Adaptive Clinical Trial Designs with Surrogates: When Should We Bother?

2021 ◽  
Author(s):  
Arielle Anderer ◽  
Hamsa Bastani ◽  
John Silberholz
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Primary Endpoint ◽  
Error Rates ◽  
Comparative Statics ◽  
Type I ◽  
Surrogate Outcome ◽  
True Outcome ◽  
Combining Data ◽  
Clinical Trial Designs

The success of a new drug is assessed within a clinical trial using a primary endpoint, which is typically the true outcome of interest—for example, overall survival. However, regulators sometimes approve drugs using a surrogate outcome—an intermediate indicator that is faster or easier to measure than the true outcome of interest—for example, progression-free survival—as the primary endpoint when there is demonstrable medical need. Although using a surrogate outcome (instead of the true outcome) as the primary endpoint can substantially speed up clinical trials and lower costs, it can also result in poor drug-approval decisions because the surrogate is not a perfect predictor of the true outcome. In this paper, we propose combining data from both surrogate and true outcomes to improve decision making within a late-phase clinical trial. In contrast to broadly used clinical trial designs that rely on a single primary endpoint, we propose a Bayesian adaptive clinical trial design that simultaneously leverages both observed outcomes to inform trial decisions. We perform comparative statics on the relative benefit of our approach, illustrating the types of diseases and surrogates for which our proposed design is particularly advantageous. Finally, we illustrate our proposed design on metastatic breast cancer. We use a large-scale clinical trial database to construct a Bayesian prior and simulate our design on a subset of clinical trials. We estimate that our design would yield a 16% decrease in trial costs relative to existing clinical trial designs, while maintaining the same Type I/II error rates. This paper was accepted by J. George Shanthikumar for the Special Issue on Data-Driven Prescriptive Analytics.

Current Advances in Clinical Trials for Rare Disease Populations: Spotlight on the Patient

2021 ◽  
Vol 16 ◽  
Author(s):  
Erica Winter ◽  
Scott Schliebner
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Drug Development ◽  
Rare Disease ◽  
Rare Diseases ◽  
Statistical Analyses ◽  
Clinical Trial Designs ◽  
Novel Approaches

: Characterized by small, highly heterogeneous patient populations, rare disease trials magnify the challenges often encountered in traditional clinical trials. In recent years, there have been increased efforts by stakeholders to improve drug development in rare diseases through novel approaches to clinical trial designs and statistical analyses. We highlight and discuss some of the current and emerging approaches aimed at overcoming challenges in rare disease clinical trials, with a focus on the ultimate stakeholder, the patient.

Phase II study of anti-EGFR rechallenge therapy with panitumumab driven by circulating tumor DNA molecular selection in metastatic colorectal cancer: The CHRONOS trial.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 3506-3506
Author(s):  
Andrea Sartore-Bianchi ◽  
Filippo Pietrantonio ◽  
Sara Lonardi ◽  
Benedetta Mussolin ◽  
Francesco Rua ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Clinical Trial ◽  
Metastatic Colorectal Cancer ◽  
Phase Ii ◽  
Primary Endpoint ◽  
Liquid Biopsy ◽  
Objective Response ◽  
Circulating Tumor Dna ◽  
Type I ◽  
Tumor Dna

