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Author: Rebecca C. Scherzer Publisher: ISBN: Category : Clinical trials Languages : en Pages : 350
Book Description
This research gives methods for sequential monitoring of survival data in clinical trials with multiple endpoints. We illustrate the use of marginal proportional hazards models and other survival models with various group sequential methods to test multiple survival endpoints at K interim analyses. To adjust for multiplicity at each interim analysis, we consider and extend methods developed by Tang and Geller (1999), Follmann, et al. (1994), and others. These methods are motivated, compared, and evaluated using survival data from a clinical study and using simulation studies.
Author: Rebecca C. Scherzer Publisher: ISBN: Category : Clinical trials Languages : en Pages : 350
Book Description
This research gives methods for sequential monitoring of survival data in clinical trials with multiple endpoints. We illustrate the use of marginal proportional hazards models and other survival models with various group sequential methods to test multiple survival endpoints at K interim analyses. To adjust for multiplicity at each interim analysis, we consider and extend methods developed by Tang and Geller (1999), Follmann, et al. (1994), and others. These methods are motivated, compared, and evaluated using survival data from a clinical study and using simulation studies.
Author: Toshimitsu Hamasaki Publisher: Springer ISBN: 4431559000 Category : Mathematics Languages : en Pages : 118
Book Description
This book focuses on group sequential methods for clinical trials with co-primary endpoints based on the decision-making frameworks for: (1) rejecting the null hypothesis (stopping for efficacy), (2) rejecting the alternative hypothesis (stopping for futility), and (3) rejecting the null or alternative hypothesis (stopping for either futility or efficacy), where the trial is designed to evaluate whether the intervention is superior to the control on all endpoints. For assessing futility, there are two fundamental approaches, i.e., the decision to stop for futility based on the conditional probability of rejecting the null hypothesis, and the other based on stopping boundaries using group sequential methods. In this book, the latter approach is discussed. The book also briefly deals with the group sequential methods for clinical trials designed to evaluate whether the intervention is superior to the control on at least one endpoint. In addition, the book describes sample size recalculation and the resulting effect on power and type I error rate. The book also describes group sequential strategies for three-arm clinical trials to demonstrate the non-inferiority of experimental intervention to actively control and to assess the assay sensitivity to placebo control.
Author: Christopher Jennison Publisher: CRC Press ISBN: 9781584888581 Category : Mathematics Languages : en Pages : 416
Book Description
Group sequential methods answer the needs of clinical trial monitoring committees who must assess the data available at an interim analysis. These interim results may provide grounds for terminating the study-effectively reducing costs-or may benefit the general patient population by allowing early dissemination of its findings. Group sequential methods provide a means to balance the ethical and financial advantages of stopping a study early against the risk of an incorrect conclusion. Group Sequential Methods with Applications to Clinical Trials describes group sequential stopping rules designed to reduce average study length and control Type I and II error probabilities. The authors present one-sided and two-sided tests, introduce several families of group sequential tests, and explain how to choose the most appropriate test and interim analysis schedule. Their topics include placebo-controlled randomized trials, bio-equivalence testing, crossover and longitudinal studies, and linear and generalized linear models. Research in group sequential analysis has progressed rapidly over the past 20 years. Group Sequential Methods with Applications to Clinical Trials surveys and extends current methods for planning and conducting interim analyses. It provides straightforward descriptions of group sequential hypothesis tests in a form suited for direct application to a wide variety of clinical trials. Medical statisticians engaged in any investigations planned with interim analyses will find this book a useful and important tool.
Author: Jay Bartroff Publisher: Springer Science & Business Media ISBN: 1461461146 Category : Medical Languages : en Pages : 250
Book Description
Sequential Experimentation in Clinical Trials: Design and Analysis is developed from decades of work in research groups, statistical pedagogy, and workshop participation. Different parts of the book can be used for short courses on clinical trials, translational medical research, and sequential experimentation. The authors have successfully used the book to teach innovative clinical trial designs and statistical methods for Statistics Ph.D. students at Stanford University. There are additional online supplements for the book that include chapter-specific exercises and information. Sequential Experimentation in Clinical Trials: Design and Analysis covers the much broader subject of sequential experimentation that includes group sequential and adaptive designs of Phase II and III clinical trials, which have attracted much attention in the past three decades. In particular, the broad scope of design and analysis problems in sequential experimentation clearly requires a wide range of statistical methods and models from nonlinear regression analysis, experimental design, dynamic programming, survival analysis, resampling, and likelihood and Bayesian inference. The background material in these building blocks is summarized in Chapter 2 and Chapter 3 and certain sections in Chapter 6 and Chapter 7. Besides group sequential tests and adaptive designs, the book also introduces sequential change-point detection methods in Chapter 5 in connection with pharmacovigilance and public health surveillance. Together with dynamic programming and approximate dynamic programming in Chapter 3, the book therefore covers all basic topics for a graduate course in sequential analysis designs.
