{"content":[{"text":"Received: Added at production \n DOI: xxx/xxxx","bbox":{"left":0.075313,"top":0.028175,"width":0.14997899999999997,"height":0.03566300000000001}},{"text":"Revised:","bbox":{"left":0.249558,"top":0.028175,"width":0.040617000000000014,"height":0.017596}},{"text":"Added at production","bbox":{"left":0.297708,"top":0.028175,"width":0.09604200000000002,"height":0.017596}},{"text":"Accepted:","bbox":{"left":0.418016,"top":0.028175,"width":0.04736499999999999,"height":0.017596}},{"text":"Added at production","bbox":{"left":0.472914,"top":0.028175,"width":0.0960419999999999,"height":0.017596}},{"text":"ARTICLE TYPE \n A Bayesian adaptive design for dual-agent phase I-II cancer \n clinical trials combining eﬃcacy data across stages \n José L. Jiménez*1 | Haiyan Zheng2","bbox":{"left":0.075313,"top":0.086597,"width":0.7665360000000001,"height":0.129612}},{"text":"2021 \n Jun \n 15 \n [stat.ME] \n arXiv:2106.08277v1","bbox":{"left":0.018378,"top":0.256124,"width":0.044820000000000006,"height":0.46012}},{"text":"1Clinical Development and Analytics, \n Novartis Pharma A.G., Basel, Switzerland \n 2MRC Biostatistics Unit, University of \n Cambridge, Cambridge, UK \n Correspondence \n *José L. Jiménez, Novartis Pharma A.G., \n Basel, Switzerland \n Email: jose_luis.jimenez@novartis.com","bbox":{"left":0.075313,"top":0.238338,"width":0.232385,"height":0.12835000000000002}},{"text":"Summary \n Integrated phase I-II clinical trial designs are eﬃcient approaches to accelerate drug \n development. In cases where eﬃcacy cannot be ascertained in a short period of \n time, two-stage approaches are usually employed. When diﬀerent patient popula- \n tions are involved across stages, it is worth of discussion about the use of eﬃcacy \n data collected from both stages. In this paper, we focus on a two-stage design that \n aims to estimate safe dose combinations with a certain level of eﬃcacy. In stage I, \n conditional escalation with overdose control (EWOC) is used to allocate successive \n cohorts of patients. The maximum tolerated dose (MTD) curve is estimated based \n on a Bayesian dose-toxicity model. In stage II, we consider an adaptive allocation \n of patients to drug combinations that have a high probability of being eﬃcacious \n along the obtained MTD curve. A robust Bayesian hierarchical model is proposed \n to allow sharing of information on the eﬃcacy parameters across stages assum- \n ing the related parameters are either exchangeable or nonexchangeable. Under the \n assumption of exchangeability, a random-eﬀects distribution is speciﬁed for the main \n eﬀects parameters to capture uncertainty about the between-stage diﬀerences. The \n proposed methodology is assessed with extensive simulations motivated by a real \n phase I-II drug combination trial using continuous doses. \n KEYWORDS: \n Adaptive randomization, Bayesian random-eﬀects model, Continuous dose combinations, EWOC, Phase \n I-II","bbox":{"left":0.352297,"top":0.252428,"width":0.558988,"height":0.40785899999999997}},{"text":"1 INTRODUCTION \n The primary objective of early phase clinical trials is to identify a dose that is safe and eﬃcacious. In single-agent settings \n where eﬃcacy could be observed relatively soon after administering the dose (e.g., after one or two cycles of therapy), one- \n stage designs where the joint probability of toxicity and eﬃcacy is sequentially updated after each cohort of patients are usually \n employed with either single agents1,2,3,4,5,6,7,8 or drug combinations9,10. \n When eﬃcacy cannot be ascertained in a short period of time, it is not uncommon to employ two-stage designs where a set \n of maximum tolerated dose combinations are ﬁrst selected using safety data only, and then tested for eﬃcacy in a second stage. \n In this type of trials, we may ﬁnd diﬀerent patient populations across stages11,12,13. For drug combination trials, a number of \n methodologies for two-stage designs have been proposed for binary eﬃcacy endpoints14,15,16,17 and for time to event eﬃcacy \n endpoints18. From our point of view, current two-stage designs have two key limitations:","bbox":{"left":0.075313,"top":0.712093,"width":0.849376,"height":0.18764599999999998}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}