3506 Background: Despite advances in molecular segmentation of metastatic colorectal cancer (mCRC), beyond RAS status therapeutic actionability remains confined to the limited subgroups of ERBB2 amplified, BRAF mutated and MSI-H patients. Optimization of available treatments is therefore warranted. Rechallenge with anti-EGFR monoclonal antibodies is often empirically used with some benefit as late-line therapy. We previously found that mutant RAS and EGFR ectodomain clones, which emerge in blood during EGFR blockade, decline upon antibody withdrawal leading to regain drug sensitivity. Based on this rationale, we designed CHRONOS, a multicenter phase II trial of anti-EGFR therapy rechallenge guided by monitoring of the mutational status of RAS, BRAF and EGFR in circulating tumor DNA (ctDNA). To our knowledge, this is the first interventional clinical trial of liquid biopsy for driving anti-EGFR rechallenge therapy in mCRC. Methods: Eligible patients were PS ECOG 0-2 RAS/BRAF WT mCRC having first achieved an objective response and then progression in any treatment line with an anti-EGFR antibody containing regimen, displaying RAS, BRAF and EGFR ectodomain WT status in ctDNA at molecular screening after progression to the last anti-EGFR-free regimen. Clonal evolution in ctDNA was analyzed by ddPCR and next generation sequencing. Panitumumab 6 mg/kg was administered IV every two weeks until progression. The primary endpoint was objective response rate (ORR) by RECIST version 1.1 with independent central review. 27 total patients and 6 responses were required to declare the study positive (power = 85%, type I error = 0.05). Results: Between Aug 19, 2019 and Nov 6, 2020 52 patients were screened by liquid biopsy and 36 (69%) were negative in ctDNA for RAS/BRAF/EGFR mutations. Of these, 27 patients were enrolled in 4 centers. Median age was 64 years (range: 42-80). PS ECOG was 0/50%, 1/46%, 2/4%. Previous anti-EGFR was administered in 1st line in 63%, 2nd in 15% and > 2nd in 22%. Median number of previous treatments was 3. The primary endpoint was met, with 8/27 partial responses (PR) observed (2 unconfirmed) (ORR = 30%, 95% CI: 12-47%). Stable disease (SD) was obtained in 11/27 (40%, 95% CI: 24-59%), lasting > 4 months in 8/11. Disease control rate (PR plus SD > 4 months) was therefore obtained in 16/27 (59%, 95% CI: 41-78%). Median progression-free survival was 16 weeks. Median duration of response was 17 weeks (1 ongoing). Maximal grade toxicity was G3, limited to dermatological and occurring in 19% of patients. ctDNA dynamics were studied in all patients. Conclusions: Liquid biopsy-driven rechallenge with anti-EGFR antibodies leads to further objective responses in one third of patients. Genotyping tumor DNA in the blood to direct therapy can be effectively incorporated in the management of advanced CRCs. Clinical trial information: 2016-002597-12.

Clinical Trial Design and Statistics

2018 ◽  
Author(s):  
Julie Ann Sosa
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Analysis ◽  
Trial Design ◽  
New Technologies ◽  
Drug Application ◽  
Clinical Trial Design ◽  
Double Blind Study ◽  
Type I ◽  
End Points

A clinical trial is a planned experiment designed to prospectively measure the efficacy or effectiveness of an intervention by comparing outcomes in a group of subjects treated with the test intervention with those observed in one or more comparable group(s) of subjects receiving another intervention.  Historically, the gold standard for a clinical trial has been a prospective, randomized, double-blind study, but it is sometimes impractical or unethical to conduct such in clinical medicine and surgery. Conventional outcomes have traditionally been clinical end points; with the rise of new technologies, however, they are increasingly being supplemented and/or replaced by surrogate end points, such as serum biomarkers. Because patients are involved, safety considerations and ethical principles must be incorporated into all phases of clinical trial design, conduct, data analysis, and presentation. This review covers the history of clinical trials, clinical trial phases, ethical issues, implementing the study, basic biostatistics for data analysis, and other resources. Figures show drug development and clinical trial process, and type I and II error. Tables list Food and Drug Administration new drug application types, and types of missing data in clinical trials. This review contains 2 highly rendered figures, 2 tables, and 38 references

scholarly journals Improved Small Sample Inference Methods for a Mixed-Effects Model for Repeated Measures Approach in Incomplete Longitudinal Data Analysis

Stats ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 174-188
Author(s):  
Yoshifumi Ukyo ◽  
Hisashi Noma ◽  
Kazushi Maruo ◽  
Masahiko Gosho
Keyword(s):  
Clinical Trial ◽  
Repeated Measures ◽  
Type I Error ◽  
Mixed Effects ◽  
Error Rates ◽  
Small Sample ◽  
Mixed Effects Model ◽  
Model Complexity ◽  
Type I ◽  
Inference Methods

