CoxFlex: Estimate the TD and/or NL effect(s) of continuous...

In ywang297/CoxFlex: Flexible Modeling of the Time-to-Event Data

Estimate the TD and/or NL effect(s) of continuous covariate(s) in time-to-event analysis

Description

Estimate the TD and/or NL effect(s) of continuous covariate(s) in time-to-event analysis

Usage

CoxFlex(data, Type, variables, TD, NL, m, p, knots)

Arguments

data	A data frame in the long (interval) format with one line per unit of time.
Type	A vector consisting the name of variables representing the start and stop of each time interval and event indicator. e.g. c("start","stop","event").
variables	A vector consisting the name of varialbes that will be adjusted in the model.
TD	A vector of binary indicators representing whether the corresponding variable has a time-dependent effect or not (1=Yes, 0=No)
NL	A vector of binary indicators representing whether the corresponding variable has a non-linear effect or not (1=Yes, 0=No)
m	The number of interior knots used in the regression B-spline
p	The degree of the regression B-spline
knots	Default value is -999

Value

Returns a list of the following items:

PLL	partial loglikelihood of the final model
NP	number of parameters estimated in the model
NE	number of events in the dataset
knots_covariates	knots for splines of NL effects for corresponding covariates
knots_time	knots for splines of the TD effect
coefficients_splines_NL	estimated splines coefficients for NL effects
coefficients_splines_TD	estimated splines coefficients for TD effects
variables	name of the covariates in the order they are adjusted in the model
coef	estimated coefficients for covariates not having TD and NL effects
sd	standard errors of the corresponding estimated coefficients
pval	p-values for corresponding estimated effects

Note

Note that the TD and NL arguments have different meanings than that in the BackSelection()

ywang297/CoxFlex documentation built on March 18, 2024, 3:26 a.m.