scholarly journals Sequential Multiple Assignment Randomized Trials: An Opportunity for Improved Design of Stroke Reperfusion Trials

2015 ◽  
Author(s):  
William Meurer ◽  
Nicholas J. Seewald ◽  
Kelley Kidwell
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Treatment Guidelines ◽  
Rescue Therapy ◽  
Phase Iii ◽  
Treatment Regimens ◽  
Multiple Assignment ◽  
Dynamic Treatment ◽  
Design Characteristics ◽  
Improved Design

AbstractBackground: Modern clinical trials in stroke reperfusion fall into two categories: alternative systemic pharmacological regimens to alteplase and "rescue" endovascular approaches using targeted thrombectomy devices and/or medications delivered directly for persistently vessel occlusions. Clinical trials in stroke have not evaluated how initial pharmacological thrombolytic management might influence subsequent rescue strategy. A sequential multiple assignment randomized trial (SMART) is a novel trial design that can test these dynamic treatment regimens and lead to treatment guidelines which more closely mimic practice.Aim: To characterize a SMART design in comparison to traditional approaches for stroke reperfusion trials.Methods: We conducted a numerical simulation study that evaluated the performance of contrasting acute stroke clinical trial designs of both initial reperfusion and rescue therapy. We compare a SMART design where the same patients are followed through initial reperfusion and rescue therapy within one trial to a standard phase III design comparing two reperfusion treatments and a separate phase II futility design of rescue therapy in terms of sample size, power, and ability to address particular research questions.Results: Traditional trial designs can be well powered and have optimal design characteristics for independent treatment effects. When treatments, such as the reperfusion and rescue therapies, may interact, commonly used designs fail to detect this. A SMART design, with similar sample size to standard designs, can detect treatment interactions.Conclusions: The use of SMART designs to investigate effective and realistic dynamic treatment regimens is a promising way to accelerate the discovery of new, effective treatments for stroke.

scholarly journals Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome

2019 ◽  
Vol 29 (7) ◽  
pp. 1891-1912
Author(s):  
Nicholas J Seewald ◽  
Kelley M Kidwell ◽  
Inbal Nahum-Shani ◽  
Tianshuang Wu ◽  
James R McKay ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Sample Size ◽  
Treatment Regimen ◽  
Statistical Efficiency ◽  
Entire Study ◽  
Treatment Regimens ◽  
Longitudinal Outcome ◽  
Multiple Assignment ◽  
Dynamic Treatment ◽  
Inflation Factor

Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen is a sequence of prespecified decision rules which can be used to guide the delivery of a sequence of treatments or interventions that is tailored to the changing needs of the individual. The sequential multiple-assignment randomized trial is a research tool which allows for the construction of effective dynamic treatment regimens. We derive easy-to-use formulae for computing the total sample size for three common two-stage sequential multiple-assignment randomized trial designs in which the primary aim is to compare mean end-of-study outcomes for two embedded dynamic treatment regimens which recommend different first-stage treatments. The formulae are derived in the context of a regression model which leverages information from a longitudinal outcome collected over the entire study. We show that the sample size formula for a sequential multiple-assignment randomized trial can be written as the product of the sample size formula for a standard two-arm randomized trial, a deflation factor that accounts for the increased statistical efficiency resulting from a longitudinal analysis, and an inflation factor that accounts for the design of a sequential multiple-assignment randomized trial. The sequential multiple-assignment randomized trial design inflation factor is typically a function of the anticipated probability of response to first-stage treatment. We review modeling and estimation for dynamic treatment regimen effect analyses using a longitudinal outcome from a sequential multiple-assignment randomized trial, as well as the estimation of standard errors. We also present estimators for the covariance matrix for a variety of common working correlation structures. Methods are motivated using the ENGAGE study, a sequential multiple-assignment randomized trial aimed at developing a dynamic treatment regimen for increasing motivation to attend treatments among alcohol- and cocaine-dependent patients.

scholarly journals Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations

2017 ◽  
Vol 26 (4) ◽  
pp. 1572-1589 ◽  
Author(s):  
Timothy NeCamp ◽  
Amy Kilbourne ◽  
Daniel Almirall
Keyword(s):  
Randomized Trial ◽  
Randomized Trials ◽  
Treatment Regimens ◽  
Patient Level ◽  
Multiple Assignment ◽  
Dynamic Treatment ◽  
The Mean ◽  
Cluster Randomized ◽  
The Individual ◽  
Cluster Level

Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

Reducing patient eligibility criteria in cancer clinical trials.

