Iruku Praveena, Goros Martin, Gelfond Jonathan, Chang Jenny, Padalecki Susan, Mesa Ruben, Kaklamani Virginia G
Department of Hematology/Oncology, University of Colorado Health, Colorado Springs, CO, USA.
Department of Epidemiology and Biostatistics, University of Texas Health Science Center San Antonio, San Antonio, TX, USA.
Contemp Clin Trials Commun. 2019 Jul 19;15:100421. doi: 10.1016/j.conctc.2019.100421. eCollection 2019 Sep.
As cancer center funds are allocated toward several resources, clinical trial offices and the clinical trial infrastructure is constantly scrutinized. It has been shown that 20% of clinical trials fail to achieve their accrual goal and in an institutional level several trials are open with poor accrual. We sought to identify factors that are associated with clinical trial accrual and develop a model to predict clinical trial accrual.
We identified all clinical trials from 1999 to 2015 at UT Health Cancer Center San Antonio. We included observational as well as interventional clinical trials. We collected several variables such as type of study, type of malignancy, trial phase, PI of study.
In total we included 297 clinical trials. We identified several factors to be associated with clinical trial accrual (Sponsor type, trial phase, disease category, type of trial, disease state and whether the trial involved a new investigational agent). We developed a predictive model with an AUC of 0.65 that showed that observational, interventional, industry-sponsored trials and trials authored by the local PI were more likely to achieve their accrual goal.
We were able to identify several factors that were significantly associated with clinical trial accrual. Based on these factors we developed a prediction model for clinical trial accrual. We believe that use of this model can help improve our cancer centers clinical trial portfolio and help in fund allocation.
由于癌症中心的资金被分配用于多种资源，临床试验办公室和临床试验基础设施一直受到严格审查。研究表明，20%的临床试验未能实现其入组目标，在机构层面，有多项试验入组情况不佳。我们试图确定与临床试验入组相关的因素，并建立一个预测临床试验入组的模型。
我们确定了1999年至2015年在圣安东尼奥德克萨斯大学健康科学中心癌症中心开展的所有临床试验。我们纳入了观察性和干预性临床试验。我们收集了多个变量，如研究类型、恶性肿瘤类型、试验阶段、研究的主要研究者。
我们总共纳入了297项临床试验。我们确定了几个与临床试验入组相关的因素（申办方类型、试验阶段、疾病类别、试验类型、疾病状态以及试验是否涉及新的研究药物）。我们开发了一个预测模型，其曲线下面积为0.65，表明观察性、干预性、行业申办的试验以及由当地主要研究者撰写的试验更有可能实现其入组目标。
我们能够确定几个与临床试验入组显著相关的因素。基于这些因素，我们开发了一个临床试验入组预测模型。我们相信使用这个模型有助于改善我们癌症中心的临床试验组合，并有助于资金分配。
