ODACAT_nominal

A simulated data set for ODACAT 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 

ODACAT_nominal function

A data frame with 300 rows and 5 variables:<dl>
 <dt>id.site</dt>
<dd>site id, 102 'site1', 100 'site2', 98 'site3'</dd> <dt>outcome</dt>
<dd>3-category outcome, possible values are 1,2,3. Category 3 will be used as reference</dd> <dt>X1</dt>
<dd>the first covariate, continuous</dd> <dt>X2</dt>
<dd>the second covariate, binary</dd> <dt>X3</dt>
<dd>the third covariate, binary</dd>
</dl>

Format

ODACAT simulated data — ODACAT_nominal

A data frame with 300 rows and 5 variables:<dl>
 <dt>id.site</dt>
<dd>site id, 102 'site1', 100 'site2', 98 'site3'</dd>

 <dt>outcome</dt>
<dd>3-category outcome, possible values are 1,2,3. Category 3 will be used as reference</dd>

 <dt>X1</dt>
<dd>the first covariate, continuous</dd>

 <dt>X2</dt>
<dd>the second covariate, binary</dd>

 <dt>X3</dt>
<dd>the third covariate, binary</dd>


</dl>

ODACAT simulated data

ODACAT_nominal: ODACAT simulated data

Description

Usage

Arguments

Format