1996 ◽  
Vol 14 (4) ◽  
pp. 1364-1370 ◽  
Author(s):  
S L George
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Acute Lymphocytic Leukemia ◽  
Lymphocytic Leukemia ◽  
Phase Iii ◽  
Eligibility Criterion ◽  
Cancer Clinical Trials ◽  
Recent Trial ◽  
Eligibility Criteria ◽  
Eligibility Requirements

PURPOSE To discuss patient eligibility criteria in phase III cancer clinical trials in the larger setting of the complexity of these trials, to review the various reasons for imposing restrictive eligibility requirements, to discuss the problems caused by these requirements, to argue that these requirements should be greatly relaxed in most cancer clinical trials, to provide some guiding principles and practical suggestions to facilitate such a relaxation, and to give an example of how eligibility requirements were reduced in a recent clinical trial in acute lymphocytic leukemia. METHODS Implicit and explicit reasons for including eligibility criteria in clinical trials are reviewed. Safety concerns and sample size issues receive special attention. The types of problems restrictive eligibility criteria cause with respect to scientific interpretation, medical applicability, complexity, costs, and patient accrual are described. RESULTS A list of three items that each eligibility criterion should meet in order to be included is proposed and applied to a recent trial in acute lymphocytic leukemia. CONCLUSION Phase III clinical trials in cancer should have much broader eligibility criteria than the traditionally restrictive criteria commonly used. Adoption of less restrictive eligibility criteria for most studies would allow broader generalizations, better mimic medical practice, reduce complexity and costs, and permit more rapid accrual without compromising patient safety or requiring major increases in sample size.

scholarly journals Comparison of futility monitoring guidelines using completed phase III oncology trials

2016 ◽  
Vol 14 (1) ◽  
pp. 48-58 ◽  
Author(s):  
Qiang Zhang ◽  
Boris Freidlin ◽  
Edward L Korn ◽  
Susan Halabi ◽  
Sumithra Mandrekar ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Alternative Hypothesis ◽  
Phase Iii ◽  
Interim Result ◽  
Type I ◽  
Conditional Power ◽  
Phase Iii Clinical Trials ◽  
The Impact ◽  
Analytical Approaches

Background: Futility (inefficacy) interim monitoring is an important component in the conduct of phase III clinical trials, especially in life-threatening diseases. Desirable futility monitoring guidelines allow timely stopping if the new therapy is harmful or if it is unlikely to demonstrate to be sufficiently effective if the trial were to continue to its final analysis. There are a number of analytical approaches that are used to construct futility monitoring boundaries. The most common approaches are based on conditional power, sequential testing of the alternative hypothesis, or sequential confidence intervals. The resulting futility boundaries vary considerably with respect to the level of evidence required for recommending stopping the study. Purpose: We evaluate the performance of commonly used methods using event histories from completed phase III clinical trials of the Radiation Therapy Oncology Group, Cancer and Leukemia Group B, and North Central Cancer Treatment Group. Methods: We considered published superiority phase III trials with survival endpoints initiated after 1990. There are 52 studies available for this analysis from different disease sites. Total sample size and maximum number of events (statistical information) for each study were calculated using protocol-specified effect size, type I and type II error rates. In addition to the common futility approaches, we considered a recently proposed linear inefficacy boundary approach with an early harm look followed by several lack-of-efficacy analyses. For each futility approach, interim test statistics were generated for three schedules with different analysis frequency, and early stopping was recommended if the interim result crossed a futility stopping boundary. For trials not demonstrating superiority, the impact of each rule is summarized as savings on sample size, study duration, and information time scales. Results: For negative studies, our results show that the futility approaches based on testing the alternative hypothesis and repeated confidence interval rules yielded less savings (compared to the other two rules). These boundaries are too conservative, especially during the first half of the study (<50% of information). The conditional power rules are too aggressive during the second half of the study (>50% of information) and may stop a trial even when there is a clinically meaningful treatment effect. The linear inefficacy boundary with three or more interim analyses provided the best results. For positive studies, we demonstrated that none of the futility rules would have stopped the trials. Conclusion: The linear inefficacy boundary futility approach is attractive from statistical, clinical, and logistical standpoints in clinical trials evaluating new anti-cancer agents.

