Study design in computational biostatistics — rigorous treatise

Introduction

What this guide covers

Rigorous mathematical foundations, causal inference modeling, statistical power analysis and algorithmic optimization for clinical and epidemiological research.

Overview

Topics covered

• Introduction
• Design of Studies in Biostatistics
• Epistemological Foundations and Causal Inference
• Directed Acyclic Graphs (DAG) and Path Blocking
• Observational Designs and Computational Fitting Methods
• Multivariate Confounding Adjustment Using Propensity Score (PS)

Related concepts

Main connections

• Survival analysis & Kaplan-Meier
• Bayes' theorem & priors
• Variance & standard deviation
• Causal inference & potential outcomes
• Clinical trials & RCT design
• Permutation & exact tests

Compatible notation

Mathematical sample in LaTeX

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