Biostatistics Research - Cognitive Health Study
Overview
Large-scale statistical analysis investigating medication effects on cognitive outcomes using a national dataset. Study currently under peer review for publication.
Research Scope
Analyzing the relationship between commonly prescribed medications and cognitive trajectories in older adults using advanced statistical methods and machine learning techniques.
Dataset
- Source: National research database
- Scale: 30,000+ participants
- Observations: 130,000+ longitudinal data points
- Time Span: Multi-year follow-up
- Variables: Demographics, clinical measures, cognitive assessments
Technical Approach
Statistical Methods
Primary Analyses:
- Survival analysis with time-varying covariates
- Mixed-effects models for repeated measures
- Propensity score matching for causal inference
- Multiple imputation for missing data
Advanced Techniques:
# Example: Propensity Score Implementation
class PropensityAnalysis:
def __init__(self, treatment_var, covariates):
self.treatment = treatment_var
self.covariates = covariates
self.matched_data = None
def calculate_scores(self, data):
"""
Calculate propensity scores using logistic regression
"""
from sklearn.linear_model import LogisticRegression
X = data[self.covariates]
y = data[self.treatment]
model = LogisticRegression(max_iter=1000)
model.fit(X, y)
scores = model.predict_proba(X)[:, 1]
return scores
def match_subjects(self, data, caliper=0.01):
"""
Perform 1:1 matching within caliper distance
"""
# Matching algorithm implementation
pass
Machine Learning Applications
Predictive Modeling:
- Feature engineering from clinical data
- Model selection with cross-validation
- Ensemble methods for robust predictions
- Interpretable ML for clinical insights
Quality Control:
- Automated data validation pipelines
- Outlier detection algorithms
- Consistency checks across time points
- Missing data pattern analysis
Technical Infrastructure
Data Processing Pipeline
# Pipeline Architecture
class ResearchPipeline:
def __init__(self):
self.stages = [
'data_ingestion',
'quality_control',
'feature_engineering',
'statistical_analysis',
'visualization',
'reporting'
]
def process(self):
# Each stage implemented as separate module
# Full reproducibility with version control
pass
Computing Environment
Technologies Used:
- Jupyter notebooks for statistical analysis
- Python for data processing and ML
- PostgreSQL for data management
- Docker for reproducible environment
- Git for version control
Performance Optimization:
- Parallel processing for bootstrapping
- Efficient memory management for large datasets
- Optimized SQL queries for data extraction
- Cached intermediate results
Engineering Contributions
Data Pipeline Development
- Built automated data extraction and cleaning pipeline
- Implemented validation rules for clinical variables
- Created reproducible analysis framework
- Developed unit tests for statistical functions
Visualization System
- Interactive dashboards for exploratory analysis
- Publication-quality figure generation
- Automated table formatting for manuscripts
- Dynamic reporting system
Code Quality
# Example: Validated Statistical Function
def calculate_hazard_ratio(data, exposure, outcome, covariates):
"""
Calculate adjusted hazard ratio with confidence intervals
Parameters:
-----------
data : pandas.DataFrame
Study dataset
exposure : str
Exposure variable name
outcome : str
Outcome variable name
covariates : list
Adjustment variables
Returns:
--------
dict : Hazard ratio, CI, p-value
"""
# Implementation with extensive validation
# Unit tests ensure statistical accuracy
pass
Deliverables
Technical Outputs
- Reproducible analysis code (1000+ lines)
- Automated reporting pipeline
- Statistical validation framework
- Data quality assessment tools
Documentation
- Statistical analysis plan (SAP)
- Data dictionary and codebook
- Technical documentation
- Version control history
Skills Demonstrated
Statistical Programming:
- Jupyter notebooks for reproducible analysis
- Python scientific stack (pandas, scipy, statsmodels, lifelines)
- SQL for complex data queries
- Reproducible research practices
Data Engineering:
- Large dataset management (100GB+)
- Pipeline automation
- Performance optimization
- Quality assurance systems
Approach:
- HIPAA-compliant data handling
- Version control for analysis code
- Containerized environments
- Comprehensive documentation
Specific findings and methodology withheld pending publication.