# PDA: Privacy-preserving Distributed Algorithm Fit Privacy-preserving Distributed Algorithms for linear, logistic, Poisson and Cox PH regression with possible heterogeneous data across sites. ```r 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 ## Returns control control ## Examples ```r 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 ``` ## 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 tools:::Rd_expr_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, tools:::Rd_expr_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, tools:::Rd_expr_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, tools:::Rd_expr_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, tools:::Rd_expr_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**, tools:::Rd_expr_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. tools:::Rd_expr_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 ## See Also `pdaPut`, `pdaList`, `pdaGet`, `getCloudConfig` and `pdaSync`.

pda function