Eylea Intervals Analysis Module
Eylea Injection Intervals Analysis
This script analyzes the injection intervals data from the SQLite database to identify patterns in treatment, specifically looking for two groups: - Group LH: 7 injections in first year, then continuing with injections every ~2 months - Group MR: 7 injections in first year, then a pause before resumption of treatment
The script also performs Principal Component Analysis (PCA) to identify patterns in treatment intervals and visual acuity measures (previous VA, current VA, next VA).
- analysis.eylea_intervals_analysis.connect_to_db()[source]
Connect to the SQLite database.
- Return type:
Connection
- analysis.eylea_intervals_analysis.load_interval_data()[source]
Load the interval_va_data table into a Polars DataFrame.
- Return type:
DataFrame
- analysis.eylea_intervals_analysis.load_interval_summary()[source]
Load the interval_summary table into a Polars DataFrame.
- Return type:
DataFrame
- analysis.eylea_intervals_analysis.analyze_first_year_injections(df)[source]
Analyze the first year of injections for each patient.
- Return type:
DataFrame- Parameters:
df (DataFrame)
- Args:
df: DataFrame with interval_va_data
- Returns:
DataFrame with first year injection analysis
- analysis.eylea_intervals_analysis.identify_treatment_groups(df)[source]
Identify the two treatment groups: - Group LH: 7 injections in first year, then continuing with injections every ~2 months - Group MR: 7 injections in first year, then a pause before resumption of treatment
- Return type:
DataFrame- Parameters:
df (DataFrame)
- Args:
df: DataFrame with first year injection analysis
- Returns:
DataFrame with group assignments
- analysis.eylea_intervals_analysis.cluster_treatment_patterns(df)[source]
Use K-means clustering to identify treatment pattern groups.
- Return type:
DataFrame- Parameters:
df (DataFrame)
- Args:
df: DataFrame with first year injection analysis
- Returns:
DataFrame with cluster assignments
- analysis.eylea_intervals_analysis.analyze_intervals_by_group(df, interval_data)[source]
Analyze and visualize injection intervals by treatment group.
- Return type:
None- Parameters:
df (DataFrame)
interval_data (DataFrame)
- Args:
df: DataFrame with group assignments interval_data: Raw interval data
- analysis.eylea_intervals_analysis.prepare_va_interval_data_for_pca(interval_data)[source]
Prepare data for PCA analysis by calculating next VA for each record.
This function processes the interval data to create a dataset with: - treatment_interval (interval_days) - previous_va (prev_va) - current_va - next_va (calculated by joining with next record)
- Return type:
DataFrame- Parameters:
interval_data (DataFrame)
- Args:
interval_data: Raw interval data from the database
- Returns:
DataFrame with prepared features for PCA analysis
- analysis.eylea_intervals_analysis.perform_va_interval_pca(interval_data)[source]
Perform PCA analysis on treatment intervals and visual acuity measures.
This function identifies patterns in: - Treatment interval - Previous VA - Current VA - Next VA
- Return type:
None- Parameters:
interval_data (DataFrame)
- Args:
interval_data: Raw interval data from the database
- analysis.eylea_intervals_analysis.analyze_va_by_group(df, interval_data)[source]
Analyze and visualize visual acuity by treatment group.
- Return type:
None- Parameters:
df (DataFrame)
interval_data (DataFrame)
- Args:
df: DataFrame with group assignments interval_data: Raw interval data
- analysis.eylea_intervals_analysis.main()[source]
Main analysis function.