In neelsoumya/dsSurvivalClient: dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD

knitr::opts_chunk$set(echo=TRUE)

Summary

This is a document that outlines a vignette for implementing survival models and meta-analyzing hazard ratios in the DataSHIELD platform.

Survival analysis in DataSHIELD

We outline code for implementing survival models and meta-analysis of hazard ratios in DataSHIELD.

All code is available here:

https://github.com/neelsoumya/dsSurvival
https://github.com/neelsoumya/dsSurvivalClient
https://github.com/neelsoumya/dsBase
https://github.com/neelsoumya/dsBaseClient

Installation

Install R Studio and the development environment as described below:

https://data2knowledge.atlassian.net/wiki/spaces/DSDEV/pages/12943461/Getting+started

Then install the virtual machines as described below:

https://data2knowledge.atlassian.net/wiki/spaces/DSDEV/pages/931069953/Installation+Training+Hub-+DataSHIELD+v6

Install the necessary packages by running the following commands in R Studio:

install.packages('devtools')

library(devtools)

devtools::install_github('neelsoumya/dsBaseClient')

devtools::install_github('neelsoumya/dsBase')

devtools::install_github('neelsoumya/dsSurvivalClient')

Install dsBase (neelsoumya/dsBase main branch) and dsSurvival (neelsoumya/dsSurvival main branch) on the Opal virtual machine.

Computational workflow

The computational steps are outlined below. The first step is connecting to the server and loading the survival data. We assume that the reader is familiar with these details.

library(knitr)
library(rmarkdown)
library(tinytex)
library(survival)
library(metafor)
library(ggplot2)
library(survminer)
library(dsSurvivalClient)
require('DSI')
require('DSOpal')
require('dsBaseClient')

builder <- DSI::newDSLoginBuilder()

builder$append(server = "study1", 
               url = "http://192.168.56.100:8080/", 
               user = "administrator", password = "datashield_test&", 
               table = "SURVIVAL.EXPAND_NO_MISSING1", driver = "OpalDriver")
builder$append(server = "study2", 
               url = "http://192.168.56.100:8080/", 
               user = "administrator", password = "datashield_test&", 
               table = "SURVIVAL.EXPAND_NO_MISSING2", driver = "OpalDriver")
builder$append(server = "study3", 
               url = "http://192.168.56.100:8080/", 
               user = "administrator", password = "datashield_test&", 
               table = "SURVIVAL.EXPAND_NO_MISSING3", driver = "OpalDriver")

logindata <- builder$build()

connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol = "D")

####################
# Load library
####################
library(knitr)
library(rmarkdown)
library(tinytex)
library(survival)
library(metafor)
library(ggplot2)
library(survminer)
#library(dsSurvival)
library(dsSurvivalClient)
require('DSI')
require('DSOpal')
require('dsBaseClient')

#######################
# Get data
#######################
builder <- DSI::newDSLoginBuilder()

# TODO: use open server
# builder$append(server="server1", url="https://opal-sandbox.mrc-epid.cam.ac.uk",
#                user="dsuser", password="password", 
#               table = "SURVIVAL.EXPAND_NO_MISSING1")

# builder$append(server="server1", url="https://opal-sandbox.mrc-epid.cam.ac.uk",
#               user="dsuser", password="password", 
#               table = "SURVIVAL.EXPAND_NO_MISSING2")

# builder$append(server="server1", url="https://opal-sandbox.mrc-epid.cam.ac.uk",
#               user="dsuser", password="password", 
#               table = "SURVIVAL.EXPAND_NO_MISSING3")         


builder$append(server = "study1", 
               url = "http://192.168.56.100:8080/", 
               user = "administrator", password = "datashield_test&", 
               table = "SURVIVAL.EXPAND_NO_MISSING1", driver = "OpalDriver")
builder$append(server = "study2", 
               url = "http://192.168.56.100:8080/", 
               user = "administrator", password = "datashield_test&", 
               table = "SURVIVAL.EXPAND_NO_MISSING2", driver = "OpalDriver")
builder$append(server = "study3", 
               url = "http://192.168.56.100:8080/", 
               user = "administrator", password = "datashield_test&", 
               table = "SURVIVAL.EXPAND_NO_MISSING3", driver = "OpalDriver")

logindata <- builder$build()

##############
# login
##############

# Log onto the remote Opal training servers
connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol = "D")

Creating server-side variables for survival analysis

We now outline some steps for analysing survival data.

make sure that the outcome variable is numeric

ds.asNumeric(x.name = "D$cens",
             newobj = "EVENT",
             datasources = connections)

ds.asNumeric(x.name = "D$survtime",
             newobj = "SURVTIME",
             datasources = connections)

convert time id variable to a factor

ds.asFactor(input.var.name = "D$time.id",
            newobj = "TID",
            datasources = connections)

create in the server-side the log(survtime) variable

ds.log(x = "D$survtime",
       newobj = "log.surv",
       datasources = connections)

create start time variable

ds.asNumeric(x.name = "D$starttime",
             newobj = "STARTTIME",
             datasources = connections)

ds.asNumeric(x.name = "D$endtime",
             newobj = "ENDTIME",
             datasources = connections)

