Minority Recruitment Trends in Phase III Prostate Cancer Clinical Trials (2003 to 2014): Progress and Critical Areas for Improvement

Balakrishnan, Ashwin; Palmer, Nynikka R.; Fergus, Kirkpatrick B.; Gaither, Thomas W.; Baradaran, Nima; Breyer, Benjamin N.

doi:10.1016/j.juro.2018.09.029

Cited by 33 publications

(34 citation statements)

References 3 publications

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“…This finding suggests that the RCT population is not representative of the average patient seeking care at a comprehensive cancer center, which has been noted in other RCTs of patients with prostate cancer. 8 However, because potential biases in the collection of race information could exist, both in the original RCT data and in our EHR data, robust examination of bias and algorithmic fairness is warranted when either type of data is used for predictive models. 27 By extending the results of RCT models to EHRs, one can optimize the inclusion of diverse populations who are eligible for an intervention but may not meet the strict inclusion criteria of an RCT.…”

Section: Discussionmentioning

confidence: 99%

Assessment of a Clinical Trial–Derived Survival Model in Patients With Metastatic Castration-Resistant Prostate Cancer

et al. 2021

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IMPORTANCE Randomized clinical trials (RCTs) are considered the criterion standard for clinical evidence. Despite their many benefits, RCTs have limitations, such as costliness, that may reduce the generalizability of their findings among diverse populations and routine care settings. OBJECTIVE To assess the performance of an RCT-derived prognostic model that predicts survival among patients with metastatic castration-resistant prostate cancer (CRPC) when the model is applied to real-world data from electronic health records (EHRs). DESIGN, SETTING, AND PARTICIPANTS The RCT-trained model and patient data from the RCTs were obtained from the Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge for prostate cancer, which occurred from March 16 to July 27, 2015. This challenge included 4 phase 3 clinical trials of patients with metastatic CRPC. Real-world data were obtained from the EHRs of a tertiary care academic medical center that includes a comprehensive cancer center. In this study, the DREAM challenge RCT-trained model was applied to real-world data from January 1, 2008, to December 31, 2019; the model was then retrained using EHR data with optimized feature selection. Patients with metastatic CRPC were divided into RCT and EHR cohorts based on data source. Data were analyzed from March 23, 2018, to October 22, 2020. EXPOSURES Patients who received treatment for metastatic CRPC. MAIN OUTCOMES AND MEASURES The primary outcome was the performance of an RCT-derived prognostic model that predicts survival among patients with metastatic CRPC when the model is applied to real-world data. Model performance was compared using 10-fold cross-validation according to time-dependent integrated area under the curve (iAUC) statistics. RESULTS Among 2113 participants with metastatic CRPC, 1600 participants were included in the RCT cohort, and 513 participants were included in the EHR cohort. The RCT cohort comprised a larger proportion of White participants (1390 patients [86.9%] vs 337 patients [65.7%]) and a smaller proportion of Hispanic participants (14 patients [0.9%] vs 42 patients [8.2%]), Asian participants (41 patients [2.6%] vs 88 patients [17.2%]), and participants older than 75 years (388 patients [24.3%] vs 191 patients [37.2%]) compared with the EHR cohort. Participants in the RCT cohort also had fewer comorbidities (mean [SD], 1.6 [1.8] comorbidities vs 2.5 [2.6] comorbidities, respectively) compared with those in the EHR cohort. Of the 101 variables used in the RCT-derived model, 10 were not available in the EHR data set, 3 of which were among the top 10 features in the DREAM challenge RCT model. The best-performing EHR-trained model included only 25 of the 101 variables included in the RCT-trained model. The performance of the RCT-trained and EHR-trained models was adequate in the EHR cohort (mean [SD] iAUC, 0.722 [0.118] and 0.762 [0.106], respectively); model optimization was associated with improved performance of the best-performing EHR model (mean [SD] iAUC, (continued) Key Points ...

