Survival data adjusted analyses

The CUA is a form of cost-effectiveness analysis in which the health outcomes are measured in terms of quality-adjusted life-years (QALYs) gained. The QALY is a measure that associates quantity of life (e.g. survival data and life... 

In Chapter 6 we covered methods for adjusted analyses and analysis of covariance in relation to continuous (ANOVA and ANCOVA) and binary and ordinal data (CMH tests and logistic regression). Similar methods exist for survival data. As with these earlier methods, particularly in relation to binary and ordinal data, there are numerous advantages in accounting for such factors in the analysis. If the randomisation has been stratified, then such factors should be incorporated into the analysis in order to preserve the properties of the resultant p-values. 

The most popular method for analysis of covariance is the proportional hazards model. This model, originally developed by Cox (1972), is now used extensively in the analysis of survival data to incorporate and adjust for both centre and covariate effects. The model assumes that the hazard ratio for the treatment effect is constant. 

The CUA is a form of cost-effectiveness analysis in which the health outcomes are measured in terms of quality-adjusted life-years (QALYs) gained. The QALY is a measure that associates quantity of life (for example survival data and life expectancy) with quality of life, by amalgamating them into a single index. One QALY is equal to a year of full life quality. Because of its universal denominator which allows comparisons across divergent areas, CUA is a tool that can (in theory) be used by policy makers... 

Compared with the standard health economic methodology applied in COI studies for other diseases (e.g. Keith and Shackleton 2006 Welte et al. 2000 Leidl et al. 1999 Henke et al. 1997 Xie et al. 1996), the quality of health economic analysis of HIV/AIDS is not always satisfactory as far as costs are concerned (Levy et al. 2006). Sometimes it is not clear whether researchers included both inpatient and outpatient medications in their analyses. Equally important, many of the early studies used costs and charges interchangeably data using charges may not accurately reflect true costs. Drummond and Davis (1988) also argued that there have been incorrect estimates of the survival times and costs in aU these early studies, since there were no explicit adjustments made for disease severity. 

The types of data available at the end of a clinical trial will depend upon the trial s sample size, duration, and clinical endpoint. There are two categories of clinical endpoints considered in pharmacoeco-nomic analysis intermediate endpoints and final endpoints. An intermediate endpoint is a clinical parameter, such as systolic blood pressure, which varies as a result of therapy. A final endpoint is an outcome variable, such as change in survival, or quality-adjusted survival, that is common to several economic trials, which allows for comparisons of economic data across clinical studies and is of relevance to policy makers. 

Orthopedics has recognized the importance of measuring outcomes in terms of quality-adjusted life-years instead of length of implant survival.Similarly, pharmacy must implement software documentation solutions that facilitate outcomes monitoring beyond cost savings. Software is needed with the ability to calculate, in a cost-benefit analysis, the clinical impact of pharmacist interventions as they affect therapeutic, financial, and humanistic outcomes. The current array of products could be better integrated into documentation software to facilitate tabulation of these data. With the power of the Internet to manipulate data in a dynamic database, it would even be possible for hospitals to compare their outcomes on a local, regional, or national basis. Furthermore, the database could... 

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