Package index • eventglm

Main functions

Most users will only need to use these functions.

cumincglm(): Generalized linear models for cumulative incidence

rmeanglm(): Generalized linear models for the restricted mean survival

eventglm: Regression Models for Event History Outcomes

Methods

S3 methods for objects returned by the main functions

print(<pseudoglm>): Print method for pseudoglm

residuals(<pseudoglm>): Pseudo-observation scaled residuals

summary(<pseudoglm>): Summary method

vcov(<pseudoglm>): Compute covariance matrix of regression coefficient estimates

confint(<pseudoglm>): Confidence Intervals for pseudoglm Model Parameters

Data

Built in datasets

colon: Chemotherapy for Stage B/C colon cancer

mgus2: Monoclonal gammopathy data

Pseudo observation modules

Functions implementing methods for computing pseudo observations

pseudo_independent(): Compute pseudo observations under independent censoring

pseudo_stratified(): Compute pseudo observations using stratified jackknife

pseudo_aareg(): Compute censoring weighted pseudo observations

pseudo_coxph(): Compute censoring weighted pseudo observations

pseudo_infjack(): Compute infinitesimal jackknife pseudo observations

Utilities

Utilities and internal functions

get_pseudo_cuminc(): Utility to get jackknife pseudo observations of cumulative incidence

get_pseudo_rmean(): Utility to get jackknife pseudo observations of restricted mean

calc_ipcw_pos(): Compute inverse probability of censoring weights pseudo observations

match_cause(): Match cause specification against model response

check_mod_cens(): Error check censoring model

pseudo_rmst2(): Compute pseudo-observations for the restricted mean survival

jackknife.competing.risks2(): Compute jackknife pseudo-observations of the cause-specific cumulative incidence for competing risks

jackknife.survival2(): Compute jackknife pseudo-observations of the survival function

leaveOneOut.competing.risks(): Compute jackknife pseudo-observations of the cause-specific cumulative incidence for competing risks

leaveOneOut.competing.risks2(): Compute jackknife pseudo-observations of the cause-specific cumulative incidence for competing risks

leaveOneOut.survival(): Compute leave one out jackknife contributions of the survival function

leaveOneOut.survival2(): Compute leave one out jackknife contributions of the survival function