B Cabarrou1, J M Boher2, E Bogart3, E Tresch-Bruneel3, N Penel4, A Ravaud5, B Escudier6, C Mahier Ait-Oukhatar7, J P Delord8, H Roché8, T Filleron9. 1. Department of Biostatistics, Institut Claudius Regaud, IUCT-O Toulouse, Toulouse. 2. Department of Biostatistics, Institut Paoli Calmette, Marseille. 3. Department of Biostatistics. 4. Department of Medical Oncology, Centre Oscar Lambret, Lille. 5. Department of Medical Oncology, Bordeaux University Hospital, Bordeaux. 6. Department of Medical Oncology, Gustave Roussy, Villejuif. 7. Research and Development Department, Unicancer, Paris. 8. Department of Medical Oncology, Institut Claudius Regaud, IUCT-O Toulouse, Toulouse, France. 9. Department of Biostatistics, Institut Claudius Regaud, IUCT-O Toulouse, Toulouse filleron.thomas@iuct-oncopole.fr.
Abstract
BACKGROUND: In the era of personalized medicine, molecularly targeted therapies (MTT) have modified the outcome of some cancer types. The price of tumor control needs to be balanced with toxicity since these new therapies are administered continuously for several months or sometimes for several years. For cytotoxic drugs, the incidence of adverse event (AE) was traditionally reported as frequency and intensity. This simple measure is not sufficient to capture the recurrent nature and duration of AE. This paper presents two methods to better describe the toxicity burden across the time: prevalence and Q-TWiST. PATIENTS AND METHODS: Limitation of worst-grade method and advantages of prevalence and Q-TWiST in the analysis of toxicity were illustrated using data from a phase II trial and a hypothetically simulated clinical trial. RESULTS: Prevalence integrates the recurrent nature of AE. Using prevalence, it is possible to obtain a time profile of AE. Q-TWiST method evaluates the weighted time spent in each health state and also considers the recurrent nature of side-effects in order to assess the 'risk-benefit' ratio of a treatment. When interpreting Q-TWiST results, it is necessary to take into account overall survival and progression-free survival and to define a clinically relevant difference according to the setting. CONCLUSION: The two methods presented here capture different effects. They are helpful for physicians in their treatment choice (balance benefit risk), to counsel patients and to optimize supportive care. In order to ensure consistency and provide critical information required for medical decision-making, it is important to encourage the use of alternative statistical methods in the analysis of toxicities associated with MTT. CLINICAL TRIAL: NCT00541008.
BACKGROUND: In the era of personalized medicine, molecularly targeted therapies (MTT) have modified the outcome of some cancer types. The price of tumor control needs to be balanced with toxicity since these new therapies are administered continuously for several months or sometimes for several years. For cytotoxic drugs, the incidence of adverse event (AE) was traditionally reported as frequency and intensity. This simple measure is not sufficient to capture the recurrent nature and duration of AE. This paper presents two methods to better describe the toxicity burden across the time: prevalence and Q-TWiST. PATIENTS AND METHODS: Limitation of worst-grade method and advantages of prevalence and Q-TWiST in the analysis of toxicity were illustrated using data from a phase II trial and a hypothetically simulated clinical trial. RESULTS: Prevalence integrates the recurrent nature of AE. Using prevalence, it is possible to obtain a time profile of AE. Q-TWiST method evaluates the weighted time spent in each health state and also considers the recurrent nature of side-effects in order to assess the 'risk-benefit' ratio of a treatment. When interpreting Q-TWiST results, it is necessary to take into account overall survival and progression-free survival and to define a clinically relevant difference according to the setting. CONCLUSION: The two methods presented here capture different effects. They are helpful for physicians in their treatment choice (balance benefit risk), to counsel patients and to optimize supportive care. In order to ensure consistency and provide critical information required for medical decision-making, it is important to encourage the use of alternative statistical methods in the analysis of toxicities associated with MTT. CLINICAL TRIAL: NCT00541008.
Authors: Gita Thanarajasingam; John P Leonard; Thomas E Witzig; Thomas M Habermann; Kristie A Blum; Nancy L Bartlett; Christopher R Flowers; Brandelyn N Pitcher; Sin-Ho Jung; Pamela J Atherton; Angelina Tan; Paul J Novotny; Amylou C Dueck Journal: Lancet Haematol Date: 2020-06 Impact factor: 18.959
Authors: Amit M Oza; Domenica Lorusso; Carol Aghajanian; Ana Oaknin; Andrew Dean; Nicoletta Colombo; Johanne I Weberpals; Andrew R Clamp; Giovanni Scambia; Alexandra Leary; Robert W Holloway; Margarita Amenedo Gancedo; Peter C Fong; Jeffrey C Goh; David M O'Malley; Deborah K Armstrong; Susana Banerjee; Jesus García-Donas; Elizabeth M Swisher; David Cella; Juliette Meunier; Sandra Goble; Terri Cameron; Lara Maloney; Ann-Christin Mörk; Josh Bedel; Jonathan A Ledermann; Robert L Coleman Journal: J Clin Oncol Date: 2020-08-24 Impact factor: 44.544