The mixed-effects model for repeated measures (MMRM) approach has been widely applied for longitudinal clinical trials. Many of the standard inference methods of MMRM could possibly lead to the inflation of type I error rates for the tests of treatment effect, when the longitudinal dataset is small and involves missing measurements. We propose two improved inference methods for the MMRM analyses, (1) the Bartlett correction with the adjustment term approximated by bootstrap, and (2) the Monte Carlo test using an estimated null distribution by bootstrap. These methods can be implemented regardless of model complexity and missing patterns via a unified computational framework. Through simulation studies, the proposed methods maintain the type I error rate properly, even for small and incomplete longitudinal clinical trial settings. Applications to a postnatal depression clinical trial are also presented.

Predictive value of phase II clinical trials in pancreatic cancer: Rethinking the road to progress.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 4036-4036 ◽  
Author(s):  
Daniel M. Halperin ◽  
J. Jack Lee ◽  
James C. Yao
Keyword(s):  
Clinical Trials ◽  
Phase Ii ◽  
Predictive Value ◽  
Error Rates ◽  
Phase Iii ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Phase Ii Trials ◽  
Fda Approval

4036 Background: Few new therapies for pancreatic adenocarcinoma (PC) have been approved by the Food and Drug Administration (FDA) or recommended by the National Comprehensive Cancer Network (NCCN), reflecting frequent failures in phase III trials. We hypothesize that the high failure rate in large trials is due to a low predictive value for “positive” phase II studies. Methods: Given a median time from initiation of clinical trials to FDA approval of 6.3 years, we conducted a systematic search of the clinicaltrials.gov database for phase II interventional trials of antineoplastic therapy in PC initiated from 1999-2004. We reviewed drug labels and NCCN guidelines for FDA approval and guideline recommendations. Results: We identified 70 phase II trials that met our inclusion criteria. Forty-five evaluated compounds without preexisting FDA approval, 23 evaluated drugs approved in other diseases, and 2 evaluated cellular therapies. With a median follow-up of 12.5 years, none of these drugs gained FDA approval in PC. Four trials, all combining chemotherapy with radiation, eventually resulted in NCCN recommendations. Forty-two of the trials have been published. Of 16 studies providing pre-specified type I error rates, these rates were ≥0.1 in 8 studies, 0.05 in 6 studies and <0.025 in 2 studies. Of 21 studies specifying type II error rates, 7 used >0.1, 10 used 0.1, and 4 used <0.1. Published studies reported a median enrollment of 47 subjects. Fourteen trials reported utilizing a randomized design. Conclusions: The low rate of phase II trials resulting in eventual regulatory approval of therapies for PC reflects the challenge of conquering a tough disease as well as deficiencies in the statistical designs. New strategies are necessary to quantify and improve odds of success in drug development. Statistical parameters of individual or coupled phase II trials should be tailored to achieve the desired predictive value prior to initiating pivotal phase III studies. Positive predictive value of a phase II study assuming a 1%, 2%, or 5% prior probability of success and 10% type II error rate. [Table: see text]

Phase II clinical trial of intravesical bacillus Calmette-Guerin (BCG) followed by sunitinib for the treatment of high-risk nonmuscle-invasive bladder cancer (NMIBC).

2015 ◽  
Vol 33 (7_suppl) ◽  
pp. 293-293 ◽  
Author(s):  
Alexander M. Helfand ◽  
Cheryl T. Lee ◽  
Khaled Hafez ◽  
Maha Hussain ◽  
Monica Liebert ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Phase Ii ◽  
Primary Endpoint ◽  
Type I Error ◽  
Combined Treatment ◽  
Progression Free Survival ◽  
Complete Response ◽  
Type I ◽  
Incomplete Response ◽  
Hand Foot Syndrome