# make sure that the outcome is numeric 
ds.asNumeric(x.name = "D$cens",
             newobj = "EVENT",
             datasources = connections)

ds.asNumeric(x.name = "D$survtime",
             newobj = "SURVTIME",
             datasources = connections)

# convert time id variable to a factor 
ds.asFactor(input.var.name = "D$time.id",
            newobj = "TID",
            datasources = connections)

# create in the server-side the log(survtime) variable
ds.log(x = "D$survtime",
       newobj = "log.surv",
       datasources = connections)

# create start time variable
ds.asNumeric(x.name = "D$starttime",
             newobj = "STARTTIME",
             datasources = connections)

# create end time variable
ds.asNumeric(x.name = "D$endtime",
             newobj = "ENDTIME",
             datasources = connections)


# check which variables exist
dsBaseClient::ds.ls()

# call new function modify NAMESPACE and DESCRIPTION
# call coxph server side
# client side function is here:
# https://github.com/neelsoumya/dsBaseClient/blob/absolute_newbie_client/R/ds.coxph.SLMA.R
# server side function is here:
# https://github.com/neelsoumya/dsBase/blob/absolute_newbie/R/coxphSLMADS.R

Create survival object and call ds.coxph.SLMA()

use constructed Surv object in ds.coxph.SLMA()

dsSurvivalClient::ds.Surv(time='STARTTIME', time2='ENDTIME', 
                      event = 'EVENT', objectname='surv_object',
                      type='counting')

coxph_model_full <- dsSurvivalClient::ds.coxph.SLMA(formula = 'surv_object~D$age+D$female')

use direct inline call to survival::Surv()

dsSurvivalClient::ds.coxph.SLMA(formula = 'survival::Surv(time=SURVTIME,event=EVENT)~D$age+D$female', 
                                dataName = 'D', 
                                datasources = connections)

call with survival::strata()

coxph_model_strata <- dsSurvivalClient::ds.coxph.SLMA(formula = 'surv_object~D$age + 
                          survival::strata(D$female)')

summary(coxph_model_strata)

# 1. use constructed surv object in coxph
dsSurvivalClient::ds.Surv(time='STARTTIME', time2='ENDTIME', event = 'EVENT', objectname='surv_object', type='counting')
coxph_model_full <- dsSurvivalClient::ds.coxph.SLMA(formula = 'surv_object~D$age+D$female')

# 2. use direct inline call
dsSurvivalClient::ds.coxph.SLMA(formula = 'survival::Surv(time=SURVTIME,event=EVENT)~D$age+D$female', dataName = 'D', datasources = connections)

# 3. Call with strata()
coxph_model_strata <- dsSurvivalClient::ds.coxph.SLMA(formula = 'surv_object~D$age+survival::strata(D$female)')
summary(coxph_model_strata)

Summary of survival objects

We can also summarize a server-side object of type survival::Surv() using a call to ds.coxphSummary(). This will provide a non-disclosive summary of the server-side object. An example call is shown below:

dsSurvivalClient::ds.coxphSummary(x = 'coxph_serverside')

#################################
# summary of coxphSLMA
#################################
# TODO:
# dsBaseClient::ds.summary(x = 'surv_object')
# dsBaseClient::ds.class(x = 'surv_object')
# dsBaseClient::ds.mean(x='surv_object')


#################################
# TODO: Plot survival curves
#################################
# fit <- survival::survfit(formula = 'surv_object~D$age+D$female', data = 'D')
# need ds.survfit() and survfitDS()
# fit_model <- ds.survfit(coxph_model[1])
# plot(fit_model)

# TODO:
# plot(survfit_km, fun="cloglog")

# TODO: 
# ggplot like functionality see other functions

# In dsBaseClient::
# ds.survfit()
#         datashield.aggregate("survfitDS", ....)
#          return (the fit model)

# In dsBase::
# survfitDS(coxph_model)  
#               fit_model <- survival::survfit(coxph_model, newdata = 'D')
#               return (fit_model)

# TODO: dsSurvivalClient::ds.survfit(formula='surv_object~1', objectname='survfit_object')
# verify that object has been created
dsBaseClient::ds.ls()

# ERROR summary of survfit not allowed
# dsBaseClient::ds.summary(x='survfit_object')
# dsBaseClient::ds.mean(x='survfit_object')

Diagnostics for Cox proportional hazards models

We have also created functions to test for the assumptions of Cox proportional hazards models.

dsSurvivalClient::ds.coxphSLMAassign(formula = 'surv_object~D$age+D$female',
                            objectname = 'coxph_serverside')

dsSurvivalClient::ds.cox.zphSLMA(fit = 'coxph_serverside')

dsSurvivalClient::ds.coxphSummary(x = 'coxph_serverside')

