meta-analyses, and software developers. 

Listen to the discussion about sensitivity and specificity in diagnostic tests. Sensitivity is the ability of a test to correctly identify people who have the disease, while specificity is the ability of a test to correctly identify people who do not have the disease. A high sensitivity rules in the disease, while a high specificity rules out the disease. It is possible to conduct systematic reviews and meta-analyses on the sensitivity and specificity of different diagnostic tests. : In this episode, we cover systematic reviews and meta-analysis. Systematic reviews are a type of research review that uses a systematic and repeatable approach to answer a specific question. They are a high level of evidence and are useful in examining the literature for a particular topic. Meta-analysis, which will be covered in more detail in next week's episode, involves pooling data from multiple studies to find patterns or trends. Then we dive into the different strengths and weaknesses of systematic reviews. 

This episode discusses qualitative research, which is a form of research that involves collecting and analyzing non-numerical data, such as interviews and observations. Qualitative research can provide insights and understanding of people's experiences and perceptions, and can be used to inform quantitative research. Its strengths include flexibility and the generation of meaningful insights, while its disadvantages include being labor-intensive and difficulty with generalization. Overall, qualitative research is an important approach to understanding and informing research in various fields. Qualitative research is used to describe the lived experiences of participants and understand their perspectives. It can involve different methods such as grounded theory, ethnography, and narrative research. Data is collected through methods like interviews, focus groups, surveys, and secondary data analysis. The data is then transcribed and analyzed using codes and themes to understand patterns and perspectives. Qualitative research is useful for understanding complex issues and answering questions that cannot be addressed using quantitative methods. 

This podcast episode talks about randomized controlled trials (RCTs) and how they are used in research to evaluate the effectiveness of interventions. The hosts also discuss the importance of adjusting for confounding in RCTs and how multiple regression can be used to do this in the analysis phase. They also touch on the assumptions of multiple regression, such as linearity, independence, normality, and equal variance. This episode of the Podiatry Research podcast discusses randomized controlled trials, which are considered the gold standard of research designs. In this type of study, participants are randomly assigned to either receive an intervention or not, in order to see if there is an impact on a certain outcome. Randomized controlled trials help eliminate confounding and bias, and demonstrate a clear cause-and-effect relationship. They also have some limitations, such as being time and resource consuming, and potential for harm to participants. 

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.