How can statistical modeling identify disease risk factors?

3 How to choose a statistical model for disease risk analysis?

Statistical models are an important tool for disease risk analysis, and there are various types available depending on the research question, data available, and assumptions made. Regression models, for instance, can estimate the relationship between predictor variables and an outcome variable. Survival models analyze the time until an event occurs, and account for censoring when some individuals are lost to follow-up. Multilevel models consider the hierarchical structure of the data, such as individuals within groups or clusters. Bayesian models incorporate prior knowledge or beliefs into the analysis and update them based on the data. All of these models can provide valuable insights into disease risk.

4 How to evaluate a statistical model for disease risk analysis?

Once a statistical model is fitted to the data, it is important to assess its validity, accuracy, and usefulness. To do this, one can use criteria such as goodness-of-fit which measures how well the model fits the data and how much of the variability in the data is explained by the model. Assumption checking is also important to verify whether the assumptions made by the model are met by the data. Model comparison can be used to compare the performance of different models and select the best one based on criteria such as parsimony or information criteria. Finally, model validation tests how well the model can generalize and predict new or unseen data, and how robust and stable it is to changes in the data or parameters. This can be done by techniques such as split-sample or bootstrap.

5 How to communicate and interpret a statistical model for disease risk analysis?

The final step of statistical modeling is to communicate and interpret the results and implications of the model for the audience and the context. To do this effectively, it's important to use clear and concise language, report the main findings and conclusions, highlight key messages and recommendations, provide estimates with their confidence intervals, use tables, graphs, and visualizations to summarize the data and model, acknowledge limitations and uncertainties, discuss sources of bias, error, and variability, and provide code and data used to create and test the model in an accessible and reproducible way.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?