A diagnostic summary is shown below.

dsSurvivalClient::ds.coxphSLMAassign(formula = 'surv_object~D$age+D$female', objectname = 'coxph_serverside')

dsSurvivalClient::ds.cox.zphSLMA(fit = 'coxph_serverside')

dsSurvivalClient::ds.coxphSummary(x = 'coxph_serverside')

Meta-analyze hazard ratios

We now outline how the hazard ratios from the survival models are meta-analyzed. We use the metafor package for meta-analysis. We show the summary of an example meta-analysis and a forest plot below. The forest plot shows a basic example of meta-analyzed hazard ratios from a survival model (analyzed in dsSurvivalClient).

The log-hazard ratios and their standard errors from each study can be found after running ds.coxphSLMA()

The hazard ratios can then be meta-analyzed:

input_logHR = c(coxph_model_full$study1$coefficients[1,2], 
        coxph_model_full$study2$coefficients[1,2], 
        coxph_model_full$study3$coefficients[1,2])

input_se    = c(coxph_model_full$study1$coefficients[1,3], 
        coxph_model_full$study2$coefficients[1,3], 
        coxph_model_full$study3$coefficients[1,3])

metafor::rma(log_hazard_ratio, sei = se_hazard_ratio, method = 'REML')

A summary of this meta-analyzed model is shown below.

# TODO: for each study
for (i_temp_counter in c(1:length(coxph_model_full)))
{

}

# list of hazard ratios for first parameter (age) over 3 studies 
input_logHR = c(coxph_model_full$study1$coefficients[1,2], coxph_model_full$study2$coefficients[1,2], coxph_model_full$study3$coefficients[1,2])
input_se    = c(coxph_model_full$study1$coefficients[1,3], coxph_model_full$study2$coefficients[1,3], coxph_model_full$study3$coefficients[1,3])

meta_model <- metafor::rma(input_logHR, sei = input_se, method = 'REML')
summary(meta_model)

#######################################################
# forest plots of final meta-analyzed hazard ratios
#######################################################

We now show a forest plot with the meta-analyzed hazard ratios. The hazard ratios come from the dsSurvivalClient function ds.coxphSLMA(). The hazard ratios are meta-analyzed using the metafor package.

metafor::forest.rma(x = meta_model)

Plotting of privacy-preserving survival curves

We also plot privacy preserving survival curves. Please note that is work in progress and is only available on a separate development branch. There will be a full release in v1.1.0.

dsSurvivalClient::ds.survfit(formula='surv_object~1', objectname='survfit_object')

dsSurvivalClient::ds.plotsurvfit(formula = 'survfit_object')

dsSurvivalClient::ds.survfit(formula='surv_object~1', objectname='survfit_object')

dsSurvivalClient::ds.plotsurvfit(formula = 'survfit_object')

# dsSurvivalClient::ds.plotsurvfit(formula = 'survfit_object', method_anonymization = 1, knn = 20)

\newpage

#############################################
# disconnect
#############################################
DSI::datashield.logout(conns = connections)

Acknowledgements

We acknowledge the help and support of the DataSHIELD technical team. We are especially grateful to Yannick Marcon, Paul Burton, Demetris Avraam, Stuart Wheater, Patricia Ryser-Welch, Xavier Escriba, Juan Gonzalez and Wolfgang Vichtbauer for fruitful discussions and feedback.

References

https://github.com/datashield
http://www.metafor-project.org
https://github.com/neelsoumya/dsBase
https://github.com/neelsoumya/dsBaseClient
https://github.com/neelsoumya/dsSurvival
https://github.com/neelsoumya/dsSurvivalClient
https://github.com/neelsoumya/datashield_testing_basic

neelsoumya/dsSurvivalClient documentation built on July 1, 2023, 10:32 p.m.

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neelsoumya/dsSurvivalClient
dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD

In neelsoumya/dsSurvivalClient: dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD

Summary

Survival analysis in DataSHIELD

Installation

Computational workflow

Creating server-side variables for survival analysis

Create survival object and call ds.coxph.SLMA()

Summary of survival objects

Diagnostics for Cox proportional hazards models

Meta-analyze hazard ratios

Plotting of privacy-preserving survival curves

Acknowledgements

References

R Package Documentation

Browse R Packages

We want your feedback!

neelsoumya/dsSurvivalClient dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD

In neelsoumya/dsSurvivalClient: dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD

Summary

Survival analysis in DataSHIELD

Installation

Computational workflow

Creating server-side variables for survival analysis

Create survival object and call ds.coxph.SLMA()

Summary of survival objects

Diagnostics for Cox proportional hazards models

Meta-analyze hazard ratios

Plotting of privacy-preserving survival curves

Acknowledgements

References

R Package Documentation

Browse R Packages

We want your feedback!

neelsoumya/dsSurvivalClient
dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD