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Technical data of product parameters
Another advantage of the full Bayes and hierarchical Bayes methods is being able to explicitly model
spatial correlation and other issues related to unobserved heterogeneity. Spatial correlation can be an
DCS module LYA210Awhere both treatment and comparison sites are close to each other. This method allows the
analyst to make use of the added information related to the spatial distribution of the sites (sites that
are closer together are more likely to have similar factors than sites that are further apart). Unobserved
heterogeneity is an issue when the unobserved characteristics are correlated with a variable that is
included in a model. The full Bayes and hierarchical Bayes methods can help to reduce the issue by
distinguishing heterogeneity from “noise” in the data (Orme, 2000). These Bayesian methods also allow
the use of prior knowledge of CMFs estimated from other studies. Recently, full Bayes methods haveDCS module LYA210A
been employed in several before-after studies (e.g., see Lan et al., 2009; El-Basyouny and Sayed, 2011).
Similar to a before-after study with the empirical Bayes method, there is no accepted method for
calculating the required sample size for a before-after study employing the full Bayes method. As
NCHRP 20-7(314) Final Report – Recommended Protocols for Developing Crash Modification DCS module LYA210A earlier with the before-after empirical Bayes method, a conservative estimate of the sampleDCS module LYA210A
size can be determined assuming a before-after study design with a comparison group.
Intervention and Time Series Analysis Methods
Intervention and time series analysis methods have been used in safety for many years. One of the
most common methods is the use of the Autoregressive Integrated Moving Average (ARIMA) method
(Box and Jenkins, 1976) that explicitly accounts for serial correlation and seasonality. The ARIMA
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