Dataset Information

Toward Rigorous Data Harmonization in Cancer Epidemiology Research: One Approach.

ABSTRACT: Cancer epidemiologists have a long history of combining data sets in pooled analyses, often harmonizing heterogeneous data from multiple studies into 1 large data set. Although there are useful websites on data harmonization with recommendations and support, there is little research on best practices in data harmonization; each project conducts harmonization according to its own internal standards. The field would be greatly served by charting the process of data harmonization to enhance the quality of the harmonized data. Here, we describe the data harmonization process utilized at the Fred Hutchinson Cancer Research Center (Seattle, Washington) by the coordinating centers of several research projects. We describe a 6-step harmonization process, including: 1) identification of questions the harmonized data set is required to answer; 2) identification of high-level data concepts to answer those questions; 3) assessment of data availability for data concepts; 4) development of common data elements for each data concept; 5) mapping and transformation of individual data points to common data elements; and 6) quality-control procedures. Our aim here is not to claim a "correct" way of doing data harmonization but to encourage others to describe their processes in order that we can begin to create rigorous approaches. We also propose a research agenda around this issue.

SUBMITTER: Rolland B

PROVIDER: S-EPMC4675662 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Toward Rigorous Data Harmonization in Cancer Epidemiology Research: One Approach.

Rolland Betsy B Reid Suzanna S Stelling Deanna D Warnick Greg G Thornquist Mark M Feng Ziding Z Potter John D JD

American journal of epidemiology 20151120 12

Cancer epidemiologists have a long history of combining data sets in pooled analyses, often harmonizing heterogeneous data from multiple studies into 1 large data set. Although there are useful websites on data harmonization with recommendations and support, there is little research on best practices in data harmonization; each project conducts harmonization according to its own internal standards. The field would be greatly served by charting the process of data harmonization to enhance the qua ...[more]

PMID: 26589709

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Project description:BackgroundProper understanding of the roles of, and interactions between genetic, lifestyle, environmental and psycho-social factors in determining the risk of development and/or progression of chronic diseases requires access to very large high-quality databases. Because of the financial, technical and time burdens related to developing and maintaining very large studies, the scientific community is increasingly synthesizing data from multiple studies to construct large databases. However, the data items collected by individual studies must be inferentially equivalent to be meaningfully synthesized. The DataSchema and Harmonization Platform for Epidemiological Research (DataSHaPER; http://www.datashaper.org) was developed to enable the rigorous assessment of the inferential equivalence, i.e. the potential for harmonization, of selected information from individual studies.MethodsThis article examines the value of using the DataSHaPER for retrospective harmonization of established studies. Using the DataSHaPER approach, the potential to generate 148 harmonized variables from the questionnaires and physical measures collected in 53 large population-based studies (6.9 million participants) was assessed. Variable and study characteristics that might influence the potential for data synthesis were also explored.ResultsOut of all assessment items evaluated (148 variables for each of the 53 studies), 38% could be harmonized. Certain characteristics of variables (i.e. relative importance, individual targeted, reference period) and of studies (i.e. observational units, data collection start date and mode of questionnaire administration) were associated with the potential for harmonization. For example, for variables deemed to be essential, 62% of assessment items paired could be harmonized.ConclusionThe current article shows that the DataSHaPER provides an effective and flexible approach for the retrospective harmonization of information across studies. To implement data synthesis, some additional scientific, ethico-legal and technical considerations must be addressed. The success of the DataSHaPER as a harmonization approach will depend on its continuing development and on the rigour and extent of its use. The DataSHaPER has the potential to take us closer to a truly collaborative epidemiology and offers the promise of enhanced research potential generated through synthesized databases.

Project description:BackgroundFactors influencing the health of populations are subjects of interdisciplinary study. However, datasets relevant to public health often lack interdisciplinary breath. It is difficult to combine data on health outcomes with datasets on potentially important contextual factors, like political violence or development, due to incompatible levels of geographic support; differing data formats and structures; differences in sampling procedures and wording; and the stability of temporal trends. We present a computational package to combine spatially misaligned datasets, and provide an illustrative analysis of multi-dimensional factors in health outcomes.MethodsWe rely on a new software toolkit, Sub-National Geospatial Data Archive (SUNGEO), to combine data across disciplinary domains and demonstrate a use case on vaccine hesitancy in Low and Middle-Income Countries (LMICs). We use data from the World Bank's High Frequency Phone Surveys (HFPS) from Kenya, Indonesia, and Malawi. We curate and combine these surveys with data on political violence, elections, economic development, and other contextual factors, using SUNGEO. We then develop a stochastic model to analyze the integrated data and evaluate 1) the stability of vaccination preferences in all three countries over time, and 2) the association between local contextual factors and vaccination preferences.ResultsIn all three countries, vaccine-acceptance is more persistent than vaccine-hesitancy from round to round: the long-run probability of staying vaccine-acceptant (hesitant) was 0.96 (0.65) in Indonesia, 0.89 (0.21) in Kenya, and 0.76 (0.40) in Malawi. However, vaccine acceptance was significantly less durable in areas exposed to political violence, with percentage point differences (ppd) in vaccine acceptance of -10 (Indonesia), -5 (Kenya), and -64 (Malawi). In Indonesia and Kenya, although not Malawi, vaccine acceptance was also significantly less durable in locations without competitive elections (-19 and -6 ppd, respectively) and in locations with more limited transportation infrastructure (-11 and -8 ppd).ConclusionWith SUNGEO, researchers can combine spatially misaligned and incompatible datasets. As an illustrative example, we find that vaccination hesitancy is correlated with political violence, electoral uncompetitiveness and limited access to public goods, consistent with past results that vaccination hesitancy is associated with government distrust.

Dataset Information

Toward Rigorous Data Harmonization in Cancer Epidemiology Research: One Approach.

Publications

Toward Rigorous Data Harmonization in Cancer Epidemiology Research: One Approach.

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