Palliative chemotherapy in carcinoma nasopharynx

Introduction: Nasopharyngeal carcinoma is a rare malignancy. We conducted an audit of systemic therapies received in palliative setting in carcinoma nasopharynx and studied their outcomes. Methods: Patients who underwent first-line palliative systemic chemotherapy between January 2014 and April 2017 for carcinoma nasopharynx at the department of medical oncology at authors' institute were selected for this analysis. Toxicities, responses, progression-free survival (PFS), and overall survival (OS) were analyzed. In addition, a Quality-Adjusted Time without Symptoms or Toxicity analysis with threshold utility analysis was performed. Results: Fifty-one patients were included in this analysis. The indication of palliative chemotherapy was locoregionally recurrent disease in 25 (49.0%) patients and metastatic disease in 26 (51.0%) patients. The overall response rate was 62.0% (n = 33). The median PFS was 225 days (95% confidence interval [CI]: 164–274 days) and median OS was 513 days (95% CI: 286–931 days). The restricted mean TOX state duration was 2.6 days (95% CI: 0.3–4.9), restricted mean TWiST duration was 219.2 days (95% CI: 184.0–254.4), and restricted mean REL duration was 74.3 days (95% CI: 38.1–110.4). Conclusion: Systemic cytotoxic therapy in nasopharyngeal cancers is associated with high response rates and clinically meaningful PFS; with low duration of time spent in adverse events.

Quality-Adjusted Time Without Symptoms or Toxicity Analysis of Adjuvant Chemotherapy in Non–Small-Cell Lung Cancer: An Analysis of the National Cancer Institute of Canada Clinical Trials Group JBR.10 Trial

Purpose National Cancer Institute of Canada Clinical Trials Group JBR.10 demonstrated that adjuvant vinorelbine and cisplatin after resection of stage IB-II non–small-cell lung cancer (NSCLC) improved relapse-free and overall survival. However, many patients either are not referred for chemotherapy or decline treatment. To aid in treatment decision making, quality-adjusted survival estimates of the JBR.10 trial were derived using a quality-adjusted time without symptoms or toxicity (Q-TWiST) analysis. Methods Survival curves for treatment (N = 242) and observation groups (N = 240) were partitioned into three health states: time with ≥ grade 2 (early or late) chemotherapy-related toxicity (TOX), time in relapse (REL), and time without toxicity or relapse (TWiST). Q-TWiST = uTOX × TOX + uTWiST × TWIST + uREL × REL, where weights uTOX, uTWIST, and uREL range from 0 to 1. Threshold utility analysis was performed to test the sensitivity of the results to changes in the weights. Weights were derived in an exploratory fashion using different methods. Methods included use of arbitrary values, European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30 (EORTC QLQ-C30) quality-of-life data prospectively collected in JBR.10 (global assessment questions and symptom-based questions), and lastly weights European Quality of Life–Five Dimensions questionnaire collected from early-stage NSCLC (nontrial) patients after resection with discounting for toxicity and relapse. The α level was .05. Results Threshold utility analysis revealed that adjuvant chemotherapy was preferred for all possible weight values for relapse and toxicity (uREL, uTOX), although the result was not always statistically significant. The adjuvant chemotherapy group had better Q-TWiST in the range of 5 to 6 additional months, which was statistically significant using all methods. Conclusion Adjuvant chemotherapy in early-stage NSCLC improves quality-adjusted survival despite chemotherapy toxicity.

The Use of Test Scores for Classification Decisions with Threshold Utility

The classification problem consists of assigning subjects to one of several available treatments on the basis of their test scores, where the success of each treatment is measured by a different criterion. It is indicated how this problem can be formulated as an (empirical) Bayes decision problem. As an example, the case of classification with a threshold utility function is analyzed, and optimal assignment rules are derived. The results are illustrated empirically with data from a classification problem in which achievement test data are used to assign students to appropriate continuation schools.

An Expected Utility Model for “Optimal” Selection

The Threshold Utility Model for culture-fair selection introduced by Gross and Su (1975) and by Petersen (1974) is defined here for both quota-free and for restricted selection. In addition, a full Bayesian solution based on posterior predictive distributions is provided. By providing a mechanism for coherently combining probabilities and utilities, this model makes it possible to determine an optimal selection strategy in all situations in which a threshold utility function is applicable. This paper studies the mathematical properties of the solution in detail in the case of quota-free selection. In the more complex restricted selection case, the study lacks some detail but is more complete than that given by Gross and Su and should be adequate for most practical purposes. Several data analyses highlight the strengths and weaknesses of the model.