Latent class models for multiple ordered categorical health data: testing violation of the local independence assumption
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Latent class models for multiple ordered categorical health data: testing violation of the local independence assumption Paolo Li Donni1
· Ranjeeta Thomas2
Received: 30 December 2017 / Accepted: 25 March 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract Latent class models are now widely applied in health economics to analyse heterogeneity in multiple outcomes generated by subgroups of individuals who vary in unobservable characteristics, such as genetic information or latent traits. These models rely on the underlying assumption that associations between observed outcomes are due to their relationship to underlying subgroups, captured in these models by conditioning on a set of latent classes. This implies that outcomes are locally independent within a class. Local independence assumption, however, is sometimes violated in practical applications when there is uncaptured unobserved heterogeneity resulting in residual associations between classes. While several approaches have been proposed in the case of binary and continuous outcomes, little attention has been directed to the case of multiple ordered categorical outcome variables often used in health economics. In this paper, we develop an approach to test for the violation of the local independence assumption in the case of multiple ordered categorical outcomes. The approach provides a detailed decomposition of identified residual association by allowing it to vary across latent classes and between levels of the ordered categorical outcomes within a class. We show how this level of decomposition is important in the case of ordered categorical outcomes. We illustrate our approach in the context of health insurance and healthcare utilization in the US Medigap market. Keywords Latent class model · Local independence assumption · Health insurance · Healthcare utilization · Categorical health data
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Paolo Li Donni [email protected] Ranjeeta Thomas [email protected]
1
Dipartimento di Scienze Economiche, Statistiche e Aziendali, Università di Palermo, Viale delle Scienze, 90129 Palermo, Italy
2
Department of Health Policy, London School of Economics and Political Science, Houghton Street, London WC2 2AE, UK
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P. Li Donni, R. Thomas
1 Introduction A growing body of literature in health economics (see e.g. Ayyagari et al. 2013; Conway and Deb 2005; Deb and Trivedi 1997, 2002; Jiménez-Martín et al. 2002; Morduch and Stern 1997), behavioural and biomedical research have used applications of latent class regression models (LCM) to explain heterogeneity in outcomes that are generated by unobserved factors. For example, the outcomes of health interventions are often dependent on the case mix or heterogeneity of the target population. Typically, studies analyse heterogeneity by identifying subgroups based on observed indicators such as the age, gender, socioeconomic characteristics, diagnosis and disease stage. However, the heterogeneity caused by unmeasurable factors such as genetic traits or comorbidities cannot be fully address
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