Author: Timothy Michael Skalland Publisher: ISBN: Category : Clinical trials Languages : en Pages : 90
Book Description
Randomized trials are the gold standard for the clinical assessment of a new treatment compared to a placebo or standard of care. Often in clinical trials, patients are accrued sequentially rather than all at once. Thus, the data from such a trial becomes available sequentially to the researcher. Monitoring and testing the accrued data throughout a trial and making decisions based on on such tests that could terminate the trial early is called sequential testing. Designing and analyzing such sequential trials has garnered much attention in the statistical literature over the last 50+ years. The added flexibility and benefi ts from such a trial do not come free-of-cost. Careful considerations in the design, careful monitoring of the data throughout, and careful analysis of the data at the conclusion are necessary to preserve the integrity of such a sequential clinical trial. This thesis will be mostly concerned with a special form of sequential testing called a group sequential procedure. Such procedures have the benefi t of a reduction in expected sample size while not being burdened by continual monitoring of the data after every observation. Special topics of group sequential procedures include the concepts of overrun, secondary endpoints and adaptive group sequential procedures. Overrun is the accrual of data after the decision to terminate the trial has been reached. We investigate and compare popular approaches to the incorporation of such data into the final analysis. Through a simulation study, it is found that a random weighting of the p-values from the data up to the termination of the trial and the overrun data based the sample sizes for such data under the Sample Mean Ordering of the outcome space leads to the shortest average con fidence intervals while maintaining the nominal coverage probability. Most clinical trials are designed and evaluated using a primary endpoint for the treatment eff ect. Some trials have secondary endpoints to assess either safety or additional clinical benefi ts beyond the primary outcome. We consider the design and analysis of group sequential trials when both a primary and secondary endpoint are of interest. Our investigations are done in the setting of a gatekeeping procedure. We are able to unify and generalize global proofs to certain propositions made by other researchers when we consider testing both a primary and secondary endpoint. We further investigate secondary inference in the form of con fidence interval construction through an extensive simulation study. We find that the approach of Whitehead et al. (2000) outperforms existing methods for the settings considered. Adaptive clinical trials seek to modify some aspect of the trial after an interim look at the data in order to improve the odds of a successful trial by the end. We compare some popular choices of adaptive Phase II two-stage designs and introduce a new design while evaluating operating characteristics (Type I error, Type II error and expected sample sizes). Majority of the literature focuses on minimizing the expected sample size under the null hypothesis only. Our new Quasi-Symmetric n2-design seeks to substantially reduce the expected sample size under the parameter values close to the design alternative while minimally increasing expected sample size under the design null. We evaluate and compare such a design to existing methods.
Author: Weichung Joe Shih Publisher: CRC Press ISBN: 1000462811 Category : Medical Languages : en Pages : 320
Book Description
Statistical Design, Monitoring, and Analysis of Clinical Trials, Second Edition concentrates on the biostatistics component of clinical trials. This new edition is updated throughout and includes five new chapters. Developed from the authors’ courses taught to public health and medical students, residents, and fellows during the past 20 years, the text shows how biostatistics in clinical trials is an integration of many fundamental scientific principles and statistical methods. The book begins with ethical and safety principles, core trial design concepts, the principles and methods of sample size and power calculation, and analysis of covariance and stratified analysis. It then focuses on sequential designs and methods for two-stage Phase II cancer trials to Phase III group sequential trials, covering monitoring safety, futility, and efficacy. The authors also discuss the development of sample size reestimation and adaptive group sequential procedures, phase 2/3 seamless design and trials with predictive biomarkers, exploit multiple testing procedures, and explain the concept of estimand, intercurrent events, and different missing data processes, and describe how to analyze incomplete data by proper multiple imputations. This text reflects the academic research, commercial development, and public health aspects of clinical trials. It gives students and practitioners a multidisciplinary understanding of the concepts and techniques involved in designing, monitoring, and analyzing various types of trials. The book’s balanced set of homework assignments and in-class exercises are appropriate for students and researchers in (bio)statistics, epidemiology, medicine, pharmacy, and public health.
