Abstract
The challenges in statistics and data science are rapidly growing because 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 relationships 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.
Author supplied keywords
- B-splines
- Bernoulli
- Gaussian
- Gompertz
- Poisson
- Royston–Parmar
- Weibull
- beta
- exponential
- fractional polynomial
- gamma
- hierarchical
- log-hazard
- longitudinal models
- merlin
- modeling framework
- multilevel
- multivariate
- ordinal logistic
- ordinal probit
- outcome models
- random effects
- restricted cubic splines
- st0616
- survival models
- time-dependent effects
Cite
CITATION STYLE
Crowther, M. J. (2020). merlin—A unified modeling framework for data analysis and methods development in Stata. Stata Journal, 20(4), 763–784. https://doi.org/10.1177/1536867X20976311
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