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A Comparison of Methods for Analyzing Health-Related Quality-Of View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Volume 5 • Number 4 • 2002 VALUE IN HEALTH A Comparison of Methods for Analyzing Health- Related Quality-of-Life Measures Peter C. Austin, PhD Institute for Clinical Evaluative Sciences, North York, Ontario; Department of Public Health Sciences, University of Toronto,Toronto, Canada ABSTRACT Objectives: Self-reported health status is often measured Survey. The results are compared to three models that using psychometric or utility indices that provide a score ignore the presence of a ceiling effect. intended to summarize an individual’s health. Measure- Results: The Censored LAD model produced coefficient ments of health status can be subject to a ceiling effect. estimates that tended to be shrunk toward 0, compared Frequently, researchers want to examine relationships to the other two models. The three models produced con- between determinants of health and measures of health flicting evidence on the effect of gender on health status. status. Regression methods that ignore the censoring in Similarly, the rate of decay in health status with increas- the health status measurement can produce biased coeffi- ing age differed across the three models. The Censored cient estimates. The authors examine the performance LAD model produced results very similar to median of three different models for assessing the relationship regression. Furthermore, the censored LAD model had between demographic characteristics and health status. the lowest prediction error in an independent validation Methods: Three methods that allow one to analyze data dataset. subject to a ceiling effect are compared. The first model Conclusions: Our results highlight the need for careful is the classic Tobit model. The second and third models consideration about how best to model variation in health are robust variants of the Tobit model: symmetrically status. Based upon our study, we recommend the use of trimmed least squares and censored least absolute devia- Censored LAD regression. tions (Censored LAD) regression. These models were fit Keywords: ceiling effect, health status, Health Utilities to data from the Canadian National Population Health Index, statistical methods, Tobit model. Introduction ently problematic, leading to the likelihood that the defined boundary is silently exceeded. Although Self-reported health status is often measured using utilities are traditionally measured on a scale from psychometric or utility indices that provide a score 0 to 1, it is useful in this context to consider health intended to reflect a person’s health. Scores on psy- states better than 1. For example, the ambulation chometric scales are usually converted to a per- scale of the Health Utilities Index Mark 3 (HUI3) centage of the maximum possible score [1]. Health assigns a maximum score to an individual who is utility indices typically measure health on a scale “able to move around the neighborhood without from 0 (or a small negative number) to 1, where 0 difficulty and without walking equipment.” [3] indicates dead and 1 indicates perfect health [2]. Thus, a person who can walk but is unable to An essential issue in interpreting a health status run or engage in sports and other vigorous activi- index is the meaning of the extreme values of ties might (depending on the responses to the other the index. Ceiling values are intended to represent questions) be classified as perfectly healthy. Feeny et states of perfection. Defining perfection is inher- al. [3], comment that the Health Utilities Index may be subject to a ceiling effect and note that one can add supranormal levels for descriptive purposes. An upper limit on health status scores allows no Address correspondence to: Dr. Peter Austin, Institute for Clinical Evaluative Sciences, G-160, 2075 Bayview Avenue, explicit room for a treatment effect if the individ- North York, Ontario M4N 3M5, Canada. ual is in perfect health at baseline. A further argu- E-mail: [email protected] ment for accounting for the censoring present in © ISPOR 1098-3015/02/$15.00/329 329–337 329 330 Austin health status scores is that it allows one to better pain/discomfort. There are five or six levels per estimate a treatment effect under these conditions. attribute, resulting in 972,000 unique health states. In studying the relationships between health A multiplicative scoring function is used to calcu- status and predictors of health status such as age, late the index. Because some health states are con- gender, or socioeconomic status, investigators sidered worse than dead, the index can take values frequently construct regression models to quantify from -0.03 to 1. The HUI3 health states from the how health status varies with changes in subject NPHS were valued using the provisional scoring characteristics. When a ceiling effect is present, system, based on the HUI2 utility weights. standard regression models ignore the censoring Cross-sectional data from the 1994/95 NPHS that has