scholarly journals Viral kinetic modeling and clinical trial simulation predicts disruption of respiratory disease trials by non-pharmaceutical COVID-19 interventions

2021 ◽  
Author(s):  
Simon Arsène ◽  
Claire Couty ◽  
Igor Faddeenkov ◽  
Natacha Go ◽  
Solène Granjeon-Noriot ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Sample Size ◽  
Clinical Relevance ◽  
Trial Design ◽  
Disease Burden ◽  
Historical Data ◽  
Transmission Rate ◽  
Clinical Trial Simulation ◽  
Type I ◽  
Eligibility Criteria

Clinical research in infectious respiratory diseases has been profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19. On top of trial delays or even discontinuation which have been observed in all disease areas, NPIs altered transmission pattern of many seasonal respiratory viruses which followed regular patterns for decades before the pandemic. Clinical trial design based on pre-pandemic historical data therefore needs to be put in question. In this article, we show how knowledge-based mathematical modeling can be used to address this issue. We set up an epidemiological model of respiratory tract infection (RTI) sensitive to a time dependent between-host transmission rate and coupled this model to a mechanistic description of viral RTI episodes in an individual patient. By reducing the transmission rate when the lockdown was introduced in the United Kingdom in March 2020, we were able to reproduce the perturbed 2020 RTI disease burden data. Using this setup, we simulated several NPIs scenarios of various strength (none, mild, medium, strong) and conducted placebo-controlled in silico clinical trials in pediatric patients with recurrent RTIs (RRTI) quantifying annual RTI rate distributions. In interventional arms, virtual patients aged 1-5 years received the bacterial lysate OM-85 (approved in several countries for the prevention of pediatric RRTIs) through a pro-type I immunomodulation mechanism of action described by a physiologically based pharmacokinetics and pharmacodynamics approach (PBPK/PD). Our predictions showed that sample size estimates based on the ratio of RTI rates (or the post-hoc power of fixed sample size trials) are not majorly impacted under NPIs which are less severe (none, mild and medium NPIs) than a strict lockdown (strong NPI). However, NPIs show a stronger impact on metrics more relevant for assessing the clinical relevance of the effect such as absolute benefit. This dichotomy shows the risk that successful trials (even with their primary endpoints being met) still get challenged in risk benefit assessment during the review of market authorization. Furthermore, we found that a mild NPI scenario already affected the time to recruit significantly when sticking to eligibility criteria complying with historical data. In summary, our model predictions can help rationalize and forecast post-COVID-19 trial feasibility. They advocate for gauging absolute and relative benefit metrics as well as clinical relevance for assessing efficacy hypotheses in trial design and they question eligibility criteria misaligned with the actual disease burden.

Abstract TP236: The Stroke Hyperglycemia Insulin Network Effort (SHINE) Trial: A Case Study of a Bayesian Trial Design

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Kristine R Broglio ◽  
Jason T Connor ◽  
William J Meurer ◽  
Valerie L Durkalski ◽  
Karen C Johnston ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Sample Size ◽  
Primary Endpoint ◽  
Trial Design ◽  
Type I Error ◽  
Phase Iii ◽  
Absolute Difference ◽  
Type I ◽  
Operating Characteristics ◽  
Patient Resources

Introduction: The “Adaptive Designs Accelerating Promising Trials into Treatments (ADAPT-IT)” project is a collaborative effort supported by the NIH and FDA to explore how adaptive clinical trial design might improve the evaluation of drugs and medical devices. We use the NINDS-supported Neurological Emergencies Treatment Trials network as a "laboratory" in which to study the development of adaptive clinical trial designs. The Stroke Hyperglycemia Insulin Network Effort (SHINE) trial was fully funded by the NIH-NINDS at the start of ADAPT-IT and is a currently ongoing phase III trial of tight glucose control in hyperglycemic acute ischemic stroke patients. Within ADAPT-IT, a Bayesian alternative design was developed. The primary endpoint is a severity-adjusted dichotomized 90-day modified Rankin scale (mRS). Objective: To present both designs and compare their operating characteristics. Methods: 10000 trials are simulated under treatment effects ranging from 0% to 7%. We present the mean sample size and probabilities of trial success or futility. Results: The SHINE trial design includes a group sequential procedure with 4 interim analyses to monitor for early efficacy and futility. A maximum of 1400 patients provide 80% power to detect a 7% absolute difference between treatment arms with an overall Type I error rate of 5%. The expected sample size is 1050 patients. This design also incorporates sample size re-estimation and response adaptive randomization. The Bayesian alternative would enroll a maximum of 1400 patients, equally randomized, but employs more frequent interim looks based on predictive probabilities and incorporates a longitudinal model of the primary endpoint. This design has similar power and Type I error and would enroll a mean of 733 and 979 patients under the null and alternative hypotheses respectively. Conclusions: Simulations suggest that the designs have similar power and Type I error. The Bayesian alternative, with more frequent looks, has a greater chance of stopping early for overwhelming efficacy or futility. The Bayesian alternative will be retrospectively executed upon completion of SHINE to later compare the designs based on their use of patient resources, time, and strength of conclusions in a real world setting.

