Hello, we are Qiong Louie-Gao and Humberto Reynoso-Vallejo, members of the research team evaluating Chapter 224, the Health Care Cost Containment Law, from the Office of the State Auditor in Massachusetts.
In order to implement a comprehensive evaluation on the impact of Chapter 224, our research team is committed to find the most effective statistical procedures to use in order to create a solid baseline that can lead to a sound quantitative analysis of the entire longitudinal project. One of the procedures we found useful is predictive modeling.
Predictive modeling is a process of fitting a statistical model based on existing relationships among variables and making an informed prediction for future behavior(s). In our study, a primary goal is to find the trend or pattern of our variables using a number of data points before the implementation of Chapter 224 (prior to implementation in 2012) as a baseline. We then use the parameter estimates of the baseline to predict the values after 2013. A solid baseline with predicted values will be compared with the actual data once we conclude the longitudinal quantitative analysis.
Hot Tip: This method can be used for both individual data and aggregate data. For individual data, each individual may have repeated measures that may produce a correlation. Generalized Estimating Equations (see Zeger & Liang, 1986) and Linear Mixed-Effect Models (see Cnaan, Laird, & Slasor, 2005) are the two general statistical methods for the longitudinal data analysis. Note that General Estimating Equations represent the effects on the population average, whereas Linear Mixed-Effect Models aims to estimate the effects on a subject-specific basis. For aggregate data, a linear regression model can be used to estimate the slope of the time series.
Hot Tip: Data fluctuation or outliers will compromise the results. To account for this, some statistical techniques such as “moving average” may be used to smooth out the data.
Hot Tip: Carefully select covariates to control confounding and avoid multicollinearity (see Steen, et al, 2002) based on background information. Some variable-selection approaches can be used, e.g. step-wise, forward, and backward selection procedures.
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