Reducing bias in secondary data analysis via an Explore and Confirm Analysis Workflow (ECAW): a proposal and survey of observational researchers

Author:

Thibault Robert T.12ORCID,Kovacs Marton34ORCID,Hardwicke Tom E.5ORCID,Sarafoglou Alexandra6ORCID,Ioannidis John P. A.17ORCID,Munafò Marcus R.28ORCID

Affiliation:

1. Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA 94305-6104, USA

2. School of Psychological Science, University of Bristol, Bristol, UK

3. Doctoral School of Psychology, ELTE Eotvos Lorand University, Budapest, Hungary

4. Institute of Psychology, ELTE Eotvos Lorand University, Budapest, Hungary

5. Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Australia

6. Department of Psychology, University of Amsterdam, Amsterdam, Noord-Holland, The Netherlands

7. Meta-Research Innovation Center Berlin (METRIC-B), QUEST Center for Transforming Biomedical Research, Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Berlin, Germany

8. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

Abstract

Background. Although preregistration can reduce researcher bias and increase transparency in primary research settings, it is less applicable to secondary data analysis. An alternative method that affords additional protection from researcher bias, which cannot be gained from conventional forms of preregistration alone, is an Explore and Confirm Analysis Workflow (ECAW). In this workflow, a data management organization initially provides access to only a subset of their dataset to researchers who request it. The researchers then prepare an analysis script based on the subset of data, upload the analysis script to a registry, and then receive access to the full dataset. ECAWs aim to achieve similar goals to preregistration, but make access to the full dataset contingent on compliance. The present survey aimed to garner information from the research community where ECAWs could be applied—employing the Avon Longitudinal Study of Parents and Children (ALSPAC) as a case example. Methods. We emailed a Web-based survey to researchers who had previously applied for access to ALSPAC's transgenerational observational dataset. Results. We received 103 responses, for a 9% response rate. The results suggest that—at least among our sample of respondents—ECAWs hold the potential to serve their intended purpose and appear relatively acceptable. For example, only 10% of respondents disagreed that ALSPAC should run a study on ECAWs (versus 55% who agreed). However, as many as 26% of respondents agreed that they would be less willing to use ALSPAC data if they were required to use an ECAW (versus 45% who disagreed). Conclusion. Our data and findings provide information for organizations and individuals interested in implementing ECAWs and related interventions. Preregistration . https://osf.io/g2fw5 Deviations from the preregistration are outlined in electronic supplementary material A.

Funder

Arnold Ventures

Canadian Institutes of Health Research

Publisher

The Royal Society

Subject

Multidisciplinary

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