Question

With the advancement in technology, how has increased access to secondary data changed the hypothesis formulation and synthesis approaches (biostatistics)

Answer 1

The secondary analysis of existing data has become an increasingly popular method of enhancing the overall efficiency of the health research enterprise. But this effort depends on governments, funding agencies, and researchers making the data collected in primary research studies and in health-related registry systems available to qualified researchers who were not involved in the original research or in the creation and maintenance of the registry systems. The benefits of doing this are clear but the barriers are many, so the effort of increasing access to such material has been slow, particularly in low- and middle-income countries.

There is frequently confusion about the use of the terms ‘primary data’, ‘primary data analysis’, ‘secondary data’, and ‘secondary data analysis’. This confusion arises because it is never completely clear whether data employed in an analysis should be considered ‘primary data’ or ‘secondary data’. Based on the usage of the National Institute of Health (NIH) in the United States, ‘primary data analysis’ is limited to the analysis of data by members of the research team that collected the data, which are conducted to answer the original hypotheses proposed in the study. All other analyses of data collected for specific research studies or analyses of data collected for other purposes (including registry data) are considered ‘secondary analyses of existing data’, whether or not the persons conducting the analyses participated in the collection of the data. This replacement of the traditional term ‘secondary data analysis’ with the term ‘secondary analysis of existing data’ is a much clearer categorization because it avoids the confusion of trying to decide whether the data employed in analysis is ‘primary data’ or ‘secondary data’.

Of course, there are cases where the distinction is less clear. One example would be the analysis of data by a researcher who has no connection with the data collection team to address a research question that overlaps with the hypotheses considered in the original study. Another example would be when a member of the original research team subsequently revisits the original hypothesis in an analysis that uses different statistical methods. These situations commonly occur in the analyses of large-scale population surveys where the research questions are generally broad (e.g., sociodemographic correlates of depression) and when the participating researchers share the cleaned data with the broader research community. In both of these situations, based on a strict application of the NIH usage, the analyses would be considered ‘secondary analysis of existing data’ NOT ‘primary data analysis’ and NOT ‘secondary data analysis’. In fact, we recommend avoiding the ambiguous term ‘secondary data analysis’ entirely.  One advantage of unobtrusive research is that you may be able to skip the data collection phase altogether. To many, skipping the data collection phase is preferable since it allows the researcher to proceed directly to answering their question through data analysis. When researchers analyze data originally gathered by another person or entity, they engage in secondary data analysis. Researchers gain access to data collected by other researchers, government agencies, and other unique sources by making connections with individuals engaged in primary research or accessing their data via publicly available sources.