ADAP_data

A simulated data set for ADAP demonstration

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 

ADAP_data function

A list containing the following elements:<dl>
 <dt>sites</dt>
<dd>site id, 300 'site1', 300 'site2', 300 'site3'</dd> <dt>status</dt>
<dd>binary outcome of length 900</dd> <dt>x</dt>
<dd>900 by 49 matrix generated by standard normal distribution, representing the covariates</dd>
 </dl>

Format

ADAP simulated data — ADAP_data

A list containing the following elements:<dl>
 <dt>sites</dt>
<dd>site id, 300 'site1', 300 'site2', 300 'site3'</dd>

 <dt>status</dt>
<dd>binary outcome of length 900</dd>

 <dt>x</dt>
<dd>900 by 49 matrix generated by standard normal distribution, representing the covariates</dd>
 

</dl>

ADAP simulated data

ADAP_data: ADAP simulated data

Description

Usage

Arguments

Format