Generation of “Virtual” Control Groups for Single Arm Prostate Cancer Adjuvant Trials

Jia, Zhenyu; Lilly, Michael B.; Koziol, James A.; Chen, Xin; Xia, Xiao Qin; Wang, Yipeng; Skarecky, Douglas; Sutton, Manuel; Sawyers, Anne; Ruckle, Herbert; Carpenter, Philip M.; Wang‐Rodriguez, Jessica; Jiang, Jun; Deng, Mingsen; Pan, Cong; Zhu, Jian; McLaren, Christine E.; Gurley, Michael; Lee, Chung; McClelland, Michael; Ahlering, Thomas E.; Kattan, Michael W.; Mercola, Dan

doi:10.1371/journal.pone.0085010

Cited by 12 publications

(7 citation statements)

References 29 publications

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“…There are previous studies that have used different RWD sources to replicate the control arm from oncology RCTs. [28][29][30][31] Recently, Carrigan et al used electronic health record (EHR) data to derive control arms for 11 non-small cell lung cancer trials. 31 In 10 of the 11 RCTs, the OS HRs using the RWD comparator arm were similar to the HRs reported in the RCTs.…”

Section: Discussionmentioning

confidence: 99%

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Replication of Oncology Randomized Trial Results using Swedish Registry Real World‐Data: A Feasibility Study

Jemielita

Widman

Fox

et al. 2021

Clin Pharma and Therapeutics

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Nationwide healthcare registries are potential important real-world data (RWD) sources for assessing drug effectiveness in oncology. However, it is unclear whether registry-derived RWD are suitable for clinical development.In this study, we replicate results from the comparator arm of two previously published oncology randomized controlled trials (RCTs) using RWD from Swedish nationwide healthcare registries. For replication 1, the RCT included 553 patients and the RWD included 283 patients treated with sorafenib for advanced hepatocellular cancer. The median overall survival (OS) was 11.2 (95% confidence interval (CI): 10.1-13.2) months in the RCT and 8.2 (95% CI: 7.0-9.9) months in the RWD, unadjusted hazard ratio (HR) 0.75 (95% CI: 0.63-0.88). For time-to-treatment discontinuation (TTD), the HR was 1.00 (95% CI: 0.87-1.16). For replication 2, the RCT included 154 patients and the RWD included 704 patients treated with melphalan, prednisone, and thalidomide for untreated multiple myeloma. The median OS was 52.6 (95% CI: 40-not applicable) months in the RCT and 36.9 (95% CI: 33.8-40.5) months in the RWD, unadjusted HR 0.67 (95% CI: 0.51-0.87). For TTD, the HR was 0.89 (95% CI: 0.74-1.06). The results were similar when applying various propensity-based confounding adjustments. In conclusion, OS was shorter in the RWD, whereas TTD was similar. Importantly, the data necessary (ex: eligibility criteria and baseline confounders) for replicating RCTs was mostly not available and these results further underscore the importance of developing frameworks for capturing relevant patient-level RWD for clinical and regulatory decision making in oncology.The randomized controlled trial (RCT) is the standard study design for assessing drug efficacy. RCTs are, however, often slow and costly, and many RCTs are hampered by low external validity because of the highly selective patient population. There is growing interest in using real-world data (RWD) and observational study design to generate real-world evidence (RWE) for assessing drug effectiveness, for example, to use RWD/RWE to derive a synthetic control arm to benchmark a single-arm clinical

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Section: Discussionmentioning

confidence: 99%

“…There are previous studies that have used different RWD sources to replicate the control arm from oncology RCTs 28–31 . Recently, Carrigan et al .…”

Section: Discussionmentioning

confidence: 99%

Replication of Oncology Randomized Trial Results using Swedish Registry Real World‐Data: A Feasibility Study

Jemielita

Widman

Fox

et al. 2021

Clin Pharma and Therapeutics

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“…Other research efforts for establishing ECs have also shown early promise. [19][20][21][22] Jia et al 22 examined an application in prostate cancer using a modeling approach based on existing trial data with reasonable success. Other studies drawing on aggregate findings from the clinical trial literature have been performed in acute myeloid leukemia 20 and anaplastic lymphoma kinase-targeted Figure 4 Estimates of relative risk across different adjustment methods comparing external control analysis to trial result: randomized controlled trial NCT0236143.…”

