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

Meat consumption and incident type 2 diabetes: an individual-participant federated meta-analysis of 1·97 million adults with 100 000 incident cases from 31 cohorts in 20 countries

BACKGROUND

Meat consumption could increase the risk of type 2 diabetes. However, evidence is largely based on studies of European and North American populations, with heterogeneous analysis strategies and a greater focus on red meat than on poultry. We aimed to investigate the associations of unprocessed red meat, processed meat, and poultry consumption with type 2 diabetes using data from worldwide cohorts and harmonised analytical approaches.

METHODS

This individual-participant federated meta-analysis involved data from 31 cohorts participating in the InterConnect project. Cohorts were from the region of the Americas (n=12) and the Eastern Mediterranean (n=2), European (n=9), South-East Asia (n=1), and Western Pacific (n=7) regions. Access to individual-participant data was provided by each cohort; participants were eligible for inclusion if they were aged 18 years or older and had available data on dietary consumption and incident type 2 diabetes and were excluded if they had a diagnosis of any type of diabetes at baseline or missing data. Cohort-specific hazard ratios (HRs) and 95% CIs were estimated for each meat type, adjusted for potential confounders (including BMI), and pooled using a random-effects meta-analysis, with meta-regression to investigate potential sources of heterogeneity.

FINDINGS

Among 1 966 444 adults eligible for participation, 107 271 incident cases of type 2 diabetes were identified during a median follow-up of 10 (IQR 7-15) years. Median meat consumption across cohorts was 0-110 g/day for unprocessed red meat, 0-49 g/day for processed meat, and 0-72 g/day for poultry. Greater consumption of each of the three types of meat was associated with increased incidence of type 2 diabetes, with HRs of 1·10 (95% CI 1·06-1·15) per 100 g/day of unprocessed red meat (I2=61%), 1·15 (1·11-1·20) per 50 g/day of processed meat (I2=59%), and 1·08 (1·02-1·14) per 100 g/day of poultry (I2=68%). Positive associations between meat consumption and type 2 diabetes were observed in North America and in the European and Western Pacific regions; the CIs were wide in other regions. We found no evidence that the heterogeneity was explained by age, sex, or BMI. The findings for poultry consumption were weaker under alternative modelling assumptions. Replacing processed meat with unprocessed red meat or poultry was associated with a lower incidence of type 2 diabetes.

INTERPRETATION

The consumption of meat, particularly processed meat and unprocessed red meat, is a risk factor for developing type 2 diabetes across populations. These findings highlight the importance of reducing meat consumption for public health and should inform dietary guidelines.

FUNDING

The EU, the Medical Research Council, and the National Institute of Health Research Cambridge Biomedical Research Centre.

Survival Analysis Without Sharing of Individual Patient Data by Using a Gaussian Copula

Cox regression and Kaplan-Meier estimations are often needed in clinical research and this requires access to individual patient data (IPD). However, IPD cannot always be shared because of privacy or proprietary restrictions, which complicates the making of such estimations. We propose a method that generates pseudodata replacing the IPD by only sharing non-disclosive aggregates such as IPD marginal moments and a correlation matrix. Such aggregates are collected by a central computer and input as parameters to a Gaussian copula (GC) that generates the pseudodata. Survival inferences are computed on the pseudodata as if it were the IPD. Using practical examples we demonstrate the utility of the method, via the amount of IPD inferential content recoverable by the GC. We compare GC to a summary-based meta-analysis and an IPD bootstrap distributed across several centers. Other pseudodata approaches are also considered. In the empirical results, GC approximates the utility of the IPD bootstrap although it might yield more conservative inferences and it might have limitations in subgroup analyses. Overall, GC avoids many legal problems related to IPD privacy or property while enabling approximation of common IPD survival analyses otherwise difficult to conduct. Sharing more IPD aggregates than is currently practiced could facilitate "second purpose"-research and relax concerns regarding IPD access.

Stress and anxiety during pregnancy and length of gestation: a federated study using data from five Canadian and European birth cohorts

While its etiology is not fully elucidated, preterm birth represents a major public health concern as it is the leading cause of child mortality and morbidity. Stress is one of the most common perinatal conditions and may increase the risk of preterm birth. In this paper we aimed to investigate the association of maternal perceived stress and anxiety with length of gestation. We used harmonized data from five birth cohorts from Canada, France, and Norway. A total of 5297 pregnancies of singletons were included in the analysis of perceived stress and gestational duration, and 55,775 pregnancies for anxiety. Federated analyses were performed through the DataSHIELD platform using Cox regression models within intervals of gestational age. The models were fit for each cohort separately, and the cohort-specific results were combined using random effects study-level meta-analysis. Moderate and high levels of perceived stress during pregnancy were associated with a shorter length of gestation in the very/moderately preterm interval [moderate: hazard ratio (HR) 1.92 (95%CI 0.83, 4.48); high: 2.04 (95%CI 0.77, 5.37)], albeit not statistically significant. No association was found for the other intervals. Anxiety was associated with gestational duration in the very/moderately preterm interval [1.66 (95%CI 1.32, 2.08)], and in the early term interval [1.15 (95%CI 1.08, 1.23)]. Our findings suggest that perceived stress and anxiety are associated with an increased risk of earlier birth, but only in the earliest gestational ages. We also found an association in the early term period for anxiety, but the result was only driven by the largest cohort, which collected information the latest in pregnancy. This raised a potential issue of reverse causality as anxiety later in pregnancy could be due to concerns about early signs of a possible preterm birth.

