These findings have important clinical and policy implications for the risk-benefit calculation induced by treatment in older patients with Stage III colon cancer. The results suggest that there is a benefit from chemotherapy, but the benefit is lower with older age.

Metastatic prostate cancer patients with SREs incur higher costs compared to similar patients without SREs. SRE costs among older stage IV (M1) prostate cancer patients vary by SRE type, with spinal cord compression and concurrent surgery costing at least twice as much as other SREs.

H2Bs were robustly and consistently associated with significantly lower CIGIB risk compared with PPIs in this population.

Marginal analysis evaluates changes in a regression function associated with a unit change in a relevant variable. The primary statistic of marginal analysis is the marginal effect (ME). The ME facilitates the examination of outcomes for defined patient profiles or individuals while measuring the change in original units (e.g., costs, probabilities). The ME has a long history in economics; however, it is not widely used in health services research despite its flexibility and ability to provide unique insights. This paper, the second in a two-part series, discusses practical issues that arise in the estimation and interpretation of the ME for a variety of regression models often used in health services research. Part One provided an overview of prior studies discussing ME followed by derivation of ME formulas for various regression models relevant for health services research studies examining costs and utilization. The current paper illustrates the calculation and interpretation of ME in practice and discusses practical issues that arise during the implementation including: understanding differences between software packages in terms of functionality available for calculating the ME and its confidence interval, interpretation of average marginal effect versus marginal effect at the mean, and the difference between ME and relative effects (e.g., odds ratio). Programming code to calculate ME using SAS, STATA, LIMDEP and MATLAB are also provided. The illustration, discussion, and application of ME in this two-part series support the conduct of future studies applying the concept of marginal analysis.

The results support the existing Veterans Affairs practice of offering tiotropium to patients with COPD-related hospitalizations. Periodic review of the effectiveness data to determine whether tiotropium would be cost-saving in patients with very severe COPD is suggested. Cost-effectiveness analyses that identify practical criteria-for-use should become an integral part of the formulary process.

Race disparities in medical oncologist evaluations diminished over time, possibly in response to increased provider supply or changing patient and provider attitudes, but there was no parallel reduction in disparities in conditional treatment rates. Projected decreases in oncologist supply suggest the need for further research on this relationship. Research on the role of supplemental medical insurance on disparities in treatment is needed, particularly as the cost of recommended adjuvant therapy increases.

Machine-learning techniques could be useful in exploring patterns of care. Among Medicare disabled HCV patients, the receipt of more QC indicators was associated with higher treatment rates. Future research is needed to assess determinants of differential QC receipt.

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