colorectal cancer is one of the most prevalent cancers in the world. it's survival expectancy depends on the time of diagnosis, stage and grade of the tumor, and general condition of the patient. survival expectancy of this type of cancer reveals the quality of diagnostic and therapeutic services in the community. to improve and standardize the treatment modalities in this field, much rese...

results survival probabilities at different times were determined using the cox proportional hazards and a neural network with three nodes in the hidden layer; the ratios of standard errors with these two methods to the kaplan-meier method were 1.1593 and 1.0071, respectively, revealed a significant difference between cox and kaplan-meier (p < 0.05) and no significant difference between cox and...

Colorectal cancer is one of the most prevalent cancers in the world. It's survival expectancy depends on the time of diagnosis, stage and grade of the tumor, and general condition of the patient. Survival expectancy of this type of cancer reveals the quality of diagnostic and therapeutic services in the community. To improve and standardize the treatment modalities in this field, much resea...

CONTEXT Patients with chronic heart failure have impaired long-term survival, but their own expectations regarding prognosis have not been well studied. OBJECTIVES To quantify expectations for survival in patients with heart failure, to compare patient expectations to model predictions, and to identify factors associated with discrepancies between patient-predicted and model-predicted prognos...

We show how to use a bedside approximation of life expectancy in quantitative decision-making. This method, the declining exponential approximation of life expectancy (DEALE), enables the physician to collate various survival data with information on morbidity to determine a quality-adjusted expected survival for a potential management plan. The keystone in the DEALE approach is the approximati...

It is well known that life expectancy can be expressed as an integral of the survival curve. The reverse that the survival function can be expressed as an integral of life expectancy is also true. 1. Relationship Let e(a) denote remaining life expectancy at age a and let `(a) denote the proportion surviving to age a (the survival function). Then (1) l(a) = e(0) e(a) exp ( − ∫ a

Improvements in life expectancy have been considerable over the past hundred years. Forecasters have taken to applying historical trends under an assumption of continuing improvements in life expectancy in the future. A linear mixed effects model was used to estimate the trends in global and regional rates of improvements in life expectancy, child, adult, and senior survival, in 166 countries b...

Parameter estimates from commonly used multivariable parametric survival regression models do not directly quantify differences in years of life expectancy. Gaussian linear regression models give results in terms of absolute mean differences, but are not appropriate in modeling life expectancy, because in many situations time to death has a negative skewed distribution. A regression approach us...