ODAL.derive

internal

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 

ODAL.derive function

<dl><dt>ipdata</dt>
<dd>individual participant data</dd>
<dt>control</dt>
<dd>pda control data</dd>
<dt>config</dt>
<dd>local site configuration</dd></dl>

Arguments

ODAL derivatives — ODAL.derive

<dl>

<dt>ipdata</dt>
<dd>individual participant data</dd>


<dt>control</dt>
<dd>pda control data</dd>


<dt>config</dt>
<dd>local site configuration</dd>

</dl>

ODAL derivatives

ODAL.derive: ODAL derivatives

Description

Usage

Value

Arguments