Exploring therapeutic decisions in elderly patients with non-small cell lung cancer: results and conclusions from North Central Cancer Treatment Group Study N0222

Prognostic significance of baseline fatigue for overall survival: A patient-level meta-analysis of 43 oncology clinical trials with 3915 patients

We have previously identified a single-item measure for baseline overall quality of life (QOL) as a strong prognostic factor for survival, and that fatigue was an important component of patient QOL. To explore whether patient-reported fatigue was supplemental or redundant to the prognostic information of overall QOL, we performed a patient-level pooled analysis of 43 North Central Cancer Treatment Group (NCCTG) and Mayo Clinic Cancer Center (MCCC) oncology clinical trials assessing the effect of baseline fatigue on overall survival (OS). 3,915 patients participating in 43 trials provided data at baseline for fatigue on a single-item 0-100 point scale. OS was tested for association with clinically deficient fatigue (CDF, score 0-50, n = 1,497) versus not clinically deficient fatigue (nCDF, score 51-100, n = 2,418). We explored whether fatigue contributed to overall survival in the presence of performance status and overall QOL. We used Cox proportional hazards models that adjusted for the effects of overall QOL, performance score, race, disease site, age and gender. Baseline fatigue was a strong predictor of OS for the entire patient cohort (CDF vs. nCDF: 31.5 months vs > 83.9 months, p < 0.0001). The effect sizes of fatigue on survival were more variable across different disease sites than was seen for overall QOL (GI, esophageal, head and neck, prostate, lung, breast and others). After controlling for covariates, including performance status and overall QOL, baseline fatigue remained a strong prognostic factor in multivariate models (CDF vs. nCDF: HR = 1.23, p = 0.02). Baseline fatigue is a strong and independent prognostic factor for OS over and above performance status (PS) and overall QOL in a wide variety of oncology patient populations. Single-item measures of overall QOL and fatigue can help to identify vulnerable subpopulations among cancer patients. We recommend these single-item measures for routine inclusion as a stratification factor or key covariate in the design and analysis of oncology treatment trials.

Impact of disease progression date determination on progression-free survival estimates in advanced lung cancer

BACKGROUND

In patients with advanced lung cancer, overall survival is largely influenced by progression status. Because progression-free survival (PFS)-based endpoints are controversial, the authors evaluated the impact of the progression date (PD) determination approach on PFS estimates.

METHODS

Individual patient data from 21 trials (14 North Central Cancer Treatment Group trials and 7 Southwest Oncology Group trials) were used. The reported PD (RPD) was defined as either the radiographic scan date or the clinical deterioration date. PD was determined using Method 1 (M1), the RPD; M2, 1 day after the last progression-free scan; M3, midpoint between the last progression-free scan and the RPD; and M4, an interval-censoring approach. PFS was estimated using Kaplan-Meier (M1-M3), and maximum-likelihood (M4) methods. Simulation studies were performed to understand the impact of the length of time elapsed between the last progression-free scan and the PD on time-to-progression estimates.

RESULTS

PFS estimates using the RPD were the highest, and M2 was the most conservative. M3 and M4 were similar because the majority of progressions occurred during treatment (ie, frequent disease assessments). M3 was influenced less by the length of the assessment schedules (percentage difference from the true time-to-progression, <1.5%) compared with M1 (11% to 30%) and M2 (-8% to -29%). The overall study conclusion was unaffected by the method used for randomized trials.

CONCLUSIONS

The magnitude of difference in the PFS estimates was large enough to alter trial conclusions in patients with advanced lung cancer. The results indicate that standards for PD determination, the use of sensitivity analyses, and randomized trials are critical when designing trials and reporting efficacy using PFS-based endpoints.