Structured Research Design Pipeline for Survival Analysis

The diagram illustrates a structured six-step research pipeline designed for implementing and evaluating survival analysis models within a retrospective cohort study. Each stage represents a critical component of the workflow, ensuring methodological rigor, reproducibility, and clinical relevance.

The first stage, Data Collection, involves gathering information from multiple linked healthcare datasets. This step is foundational, as the quality and comprehensiveness of the data directly influence the reliability of subsequent analyses. Following this, Cohort Identification focuses on isolating the target population—in this case, care home residents—using address-matching algorithms. This ensures that the study population is accurately defined and relevant to the research objectives.

Next, Data Processing encompasses essential tasks such as cleaning, harmonization, imputation of missing values, and feature engineering. These processes standardize the data and prepare it for robust modeling, reducing biases and improving interpretability. The fourth stage, Model Development, involves implementing and comparing three survival analysis approaches: Cox proportional hazards, Random Survival Forests (RSF), and Gradient Boosting Machines (GBM). This comparative approach enables the identification of the most effective algorithm under identical conditions.

The fifth stage, Model Evaluation, applies comprehensive performance metrics to assess predictive accuracy and generalizability. This includes measures tailored to survival analysis, ensuring that the models are clinically meaningful. Finally, Interpretation translates model outputs into actionable insights for healthcare decision-making, emphasizing clinical applicability and transparency.

The pipeline was implemented using Python 3.12 and key libraries such as pandas, numpy, scikit-survival, lifelines, xgboost, and lightgbm. To guarantee reproducibility, random seeds were fixed at 42, version control was maintained, environment files were provided, and all preprocessing and training steps were logged.

This systematic design ensures fairness in model comparison, robustness in statistical validation, and clarity in interpretation, ultimately supporting evidence-based clinical applications.