kidney_data

A dataset containing survival analysis information related to kidney disease patients.

Estimation of marginal hazard ratios in clustered failure time data. It implements the weighted generalized estimating equation approach based on a semiparametric marginal proportional hazards model (See Niu, Y. Peng, Y.(2015). "A new estimating equation approach for marginal hazard ratio estimation"), accounting for within-cluster correlations. 5 different correlation structures are supported. The package is designed for researchers in biostatistics and epidemiology who require accurate and efficient estimation methods for survival analysis in clustered data settings.

Junyi Chen

marcox

Marginal Hazard Ratio Estimation in Clustered Failure Time Data

Siqi Zhou

Shida Li

Yi Niu

kidney_data function

A data frame with 76 rows and 5 variables:<dl>
 <dt><code>time</code></dt>
<dd>Numeric: Time to event or censoring (in days).</dd> <dt><code>cens</code></dt>
<dd>Binary (0/1): Censoring indicator (1 = event occurred, 0 = censored).</dd> <dt><code>age</code></dt>
<dd>Numeric: Age of the patient in years.</dd> <dt><code>sex</code></dt>
<dd>Binary (0/1): Sex of the patient (1 = male, 0 = female).</dd> <dt><code>type</code></dt>
<dd>Categorical (0,1,2,3): Kidney disease type classification.</dd> 
</dl>

Format

Kidney Disease Study Data — kidney_data

A data frame with 76 rows and 5 variables:<dl>
 <dt><code>time</code></dt>
<dd>Numeric: Time to event or censoring (in days).</dd>

 <dt><code>cens</code></dt>
<dd>Binary (0/1): Censoring indicator (1 = event occurred, 0 = censored).</dd>

 <dt><code>age</code></dt>
<dd>Numeric: Age of the patient in years.</dd>

 <dt><code>sex</code></dt>
<dd>Binary (0/1): Sex of the patient (1 = male, 0 = female).</dd>

 <dt><code>type</code></dt>
<dd>Categorical (0,1,2,3): Kidney disease type classification.</dd>

 
</dl>

kidney_data: Kidney Disease Study Data

Description

Usage

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Examples