Cox model with fully parameterised baseline hazard function

Coxph

Formula-based user-interfaces to specific transformation models
implemented in package 'mlt' (<DOI:10.32614/CRAN.package.mlt>, <DOI:10.32614/CRAN.package.mlt.docreg>).
Available models include Cox models, some parametric
survival models (Weibull, etc.), models for ordered categorical variables,
normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression
(Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory
is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to
transformation models for clustered data is provided (Barbanti and Hothorn, 2022,
<DOI:10.1093/biostatistics/kxac048>). Multivariate conditional transformation models
(Klein et al, 2022, <DOI:10.1111/sjos.12501>) and shift-scale transformation models (Siegfried et al, 2023,
<DOI:10.1080/00031305.2023.2203177>) can be fitted as well. The package contains an implementation of
a doubly robust score test, described in Kook et al. (2024, <DOI:10.1080/01621459.2024.2395588>).

Torsten Hothorn

tram

Transformation Models

Luisa Barbanti

Sandra Siegfried

Lucas Kook

Brian Ripley

Bill Venables

Nadja Klein

Coxph function

Cox Proportional Hazards Model — Coxph

<dl>

 <dt>formula</dt>
<dd>an object of class <code>"formula"</code>: a symbolic description 
 of the model structure to be
 fitted. The details of model specification are given under
 <code>tram</code> and in the package vignette.</dd>

 <dt>data</dt>
<dd>an optional data frame, list or environment (or object
 coercible by <code>as.data.frame</code> to a data frame) containing the
 variables in the model. If not found in <code>data</code>, the
 variables are taken from <code>environment(formula)</code>.</dd>

 <dt>subset</dt>
<dd>an optional vector specifying a subset of observations to be
 used in the fitting process.</dd>

 <dt>weights</dt>
<dd>an optional vector of weights to be used in the fitting
 process. Should be <code>NULL</code> or a numeric vector. If present,
 the weighted log-likelihood is maximised.</dd>

 <dt>offset</dt>
<dd>this can be used to specify an _a priori_ known component to
 be included in the linear predictor during fitting. This
 should be <code>NULL</code> or a numeric vector of length equal to the
 number of cases.</dd>

 <dt>cluster</dt>
<dd>optional factor with a cluster ID employed for computing
 clustered covariances.</dd>

 <dt>na.action</dt>
<dd>a function which indicates what should happen when the data
 contain <code>NA</code>s. The default is set to <code>na.omit</code>.</dd>

 <dt>...</dt>
<dd>additional arguments to <code>tram</code>.</dd>

</dl>

Arguments

Cox Proportional Hazards Model

Formula-based user-interfaces to specific transformation models
implemented in package 'mlt'. Available models include Cox models, some parametric
survival models (Weibull, etc.), models for ordered categorical variables,
normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression
(Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory
is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to
transformation models for clustered data is provided (Hothorn, 2019, <arxiv:1910.09219>).

Coxph: Cox Proportional Hazards Model

Description

Usage

Arguments

Value

Details

References

Examples