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pooling (version 1.1.2)

Fit Poolwise Regression Models

Description

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) .

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Version

Install

install.packages('pooling')

Monthly Downloads

95

Version

1.1.2

License

GPL-3

Maintainer

Dane Van Domelen

Last Published

February 13th, 2020

Functions in pooling (1.1.2)

p_gdfa_constant

Gamma Discriminant Function Approach for Estimating Odds Ratio with Exposure Measured in Pools and Potentially Subject to Multiplicative Lognormal Errors (Constant Odds Ratio Version)
dat_p_linreg_yerrors

Dataset for Examples in p_linreg_yerrors
p_dfa_xerrors

Discriminant Function Approach for Estimating Odds Ratio with Normal Exposure Measured in Pools and Potentially Subject to Errors
dat_cond_logreg

Dataset for Examples in cond_logreg
p_gdfa

Gamma Discriminant Function Approach for Estimating Odds Ratio with Exposure Measured in Pools and Potentially Subject to Multiplicative Lognormal Errors
p_dfa_xerrors2

Discriminant Function Approach for Estimating Odds Ratio with Gamma Exposure Measured in Pools and Potentially Subject to Errors
cond_logreg

Conditional Logistic Regression with Measurement Error in One Covariate
dat_p_gdfa

Dataset for Examples in p_gdfa
dat_p_ndfa

Dataset for Examples in p_ndfa
form_pools

Created a Pooled Dataset from a Subject-Specific One
p_ndfa_constant

Normal Discriminant Function Approach for Estimating Odds Ratio with Exposure Measured in Pools and Potentially Subject to Additive Normal Errors (Constant Odds Ratio Version)
poolvar_t

Visualize Ratio of Variance of Each Pooled Measurement to Variance of Each Unpooled Measurement as Function of Pool Size
simdata

Dataset for a Paper Under Review
p_ndfa

Normal Discriminant Function Approach for Estimating Odds Ratio with Exposure Measured in Pools and Potentially Subject to Additive Normal Errors
p_logreg_xerrors2

Poolwise Logistic Regression with Gamma Exposure Subject to Errors
p_gdfa_nonconstant

Gamma Discriminant Function Approach for Estimating Odds Ratio with Exposure Measured in Pools and Potentially Subject to Multiplicative Lognormal Errors (Non-constant Odds Ratio Version)
plot_ndfa

Plot Log-OR vs. X for Normal Discriminant Function Approach
plot_gdfa

Plot Log-OR vs. X for Gamma Discriminant Function Approach
pdat1

Dataset for Examples in p_dfa_xerrors and p_logreg_xerrors
p_linreg_yerrors

Linear Regression of Y vs. Covariates with Y Measured in Pools and (Potentially) Subject to Additive Normal Errors
pdat2

Dataset for Examples in p_dfa_xerrors2 and p_logreg_xerrors2
plot_dfa

Plot Log-OR vs. X for Normal Discriminant Function Approach
plot_dfa2

Plot Log-OR vs. X for Gamma Discriminant Function Approach
p_ndfa_nonconstant

Normal Discriminant Function Approach for Estimating Odds Ratio with Exposure Measured in Pools and Potentially Subject to Additive Normal Errors (Non-constant Odds Ratio Version)
poolcushion_t

Visualize T-test Power for Pooling Design as Function of Processing Error Variance
poolcost_t

Visualize Total Costs for Pooling Design as a Function of Pool Size
test_pe

Test for Underestimated Processing Error Variance in Pooling Studies
p_logreg

Poolwise Logistic Regression
p_logreg_xerrors

Poolwise Logistic Regression with Normal Exposure Subject to Errors
pooling

Fit Poolwise Regression Models
poolpower_t

Visualize T-test Power for Pooling Design