lung2

A data set modified from the lung data in survival package (see demo(ODAC)).

datasets

A collection of privacy-preserving distributed algorithms for conducting multi-site data analyses. The regression analyses can be linear regression for continuous outcome, logistic regression for binary outcome, Cox proportional hazard regression for time-to event outcome, Poisson regression for count outcome, or multi-categorical regression for nominal or ordinal outcome. The PDA algorithm runs on a lead site and only requires summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online system (<https://pda-ota.pdamethods.org/>) for safe and convenient collaboration. For more information, please visit our software websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.

Jiajie Chen

Privacy-Preserving Distributed Algorithms

Chongliang Luo

Rui Duan

Mackenzie Edmondson

Jiayi Tong

Xiaokang Liu

Kenneth Locke

Yong Chen

Penn Computing Inference Learning (PennCIL) lab 

lung2 function

A data frame with 228 rows and 5 variables:<dl>
 <dt>site</dt>
<dd>simulated site id, 86 'site1', 83 'site2' and 59 'site3'</dd> <dt>time</dt>
<dd>survival time in days</dd> <dt>status</dt>
<dd>censoring status 0=censored, 1=dead</dd> <dt>age</dt>
<dd>age in years</dd> <dt>sex</dt>
<dd>1 for female and 0 for male</dd>
</dl>

Format

Lung cancer survival time data — lung2

A data frame with 228 rows and 5 variables:<dl>
 <dt>site</dt>
<dd>simulated site id, 86 'site1', 83 'site2' and 59 'site3'</dd>

 <dt>time</dt>
<dd>survival time in days</dd>

 <dt>status</dt>
<dd>censoring status 0=censored, 1=dead</dd>

 <dt>age</dt>
<dd>age in years</dd>

 <dt>sex</dt>
<dd>1 for female and 0 for male</dd>


</dl>

Lung cancer survival time data

lung2: Lung cancer survival time data

Description

Usage

Arguments

Format