A systematic review of economic evaluations for interventions of cardiovascular diseases

Background. Cardiovascular disease (CVD) exhibits the most striking public health significance due to its high prevalence and mortality as well as huge economic burdens all over the world, especially in industrialized countries. Major risk factors of CVDs have been the targets of population-wide prevention in the United States. Economic evaluations provide structured information in regard to the efficiency of resource utilization which can inform decisions of resource allocation. The main purpose of this review is to investigate the pattern of study design of economic evaluations for interventions of CVDs. ^ Methods. Primary journal articles published during 2003-2008 were systematically retrieved via relevant keywords from Medline, NHS Economic Evaluation Database (NHS EED) and EBSCO Academic Search Complete. Only full economic evaluations for narrowly defined CVD interventions were included for this review. The methodological data of interest were extracted from the eligible articles and reorganized in Microsoft Access database. Chi-square tests in SPSS were used to analyze the associations between pairs of categorical data. ^ Results. One hundred and twenty eligible articles were reviewed after two steps of literature selection with explicit inclusion and exclusion criteria. Descriptive statistics were reported regarding the evaluated interventions, outcome measures, unit costing and cost reports. The chi-square test of the association between prevention level of intervention and category of time horizon showed no statistical significance. The chi-square test showed that sponsor type was significantly associated with whether new or standard intervention being concluded as more cost effective. ^ Conclusions. Tertiary prevention and medication interventions are the major interests for economic evaluators. The majority of the evaluations were claimed from either a provider’s or a payer’s perspective. Almost all evaluations adopted gross costing strategy for unit cost data rather than micro costing. EQ-5D is the most commonly used instrument for subjective outcome measurement. More than half of the evaluations used decision analytic modeling techniques. The lack of consistency in study design standards in published evaluations appears in several aspects. Prevention level of intervention is not likely to be a factor for evaluators to decide whether to design an evaluation in a lifetime horizon or not. Published evaluations sponsored by industry are more likely to conclude that new intervention is more cost effective than standard intervention.^

Structural equation modeling of the medical outcome study: Short Form 36 (SF-36)

The factorial validity of the SF-36 was evaluated using confirmatory factor analysis (CFA) methods, structural equation modeling (SEM), and multigroup structural equation modeling (MSEM). First, the measurement and structural model of the hypothesized SF-36 was explicated. Second, the model was tested for the validity of a second-order factorial structure, upon evidence of model misfit, determined the best-fitting model, and tested the validity of the best-fitting model on a second random sample from the same population. Third, the best-fitting model was tested for invariance of the factorial structure across race, age, and educational subgroups using MSEM.^ The findings support the second-order factorial structure of the SF-36 as proposed by Ware and Sherbourne (1992). However, the results suggest that: (a) Mental Health and Physical Health covary; (b) general mental health cross-loads onto Physical Health; (c) general health perception loads onto Mental Health instead of Physical Health; (d) many of the error terms are correlated; and (e) the physical function scale is not reliable across these two samples. This hierarchical factor pattern was replicated across both samples of health care workers, suggesting that the post hoc model fitting was not data specific. Subgroup analysis suggests that the physical function scale is not reliable across the "age" or "education" subgroups and that the general mental health scale path from Mental Health is not reliable across the "white/nonwhite" or "education" subgroups.^ The importance of this study is in the use of SEM and MSEM in evaluating sample data from the use of the SF-36. These methods are uniquely suited to the analysis of latent variable structures and are widely used in other fields. The use of latent variable models for self reported outcome measures has become widespread, and should now be applied to medical outcomes research. Invariance testing is superior to mean scores or summary scores when evaluating differences between groups. From a practical, as well as, psychometric perspective, it seems imperative that construct validity research related to the SF-36 establish whether this same hierarchical structure and invariance holds for other populations.^ This project is presented as three articles to be submitted for publication. ^

Slope estimation in a parametric measurement error model: A simulation study

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.^