Biostatistics for medical research explained

We simplify biostatistics for medical professionals and students—making complex methods clear, practical, and clinically relevant. Through easy-to-follow explanations, intuitive visuals, and essential math, we bridge the gap between statistics and medical research.

Types of research

Research can roughly be divided into the following types:

Descriptive Analysis: To summarize the data set without interpretation.

Exploratory Analysis: To find patterns, trends, or relationships to generate hypotheses.

Inferential Analysis: To estimate how findings in a sample hold for a larger population.

Predictive Analysis: To predict outcomes for individuals based on features.

Causal Analysis: To estimate what happens on average when one variable changes another (e.g. average treatment effect).

Mechanistic Analysis: Typically to establish how one variable deterministically influences another.

We will focus on the following on two aspects:

1.  Interpretation and use of statistical evaluation measures

• Statistical evaluation measures

• p-values

• Brier score and scaled Brier score

• Pseudo R2 measures

• Calibration

2. Interpretation of model coefficients for regression models in

• the 4 archetypical statistical models

• Logistic regression

• Linear regression

• Cox regression

• Poisson regression

• Extensions:

  • Extensions logistic regression

    • Ordinal logistic regression

  • Extensions linear regression

  • Extensions Cox regression

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References:

Leek, J., & Peng, R. D. (2015, February 26). What is the question? Science. https://doi.org/10.1126/science.aaa6146

Kiefer, J. C. (1987). Introduction to statistical inference (G. Lorden, Ed.). Springer.