tvef.zero

Testing the significance of the covariates from a <code>coxtv</code> or <code>coxtp</code> object using a Wald test statistic.
The null hypothesis \(H_0: \beta(t) = 0\) for any \(t\), where \(t\) denotes the event time.

Fit Cox non-proportional hazards models with time-varying coefficients.
Both unpenalized procedures (Newton and proximal Newton) and penalized procedures
(P-splines and smoothing splines) are included using B-spline basis functions for
estimating time-varying coefficients. For penalized procedures, cross validations,
mAIC, TIC or GIC are implemented to select tuning parameters. Utilities for
carrying out post-estimation visualization, summarization, point-wise confidence
interval and hypothesis testing are also provided.
For more information, see Wu et al. (2022) <doi: 10.1007/s10985-021-09544-2> and
Luo et al. (2023) <doi:10.1177/09622802231181471>.

Lingfeng Luo

surtvep

Cox Non-Proportional Hazards Model with Time-Varying
Coefficients

Wenbo Wu

Kevin He

tvef.zero function

<dl><dt>fit</dt>
<dd>fitted <code>coxtv</code> or <code>coxtp</code> model.</dd>
<dt>parm</dt>
<dd>the names of parameters to be tested.</dd></dl>

Arguments

testing the significance of the covariates from a coxtv or coxtp object — tvef.zero

<dl>

<dt>fit</dt>
<dd>fitted <code>coxtv</code> or <code>coxtp</code> model.</dd>


<dt>parm</dt>
<dd>the names of parameters to be tested.</dd>

</dl>

tvef.zero: testing the significance of the covariates from a `coxtv` or `coxtp` object

Description

Usage

Value

Arguments

See Also

Examples