Guertin JR, Conombo B, Langevin R, et al. A Systematic Review of Methods Used for Confounding Adjustment in Observational Economic Evaluations in Cardiology Conducted between 2013 and 2017. Medical Decision Making. 2020;40(5):582-595. doi:10.1177/0272989X20937257

Policy Learning with Observational Data: The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients [pdf] (Job Market Paper)

Abstract: Decision-makers frequently must choose a single action from a finite set of alternatives—for example, physicians selecting a treatment, investors choosing a portfolio risk level, or insurers setting premiums. To improve outcomes, policymakers often issue policy rules or guidelines to inform such choices. This paper develops a method to derive optimal policy rules from observational data under relatively weak assumptions about the underlying structure of the heterogeneous sampled population. Conditional average treatment effects are consistently estimated using a weighted K-means algorithm, assuming the outcome model is correctly specified within each homogeneous subgroup. Optimal and feasible policy rules are then implemented via a standard decision tree, allowing for both perfect and imperfect adherence to treatment. The methodology is applied to treatment options for Hepatitis C (HCV) among patients co-infected with human immunodeficiency virus (HIV), a setting in which no uniform guideline exists for modern pharmaceutical therapies. The results identify a subgroup of patients with approximately an 80% probability of spontaneous HCV clearance without treatment. Estimation results also show that reallocating treatments among treated individuals could have reduced total treatment costs by CAN$2.7–3.3 million while slightly increasing aggregate health benefits relative to the status quo. These findings demonstrate that the proposed approach can generate improved, data-driven treatment guidelines for the management of HIV/HCV co-infected patients.

Bias-Reduced Estimation of Finite Mixtures: An Application to Latent Group Panel Structures [arXiv] [GitHub] 

Abstract: Finite mixture models are widely used in econometric analyses to capture unobserved heterogeneity. This paper shows that maximum likelihood estimation of finite mixtures of parametric densities can suffer from substantial finite-sample bias in all parameters under mild regularity conditions. The bias arises from the influence of outliers in component densities with unbounded or large support and increases with the degree of overlap among mixture components. I show that maximizing the classification-mixture likelihood function, equipped with a consistent classifier, yields parameter estimates that are less biased than those obtained by standard maximum likelihood estimation (MLE). I then derive the asymptotic distribution of the resulting estimator and provide conditions under which oracle efficiency is achieved. Monte Carlo simulations show that conventional mixture MLE exhibits pronounced finite-sample bias, which diminishes as the sample size or the statistical distance between component densities tends to infinity. The simulations further show that the proposed estimation strategy generally outperforms standard MLE in finite samples in terms of both bias and mean squared errors under relatively weak assumptions. An empirical application to latent group panel structures using health administrative data shows that the proposed approach reduces out-of-sample prediction error by approximately 17.6% relative to the best results obtained from standard MLE procedures.

Bending the Cost Curve in Health Care: The Role of Community-Based Organizations [pdf]

Abstract: Healthcare costs have been rising worldwide in recent decades, hence putting pressure simultaneously on households, governments, insurers, and healthcare providers. This paper demonstrates that higher funding for community-based organizations (CBOs) is likely to yield substantial long-term savings for healthcare systems through improvements in social determinants of health (SDoH). Estimation of elasticities over time is performed via a first-differenced two-way fixed effects (FD-TWFE) estimator that accounts for the structural breaks caused by the COVID-19 pandemic. I show that this estimator is more efficient and less prone to biases coming from feedback effects compared to other fixed effects estimators when the error part is strongly persistent over time. I use regional-level health administrative data from Quebec, Canada, between 2009 and 2023, to estimate all savings generated by CBO public funding. Estimation results show that each additional dollar given to CBOs in the province yields average savings of around CAN$8.00 2 to 7 years later. Average savings tend to increase over the study period, namely those generated by CBOs funded by Quebec's Ministry of Health and Social Services (MSSS). The estimated savings also vary across groups of regions, with more densely populated regions featuring large and statistically significant savings over the entire study period. This heterogeneity could be leveraged to help less effective regions reduce the growth of healthcare expenditures over time.

Ammi, M., Langevin, R., Strumpf, E. C., & Arpin, E. (2024). Effets de la pandémie de COVID-19 sur la réallocation des dépenses de santé publique par fonction : estimation de court terme et analyse prédictive contrefactuelle (2024RP-11, Rapports de projets, CIRANO. In French.) https://doi.org/10.54932/LSLR2977

Ammi, M., Langevin, R., Strumpf, E. C., & Arpin, E. (2024). S’attaquer aux crises épidémiologiques : oui, mais à quel prix ? (2024PJ-07, Revue PERSPECTIVES, CIRANO. In French.) https://doi.org/10.54932/TUPX6305

Langevin, R., Couturier, E. (2023). Spirale salaires-inflation: mythe ou réalité? Institut de recherche et d’informations socioéconomiques (IRIS).

Devin N., Langevin R. (2022), Market Basket Measure Research: Additional Income Inequality Indicators using the Market Basket Measure, Income Research Paper Series, Statistics Canada, Catalogue no. 75F0002M

Langevin, R. (2021). Hausser l’impôt des plus riches: des bénéfices qui dépassent les inconvénients, Institut de recherche et d’informations socioéconomiques (IRIS).

Langevin, R. (2019). Les projets de loi sur la laïcité augmentent-ils le nombre de crimes haineux au Québec? Ligue des droits et libertés, section Québec.

Langevin, R., Guay, E. (2018). L’austérité a-t-elle contribué à la relance économique au Québec? Institut de recherche et d’informations socioéconomiques (IRIS).

Dufour, M., Langevin, R. (2016). Quels seraient les effets réels d’une hausse marquée du salaire minimum? Institut de recherche et d’informations socioéconomiques (IRIS).