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      Review of Statistical Methods for Analysing Healthcare Resources and Costs

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          We review statistical methods for analysing healthcare resource use and costs, their ability to address skewness, excess zeros, multimodality and heavy right tails, and their ease for general use. We aim to provide guidance on analysing resource use and costs focusing on randomised trials, although methods often have wider applicability. Twelve broad categories of methods were identified: (I) methods based on the normal distribution, (II) methods following transformation of data, (III) single-distribution generalized linear models (GLMs), (IV) parametric models based on skewed distributions outside the GLM family, (V) models based on mixtures of parametric distributions, (VI) two (or multi)-part and Tobit models, (VII) survival methods, (VIII) non-parametric methods, (IX) methods based on truncation or trimming of data, (X) data components models, (XI) methods based on averaging across models, and (XII) Markov chain methods. Based on this review, our recommendations are that, first, simple methods are preferred in large samples where the near-normality of sample means is assured. Second, in somewhat smaller samples, relatively simple methods, able to deal with one or two of above data characteristics, may be preferable but checking sensitivity to assumptions is necessary. Finally, some more complex methods hold promise, but are relatively untried; their implementation requires substantial expertise and they are not currently recommended for wider applied work. Copyright © 2010 John Wiley & Sons, Ltd.

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              Estimating log models: to transform or not to transform?

               J Mullahy,  W Manning (2001)
              Health economists often use log models to deal with skewed outcomes, such as health utilization or health expenditures. The literature provides a number of alternative estimation approaches for log models, including ordinary least-squares on ln(y) and generalized linear models. This study examines how well the alternative estimators behave econometrically in terms of bias and precision when the data are skewed or have other common data problems (heteroscedasticity, heavy tails, etc.). No single alternative is best under all conditions examined. The paper provides a straightforward algorithm for choosing among the alternative estimators. Even if the estimators considered are consistent, there can be major losses in precision from selecting a less appropriate estimator.

                Author and article information

                [a ]Health Economics Research Centre, University of Oxford Oxford, UK
                [b ]Public Health and Health Policy, University of Glasgow Glasgow, UK
                [c ]Department of Probability and Statistics, University of Sheffield Sheffield, UK
                [d ]MRC Biostatistics Unit Cambridge, UK
                Author notes
                *Correspondence to: Health Economics Research Centre, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK. E-mail: boby.mihaylova@
                Health Econ
                Health Econ
                Health Economics
                John Wiley & Sons, Ltd. (Chichester, UK )
                August 2011
                26 August 2010
                : 20
                : 8
                : 897-916
                Copyright © 2010 John Wiley & Sons, Ltd.

                Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.

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