293 Background: We conducted a phase II trial to evaluate combination therapy with intravesical BCG + sunitinib for prevention of recurrence and progression of NMIBC. Methods: Patients with high-grade clinical ≤ T1N0M0 NMIBC without BCG in the past year were eligible and received induction BCG followed 2 weeks later by 28 days of sunitinib (50mg). The primary endpoint was 3 month complete response (CR) by biopsy and cytology. Patients with incomplete response were eligible for a second cycle of BCG + sunitinib. Secondary endpoints included 2-year recurrence and progression-free survival (RFS, PFS). Toxicity was graded according to the NCI CTCAE v.3.0. The Simon Minimax 2-stage study had 80% power with a 5% type I error assuming a 3m CR of 75% with sunitinib + BCG compared to 55% with BCG alone. If ≥ 25/36 evaluable patients achieved a 3m CR, then the treatment would be considered for further study. Binomial proportions, confidence intervals and Kaplan-Meier estimates are reported. Results: Of 36 evaluable patients, median age was 65.9 years (IQR 59-72). Initial stage was T1 (19), Ta (9), and CIS (8). Thirty-six percent completed sunitinib without interruption. Treatment was delayed (median 12 days (IQR 9-16)) and dose was reduced to 37.5 mg in 13 patients. One patient had reduction to 25mg with re-escalation to 37.5mg. One patient completed a 2nd cycle of BCG + sunitinib for incomplete response. BCG maintenance therapy was given to 21 patients. Of 133 adverse events in 34/36 patients, 6 (4.5%) in 5 patients were ≥ grade 3: thrombocytopenia, diarrhea (2), shingles, extremity rash/pain and hand + foot syndrome. CR at 3m included 26/36 (72%, 95% CI[55,86]) reaching the primary endpoint. The patient who completed a 2nd cycle of BCG induction and sunitinib had CR at 6 months. 2y RFS (patients with intact bladder) was 77% (95% CI[58,88]) and 2y PFS was 100%. Conclusions: The primary endpoint of the study of 25 3m CR has been reached. Combined treatment with BCG + sunitinib is associated with low rates of recurrence and progression. Adverse effects were common and frequent but few were serious. BCG + sunitinib may produce outcomes superior to BCG alone. (Study supported by Pfizer, Inc) Clinical trial information: NCT00794950.

Use of interval estimations in design and evaluation of multiregional clinical trials with continuous outcomes

2018 ◽  
Vol 28 (7) ◽  
pp. 2179-2195 ◽  
Author(s):  
Chieh Chiang ◽  
Chin-Fu Hsiao
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Type I Error ◽  
Interval Estimation ◽  
Error Rates ◽  
New Drugs ◽  
Sample Size Determination ◽  
Type I ◽  
Size Determination ◽  
Interval Estimators

Multiregional clinical trials have been accepted in recent years as a useful means of accelerating the development of new drugs and abridging their approval time. The statistical properties of multiregional clinical trials are being widely discussed. In practice, variance of a continuous response may be different from region to region, but it leads to the assessment of the efficacy response falling into a Behrens–Fisher problem—there is no exact testing or interval estimator for mean difference with unequal variances. As a solution, this study applies interval estimations of the efficacy response based on Howe’s, Cochran–Cox’s, and Satterthwaite’s approximations, which have been shown to have well-controlled type I error rates. However, the traditional sample size determination cannot be applied to the interval estimators. The sample size determination to achieve a desired power based on these interval estimators is then presented. Moreover, the consistency criteria suggested by the Japanese Ministry of Health, Labour and Welfare guidance to decide whether the overall results from the multiregional clinical trial obtained via the proposed interval estimation were also applied. A real example is used to illustrate the proposed method. The results of simulation studies indicate that the proposed method can correctly determine the required sample size and evaluate the assurance probability of the consistency criteria.

scholarly journals Do we need to adjust for interim analyses in a Bayesian adaptive trial design?