Author: Institute of Medicine Publisher: National Academies Press ISBN: 0309171148 Category : Medical Languages : en Pages : 221
Book Description
Clinical trials are used to elucidate the most appropriate preventive, diagnostic, or treatment options for individuals with a given medical condition. Perhaps the most essential feature of a clinical trial is that it aims to use results based on a limited sample of research participants to see if the intervention is safe and effective or if it is comparable to a comparison treatment. Sample size is a crucial component of any clinical trial. A trial with a small number of research participants is more prone to variability and carries a considerable risk of failing to demonstrate the effectiveness of a given intervention when one really is present. This may occur in phase I (safety and pharmacologic profiles), II (pilot efficacy evaluation), and III (extensive assessment of safety and efficacy) trials. Although phase I and II studies may have smaller sample sizes, they usually have adequate statistical power, which is the committee's definition of a "large" trial. Sometimes a trial with eight participants may have adequate statistical power, statistical power being the probability of rejecting the null hypothesis when the hypothesis is false. Small Clinical Trials assesses the current methodologies and the appropriate situations for the conduct of clinical trials with small sample sizes. This report assesses the published literature on various strategies such as (1) meta-analysis to combine disparate information from several studies including Bayesian techniques as in the confidence profile method and (2) other alternatives such as assessing therapeutic results in a single treated population (e.g., astronauts) by sequentially measuring whether the intervention is falling above or below a preestablished probability outcome range and meeting predesigned specifications as opposed to incremental improvement.
Author: Herman E. Ray Publisher: ISBN: Category : Clinical trials Languages : en Pages : 200
Book Description
The phase II clinical trial is a critical step in the drug development process. In the oncology setting, phase II studies typically evaluate one primary endpoint, which is efficacy. In practice, a binary measurement representing the response to the new treatment defines the efficacy. The single-arm, multiple-stage designs are popular and the Simon 2-Stage design is preferred. Although the study designs evaluate the efficacy, the subject's safety is an important concern. Safety is monitored through the number of grade 3 or grade 4 toxic events. The phase II clinical trial design based on the primary endpoint is typically augmented with an ad hoc monitoring rule. The studies are designed in two steps. First, the sample size and critical values are determined based on the primary endpoint. Then an ad hoc toxicity monitoring rule is applied to the study. Previous authors recommended a method to monitor toxic events after each patient is enrolled which is also known as continuous toxicity monitoring. A trial designed at the JG Brown Cancer Center combined the Simon 2-Stage design with continuous toxicity monitoring. We describe how to integrate the continuous toxicity monitoring methodology with the Simon 2-Stage design for response. Theoretical justification is given for the nominal size, power, probability of early termination (PET), and average sample size (ASN) of the combined testing procedure. A series of simulations were conducted to investigate the performance of the combined procedure. We discover that the type I error rate, type II error rate, PET, and ASN are subject to the correlation between toxicity and response. In fact, the study may have a smaller type I error rate than expected. The theoretical expressions derived to describe the operating characteristics of the combined procedure were utilized to create a new flexible, bivariate, multistage clinical trial. The design is considered flexible because it can monitor toxicity on a different schedule than response. An example is considered in which toxicity is measured after four equally spaced intervals and the response is evaluated only at the second and fourth toxicity examinations. This example corresponds to a data monitoring committee's meeting schedule that may happen every 6 months over a two year span. The effect of the correlation on the type I and type II error rates is examined through simulation. The simulations also examine the power over the range of response rates with a fixed toxicity rate in the alternative region and vice-versa. There are several single-arm, multiple-stage clinical trial designs that consider multiple endpoints at the same time. A subset of the designs includes those that consider both efficacy and toxicity as binary endpoints. A common problem, considered after the conduct of the trial, is appropriate inference given the repeated examinations of the multiple endpoints. We propose a uniformly minimum variance unbiased estimator (UMVUE) for the response in a multistage clinical trial design incorporating toxicity effects. The proposed estimator and the typical maximum likelihood estimator (MLE) are evaluated through simulation. The estimator requires further modification when continuous toxicity monitoring is combined with a multistage design for response. The modified estimator maintains low bias over the range of possible response values. The larger phase lIb or phase III clinical trial is the logical extension of the bivariate research based on exact calculations. The phase lIb or III clinical trials typically include an ad hoc toxicity monitoring rule ensuring participant protection. The designs also include provisions to allow early stopping for futility or efficacy utilizing group sequential theory or stochastic curtailment. We also examine a novel large sample clinical trial design that incorporates correlation between the response and toxicity events. The design uses the typical critical values associated with the standard normal distribution. It also searches for critical values specific to the global hypothesis associated with both response and toxicity. The bivariate test is then combined with efficacy and safety monitoring based on a flexible time-varying conditional power methodology. The type I and type II error rates of the bivariate test procedure, along with the bivariate test procedure combined with the conditional power methodology, are investigated through simulation. A modification is developed for the conditional power methodology to preserve the type I and type II error rates. In the end, the research extends the bivariate clinical trial designs in an attempt to make them more appealing in practice. Although, the research resulted in positive outcomes, additional work is required.
Author: Rebecca C. Scherzer
Author: Rebecca C. Scherzer
Author: Toshimitsu Hamasaki
Author: Christopher Jennison
Author: Yuanjun Shi
Author: Jay Bartroff
Author: Timothy Michael Skalland
Author: Weichung Joe Shih
Author: Yonghua Chen
Author: Institute of Medicine
Author: Herman E. Ray