Privacy-Preserving Distributed Algorithms
Pooled estimation for COLA-GLM and COLA-GLM-H
PDA synthesize surrogate estimates from all sites, optional
Generate pda derivatives
PDA surrogate estimation
ODACH_CC initialize
PDA synthesize surrogate estimates from all sites, optional
Generate pda ODACT derivatives
PDA ODACT surrogate estimation
ODACT initialize
PDA synthesize surrogate estimates from all sites, optional
ODAH derivatives
PDA surrogate estimation
ODAH initialize
ODAL derivatives
PDA surrogate estimation
ODAL initialize
PDA synthesize surrogate estimates from all sites, optional
ODAP derivatives
PDA surrogate estimation
ODAP initialize
ODAPB derivatives
PDA surrogate estimation
ODAPB initialize
PDA: Privacy-preserving Distributed Algorithm
use this function to guide end-users step-by-step to identify best pda...
Function to download json and return as object
Function to list available objects
Function to upload object to cloud as json
pda control synchronize
Function to perform all data processing and pooled stratified analysis
ODACATH initialize
ADAP surrogate estimation
ADAP initialize
DisC2o AIPW estimate of the ATE, synthesizing all sites
PDA DLM estimation
PDA synthesize surrogate estimates from all sites, optional
ODACAT derivatives
DLM initialize
PDA surrogate estimation
ODACAT initialize
PDA synthesize surrogate estimates from all sites, optional
ODACATH derivatives
DPQL derive
PDA surrogate estimation
PDA DPQL estimation
DPQL initialize
ADAP derivatives
COLA-GLMM
PDA COLA estimation
COLA initialize
dGEM hospital-specific effect derivation
dGEM standardized event rate estimation
dGEM initialize
PDA dGEM synthesize
DisC2o AIPW estimate of the ATE at each site
DisC2o_OM derivatives
DisC2o outcome model surrogate estimation
DisC2o_OM initialize
DisC2o_PS derivatives
PDA surrogate estimation
DisC2o PS initialize
One-shot site summaries for COLA-GLMM
gather cloud settings into a list
LATTE LATTE.estimate
LATTE initialize
Construct binary covariate pattern matrix
A flexible version of MASS::glmmPQL
Generate pda UWZ derivatives
Generate pda UWZ summary statistics before calculating derivatives
PDA surrogate estimation
ODAC initialize
A collection of privacy-preserving distributed algorithms (PDAs) for conducting federated statistical learning across multiple data sites. The PDA framework includes models for various tasks such as regression, trial emulation, causal inference, design-specific analysis, and clustering. The PDA algorithms run on a lead site and only require summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online data transfer 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/>.