Skip to contents

Joint estimation and inference

Build a joint asymptotic covariance across heterogeneous outcome models, and run PATED for prognostic-covariate-adjusted treatment-effect tests.

jointCovariance()
Fitting Regression Models for Multiple Outcomes and Returning the Matrix of Covariance
pated()
Prognostic Variables Assisted Treatment Effect Detection

Model specifications

Spec constructors describing how each component model is fit. Pass them as the ... arguments to jointCovariance() or pated().

glm_()
Creating Objects of Generalized Linear Models
coxph_()
Creating Objects of Proportional Hazards Regression Model
logrank_()
Creating Objects of Logrank Test
gee_()
Creating Objects of Generalized Estimation Equation Model
mmrm_()
Creating Objects of Mixed Models for Repeated Measures
km_()
Creating Objects of Kaplan-Meier Curve
quantile_()
Creating Objects of Group Quantile Differences

Extracting results

coef(<jointCovariance>)
Extract Model Coefficients
vcov(<jointCovariance>)
Calculate Variance-Covariance Matrix for a Fitted Model Object
summary(<jointCovariance>)
Object Summaries
print(<summary.jointCovariance>)
Title Summarize an Analysis of Multiple Outcomes.
plot(<pated>)
Plot PATED Analysis Results

Datasets and simulation

actg
ACTG 320 Clinical Trial Dataset
indo
Rectal Indomethacin for Prevention of Post-ERCP Pancreatitis
simulateMoData()
Generating Data for Simulation and Testing