An econometric method for estimating population parameters from non-random samples: An application to clinical case finding
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Type of Work47 pages
Citation of Original PublicationBurger, Rulof P.; McLaren, Zoe; An econometric method for estimating population parameters from non-random samples: An application to clinical case finding; Health Economics, 26, 9, 1110-1122, 29 August, 2017; https://doi.org/10.1002/hec.3547
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This is the peer reviewed version of the following article: Burger, Rulof P.; McLaren, Zoe; An econometric method for estimating population parameters from non-random samples: An application to clinical case finding; Health Economics, 26, 9, 1110-1122, 29 August, 2017; https://doi.org/10.1002/hec.3547, which has been published in final form at https://doi.org/10.1002/hec.3547. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited
The problem of sample selection complicates the process of drawing inference about populations. Selective sampling arises in many real world situations when agents such as doctors and customs officials search for targets with high values of a characteristic. We propose a new method for estimating population characteristics from these types of selected samples. We develop a model that captures key features of the agent's sampling decision. We use a generalized method of moments with instrumental variables and maximum likelihood to estimate the population prevalence of the characteristic of interest and the agents' accuracy in identifying targets. We apply this method to tuberculosis (TB), which is the leading infectious disease cause of death worldwide. We use a national database of TB test data from South Africa to examine testing for multidrug resistant TB (MDR-TB). Approximately one quarter of MDR-TB cases was undiagnosed between 2004 and 2010. The official estimate of 2.5% is therefore too low, and MDR-TB prevalence is as high as 3.5%. Signal-to-noise ratios are estimated to be between 0.5 and 1. Our approach is widely applicable because of the availability of routinely collected data and abundance of potential instruments. Using routinely collected data to monitor population prevalence can guide evidence-based policy making.