finda: Estimate the time-varying coefficients from a local-in-time...
In hds: Hazard Discrimination Summary

Description Usage Arguments Details Value References

finda estimates the time-varying coefficients beta(t) at a single time from a local-in-time Cox model. Think of it as a Cox model where the the coefficients are allowed to vary with time. Further details can be found in Cai and Sun (2003) and Tian et al. (2005).

1	finda(tt, times, status, covars, start = rep(0, ncol(covars)), h = 400, ...)

`tt`	Time to estimate beta(t) at
`times`	A vector of observed follow up times.
`status`	A vector of status indicators, usually 0=alive, 1=dead.
`covars`	A matrix or data frame of numeric covariate values, with a column for each covariate and each observation is on a separate row.
`start`	A vector of length p of starting values to be passed to `optim` for the numerical optimization procedure. p is the number of covariates. Defaults to all zeroes.
`h`	A single value on the time scale representing the bandwidth to use.
`...`	Additional parameters to pass to `optim`.

The naming of the function finda stands for "find a(t)", where "a(t)" is the notation used in Cai and Sun (2003) to represent the time-varying Cox model coefficients. We also refer to "a(t)" as "beta(t)" through the documentation.

The user typically will not interact with this function, as finda is wrapped by hdslc.

A vector of length p, where p is the number of covariates. The vector is the estimated beta(t) from the local-in-time Cox model at time tt.

Cai Z and Sun Y (2003). Local linear estimation for time-dependent coefficients in Cox's regression models. Scandinavian Journal of Statistics, 30: 93-111. doi:10.1111/1467-9469.00320

Tian L, Zucker D, and Wei LJ (2005). On the Cox model with time-varying regression coefficients. Journal of the American Statistical Association, 100(469):172-83. doi:10.1198/016214504000000845

hds documentation built on May 2, 2019, 9:44 a.m.