A Privacy Enhancing Technologies Operations (PETOps) platform

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Kaplan-Meier on OHDSI

This repository contains an implementation of the Kaplan-Meier curve calculation designed for federated learning environments via the vantage6 framework. It allows for the estimation of survival probabilities across distributed datasets without sharing the patient-specific information. This method supports privacy-enhancing data analysis in medical research and other fields where event-time analysis is critical.

The algorithm operates within the vantage6 infrastructure, a platform supporting federated learning, to enable institutions to perform survival analysis while maintaining data privacy. The initial idea was based on contributions from Benedetta Gottardelli ([email protected]).

This initial version has been updated and adapted for the BlueBerry project.

Follow the instructions in subsequent sections to set up and execute the federated Kaplan-Meier analysis.

Privacy Gaurds

Minimum number of organizations

In order to minimize the risk of reconstruction the number of organizations should be at least 3. The value of this threshold can be changed by setting KAPLAN_MEIER_MINIMUM_ORGANIZATIONS. Note that this threshold can be set by the aggregator party only!

Minimum number of records

The algorithm will only share information if there are at least n records present in the local dataset. This can be set using the variable KAPLAN_MEIER_MINIMUM_NUMBER_OF_RECORDS.

Fix event time column

In order to limit the options the user has for selecting the event time column the KAPLAN_MEIER_ALLOWED_EVENT_TIME_COLUMNS_REGEX can be set to a comma separated list. Each element in the list can be a regex pattern.

Noise to event times

In order to protect the individual event times noise can be added to this column. The column is user defined, see “Fixed event time column” section.

The type of noise can be set through KAPLAN_MEIER_TYPE_NOISE. This can be one of the following:

• NONE – no noise will be added to the time event columns
• GAUSSIAN – Gaussian noise will be added, the amount of noise can be controlled to a signal to noise ratio: KAPLAN_MEIER_PRIVACY_SNR_EVENT_TIME. The SNR is defined as the amount of noise compared to the standard deviation of the original signal.
• POISSON – Poisson noise will be applied.

Build

In order to build its best to use the makefile.

make image VANTAGE6_VERSION=4.5.3