Section: Discussionmentioning

confidence: 99%

Using Electronic Health Records to Derive Control Arms for Early Phase Single‐Arm Lung Cancer Trials: Proof‐of‐Concept in Randomized Controlled Trials

Carrigan¹,

Whipple²,

Capra³

et al. 2019

Clin Pharma and Therapeutics

116

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Oncology drug development increasingly relies on single-arm clinical trials. External controls (ECs) derived from electronic health record (EHR) databases may provide additional context. Patients from a US-based oncology EHR database were aligned with patients from randomized controlled trials (RCTs) and trial-specific eligibility criteria were applied to the EHR dataset. Overall survival (OS) in the EC-derived control arm was compared with OS in the RCT experimental arm. The primary outcome was OS, defined as time from randomization or treatment initiation (EHR) to death. Cox regression models were used to obtain effect estimates using EHR data. EC-derived hazard ratio estimates aligned closely with those from the corresponding RCT with one exception. Comparing log HRs among all RCT and EC results gave a Pearson correlation coefficient of 0.86. Properly selected control arms from contemporaneous EHR data could be used to put single-arm trials of OS in advanced non-small cell lung cancer into context.The need to bring safe and effective oncology treatment options to patients quickly is highlighted by the 21st Century Cures Act 1-3 and the Cancer Moonshot initiative. 4 Oncology research in recent years has included novel trial designs sometimes with only a single treatment arm. Precision oncology drugs increasingly receive accelerated or breakthrough regulatory approval based on these single-arm trials. An inherent feature of these designs is that a standard-of-care control arm is not included, leading to challenges in interpretation of efficacy. 3,[5][6][7][8][9]

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“…Jia et al [ 46 ] studied the effectiveness of adjuvant therapy for prostate cancer. Statistical models were generated to predict time of progression free survival with non adjuvant-treated patients.…”

Section: Main Textmentioning

confidence: 99%

Virtual controls as an alternative to randomized controlled trials for assessing efficacy of interventions

Strayhorn¹

2021

BMC Med Res Methodol

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Randomized controlled trials are ubiquitously spoken of as the “gold standard” for testing interventions and establishing causal relations. This article presents evidence for two premises. First: there are often major problems with randomized designs; it is by no means true that the only good design is a randomized design. Second: the method of virtual controls in some circumstances can and should replace randomized designs.Randomized trials can present problems with external validity or generalizability; they can be unethical; they typically involve much time, effort, and expense; their assignments to treatment conditions often can be maintained only for limited time periods; examination of their track record reveals problems with reproducibility on the one hand, and lack of overwhelming superiority to observational methods on the other hand.The method of virtual controls involves ongoing efforts to refine statistical models for prediction of outcomes from measurable variables, under conditions of no treatment or current standard of care. Research participants then join a single-arm study of a new intervention. Each participant’s data, together with the formulas previously generated, predict that participant’s outcome without the new intervention. These outcomes are the “virtual controls.” The actual outcomes with intervention are compared with the virtual control outcomes to estimate effect sizes. Part of the research product is the prediction equations themselves, so that in clinical practice, individual treatment decisions may be aided by quantitative answers to the questions, “What is estimated to happen to this particular patient with and without this treatment?”The method of virtual controls is especially indicated when rapid results are of high priority, when withholding intervention is likely harmful, when adequate data exist for prediction of untreated or standard of care outcomes, when we want to let people choose the treatment they prefer, when tailoring treatment decisions to individuals is desirable, and when real-world clinical information can be harnessed for analysis.

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Generation of “Virtual” Control Groups for Single Arm Prostate Cancer Adjuvant Trials

Cited by 12 publications

References 29 publications

Replication of Oncology Randomized Trial Results using Swedish Registry Real World‐Data: A Feasibility Study

Replication of Oncology Randomized Trial Results using Swedish Registry Real World‐Data: A Feasibility Study

Using Electronic Health Records to Derive Control Arms for Early Phase Single‐Arm Lung Cancer Trials: Proof‐of‐Concept in Randomized Controlled Trials

Virtual controls as an alternative to randomized controlled trials for assessing efficacy of interventions

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