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Section: Discussionmentioning

confidence: 99%

Assessment of a Clinical Trial–Derived Survival Model in Patients With Metastatic Castration-Resistant Prostate Cancer

et al. 2021

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“…It returns an output of dose data that includes drug name, frequency, intake time, and either strength and dose amount or dose given intake. 1…”

Section: Case Studymentioning

confidence: 99%

“…Population PK/PD studies in the postmarketing phase have the potential to capture patient characteristics affecting PK or PD in patients treated in real-world settings, where patients are more heterogeneous and diverse than participants in phase I-III trials who must meet strict inclusion/exclusion criteria. 1,2 Population PK/PD analyses require longitudinally measured information on dose, outcomes, and potential covariates in a large number of patients. Population PK/PD modeling approaches can fit sparse data by taking a mechanistic modeling approach and borrowing information across a large number of subjects.…”

mentioning

confidence: 99%

Development of a System for Postmarketing Population Pharmacokinetic and Pharmacodynamic Studies Using Real‐World Data From Electronic Health Records

Choi

Beck

McNeer

et al. 2020

Clin Pharma and Therapeutics

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Postmarketing population pharmacokinetic (PK) and pharmacodynamic (PD) studies can be useful to capture patient characteristics affecting PK or PD in real‐world settings. These studies require longitudinally measured dose, outcomes, and covariates in large numbers of patients; however, prospective data collection is cost‐prohibitive. Electronic health records (EHRs) can be an excellent source for such data, but there are challenges, including accurate ascertainment of drug dose. We developed a standardized system to prepare datasets from EHRs for population PK/PD studies. Our system handles a variety of tasks involving data extraction from clinical text using a natural language processing algorithm, data processing, and data building. Applying this system, we performed a fentanyl population PK analysis, resulting in comparable parameter estimates to a prior study. This new system makes the EHR data extraction and preparation process more efficient and accurate and provides a powerful tool to facilitate postmarketing population PK/PD studies using information available in EHRs.

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“…In prostate cancer, clinical trial disparities based on race/ethnicity are pronounced [17, 18]. Black men are known to have an increased risk of developing and dying from prostate cancer compared to White men [19].…”

Section: Introductionmentioning

confidence: 99%

A step towards equitable clinical trial recruitment: a protocol for the development and preliminary testing of an online prostate cancer health information and clinical trial matching tool

Borno

Bakke

Kaplan

et al. 2019

Pilot Feasibility Stud

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BackgroundRecruitment of a diverse participant pool to cancer clinical trials is an essential component of clinical research as it improves the generalizability of findings. Investigating and piloting novel recruitment strategies that take advantage of ubiquitous digital technologies has become an important component of facilitating broad recruitment and addressing inequities in clinical trial participation. Equitable and inclusive recruitment improves generalizability of clinical trial outcomes, benefiting patients, clinicians, and the research community. The increasing prevalence of online connectivity in the USA and use of the Internet as a resource for medical information provides an opportunity for digital recruitment strategies in cancer clinical trials. This study aims to measure the acceptability, preliminary estimates of efficacy, and feasibility of the Trial Library intervention, an Internet-based cancer clinical trial matching tool. This study will also examine the extent to which the Trial Library website, designed to address the linguistic and literacy needs of broader patient populations, influences patient-initiated conversations with physicians about clinical trial participation.MethodsThis is a study protocol for a non-randomized, single-arm pilot study. This is a mixed methods study design that utilizes the statistical analysis of quantitative survey data and the qualitative analysis of interview data to assess the participant experience with the Trial Library intervention. This study will examine (1) acceptability as a measure of participant satisfaction with this intervention, (2) preliminary measure of efficacy as a measure of proportion of participants with documented clinical trial discussion in the electronic medical record, and (3) feasibility of the intervention as a measure of duration of clinical visit.DiscussionThe principles that informed the design of the Trial Library intervention aim to be generalizable to clinical trials across many disease contexts. From the ground up, this intervention is built to be inclusive of the linguistic, literacy, and technological needs of underrepresented patient populations. This study will collect essential preliminary data prior to a multi-site randomized clinical trial of the Trial Library intervention.Trial registrationThis study has received institutional approval from the Committee of Human Subjects Research at the University of California, San Francisco.

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Minority Recruitment Trends in Phase III Prostate Cancer Clinical Trials (2003 to 2014): Progress and Critical Areas for Improvement

Cited by 33 publications

References 3 publications

Assessment of a Clinical Trial–Derived Survival Model in Patients With Metastatic Castration-Resistant Prostate Cancer

Assessment of a Clinical Trial–Derived Survival Model in Patients With Metastatic Castration-Resistant Prostate Cancer

Development of a System for Postmarketing Population Pharmacokinetic and Pharmacodynamic Studies Using Real‐World Data From Electronic Health Records

A step towards equitable clinical trial recruitment: a protocol for the development and preliminary testing of an online prostate cancer health information and clinical trial matching tool

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