R: PDA: Privacy-preserving Distributed Algorithm

pda {pda}

R Documentation

PDA: Privacy-preserving Distributed Algorithm

Description

Fit Privacy-preserving Distributed Algorithms for linear, logistic, Poisson and Cox PH regression with possible heterogeneous data across sites.

Usage

pda(ipdata,site_id,control,dir,uri,secret,hosdata)

Arguments

`ipdata`	Local IPD data in data frame, should include at least one column for the outcome and one column for the covariates
`site_id`	Character site name
`control`	pda control data
`dir`	directory for shared flat file cloud
`uri`	Universal Resource Identifier for this run
`secret`	password to authenticate as site_id on uri
`hosdata`	hospital-level data, should include the same name as defined in the control file

Value

control

References

Michael I. Jordan, Jason D. Lee & Yun Yang (2019) Communication-Efficient Distributed Statistical Inference,
Journal of the American Statistical Association, 114:526, 668-681
doi:10.1080/01621459.2018.1429274.
(DLM) Yixin Chen, et al. (2006) Regression cubes with lossless compression and aggregation. IEEE Transactions on Knowledge and Data Engineering, 18(12), pp.1585-1599.
(DLMM) Chongliang Luo, et al. (2020) Lossless Distributed Linear Mixed Model with Application to Integration of Heterogeneous Healthcare Data. medRxiv, doi:10.1101/2020.11.16.20230730.
(DPQL) Chongliang Luo, et al. (2021) dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling.
medRxiv, doi:10.1101/2021.05.03.21256561.
(ODAL) Rui Duan, et al. (2020) Learning from electronic health records across multiple sites:
A communication-efficient and privacy-preserving distributed algorithm.
Journal of the American Medical Informatics Association, 27.3:376–385,
doi:10.1093/jamia/ocz199.
(ODAC) Rui Duan, et al. (2020) Learning from local to global: An efficient distributed algorithm for modeling time-to-event data.
Journal of the American Medical Informatics Association, 27.7:1028–1036,
doi:10.1093/jamia/ocaa044.
(ODACH) Chongliang Luo, et al. (2021) ODACH: A One-shot Distributed Algorithm for Cox model with Heterogeneous Multi-center Data.
medRxiv, doi:10.1101/2021.04.18.21255694.
(ODAH) Mackenzie J. Edmondson, et al. (2021) An Efficient and Accurate Distributed Learning Algorithm for Modeling Multi-Site Zero-Inflated Count Outcomes. medRxiv, pp.2020-12.
doi:10.1101/2020.12.17.20248194.
(ADAP) Xiaokang Liu, et al. (2021) ADAP: multisite learning with high-dimensional heterogeneous data via A Distributed Algorithm for Penalized regression.
(dGEM) Jiayi Tong, et al. (2022) dGEM: Decentralized Generalized Linear Mixed Effects Model

Examples

require(survival)
require(data.table)
require(pda)
data(lung)

## In the toy example below we aim to analyze the association of lung status with 
## age and sex using logistic regression, data(lung) from 'survival', we randomly 
## assign to 3 sites: 'site1', 'site2', 'site3'. we demonstrate using PDA ODAL can 
## obtain a surrogate estimator that is close to the pooled estimate. We run the 
## example in local directory. In actual collaboration, account/password for pda server 
## will be assigned to the sites at the server https://pda.one.
## Each site can access via web browser to check the communication of the summary stats.

## for more examples, see demo(ODAC) and demo(ODAP)

# Create 3 sites, split the lung data amongst them
sites = c('site1', 'site2', 'site3')
set.seed(42)
lung2 <- lung[,c('status', 'age', 'sex')]
lung2$sex <- lung2$sex - 1
lung2$status <- ifelse(lung2$status == 2, 1, 0)
lung_split <- split(lung2, sample(1:length(sites), nrow(lung), replace=TRUE))
## fit logistic reg using pooled data
fit.pool <- glm(status ~ age + sex, family = 'binomial', data = lung2)


# ############################  STEP 1: initialize  ###############################
control <- list(project_name = 'Lung cancer study',
                step = 'initialize',
                sites = sites,
                heterogeneity = FALSE,
                model = 'ODAL',
                family = 'binomial',
                outcome = "status",
                variables = c('age', 'sex'),
                optim_maxit = 100,
                lead_site = 'site1',
                upload_date = as.character(Sys.time()) )


## run the example in local directory:
## specify your working directory, default is the tempdir
mydir <- tempdir()
## assume lead site1: enter "1" to allow transferring the control file  
pda(site_id = 'site1', control = control, dir = mydir)
## in actual collaboration, account/password for pda server will be assigned, thus:
## Not run: pda(site_id = 'site1', control = control, uri = 'https://pda.one', secret='abc123')
## you can also set your environment variables, and no need to specify them in pda:
## Not run: Sys.setenv(PDA_USER = 'site1', PDA_SECRET = 'abc123', PDA_URI = 'https://pda.one')
## Not run: pda(site_id = 'site1', control = control)

##' assume remote site3: enter "1" to allow tranferring your local estimate 
pda(site_id = 'site3', ipdata = lung_split[[3]], dir=mydir)

##' assume remote site2: enter "1" to allow tranferring your local estimate  
pda(site_id = 'site2', ipdata = lung_split[[2]], dir=mydir)

##' assume lead site1: enter "1" to allow tranferring your local estimate  
##' control.json is also automatically updated
pda(site_id = 'site1', ipdata = lung_split[[1]], dir=mydir)

##' if lead site1 initialized before other sites,
##' lead site1: uncomment to sync the control before STEP 2
## Not run: pda(site_id = 'site1', control = control)
## Not run: config <- getCloudConfig(site_id = 'site1')
## Not run: pdaSync(config)

#' ############################'  STEP 2: derivative  ############################ 
##' assume remote site3: enter "1" to allow tranferring your derivatives  
pda(site_id = 'site3', ipdata = lung_split[[3]], dir=mydir)

##' assume remote site2: enter "1" to allow tranferring your derivatives  
pda(site_id = 'site2', ipdata = lung_split[[2]], dir=mydir)

##' assume lead site1: enter "1" to allow tranferring your derivatives  
pda(site_id = 'site1', ipdata = lung_split[[1]], dir=mydir)


#' ############################'  STEP 3: estimate  ############################ 
##' assume lead site1: enter "1" to allow tranferring the surrogate estimate  
pda(site_id = 'site1', ipdata = lung_split[[1]], dir=mydir)

##' the PDA ODAL is now completed!
##' All the sites can still run their own surrogate estimates and broadcast them.

##' compare the surrogate estimate with the pooled estimate 
config <- getCloudConfig(site_id = 'site1', dir=mydir)
fit.odal <- pdaGet(name = 'site1_estimate', config = config)
cbind(b.pool=fit.pool$coef,
      b.odal=fit.odal$btilde,
      sd.pool=summary(fit.pool)$coef[,2],
      sd.odal=sqrt(diag(solve(fit.odal$Htilde)/nrow(lung2))))
      
## see demo(ODAL) for more optional steps

[Package pda version 1.2.7 Index]