![]() ![]() Īn alternative approach is using nonlinear regression models, of which exponential conditional mean (ECM) models in generalized linear models (GLMs) are examples. The general consensus is that estimating the mean cost using a logarithmic regression model leads to biased estimation. One of these drawbacks is that the predictions are not robust enough to detect the heteroscedasticity in the transformed scale. However, it also presents several drawbacks. The logarithmic transformation with ordinary least squares (OLS) regression is a very common approach in applied economics. Further, logarithmic (or other) transformations are commonly used to decrease the skewness and drive them close to normal distribution, in order to implement linear regression models. Two-part models based on mixture models are performed when excess zeroes are present in data. These specifications of data impose a number of difficulties in using standard statistical analysis, such as implementing linear regression causes unreliable results. Healthcare costs data demonstrate the substantial positive skewness and are sometimes characterized by the use of large resources with zero cost. However, these cannot be implemented by simple statistical models as the healthcare costs data have specific characterizations. The main issues in such studies are the estimation of mean population healthcare costs and finding the best relationship between costs and covariates through regression modeling. ![]() Statistical models are often used in many healthcare economics and policy studies. However, increasing the sample size could improve the performance of the OLS-based model. Approximately results are consistent by increasing the sample size. The results showed that the Cox proportional hazard model exhibited a poor estimation of population means of healthcare costs and the β 1 even under proportional hazard data. However, GLMs, especially the Gamma regression model, behaved well in the estimation of population means of healthcare costs. We found that there was not one best model across all generated conditions. Alternative estimators, such as ordinary least squares (OLS) for Ln(y) or Log(y), Gamma, Weibull and Cox proportional hazard regression models, were compared using Monte Carlo simulation under different situations, which were generated from skewed distributions. The primary outcome was an estimation of population means of healthcare costs and the secondary outcome was the impact of a covariate on healthcare cost. The aim of this study was to investigate how well these alternative estimators perform in terms of bias and precision when the data are skewed. Some recent studies have employed generalized linear models (GLMs) and Cox proportional hazard regression as alternative estimators. Data transformation is a conventional method to decrease skewness, but there are some disadvantages. Skewed data is the main issue in statistical models in healthcare costs. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |