Combined analysis of correlated data when data cannot be pooled

Analytic and Data Sharing Options in Real-World Multidatabase Studies of Comparative Effectiveness and Safety of Medical Products

A wide range of analytic and data sharing options are available in nonexperimental multidatabase studies designed to assess the real-world benefits and risks of medical products. Researchers often consider six scientific domains when choosing among these options-study design, exposure type, outcome type, covariate summarization technique, covariate adjustment method, and data sharing approach. This article reviews available analytic and data sharing options and discusses key scientific and practical considerations when choosing among these options in multidatabase studies of comparative effectiveness and safety of medical products. The scientific considerations must be balanced against what the data-contributing sites are able or willing to share. While pooling of person-level data sets remains the most familiar and analytically flexible approach, newer analytic and data sharing approaches that share less granular summary-level information may be equally valid and preferred in some multidatabase studies, especially when sharing of person-level data is challenging or infeasible.

Do differences in religious affiliation explain high levels of excess mortality in the UK?

BACKGROUND

High levels of mortality not explained by differences in socioeconomic status (SES) have been observed for Scotland and its largest city, Glasgow, compared with elsewhere in the UK. Previous cross-sectional research highlighted potentially relevant differences in social capital, including religious social capital (the benefits of social participation in organised religion). The aim of this study was to use longitudinal data to assess whether religious affiliation (as measured in UK censuses) attenuated the high levels of Scottish excess mortality.

METHODS

The study used the Scottish Longitudinal Study (SLS) and the ONS Longitudinal Study of England and Wales. Risk of all-cause mortality (2001-2010) was compared between residents aged 35 and 74 years of Scotland and England and Wales, and between Glasgow and Liverpool/Manchester, using Poisson regression. Models adjusted for age, gender, SES and religious affiliation. Similar country-based analyses were undertaken for suicide.

RESULTS

After adjustment for age, gender and SES, all-cause mortality was 9% higher in Scotland than in England and Wales, and 27% higher in Glasgow than in Liverpool or Manchester. Religious affiliation was notably lower across Scotland; but, its inclusion in the models did not attenuate the level of Scottish excess all-cause mortality, and only marginally lowered the differences in risk of suicide.

CONCLUSIONS

Differences in religious affiliation do not explain the higher mortality rates in Scotland compared with the rest of the UK. However, it is possible that other aspects of religion such as religiosity or religious participation which were not assessed here may still be important.

DataSHIELD: taking the analysis to the data, not the data to the analysis

Background: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK’s proposed ‘care.data’ initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data.

Methods: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC.

Results: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach.

Conclusions: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property—the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.