scholarly journals Hepatitis C: A Pharmacological Therapeutic Update

2021 ◽  
Vol 10 (8) ◽  
pp. 1568
Author(s):  
Sonia Santander Ballestín ◽  
David Gómez Martín ◽  
Sara Lorente Pérez ◽  
María José Luesma Bartolomé
Keyword(s):  
Hepatitis C ◽  
Treatment Guidelines ◽  
Phase Iii ◽  
World Health ◽  
Technical Data ◽  
Treatment Regimens ◽  
Phase Ii Studies ◽  
Pubmed Database ◽  
Health Organization ◽  
Direct Acting

(1) Background: Hepatitis C is a high-prevalence disease, representing a global impact health problem. Lately, many changes have been made in treatment guidelines because of the commercialization of second-generation direct-acting antivirals due to their high effectiveness, few side effects and pangenotypic action. We address the pharmacological possibilities available and compare them with the current recommendations of the World Health Organization (WHO). (2) Methods: The search for articles was made through the PubMed database using different search strategies and we consulted technical data sheets of the treatments that have been included in the study. (3) Results: Combinations of “glecaprevir/pibrentasvir”, “sofosbuvir/velpatasvir” and “sofosbuvir/velpatasvir/voxilaprevir” have been recently incorporated. Phase II studies have shown that they are safe and effective therapies with very comfortable posologies and easy therapeutic adherence; furthermore, they suppose shorter treatment duration. Subsequently, phase III studies have shown they were effective for previously treated or compensated cirrhotic patients that previously had more complex treatment regimens. (4) Conclusions: These results suppose a simplification in Hepatitis C therapeutic approach, and open new study possibilities.

Prevalence of RAS mutations among patients with metastatic colorectal cancer by country and region in randomized clinical trials: A pooled analysis.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 520-520 ◽  
Author(s):  
Marc Peeters ◽  
George Kafatos ◽  
Aliki Taylor ◽  
Victor M. Gastanaga ◽  
Hua Yu ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sanger Sequencing ◽  
Randomized Clinical Trials ◽  
Treatment Guidelines ◽  
Performance Status ◽  
Pooled Analysis ◽  
Phase Iii ◽  
Exon 2 ◽  
Ras Mutation ◽  
Ras Mutations

520 Background: Global mCRC treatment guidelines for EGFR inhibitors require prior confirmation of wild-type RAS status (KRAS exons 2, 3, 4 and NRAS 2, 3, 4). The aim of this study was to estimate the prevalence of RAS mutations among mCRC patients by country, demographic characteristics, and clinical risk factors. A secondary aim was to estimate BRAF and KRASexon 2 mutation prevalence. Methods: Data from 5 published Amgen-sponsored randomized clinical trials (RCTs) were merged in a retrospective pooled analysis. There were 3 phase III, 1 phase II, and 1 phase Ib/II studies. For 4 out of 5 RCTs, RAStesting was conducted in a U.S. laboratory (Transgenomic Inc.) using Sanger sequencing on DNA extracted from tumor samples. For the remaining trial, a combination of next-generation sequencing and Sanger sequencing was used. Results: A total of 3,196 patients from 36 countries were included. The overall unadjusted prevalence of RAS mutations among mCRC subjects was 55.9% (95% CI, 53.9%, 57.9%); KRAS exon 2 mutation prevalence was 42.6% (40.7%, 44.5%). The prevalence by exon is given in the table below. BRAFmutation prevalence was 8.1% (6.7%, 9.6%). There were no statistically significant differences in RAS mutation prevalence by gender, age, or clinical factors such as performance status, tumour site, biopsy origin, or metastasis characteristics. Statistically significant differences in RAS mutation prevalence estimates were observed by country and by region with rates for Central West Europe being significantly lower than Eastern Europe (49.4% [44.4%, 54.3%] and 61.5%; [55.3%; 67.4%] respectively). Statistically significant RASmutation prevalence differences were observed between studies which could be due to varying patient characteristics. Conclusions: By merging data from RCTs, the analysis provides robust estimates of RASmutation prevalence. Studies using observational data are needed to confirm these findings. [Table: see text]

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