occurred among those individuals with a [11] were used for the current study. The NPHS health status that lies above the threshold for questionnaire included components on health perfect health. One approach is to treat the index status, use of health services, demographic and as if it was censored, with a score of 1 (or 100% socioeconomic status. The target population for the being the largest observable value. In this scenario, NPHS was household residents in all provinces. for a subject with an observed score of 1, all that is Patients in hospitals, and residents of long-term known is that the subject’s true health status is at care institutions were excluded from the NPHS. The least 1. In a recent article, Austin et al. [4] proposed total number of respondents was 17,626. We the use of the Tobit model for analyzing measures restricted our analysis to those individuals who of health status. In a related commentary, Grooten- were between 20 and 80 years of age. We assumed dorst [5] proposed two robust alternatives to that those subjects with HUI scores equal to 1 are maximum likelihood estimation of the Tobit model, censored observations, and that there is actually a both developed by Powell [6,7], the censored least range of supranormal health states among those absolute deviations (censored LAD) estimator, and with a reported score of 1. the symmetrically trimmed least squares estimator. Age is reported in 5-year increments in the Previous research has shown that the classic NPHS. For age to be treated as a continuous vari- Tobit model performs poorly when the distribu- able, we chose to represent age using the mid-point tional assumptions of the model are not satisfied. of the interval. For a measure of socioeconomic Maddala [8] commented that in the presence of het- status, the derived income adequacy variable was eroscedasticity, the usual Tobit estimates are incon- used, which categorizes subjects into five levels sistent, and that there is only limited information according to income adequacy. The income ade- about the direction of the bias. Furthermore, quacy variable derived by Statistics Canada as a Greene states that the Tobit estimators are incon- function of household income and size of the house- sistent in the face of non-normality of the error term hold takes on the following levels: lowest income; [9]. In studying Bayesian extensions of the Tobit lower middle income; middle income; upper middle model, Austin demonstrated that there was strong income, and highest income. We used the highest evidence in favor of heteroscedasticity, compared level of income adequacy as the reference level, and to homoscedasticity in a Tobit model relating the used indicator variables to represent each of the Health Utilities Index to subject characteristics [10]. four lower levels of income adequacy. The number The purpose of this paper is to compare the of chronic medical conditions was obtained by performance of the classic Tobit model with that summing the number of affirmative responses of censored LAD and symmetrically trimmed least regarding specifically diagnosed chronic medical squares for analyzing the relationship between conditions. demographic characteristics and the Health Utilities Index Mark 3. Data from the National Population Statistical Models Health Survey (NPHS) [11] were used for the analysis. Classic Tobit model. The Tobit model [9,13,14] is a well-known econometric regression model used in Methods the presence of censored data. Assume that the true model is given by the following equation: Data Sources The HUI Mark 3 (HUI3), developed by Torrance (1) YXi*=+ab1 1i * ++ ... bk X ki * + e i *, and colleagues [3,12], describes an individual’s health in terms of eight attributes: vision, hearing, where Yi* denotes the individual’s true health status speech, mobility, dexterity, cognition, emotion, and score. However, an individual with an observed Statistical Models for Health Status 331 health status score of 1, has a true Yi* ≥ 1.0. There- Traditional Statistical Models fore, the observed dependent variable is given by: For comparative purposes, three other models were fit to the data. First, a linear regression model, esti- =< Yii Y*, for Y i * ,1 and mated using ordinary least squares (OLS), was fit to the data. This model ignored the potential censor- ing that occurs at the ceiling, and treated the (2) Yii=≥110,*.. for Y observed health status as the true health status. The actual estimated regression equation will Second, a linear regression model, estimated using then be OLS was fit to those subjects for whom the observed HUI lay below the ceiling value. Third, a median regression model [9] was fit to the data, (3) YXi =+ab1 1i ++... bk X ki + e i . ignoring the potential censoring that occurred at the The classic Tobit model assumes that the error ceiling. Median regression describes the change in terms are normally distributed with uniform vari- median health status with changes in subject 2 characteristics, in contrast to OLS regression, which ance, ei ~ N(0,s ).
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