Health Econometrics Using Stata
Partha Deb, Edward C. Norton, Willard G. Manning
Paperback, 264 pages, ISBN: 978-1-59718-228-7, published 31 August 2017
Amazon / Google Books / Stata Press
This book is the perfect guide to understanding the various econometric methods available for modelling of costs and counts data for the individual who understands econometrics best after applying it to a dataset (like myself). Pre-requisites include a decent knowledge of Stata and a desire to apply econometric methods to a cost or count outcome variable
It’s important to say that this book does not cover all aspects of econometrics within health economics, but instead focuses on ‘modelling health care costs and counts’ (the title of the short course from which the book evolved). As expected from this range of texts, the vast majority of the book comes with detailed example Stata code for all of the methods described, with illustrations either using a publicly available sample of MEPS data or simulated data.
Like many papers in this field, the focus of the book revolves around the non-normal characteristics of health care resource use distributions. These are the mass point at zero, right-hand skew and inherent heteroskedasticity. As such the book covers the broad suite of models that have been developed in order to account for these features, ranging from two-part models, transformation of the data (and the problematic re-transformation of estimated effects) to non-linear modelling methods such as generalised linear models (GLMs). Unlike many papers in this field, the authors emphasise the need – and provide guidance on how – to delve deep into the underlying data in order to appreciate the most appropriate methods (there is even a chapter on design effects) and encourage rigorous testing of model specification. In addition, Health Econometrics Using Stata considers the important issue of endogeneity and is not solely fixated on distributional issues, providing important insight and code for estimation of non-linear models that control for potential endogeneity (interested readers may wish to heed the published cautionary notes for some of these methods, e.g. Chapman and Brooks). Finally, the book describes more advanced methods for estimating heterogeneous effects, although code is not provided for all of these methods, which is a bit of a shame (but perhaps understandable given the complexity).
This could be a very dry text, but it is not – emphatically! The personality of the authors comes through very strongly from the writing. Reading it brought back many pleasant memories from the course ‘modelling health care costs and counts’ that I sat in 2012. The book also features a dedication to Willard Manning, which is a fitting tribute to a man who was both a great academic and an outstanding mentor. One particular highlight, with which past course attendants will be familiar, is the section ‘top 10 myths in health econometrics’. This straightforward and punchy presentation, backed up by rigorous methodological research, is a great way to get these key messages across in an accessible format. Other great features of this book include the use of simulations to illustrate important features of the econometric models (with code provided to recreate) and a personal highlight (granted, a niche interest…) was the code to generate appropriate standard errors when using the poisson family within GLMs for costs.
Of course, Health Econometrics Using Stata cannot be comprehensive and there are developments in this field that are not covered. Most notably, there is no discussion of how to model these data in a panel/longitudinal setting, which is crucially important for estimating parameters for decision models, for example. Potential issues around missing data and censoring are also not discussed. Also, this text does not cover advances in flexible parametric modelling, which enable modelling of data that are both highly skewed and leptokurtic (see Jones 2017 for an excellent summary of this literature along with a primer on data visualisation using Stata).
I heartily recommend Health Econometrics Using Stata to interested colleagues who want practical advice – on model selection and specification testing with cost and count outcome data – from some of the top specialists in our field, in their own words.
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