What do you do if healthcare professionals need help analyzing and interpreting medical data?

In my experience the first step is usually to identify what is problem, what kind of data is available and identify methods to analyse data which will best give them validation for clinical decision making. Sometimes the retrospective data might not give sufficient data for analysis we might have to compromise and deviate from routine statistical analysis and work with available data to give best output.

Understanding the problem and context is crucial for any data analysis. Let's break down the key points: 1. Goal of the Analysis: It's essential to clarify the purpose of the analysis. 2. Research Questions or Hypotheses: What specific questions do we want to answer? 3. Variables and Measurements of Interest: Identify the relevant variables. 4. Data Sources and Quality: Determine where the data comes from! 6. Scope and Feasibility: Understand the limitations & assess computational resources and time constraints. 7. Expectations and Limitations: Set realistic expectations with the healthcare professional & acknowledge any limitations. Remember, effective communication with the team is essential throughout the process.

Offer training sessions on statistical analysis tailored to healthcare contexts. Provide resources, support, and collaborate to ensure accurate interpretation and informed decision-making.

Many healthcare professionals have been taught to avoid statistical analysis through their past interactions seeking help to analyze data they spent time and effort to collect by well meaning individuals who criticize the data as being collected in an inferior manner. It is important to remember we are all learning and future projects will be done better than current projects as a result. Negative comments regarding data can be perceived as so great of a discouragement there will be no desire to do a future project. It is incumbent on each of us to see the positives within the project and data by providing the best analysis possible. In this way trust is built and future investigation and collaboration is encouraged and strengthened.

Goal is to understand the problem in hand -> convert into appropriate research question/s -> convert the research question/s into objective/s.

A statistical analysis plan is crucial. Choose the analysis plan that answers the objectives of the study. Multiple things can be done with a single set of data. However, one must not be over-ambitious in laying out too many analysis plans (those that are not relevant to the objective). If done so, we might be unable to answer the research question in the first place. Things to consider in the methods section include: >study design >study setting >study population (to which the results would be generalized (if possible and valid) > statistical analysis plan (may include data preprocessing steps) > software that is to be used for analysis.

Choosing the methods depend on many factors as the type of variables, your hypothesis, you might first want to conduct an exploratory factor analysis if your type of variables is Likert scale with latent variables as we are talking about patient feedback. You also want to test hypothesis , validate your measurement model ,checking the fitness indices and evaluate your model

While interpreting results, it is essential to look for the clinical significance of the results, even if statistical significance is achieved. An interpretation involving both clinical and statistical significance adds more clarity and solves the purpose of analysis.

It's important to have a good communication when talking to non-stats workers/researchers. They want to see the business/result and we bring data. These two must match and tell the same story. It's important to have the research by the statistician's side during the whole experiment development so they can relate the numbers with the findings.

Remember the "storyline" of your results and try to keep numbers, formulas, and statistical jargon to a minimum. It is better to talk in terms of constructs than variables/operationalizations (e.g. use "high alcohol intake predicted depression" rather than "The AUDIT scores predicted BDI").