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 Beyond p-values: A phase II dual-criterion design with statistical significance and clinical relevance

2018 ◽  
Vol 15 (5) ◽  
pp. 452-461 ◽  
Author(s):  
Satrajit Roychoudhury ◽  
Nicolas Scheuer ◽  
Beat Neuenschwander
Keyword(s):  
Sample Size ◽  
Phase Ii ◽  
Clinical Relevance ◽  
Statistical Significance ◽  
Effect Estimate ◽  
Type I ◽  
Operating Characteristics ◽  
Phase Ii Trials ◽  
Standard Design ◽  
Standard Designs

Background Well-designed phase II trials must have acceptable error rates relative to a pre-specified success criterion, usually a statistically significant p-value. Such standard designs may not always suffice from a clinical perspective because clinical relevance may call for more. For example, proof-of-concept in phase II often requires not only statistical significance but also a sufficiently large effect estimate. Purpose We propose dual-criterion designs to complement statistical significance with clinical relevance, discuss their methodology, and illustrate their implementation in phase II. Methods Clinical relevance requires the effect estimate to pass a clinically motivated threshold (the decision value (DV)). In contrast to standard designs, the required effect estimate is an explicit design input, whereas study power is implicit. The sample size for a dual-criterion design needs careful considerations of the study’s operating characteristics (type I error, power). Results Dual-criterion designs are discussed for a randomized controlled and a single-arm phase II trial, including decision criteria, sample size calculations, decisions under various data scenarios, and operating characteristics. The designs facilitate GO/NO-GO decisions due to their complementary statistical–clinical criterion. Limitations While conceptually simple, implementing a dual-criterion design needs care. The clinical DV must be elicited carefully in collaboration with clinicians, and understanding similarities and differences to a standard design is crucial. Conclusion To improve evidence-based decision-making, a formal yet transparent quantitative framework is important. Dual-criterion designs offer an appealing statistical–clinical compromise, which may be preferable to standard designs if evidence against the null hypothesis alone does not suffice for an efficacy claim.

Could a biomarkers-based patients' selection help to plan a clinical trial with targeted agents (TA) for metastatic renal cell carcinoma (mRCC)? Interaction and power analysis of randomized trials (RCTs).

2013 ◽  
Vol 31 (6_suppl) ◽  
pp. 399-399
Author(s):  
Francesco Massari ◽  
Francesca Maines ◽  
Sara Pilotto ◽  
Maria Bonomi ◽  
Diana Giannarelli ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Overall Survival ◽  
Sample Size ◽  
Power Analysis ◽  
Trial Design ◽  
Random Effect ◽  
Random Effect Model ◽  
Clinical Trial Design ◽  
Attrition Rate ◽  
Intention To Treat

399 Background: To predict the clinical benefit of TA in mRCC, several studies have analyzed some angiogenesis-related biomarkers including soluble VEGF and VEGF-Receptors, VEGF polymorphisms, IL-8 polymorphisms, and VHL mutations. We performed a power analysis to assess how much a biomarker-based approach can affect the sample size of a trial in mRCC. Methods: Hazard ratios (HR) for survival with 95% confidence intervals (CI) for overall survival were extracted and cumulated according to a random-effect model from RCTs. A sensitivity analysis according to ‘biomarker-selection’ approach and to ‘unselected’ fashion was accomplished in order to test for interaction. Testing for heterogeneity was performed as well. Results: A correlation between a featured biomarker and treatment effect was reported in three RCTs (1987 patients). The attrition rate for the survival analysis according to the molecular analysis was 45% (range 20 to 60%). In spite of the extremely different bio-markers’ profiles, demonstrated by the significant observed heterogeneity (p = 0.002), we found an Hazard Ratio (HR) for overall survival of 0.60 (95% CI 0.40-0.89; p = 0.013) and a significant interaction according to strategy (‘biomarker-selected’ vs.‘unselected’) (p = 0.025), supporting a differential effect of these drugs when administered according to a predictive phenotype. In the ‘unselected’ sample, considering the intention to treat analysis (all randomized patients) the HR was 0.90 (95% 0.80-1.00; p = 0.06). From a clinical trial design perspective, if targeting these expected survival differences, with a power 80%, and a alpha-error 0.05, the required events are described in the table. Conclusions: With the aim to avoid the attrition of the heterogeneity of biomarkers’ selection and drugs, we speculate that the sample size of a planned clinical trial design in mRCC according to a biomolecular classifier may help to shorten the gap between clinical research and practice. [Table: see text]