2019 ◽  
Author(s):  
Elizabeth Ryan ◽  
Kristian Brock ◽  
Simon Gates ◽  
Daniel Slade
Keyword(s):  
Clinical Trials ◽  
Case Studies ◽  
Type I Error ◽  
Error Rates ◽  
Adaptive Designs ◽  
Alternative Methods ◽  
Type I ◽  
Early Stopping ◽  
Interim Analyses ◽  
Stopping For Futility

Abstract Background Bayesian adaptive methods are increasingly being used to design clinical trials and offer a number of advantages over traditional approaches. Decisions at analysis points are usually based on the posterior distribution of the parameter of interest. However, there is some confusion amongst statisticians and trialists as to whether control of type I error is required for Bayesian adaptive designs as this is a frequentist concept. Methods We discuss the arguments for and against adjusting for multiplicities in Bayesian trials with interim analyses. We present two case studies demonstrating the effect on type I/II error rates of including interim analyses in Bayesian clinical trials. We propose alternative approaches to adjusting stopping boundaries to control type I error, and also alternative methods for decision-making in Bayesian clinical trials. Results In both case studies we found that the type I error was inflated in the Bayesian adaptive designs through incorporation of interim analyses that allowed early stopping for efficacy and do not make adjustments to account for multiplicity. Incorporation of early stopping for efficacy also increased the power in some instances. An increase in the number of interim analyses that only allowed early stopping for futility decreased the type I error, but also decreased power. An increase in the number of interim analyses that allowed for either early stopping for efficacy or futility generally increased type I error and decreased power. Conclusions If one wishes to demonstrate control of type I error in Bayesian adaptive designs then adjustments to the stopping boundaries are usually required for designs that allow for early stopping for efficacy as the number of analyses increase. If the designs only allow for early stopping for futility then adjustments to the stopping boundaries are not needed to control type I error, but may be required to ensure adequate power. If one instead uses a strict Bayesian approach then type I errors could be ignored and the designs could instead focus on the posterior probabilities of treatment effects of particular values.

scholarly journals Do we need to adjust for interim analyses in a Bayesian adaptive trial design?

2020 ◽  
Author(s):  
Elizabeth Ryan ◽  
Kristian Brock ◽  
Simon Gates ◽  
Daniel Slade
Keyword(s):  
Clinical Trials ◽  
Case Studies ◽  
Type I Error ◽  
Error Rates ◽  
Adaptive Designs ◽  
Alternative Methods ◽  
Type I ◽  
Early Stopping ◽  
Interim Analyses ◽  
Stopping For Futility

Abstract Background: Bayesian adaptive methods are increasingly being used to design clinical trials and offer several advantages over traditional approaches. Decisions at analysis points are usually based on the posterior distribution of the treatment effect. However, there is some confusion as to whether control of type I error is required for Bayesian designs as this is a frequentist concept.Methods: We discuss the arguments for and against adjusting for multiplicities in Bayesian trials with interim analyses. With two case studies we illustrate the effect of including interim analyses on type I/II error rates in Bayesian clinical trials where no adjustments for multiplicities are made. We propose several approaches to control type I error, and also alternative methods for decision-making in Bayesian clinical trials.Results: In both case studies we demonstrated that the type I error was inflated in the Bayesian adaptive designs through incorporation of interim analyses that allowed early stopping for efficacy and without adjustments to account for multiplicity. Incorporation of early stopping for efficacy also increased the power in some instances. An increase in the number of interim analyses that only allowed early stopping for futility decreased the type I error, but also decreased power. An increase in the number of interim analyses that allowed for either early stopping for efficacy or futility generally increased type I error and decreased power.Conclusions: Currently, regulators require demonstration of control of type I error for both frequentist and Bayesian adaptive designs, particularly for late-phase trials. To demonstrate control of type I error in Bayesian adaptive designs, adjustments to the stopping boundaries are usually required for designs that allow for early stopping for efficacy as the number of analyses increase. If the designs only allow for early stopping for futility then adjustments to the stopping boundaries are not needed to control type I error. If one instead uses a strict Bayesian approach, which is currently more accepted in the design and analysis of exploratory trials, then type I errors could be ignored and the designs could instead focus on the posterior probabilities of treatment effects of clinically-relevant values.

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