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Quality-adjusted life year (QALY) estimation is a well-known but little used technique to compare survival adjusted for complications.
The contribution of quality of life in relation to survival adjusted for known prognostic factors was determined using Cox's proportional hazard model.
The purpose of this paper is to describe features and use of two R functions, main.preval.func and main.wpreval.func, which were designed for the analysis of survival adjusted for quality of life.
The hazard ratio for overall survival, adjusted for other confounding variables, was 0.965 955% confidence interval: 0.73 1.28).
Cox regression was used to estimate the effect of lymph node count on survival adjusted for possible confounders.
Survival curves for overall survival adjusted for patients' age, tumor grade, histology and platinum response status is shown in Figure 2.
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The second part of the analysis was to model the relationship between a day or more of exposure to workload/staffing ratios above or below the identified threshold and changes in survival, adjusting for the same confounders as in the net-benefit regression analysis.
We built multivariable survival models to quantify the mediational effect of individual smoking on overall and OPC-specific survival, adjusting for regional smoking, demographics, and clinical characteristics.
We estimate the contribution of antioxidants and other nurtients, both separately and in combination with each other, to glioma survival adjusting for known risk factors and confounders.
Using a stepwise selection model, we were also able to show that pERK.gr was associated with survival adjusting for other markers (data not shown).
p53 AAb independently related to poor survival adjusting for stage (P= 0.026), grade (P= 0.029) and residual disease after surgery (P= 0.005).
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