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The different percentage of zero cost observations in the two groups determines a relevant variation from the ratios estimated previously in the one part models.
Standard errors of the parameter estimates in the regression model were adjusted for the multiple annual cost observations per patient [ 24].
The estimated cost ratio between patients with and without diabetes, considering the presence of zero cost observations in the two groups, was 4.10.
Despite the low computational cost, observations that are close to the thresholds are likely to be misclassified, as analytical methods associate an error term with each observation (Cakmak et al., 2012; Cicek and Ozsoyoglu, 2012).
Our data are affected by skewness and relevant presence of zero cost observations, so the focus of our paper will be on these two criticisms and how to manage them using different statistical models.
Incomplete annual cost observations included years in which a patient either died or experienced a new breast cancer event, and hence did not remain in the same health state for a full year.
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Presence of zero cost observation was handled by means of a two part model.
In online learning, the function c ¯ is estimated from a sample-path of state-cost observations.
Moreover, the number of state-cost observations needed to estimate the cost-to-go function is orders of magnitude smaller than that required by traditional online learning techniques.
The proposed algorithm achieves a considerable accuracy in the estimation of c ¯ after a very short number of state-cost observations, whereas standard learning converges slowly to c ¯.
The distribution of health-care costs was skewed to the left with a tail of high-cost observations and the cost equations were estimated using the logarithmic transformation on cost [ 17].
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