Regional and temporal differences in the associations between cardiovascular disease and its classic risk factors: An analysis of 49 cohorts from 11 European countries

AIMS

The regional and temporal differences in the associations between cardiovascular disease (CVD) and its classic risk factors are unknown. The current study examined these associations in different European regions over a 30-year period.

METHODS

The study sample comprised 553818 individuals from 49 cohorts in 11 European countries (baseline: 1982-2012) who were followed up for a maximum of 10 years. Risk factors (sex, smoking, diabetes, non-HDL [high-density lipoprotein] cholesterol, systolic blood pressure [BP], and body mass index [BMI]) and CVD events (coronary heart disease or stroke) were harmonized across cohorts. Risk factor-outcome associations were analysed using multivariable-adjusted Cox regression models, and differences in associations were assessed using meta-regression.

RESULTS

The differences in the risk factor-CVD associations between central Europe, northern Europe, southern Europe, and the United Kingdom were generally small. Men had a slightly higher hazard ratio (HR) in southern Europe (p = 0.043 for overall difference) and those with diabetes had a slightly lower HR in central Europe (p = 0.022 for overall difference) compared with the other regions. Of the six CVD risk factors, minor HR decreases per decade were observed for non-HDL cholesterol (7% per mmol/L; 95% confidence interval [CI], 3-10%) and systolic BP (4% per 20 mmHg; 95% CI, 1-8%), while a minor HR increase per decade was observed for BMI (7% per 10 kg/m2; 95% CI, 1-13%).

CONCLUSION

The results demonstrate that all classic CVD risk factors are still relevant in Europe, irrespective of regional area. Preventive strategies should focus on risk factors with the greatest population attributable risk.

dsSurvival 2.0: privacy enhancing survival curves for survival models in the federated DataSHIELD analysis system

OBJECTIVE

Survival models are used extensively in biomedical sciences, where they allow the investigation of the effect of exposures on health outcomes. It is desirable to use diverse data sets in survival analyses, because this offers increased statistical power and generalisability of results. However, there are often challenges with bringing data together in one location or following an analysis plan and sharing results. DataSHIELD is an analysis platform that helps users to overcome these ethical, governance and process difficulties. It allows users to analyse data remotely, using functions that are built to restrict access to the detailed data items (federated analysis). Previous works have provided survival modelling functionality in DataSHIELD (dsSurvival package), but there is a requirement to provide functions that offer privacy enhancing survival curves that retain useful information.

RESULTS

We introduce an enhanced version of the dsSurvival package which offers privacy enhancing survival curves for DataSHIELD. Different methods for enhancing privacy were evaluated for their effectiveness in enhancing privacy while maintaining utility. We demonstrated how our selected method could enhance privacy in different scenarios using real survival data. The details of how DataSHIELD can be used to generate survival curves can be found in the associated tutorial.

Federated analyses of multiple data sources in drug safety studies

PURPOSE

Studies of rare side effects of new drugs with limited exposure may require pooling of multiple data sources. Federated Analyses (FA) allow real-time, interactive, centralized statistical processing of individual-level data from different data sets without transfer of sensitive personal data.

METHODS

We review IT-architecture, legal considerations, and statistical methods in FA, based on a Swedish Medical Products Agency methodological development project.

RESULTS

In a review of all post-authorisation safety studies assessed by the EMA during 2019, 74% (20/27 studies) reported issues with lack of precision in spite of mean study periods of 9.3 years. FA could potentially improve precision in such studies. Depending on the statistical model, the federated approach can generate identical results to a standard analysis. FA may be particularly attractive for repeated collaborative projects where data is regularly updated. There are also important limitations. Detailed agreements between involved parties are strongly recommended to anticipate potential issues and conflicts, document a shared understanding of the project, and fully comply with legal obligations regarding ethics and data protection. FA do not remove the data harmonisation step, which remains essential and often cumbersome. Reliable support for technical integration with the local server architecture and security solutions is required. Common statistical methods are available, but adaptations may be required.

CONCLUSIONS

Federated Analyses require competent and active involvement of all collaborating parties but have the potential to facilitate collaboration across institutional and national borders and improve the precision of postmarketing drug safety studies.

Software Application Profile: ShinyDataSHIELD—an R Shiny application to perform federated non-disclosive data analysis in multicohort studies

Motivation

DataSHIELD is an open-source software infrastructure enabling the analysis of data distributed across multiple databases (federated data) without leaking individuals’ information (non-disclosive). It has applications in many scientific domains, ranging from biosciences to social sciences and including high-throughput genomic studies. R is the language used to interact with (and build) DataSHIELD. This creates difficulties for researchers who do not have experience writing R code or lack the time to learn how to use the DataSHIELD functions. To help new researchers use the DataSHIELD infrastructure and to improve the user-friendliness for experienced researchers, we present ShinyDataSHIELD.

Implementation

ShinyDataSHIELD is a web application with an R backend that serves as a graphical user interface (GUI) to the DataSHIELD infrastructure.

General features

The version of the application presented here includes modules to perform: (i) exploratory analysis through descriptive summary statistics and graphical representations (scatter plots, histograms, heatmaps and boxplots); (ii) statistical modelling (generalized linear fixed and mixed-effects models, survival analysis through Cox regression); (iii) genome-wide association studies (GWAS); and (iv) omic analysis (transcriptomics, epigenomics and multi-omic integration).

Availability

ShinyDataSHIELD is publicly hosted online [https://datashield-demo.obiba.org/ ], the source code and user guide are deposited on Zenodo DOI 10.5281/zenodo.6500323, freely available to non-commercial users under ‘Commons Clause’ License Condition v1.0. Docker images are also available [https://hub.docker.com/r/brgelab/shiny-data-shield ].