Q & A
Statistical Programming & Biostatistics

Q4. Can you customize statistical models based on project needs?

Statistical modelling for any given project will be dependent upon full project requirements, which means that the model can be designed to fit your specific research goals, the characteristics of your data, and your decision-making needs and preferences.

1. Understanding Project Objectives

  • The first part of customizing your statistical model is understanding what your statistical modelling goals are; this could be to predict, test, or estimate risk, etc.
  • Therefore, when customising your statistical model, it is essential to identify the critical questions of your research so that the model provides you with relevant and actionable results.

2. Data-Driven Model Selection

  • Your data type, structure, and distribution of your data will define which statistical model you can use. The available statistical models include linear regression, nonlinear regression, multivariate regression, survival analysis, longitudinal regression and advanced machine learning approaches.
  • Therefore, you will need to choose the best statistical model that fits your research context.

3. Customization of Variables and Assumptions

  • When you start building and customising your statistical model, it is important to have a list of your predictor variables and covariates. In addition, interactions and assumptions of the statistical model should also be properly defined.
  • You will select or modify these according to your domain knowledge of the subject matter as well as the project constraints and prior research.

4. Validation and Performance Tuning

  • All customised statistical models are validated using the diagnostic tests and all statistical models are validated using cross-validation and sensitivity analysis to ensure that the models are robust, reliable, and generalizable.

5. Adaptable Reporting and Interpretation

  • We will prepare your results in a way that will be appropriate for your stakeholders, whether they are in a technical team, a clinical team, or a management role.
  • We ensure that the results are actionable, easy to understand, and relate to the overall project objectives.