Xiaofei Wang

In the presence of heterogeneity between the randomized controlled trial (RCT) participants and the target population, evaluating the treatment effect solely based on the RCT often leads to biased quantification of the real-world treatment effect. To address the problem of lack of generalizability for the treatment effect estimated by the RCT sample, we leverage observational studies with large samples that are representative of the target population. This article concerns evaluating treatment effects on survival outcomes for a target population and considers a broad class of estimands that are functionals of treatment-specific survival functions, including differences in survival probability and restricted mean survival times. Motivated by two intuitive but distinct approaches, i.e., imputation based on survival outcome regression and weighting based on inverse probability of sampling, censoring, and treatment assignment, we propose a semiparametric estimator through the guidance of the efficient influence function. The proposed estimator is doubly robust in the sense that it is consistent for the target population estimands if either the survival model or the weighting model is correctly specified and is locally efficient when both are correct. In addition, as an alternative to parametric estimation, we employ the nonparametric method of sieves for flexible and robust estimation of the nuisance functions and show that the resulting estimator retains the root-n consistency and efficiency, the so-called rate-double robustness. Simulation studies confirm the theoretical properties of the proposed estimator and show that it outperforms competitors. We apply the proposed method to estimate the effect of adjuvant chemotherapy on survival in patients with early-stage resected non-small cell lung cancer.

BACKGROUND: Weight loss (WL) has been associated with shorter survival in patients with advanced cancer, while obesity has been associated with longer survival. Integrating body mass index (BMI) and WL provides a powerful prognostic tool but has not been well-studied in lung cancer patients, particularly in the setting of clinical trials. METHODS: We analysed patient data (n = 10 128) from 63 National Cancer Institute sponsored advanced non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) trials. Risk matrices were created using BMI and WL percentage, which were divided into 'grades' based on median survival. Relationships between survival, BMI and WL percentage were examined using Kaplan-Meier estimators and Cox proportional hazards (PH) models with restricted cubic splines. RESULTS: For NSCLC, a twofold difference was noted in median survival between the BMI > 28 and WL ≤ 5% group (13.5 months) compared with the BMI < 20 and WL > 5% group (6.6 months). These associations were less pronounced in SCLC. Kaplan-Meier curves showed significant survival differences between grades for both NSCLC and SCLC (log-rank, P < 0.0001). In Stage IV NSCLC, Cox PH analyses with restricted cubic splines demonstrated significant associations between BMI and survival in both WL ≤ 5% (P = 0.0004) and >5% (P = 0.0129) groups, as well as in WL > 5% in Stage III (P = 0.0306). In SCLC, these relationships were more complex. CONCLUSIONS: BMI and WL have strong associations with overall survival in patients with advanced lung cancer, with a greater impact seen in NSCLC compared with SCLC. The integration of a BMI/WL grading scale may provide additional prognostic information and should be included in the evaluation of therapeutic interventions in future clinical trials in advanced lung cancer.

BACKGROUND: For patients with locally advanced non-small cell lung cancer (NSCLC), concurrent chemoradiotherapy is the foundational treatment strategy. Adding induction chemotherapy did not achieve a superior efficacy but increased the burden from toxicity. Accordingly, we retrospectively investigated the toxicity patterns through pooling individual patient data of the Cancer and Leukemia Group B (CALGB)/Alliance trials. METHODS: We included a total of 637 patients with unresectable stage III NSCLC who received induction chemotherapy with a platinum doublet and concurrent chemoradiotherapy and experienced at least one adverse event (AE) in CALGB 9130, 9431, 9534, 30105, 30106 and 39801 trials. The following toxicity occurrence patterns were evaluated: top 10 most frequent AEs, AE distribution by grade, rate of treatment discontinuation due to AEs, associations of AE occurrence with patient characteristics and treatment phase, the time to the first grade ≥3 AE occurrence and its associations with patient characteristics and treatment phase. RESULTS: The occurrence of AEs was the main reason accounting for treatment discontinuation (60 of 637 among all patients; 18 of 112 patients who experienced the induction phase only; 42 of 525 patients who experienced both phases). All patients experienced a total of 11,786 AEs (grade ≥3: 1,049 of 5,538 in induction phase, 1,382 of 6,248 in concurrent phase). Lymphocytes and white blood count were of top 3 grade ≥3 AEs that patients experienced the most in the either phase. Multivariable analysis found AE occurrence was associated with age ≥65 [any grade: odds ratio (OR) =1.44, 95% confidence interval (CI): 1.12-1.86] and the concurrent phase (grade ≥3: OR =1.86, 95% CI: 1.41-2.47; any grade: OR =1.47, 95% CI: 1.19-1.81). Patients in the concurrent phase were more likely and earlier to develop grade ≥3 AEs than those in the induction phase [hazard ratio (HR) =4.37, 95% CI: 2.52-7.59]. CONCLUSIONS: The report provides a better understanding regarding the toxicity occurrence patterns in concurrent chemoradiotherapy after induction chemotherapy.

<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Objectives</jats:title> <jats:p>Inconsistent findings have been reported on the link between dietary carbohydrates and lung cancer incidence. We comprehensively evaluated the associations of dietary carbohydrates, fibers and their food sources with lung cancer in the PLCO Cancer Screening Trial.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>The study included 113,096 eligible participants recruited to the PLCO trial. Participants had to have completed baseline and diet history questionnaires. The incidence of lung cancer was acquired through self-report and medical record follow-up. A multivariable logistic model adjusted for confounders was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of dietary carbohydrates, fibers, whole grains, glycemic index (GI) and glycemic load (GL) for lung cancer.  Similar methods were applied in analyzing the food sources of carbohydrates and fibers. Multinomial logistic models were used for sensitivity analysis with lung cancer subtypes as outcomes.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>Dietary carbohydrates and GL were associated with a lower risk of lung cancer in the PLCO population. Among various carbohydrates, 30-gram daily consumption of dietary fibers was related to a lower risk of lung cancer (the fourth vs the first quartile OR: 0.62, 95% CI: 0.54–0.72) compared with 8.8-gram. Furthermore, consuming 2.3 servings of whole grain per day as opposed to 0.3 servings per day was associated with a lower risk of lung cancer (OR: 0.73, 95% CI: 0.64–0.83).  Nevertheless, a higher risk of lung cancer was seen for high-GI food consumption (P = 0.013) and refined carbohydrates from soft drinks (P = 0.016).</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>A higher quantity of high-quality carbohydrates and fibers from fruits, vegetables, and whole grains are associated with lower lung cancer risk. Refined carbohydrates, such as soft drinks, appear to increase risk.</jats:p> </jats:sec> <jats:sec> <jats:title>Funding Sources</jats:title> <jats:p>The University Postgraduate Fellowship from the University of Hong Kong.</jats:p> </jats:sec>