Estimation of loss of quality-adjusted life expectancy (QALE) for patients with operable versus inoperable lung cancer: Adjusting quality-of-life and lead-time bias for utility of surgery

Abstract

a b s t r a c tObjectives: This study attempts to quantify the difference in loss of quality-adjusted life expectancy (QALE) for patients with operable and inoperable non-small-cell lung cancer (NSCLC). Patients and methods: A cohort consisting of 1652 pathologically verified NSCLC patients with performance status 0-1 was monitored for 7 years (2005)(2006)(2007)(2008)(2009)(2010)(2011) to obtain the survival function. This was further extrapolated to lifetime, based on the survival ratios between patients and age-and sex-matched referents simulated from the life tables of the National Vital Statistics of Taiwan. Between 2011 and 2012, EuroQol 5-dimension questionnaires were used to prospectively measure the quality-of-life (QoL) of a 518 consecutive, cross-sectional subsample. We adjusted the lifetime survival function by the utility values of QoL for the cancer cohort to obtain the QALE, while that for the age and sex-matched referents were adjusted to the values collected from the 2009 National Health Interview Survey, and the difference between them was the loss-of-QALE. Results: The QALE for patients with operable and inoperable NSCLC were 11.66 ± 0.18 and 1.43 ± 0.05 quality-adjusted life year (QALY), with the corresponding loss-of-QALE of 5.25 ± 0.18 and 14.24 ± 0.05 QALY, respectively. The lifetime utility difference for patients with operable and inoperable NSCLC was 9.00 ± 0.18 QALY, after adjustment for QoL and lead-time bias. Conclusion: The utility gained from surgical operation for operable lung cancer is substantial, even after adjustment for lead-time bias. Future studies should compare screening programs with treatment strategies when carrying out cost-utility assessments to improve patients' values.

Estimation of losses of quality-adjusted life expectancy attributed to the combination of cognitive impairment and multimorbidity among Chinese adults aged 45 years and older

“…The current study measured the losses of QALE due to the combination of cognitive impairment and multimorbidity, and then discovered significantly additional burden from the interaction of cognitive impairment and multimorbidity at both individual and population levels. Unlike previous studies [26,43,44], our consequences were confirmed by losses of QALE based on the representative sample of a country rather than patient-based clinical studies.…”

Comparative effectiveness and cost-effectiveness of three first-line EGFR-tyrosine kinase inhibitors: Analysis of real-world data in a tertiary hospital in Taiwan

“…By using a semiparametric method explained in detail in our previous article [13], we extrapolated the survival to lifetime to estimate the life expectancy of patients receiving one of three first-line treatments. The extrapolation method has been shown to be effective via computer simulations [14], mathematical proof [15] and corroboration by examples of lung cancer cohorts [13,16,17]. The iSQoL statistical package (www.stat.sinica.…”

Estimating lifetime medical costs from censored claims data

“…The curve of logit transformation of the relative survival between the index cohort and the reference population would follow an approximately straight line if the excess hazard of the patients remains constant over time (Fang, Chang, Hsu, et al, ). This method has been successfully validated with simulation data and has been used to analyze real data in several studies (e.g., Cucchetti, Trevisani, Bucci, et al, , Hung et al, ‐, , H. Y. Lee et al, , Yang, Lee, Hsu, et al, ; Yang, Lai, Chang, et al, ). The assumption of an excess constant hazard, however, is usually untestable with a limited observed time period, because how closely the curve of logit‐transformed relative survival approaches a straight line is uncertain.…”

“…This method has been successfully validated with simulation data and has been used to analyze real data in several studies (e.g., Cucchetti, Trevisani, Bucci, et al, 2016, Hung et al, 2011, H. Y. , Yang, Lee, Hsu, et al, 2012Yang, Lai, Chang, et al, 2014). The assumption of an excess constant hazard, however, is usually untestable with a limited observed time period, because how closely the curve of logit-transformed relative survival approaches a straight line is uncertain.…”