Fit Poolwise Regression Models
Conditional Logistic Regression with Measurement Error in One Covariat...
Created a Pooled Dataset from a Subject-Specific One
Discriminant Function Approach for Estimating Odds Ratio with Normal E...
Discriminant Function Approach for Estimating Odds Ratio with Gamma Ex...
Gamma Discriminant Function Approach for Estimating Odds Ratio with Ex...
Gamma Discriminant Function Approach for Estimating Odds Ratio with Ex...
Gamma Discriminant Function Approach for Estimating Odds Ratio with Ex...
Linear Regression of Y vs. Covariates with Y Measured in Pools and (Po...
Poolwise Logistic Regression
Poolwise Logistic Regression with Normal Exposure Subject to Errors
Poolwise Logistic Regression with Gamma Exposure Subject to Errors
Normal Discriminant Function Approach for Estimating Odds Ratio with E...
Normal Discriminant Function Approach for Estimating Odds Ratio with E...
Normal Discriminant Function Approach for Estimating Odds Ratio with E...
Plot Log-OR vs. X for Normal Discriminant Function Approach
Plot Log-OR vs. X for Gamma Discriminant Function Approach
Plot Log-OR vs. X for Gamma Discriminant Function Approach
Plot Log-OR vs. X for Normal Discriminant Function Approach
Visualize Total Costs for Pooling Design as a Function of Pool Size
Visualize T-test Power for Pooling Design as Function of Processing Er...
Fit Poolwise Regression Models
Visualize T-test Power for Pooling Design
Visualize Ratio of Variance of Each Pooled Measurement to Variance of ...
Dataset for a Paper Under Review
Test for Underestimated Processing Error Variance in Pooling Studies
Functions for calculating power and fitting regression models in studies where a biomarker is measured in "pooled" samples rather than for each individual. Approaches for handling measurement error follow the framework of Schisterman et al. (2010) <doi:10.1002/sim.3823>.