Implications of a prognostic 40-gene expression profile (40-GEP) test for high-risk cutaneous squamous cell carcinoma (cSCC) on staging-based risk assessment and adjuvant therapy trial design.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e22091-e22091
Author(s):  
Chrysalyne Schmults ◽  
Kyle R. Covington ◽  
Sarah J. Kurley ◽  
Robert W. Cook
Keyword(s):  
Gene Expression ◽  
Clinical Trial ◽  
High Risk ◽  
Sample Size ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Trial Design ◽  
Multivariable Analysis ◽  
Primary Objective ◽  
Staging Systems

e22091 Background: Deaths due to cSCC are expected to exceed melanoma-specific deaths. With the demonstration of effective therapies for advanced cSCC, and as treatment of patients in the adjuvant setting is considered, accurate prognosis is critical. For improved identification of ‘high-risk’ patients, with biologically aggressive disease capable of metastasis, a prognostic 40-gene expression profile (40-GEP) test was validated using an independent cohort of patients with high-risk cSCC and known clinical outcomes. The test identified three groups with increasing metastasis risk profiles: Class 1 (low risk), Class 2A (high risk), and Class 2B (highest risk) having metastasis rates of 8.9%, 20.4%, and 60%, respectively. Multivariable analysis demonstrated prognostic efficacy of the 40-GEP test alone and in combination with clinicopathological staging systems. This study evaluated risk stratification with concurrent consideration of the 40-GEP result and the Brigham and Women’s Hospital (BWH) stage. The primary objective was evaluation of the potential impact of the 40-GEP on adjuvant clinical trial design. Methods: To determine if a 40-GEP Class 2B result could optimize clinical trial accrual, metastasis rates of BWH high-risk T stage patients (T2b-T3) alone and in combination with 40-GEP results from the validation cohort were used for two-arm trial sample size calculations. Results: Metastasis rates for cases with T2b-T3 tumors increased from 35.1% to 71.4% when selecting for T2b-T3 cases with a 40-GEP Class 2B result. To provide 80% power to detect hazard ratio of 0.6 with 3 years of follow-up (alpha = 0.05), in line with improvement rates by addition of radiation to surgery, 434 T2b-T3 patients are required for randomization. However, sample size could be reduced by 51% to 214 patients by focusing enrollment on T2b-T3 patients with a 40-GEP Class 2B result. Conclusions: These results support the incorporation of the 40-GEP test into selection processes for patients with T2b-T3 tumors who are at the highest risk for metastasis and appropriate for adjuvant clinical trials.

scholarly journals Prospective comparison of Bayesian and frequentist adaptive clinical trials : The SHADOW - SHINE project

2021 ◽  
Author(s):  
Kristine Broglio ◽  
William Meurer ◽  
Valerie Durkalski ◽  
Qi Pauls ◽  
Jason Connor ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Acute Stroke ◽  
Sample Size ◽  
Trial Design ◽  
Interim Analyses ◽  
Patient Level Data ◽  
Patient Level ◽  
Prospective Comparison ◽  
Level Data

Importance: Bayesian adaptive trial design has the potential to create more efficient clinical trials. However, one of the barriers to the uptake of Bayesian adaptive designs for confirmatory trials is limited experience with how they may perform compared to a frequentist design. Objective: Compare the performance of a Bayesian and a frequentist adaptive clinical trial design. Design: Prospective observational study comparing two trial designs using individual patient level data from a completed stroke trial, including the timing and order of enrollments and outcome ascertainment. The implemented frequentist design had group sequential boundaries for efficacy and futility interim analyses when 90-days post-randomization was met for 500, 700, 900, and 1,100 patients. The Bayesian alternative utilized predictive probability of trial success to govern early termination for efficacy and futility with a first interim analysis at 500 randomized patients, and subsequent interims after every 100 randomizations. Setting: Multi-center, acute stroke study conducted within a National Institutes of Health neurological emergencies clinical trials network. Participants: Patient level data from 1,151 patients randomized in a clinical trial comparing intensive insulin therapy to standard in acute stroke patients with hyperglycemia. Main Outcome(s) and Measure(s): Sample size at end of study. This was defined as the sample size at which each of the studies stopped accrual of patients. Results: As conducted, the frequentist design passed the futility boundary after 936 participants were randomized. Using the same sequence and timing of randomization and outcome data, the Bayesian alternative crossed the futility boundary about 3 months earlier after 800 participants were randomized. Conclusions and Relevance: Both trial designs stopped for futility prior to reaching the planned maximum sample size. In both cases, the clinical community and patients would benefit from learning the answer to the trial's primary question earlier. The common feature across the two designs was frequent interim analyses to stop early for efficacy or for futility. Differences between how this was implemented between the two trials resulted in the differences in early stopping.

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