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Evaluating the contribution of these interventions using mathematical models requires survival functions before and after their introduction.
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This model estimates required survival rates to achieve cost-effectiveness with CAS.
The validity of estimates obtained from survival models requires that several assumptions are met [59].
We need to simulate the survival times since software packages for Cox models require the individual survival time data, not the hazard function.
Because we have to build in multiple options to assure its integrity, the creative model requires robustness for survival.
This unit of analysis is not suitable for survival analysis as the models require a certain number of cases/deaths per interval.
Classification models require the use of non-censored data where survival outcome is known for every patient in the dataset at the critical decision point in time.
To achieve the same proportional reductions in C*, age dependent models required about twice as great of a reduction in survival compared to the constant models (Figure 6).
Location-scale models require ascertainment of a data-appropriate parametric function for the hazard rate; to estimate survival with a Cox model, it is necessary to (non-parametrically) estimate baseline hazard.
Different business models require different investors.
New business models require new technology.
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