Evaluating minimal important differences for the FACT-Melanoma quality of life questionnaire

Objectives. Minimal Important Differences (MIDs) establish benchmarks for interpreting mean differences in clinical trials involving quality of life outcomes and inform discussions of clinically meaningful change in patient status. As such, the purpose of this study was to assess MIDs for the Functional Assessment of Cancer Therapy–Melanoma (FACT-M). ^ Methods. A prospective validation study of the FACT-M was performed with 273 patients with stage I to IV melanoma. FACT-M, Karnofsky Performance Status (KPS), and Eastern Cooperative Oncology Group Performance Status (ECOG-PS) scores were obtained at baseline and 3 months following enrollment. Anchor- and distribution-based methods were used to assess MIDs, and the correspondence between MID ranges derived from each method was evaluated. ^ Results. This study indicates that an approximate range for MIDs of the FACT-M subscales is between 5 to 8 points for the Trial Outcome Index, 4 to 5 points for the Melanoma Combined Subscale, 2 to 4 points for the Melanoma Subscale, and 1 to 2 points for the Melanoma Surgery Subscale. Each method produced similar but not identical ranges of MIDs. ^ Conclusions. The properties of the anchor instrument employed to derive MIDs directly affect resulting MID ranges and point values. When MIDs are offered as supportive evidence of a clinically meaningful change, the anchor instrument used to derive thresholds should be clearly stated along with evidence supporting the choice of anchor instrument as the most appropriate for the domain of interest. In this analysis, the KPS was a more appropriate measure than the ECOG-PS for assessing MIDs. ^

A BAYESIAN APPROACH TO DOSE-RESPONSE ASSESSMENT AND DRUG-DRUG INTERACTION ANALYSIS: APPLICATION TO IN VITRO STUDIES

The considerable search for synergistic agents in cancer research is motivated by the therapeutic benefits achieved by combining anti-cancer agents. Synergistic agents make it possible to reduce dosage while maintaining or enhancing a desired effect. Other favorable outcomes of synergistic agents include reduction in toxicity and minimizing or delaying drug resistance. Dose-response assessment and drug-drug interaction analysis play an important part in the drug discovery process, however analysis are often poorly done. This dissertation is an effort to notably improve dose-response assessment and drug-drug interaction analysis. The most commonly used method in published analysis is the Median-Effect Principle/Combination Index method (Chou and Talalay, 1984). The Median-Effect Principle/Combination Index method leads to inefficiency by ignoring important sources of variation inherent in dose-response data and discarding data points that do not fit the Median-Effect Principle. Previous work has shown that the conventional method yields a high rate of false positives (Boik, Boik, Newman, 2008; Hennessey, Rosner, Bast, Chen, 2010) and, in some cases, low power to detect synergy. There is a great need for improving the current methodology. We developed a Bayesian framework for dose-response modeling and drug-drug interaction analysis. First, we developed a hierarchical meta-regression dose-response model that accounts for various sources of variation and uncertainty and allows one to incorporate knowledge from prior studies into the current analysis, thus offering a more efficient and reliable inference. Second, in the case that parametric dose-response models do not fit the data, we developed a practical and flexible nonparametric regression method for meta-analysis of independently repeated dose-response experiments. Third, and lastly, we developed a method, based on Loewe additivity that allows one to quantitatively assess interaction between two agents combined at a fixed dose ratio. The proposed method makes a comprehensive and honest account of uncertainty within drug interaction assessment. Extensive simulation studies show that the novel methodology improves the screening process of effective/synergistic agents and reduces the incidence of type I error. We consider an ovarian cancer cell line study that investigates the combined effect of DNA methylation inhibitors and histone deacetylation inhibitors in human ovarian cancer cell lines. The hypothesis is that the combination of DNA methylation inhibitors and histone deacetylation inhibitors will enhance antiproliferative activity in human ovarian cancer cell lines compared to treatment with each inhibitor alone. By applying the proposed Bayesian methodology, in vitro synergy was declared for DNA methylation inhibitor, 5-AZA-2'-deoxycytidine combined with one histone deacetylation inhibitor, suberoylanilide hydroxamic acid or trichostatin A in the cell lines HEY and SKOV3. This suggests potential new epigenetic therapies in cell growth inhibition of ovarian cancer cells.