Can synthetic data be a proxy for real clinical trial data? A validation study

Azizi, Zahra; Zheng, Chaoyi; Mosquera, Lucy; Pilote, Louise; Emam, Khaled El

doi:10.1136/bmjopen-2020-043497

Cited by 56 publications

(54 citation statements)

References 41 publications

(35 reference statements)

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“…Some research suggests that synthetic data can be used as a proxy for the real dataset in analyses [35,40]. We believe that the synthetic data constructed here is accurate enough to make predictions consistent with the real data, but do not advise that it currently be used in place of the real data as a proxy.…”

Section: Discussionmentioning

confidence: 82%

“…Previous literature regarding simulation of survival data tends to focus on the creation of entirely new data under a range of set parameters [1,2,[31][32][33]. Many simulation studies make use of either exponential or Weibull distributions [34,35], however these are often not flexible enough to fully capture the shape of underlying hazard functions found in real-world clinicals trial or population based data, where at least one turning point is observed in the hazard function [21]. Making use of the flexibility in a Royston-Parmar model provides a good solution for replicating real-world survival data.…”

Section: Discussionmentioning

confidence: 99%

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Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility

Smith

Lambert

Rutherford

2022

BMC Med Res Methodol

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Background A lack of available data and statistical code being published alongside journal articles provides a significant barrier to open scientific discourse, and reproducibility of research. Information governance restrictions inhibit the active dissemination of individual level data to accompany published manuscripts. Realistic, high-fidelity time-to-event synthetic data can aid in the acceleration of methodological developments in survival analysis and beyond by enabling researchers to access and test published methods using data similar to that which they were developed on. Methods We present methods to accurately emulate the covariate patterns and survival times found in real-world datasets using synthetic data techniques, without compromising patient privacy. We model the joint covariate distribution of the original data using covariate specific sequential conditional regression models, then fit a complex flexible parametric survival model from which to generate survival times conditional on individual covariate patterns. We recreate the administrative censoring mechanism using the last observed follow-up date information from the initial dataset. Metrics for evaluating the accuracy of the synthetic data, and the non-identifiability of individuals from the original dataset, are presented. Results We successfully create a synthetic version of an example colon cancer dataset consisting of 9064 patients which aims to show good similarity to both covariate distributions and survival times from the original data, without containing any exact information from the original data, therefore allowing them to be published openly alongside research. Conclusions We evaluate the effectiveness of the methods for constructing synthetic data, as well as providing evidence that there is minimal risk that a given patient from the original data could be identified from their individual unique patient information. Synthetic datasets using this methodology could be made available alongside published research without breaching data privacy protocols, and allow for data and code to be made available alongside methodological or applied manuscripts to greatly improve the transparency and accessibility of medical research.

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Section: Discussionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility

Smith

Lambert

Rutherford

2022

BMC Med Res Methodol

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“…The process involves generating synthetic data from real data using a machine learning model that captures the patterns in real data and then generates new data from that model. The generated non-identifiable data closely match the statistical properties and patterns in the original dataset, offering very similar results and leading to the same conclusions, all while preserving individuals’ privacy and without the legislative need for additional consent [ 4 , 5 ]. This method is further discussed in detail in the next section.…”

Section: Privacy and Confidentiality-preserving Solutions For Geolocationmentioning

confidence: 80%

Reconciling public health common good and individual privacy: new methods and issues in geoprivacy

et al. 2022

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This article provides a state-of-the-art summary of location privacy issues and geoprivacy-preserving methods in public health interventions and health research involving disaggregate geographic data about individuals. Synthetic data generation (from real data using machine learning) is discussed in detail as a promising privacy-preserving approach. To fully achieve their goals, privacy-preserving methods should form part of a wider comprehensive socio-technical framework for the appropriate disclosure, use and dissemination of data containing personal identifiable information. Select highlights are also presented from a related December 2021 AAG (American Association of Geographers) webinar that explored ethical and other issues surrounding the use of geospatial data to address public health issues during challenging crises, such as the COVID-19 pandemic.

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“…This is known as synthetic data and it is created using machine learning generative models. 204 It produces entirely new datasets as a proxy from the collected data, which are then tested and validated.…”

Section: Privacymentioning

confidence: 99%

Future of Surgery: Technology Enhanced Surgical Training: Report of the FOS:TEST Commission

Adebayo¹,

Aldoori²,

Allum³

et al. 2022

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Over the past 50 years the capability of technology to improve surgical care has been realised and while surgical trainees and trainers strive to deliver care and train; the technological ‘solutions’ market continues to expand. However, there remains no coordinated process to assess these technologies. The FOS:TEST Report aimed to (1) define the current, unmet needs in surgical training, (2) assess the current evidence-base of technologies that may be beneficial to training and map these onto both the patient and trainee pathway and (3) make recommendations on the development, assessment, and adoption of novel surgical technologies. The FOS:TEST Commission was formed by the Association of Surgeons in Training (ASiT), The Royal College of Surgeons of England (RCS England) Robotics and Digital Surgery Group and representatives from all trainee specialty associations. Two national datasets provided by Health Education England were used to identify unmet surgical training needs through qualitative analysis against pre-defined coding frameworks. These unmet needs were prioritised at two virtual consensus hackathons and mapped to the patient and trainee pathway and the capabilities in practice (CiPs) framework. The commission received more than 120 evidence submissions from surgeons in training, consultant surgeons and training leaders. Following peer review, 32 were selected that covered a range of innovations. Contributors also highlighted several important key considerations, including the changing pedagogy of surgical training, the ethics and challenges of big data and machine learning, sustainability, and health economics. This summates to 7 Key Recommendations and 51 concluding statements. The FOS:TEST Commission was borne out of what is a pivotal point in the digital transformation of surgical training. Academic expertise and collaboration will be required to evaluate efficacy of any novel training solution. However, this must be coupled with pragmatic assessments of feasibility and cost to ensure that any intervention is scalable for national implementation. Currently, there is no replacement for hands-on operating. However, for future UK and ROI surgeons to stay relevant in a global market, our training methods must adapt. The Future of Surgery: Technology Enhanced Surgical Training Report provides a blueprint for how this can be achieved.

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Can synthetic data be a proxy for real clinical trial data? A validation study

Cited by 56 publications

References 41 publications

Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility

Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility

Reconciling public health common good and individual privacy: new methods and issues in geoprivacy

Future of Surgery: Technology Enhanced Surgical Training: Report of the FOS:TEST Commission

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