fastCox: Cox Regression (Proportional Hazards Model) with Multiple...

In CoxPlus: Cox Regression (Proportional Hazards Model) with Multiple Causes and Mixed Effects

Cox Regression (Proportional Hazards Model) with Multiple Causes and Mixed Effects

Description

This function estimates Proportional Hazards Model when an even can have more than one causes, including support for random and fixed effects, tied events, and time-varying variables.

Usage

fastCox(head, formula, par = list(), data = NULL)

Arguments

head	A data frame with 4~5 columns: start, stop, event, weight, strata (optional).
formula	A formula specifying the independent variables
par	A optional list of parameters controlling the estimation process
data	The dataset, a data frame containing observations on the independent variables

Value

A list containing the estimated parameters

References

1. Jing Peng, Ashish Agarwal, Kartik Hosanagar, and Raghuram Iyengar. (2018). Network Overlap and Content Sharing on Social Media Platforms. Journal of Marketing Research, 55(4), p. 571-585.

2. Jing Peng, Ashish Agarwal, Kartik Hosanagar, and Raghuram Iyengar. Toward Effective Social Contagion: A Micro Level Analysis of the Impact of Dyadic Network Relationship. In Proceedings of the 2014 International Conference on Information Systems.

Examples

# Simulate a dataset. lam=exp(x), suvtime depends on lam
set.seed(123)
x = rnorm(5000)
suvtime = -log(runif(length(x)))/exp(x)
# Censor 80% of events
thd = quantile(suvtime, 0.2)
event = as.numeric(suvtime <= thd)
suvtime[suvtime>thd] = thd

# The estimates of beta should be very close to 1, the true value
head = cbind(start=0,stop=suvtime,event=event,weight=1)
est = fastCox(head,~x)
print(est$result)

CoxPlus documentation built on Nov. 19, 2025, 5:07 p.m.