Reply to: On the difficulty of achieving differential privacy in practice: user-level guarantees in aggregate location data

Bassolas, Aleix; Barbosa, Hugo; Dickinson, Brian P.; Dotiwalla, Xerxes; Eastham, P. R.; Gallotti, Riccardo; Ghoshal, Gourab; Gipson, Bryant; Hazarie, Surendra; Kautz, Henry; Küçüktunç, Onur; Lieber, Allison; Sadilek, Adam; Ramasco, José J.

doi:10.1038/s41467-021-27567-z

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…Some of the recent examples of publishing anonymized RWD include the Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS), [22,23] European population statistics, [24] and urban mobility data. [25] There are also studies analyzing the privacy risks and data accuracy trade-offs of anonymized RWD and clinical study data sets. [24][25][26][27][28] The aim of this study was to explore how well anonymized RWD performs in the creation of an ECA for an RCT, when compared to the corresponding performance prior to the anonymization, i.e., when using pseudonymized RWD.…”

Section: Introductionmentioning

confidence: 99%

“…[25] There are also studies analyzing the privacy risks and data accuracy trade-offs of anonymized RWD and clinical study data sets. [24][25][26][27][28] The aim of this study was to explore how well anonymized RWD performs in the creation of an ECA for an RCT, when compared to the corresponding performance prior to the anonymization, i.e., when using pseudonymized RWD. Furthermore, the study compared general characteristics of the same pseudonymized and anonymized RWD sets, to assess the magnitude of discrepancies caused by anonymization.…”

Section: Introductionmentioning

confidence: 99%

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Utilization of anonymization techniques to create an external control arm for clinical trial data

Mehtälä¹,

Ali²,

Miettinen³

et al. 2023

Preprint

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Background Subject-level real-world data (RWD) collected during daily healthcare practices are increasingly used in medical research to assess questions that cannot be addressed in the context of a randomized controlled trial (RCT). A novel application of RWD arises from the need to create external control arms (ECAs) for single-arm RCTs. In the analysis of ECAs against RCT data, there is an evident need to manage and analyze RCT data and RWD in the same technical environment. In the Nordic countries, legal requirements may require that the original subject-level data be anonymized, i.e., modified so that the risk to identify any individual is minimal. The aim of this study was to investigate and compare how well pseudonymized and anonymized RWD perform in the creation of an ECA for an RCT. Methods This was a hybrid observational cohort study using clinical data from the control arm of the completed randomized phase II clinical trial (PACIFIC-AF) and RWD cohort from Finnish healthcare data sources. The initial pseudonymized RWD were anonymized within the (k, ε)-anonymity framework (a model for protecting individuals against identification). Propensity score matching and weighting methods were applied to the anonymized and pseudonymized RWD, to balance potential confounders against the RCT data. Descriptive statistics for the potential confounders and overall survival analyses were conducted prior to and after matching and weighting, using both the pseudonymized and anonymized RWD sets. Results Anonymization affected the baseline characteristics of potential confounders only marginally. The greatest difference was in the prevalence of chronic obstructive pulmonary disease (4.6% vs. 5.4% in the pseudonymized compared to the anonymized data, respectively). Moreover, the overall survival changed in anonymization by only 8% (95% CI 4–22%). Both the pseudonymized and anonymized RWD were able to produce matched ECAs for the RCT data. Anonymization after matching impacted overall survival analysis by 22% (95% CI -21–87%). Conclusions Anonymization is a viable technique for cases where flexible data transfer and sharing are required. However, as anonymization necessarily affects some aspects of the original data, careful consideration of anonymization strategy is recommended.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Utilization of anonymization techniques to create an external control arm for clinical trial data

Mehtälä¹,

Ali²,

Miettinen³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The selection of anonymization techniques and privacy criteria depends on the scope of the target data, variable types, and the intended use of the resulting anonymized data. Some of the recent examples of publishing anonymized RWD include the Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS), [ 24 , 25 ] European population statistics, [ 26 ] and urban mobility data [ 27 ]. There are also studies analyzing the privacy risks and data accuracy trade-offs of anonymized RWD and clinical study data sets [ 26 – 30 ].…”

Section: Introductionmentioning

confidence: 99%

Utilization of anonymization techniques to create an external control arm for clinical trial data

Mehtälä,

Ali,

Miettinen

et al. 2023

BMC Med Res Methodol

View full text Add to dashboard Cite

Background Subject-level real-world data (RWD) collected during daily healthcare practices are increasingly used in medical research to assess questions that cannot be addressed in the context of a randomized controlled trial (RCT). A novel application of RWD arises from the need to create external control arms (ECAs) for single-arm RCTs. In the analysis of ECAs against RCT data, there is an evident need to manage and analyze RCT data and RWD in the same technical environment. In the Nordic countries, legal requirements may require that the original subject-level data be anonymized, i.e., modified so that the risk to identify any individual is minimal. The aim of this study was to conduct initial exploration on how well pseudonymized and anonymized RWD perform in the creation of an ECA for an RCT. Methods This was a hybrid observational cohort study using clinical data from the control arm of the completed randomized phase II clinical trial (PACIFIC-AF) and RWD cohort from Finnish healthcare data sources. The initial pseudonymized RWD were anonymized within the (k, ε)-anonymity framework (a model for protecting individuals against identification). Propensity score matching and weighting methods were applied to the anonymized and pseudonymized RWD, to balance potential confounders against the RCT data. Descriptive statistics for the potential confounders and overall survival analyses were conducted prior to and after matching and weighting, using both the pseudonymized and anonymized RWD sets. Results Anonymization affected the baseline characteristics of potential confounders only marginally. The greatest difference was in the prevalence of chronic obstructive pulmonary disease (4.6% vs. 5.4% in the pseudonymized compared to the anonymized data, respectively). Moreover, the overall survival changed in anonymization by only 8% (95% CI 4–22%). Both the pseudonymized and anonymized RWD were able to produce matched ECAs for the RCT data. Anonymization after matching impacted overall survival analysis by 22% (95% CI -21–87%). Conclusions Anonymization may be a viable technique for cases where flexible data transfer and sharing are required. As anonymization necessarily affects some aspects of the original data, further research and careful consideration of anonymization strategies are needed.

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Reply to: On the difficulty of achieving differential privacy in practice: user-level guarantees in aggregate location data

Cited by 2 publications

References 2 publications

Utilization of anonymization techniques to create an external control arm for clinical trial data

Utilization of anonymization techniques to create an external control arm for clinical trial data

Utilization of anonymization techniques to create an external control arm for clinical trial data

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