Conducting HighValue Secondary Dataset Analysis

Authors: Alexander K Smith John Z Ayanian Kenneth E Covinsky Bruce E Landon Ellen P McCarthy Christina C Wee Michael A Steinman

Publish Date: 2011/02/08

Volume: 26, Issue: 8, Pages: 920-929

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Abstract

Secondary analyses of large datasets provide a mechanism for researchers to address high impact questions that would otherwise be prohibitively expensive and timeconsuming to study This paper presents a guide to assist investigators interested in conducting secondary data analysis including advice on the process of successful secondary data analysis as well as a brief summary of highvalue datasets and online resources for researchers including the SGIM dataset compendium wwwsgimorg/go/datasets The same basic research principles that apply to primary data analysis apply to secondary data analysis including the development of a clear and clinically relevant research question study sample appropriate measures and a thoughtful analytic approach A realworld case description illustrates key steps 1 define your research topic and question 2 select a dataset 3 get to know your dataset and 4 structure your analysis and presentation of findings in a way that is clinically meaningful Secondary dataset analysis is a wellestablished methodology Secondary analysis is particularly valuable for junior investigators who have limited time and resources to demonstrate expertise and productivitySecondary data analysis is analysis of data that was collected by someone else for another primary purpose Increasingly generalist researchers start their careers conducting analyses of existing datasets and some continue to make this the focus of their career Using secondary data enables one to conduct studies of highimpact research questions with dramatically less time and resources than required for most studies involving primary data collection For fellows and junior faculty who need to demonstrate productivity by completing and publishing research in a timely manner secondary data analysis can be a key foundation to successfully starting a research career Successful completion demonstrates content and methodological expertise and may yield useful data for future grants Despite these attributes conducting high quality secondary data research requires a distinct skill set and substantial effort However few frameworks are available to guide new investigators as they conduct secondary data analysies1 2 3In this article we describe key principles and skills needed to conduct successful analysis of secondary data and provide a brief description of highvalue datasets and online resources The primary target audience of the article is investigators with an interest but limited prior experience in secondary data analysis as well as mentors of these investigators who may find this article a useful reference and teaching tool While we focus on analysis of large publicly available datasets many of the concepts we cover are applicable to secondary analysis of proprietary datasets Datasets we feature in this manuscript encompass a wide range of measures and thus can be useful to evaluate not only one disease in isolation but also its intersection with other clinical demographic and psychosocial characteristics of patientsMany worthwhile studies simply cannot be done in a reasonable timeframe and cost with primary data collection For example if you wanted to examine racial and ethnic differences in health services utilization over the last 10 years of life you could enroll a diverse cohort of subjects with chronic illness and wait a decade or longer for them to die or you could find a dataset that includes a diverse sample of decedents Even for less dramatic examples primary data collection can be difficult without incurring substantial costs including time and money—scarce resources for junior researchers in particular Secondary datasets in contrast can provide access to large sample sizes relevant measures and longitudinal data allowing junior investigators to formulate a generalizable answer to a high impact question For those interested in conducting primary data collection beginning with a secondary data analysis may provide a “bird’s eye view” of epidemiologic trends that future primary data studies examine in greater detailSecondary data analyses however have disadvantages that are important to consider In a study focused on primary data you can tightly control the desired study population specify the exact measures that you would like to assess and examine causal relationships eg through a randomized controlled design In secondary data analyses the study population and measures collected are often not exactly what you might have chosen to collect and the observational nature of most secondary data makes it difficult to assess causality although some quasiexperimental methods such as instrumental variable or regression discontinuity analysis can partially address this issue While not unique to secondary data analysis another disadvantage to publicly available datasets is the potential to be “scooped” meaning that someone else publishes a similar study from the same data set before you do On the other hand intentional replication of a study in a different dataset can be important in that it either supports or refutes the generalizability of the original findings If you do find that someone has published the same study using the same dataset try to find a unique angle to your study that builds on their findings

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