merlin—A unified modeling framework for data analysis and methods development in Stata

Michael J. Crowther ()
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Michael J. Crowther: University of Leicester

Stata Journal, 2020, vol. 20, issue 4, 763-784

Abstract: The challenges in statistics and data science are rapidly growing be- cause access to a multitude of data types continues to increase, as well as the sheer quantity of data. Analysts are now presented with multivariate data, sometimes measured repeatedly, and often requiring the ability to model nonlinear relation- ships and hierarchical structures. In this article, I present the merlin command, which attempts to provide an extremely general framework for data analysis. From simple settings such as fitting a linear regression model or a Weibull survival model to more complex settings such as fitting a three-level logistic mixed-effects model or a multivariate joint model of multiple longitudinal outcomes (of different types) and a recurrent event and survival with nonlinear effects, merlin can fit them all. I will take a single dataset and attempt to show you the full range of capabilities of merlin and discuss some future directions for the implementation in Stata.

Keywords: merlin; modeling framework; outcome models; survival models; longitudinal models; Gaussian; Bernoulli; beta; Poisson; ordinal logistic; ordinal probit; gamma; exponential; Gompertz; Royston–Parmar; log-hazard; Weibull; time-dependent effects; restricted cubic splines; B-splines; fractional polynomial; random effects; multilevel; multivariate; hierarchical (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1177/1536867X20976311

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