Estimation of Life Expectancy and the Expected Years of Life Lost in Patients with Major Cancers: Extrapolation of Survival Curves under High-Censored Rates

Abstract

The newly developed method is feasible and relatively accurate to project LE and EYLL, which could also be merged with data pertaining to quality of life, for a more detailed outcome assessment in the future.

Estimation of life expectancies and loss-of-life expectancies for workers with permanent occupational disabilities of the extremities – a 21-year follow-up study

“…Finally, if the assumption of constant excess hazard holds, then the logit-transformed survival ratio, indicated by W(t), between the study and reference population becomes linear during the later follow-up period, and the estimated regression line can be used to extrapolate lifelong survival beyond the follow-up period (5). We have simulated (11) and mathematically proven (5) that this is a valid method for predicting the life expectancy under a high censored rate, which is also corroborated by several real examples (5,7,9).…”

“…Hwang et al (4) have developed a semi-parametric method to incorporate the life expectancy of a background general population into the estimation process for cohorts with life-threatening conditions such as HIV (5) or cancer (6,7). That method was also applied in the estimation of life expectancy for workers with permanent occupational disabilities from 1986-2000 and demonstrated that the expected loss-of-life expectancy varied between 5-19 years, depending on the grade of major physiological dysfunction and injury type (8,9).…”

Quality-Adjusted Life Expectancy (QALE) and Loss of QALE for Patients With Ischemic Stroke and Intracerebral Hemorrhage

“…The validity of our extrapolation for long-term survival using this statistics package has been validated in previously published studies. [13][14][15] …”

Comparison of expected health impacts for major cancers: Integration of incidence rate and loss of quality-adjusted life expectancy

“…Although estimates of standardized mortality rates can still provide useful information on the impacts of diseases or technologies, which can then aid health policy decision, such comparisons for different illnesses have become less efficient, especially because for most cancers patients usually survive for more than 5-10 years, and thus one must wait for a long period of time for mortality to occur [1][2][3][4][5][6]. The current coding of one underlying cause of death might also underestimate the impact of other causes, including cancer.…”

“…Recently, the authors of this study have developed estimates of the expected years of life lost and loss-of-QALE that quantify the consequences of illness with life-year and QALY, respectively, which make possible direct comparisons of prevention with clinical care [1][2][3][4]15] if incidence rates can also be incorporated together [16]. It may thus be possible to quantify both incidence rates and how much utility is lost from developing a specific illness, rather than simply making policy decisions based on mortality rates.…”