cste_surv: Estimate the CSTE curve for time to event outcome with right...

In CSTE: Covariate Specific Treatment Effect (CSTE) Curve

Estimate the CSTE curve for time to event outcome with right censoring.

Description

Estimate the CSTE curve for time to event outcome with right censoring. The working model is

\lambda(t| X, Z) = \lambda_0(t) \exp(\beta^T(X)Z + g(X)),

which implies that CSTE(x) = \beta(x).

Usage

cste_surv(x, y, z, s, h)

Arguments

x	samples of biomarker (or covariate) which is a n*1 vector and should be scaled between 0 and 1.
y	samples of time to event which is a n*1 vector.
z	samples of treatment indicator which is a n*K matrix.
s	samples of censoring indicator which is a n*1 vector.
h	kernel bandwidth.

Value

A n*K matrix, estimation of \beta(x).

References

Ma Y. and Zhou X. (2017). Treatment selection in a randomized clinical trial via covariate-specific treatment effect curves, Statistical Methods in Medical Research, 26(1), 124-141.

See Also

cste_surv_SCB

CSTE documentation built on April 4, 2025, 12:27 a.m.