Inference for clinical trials with some protocol amendments

The use of adaptive methods in clinical development has become very popular in recent years due to its flexibility in modifying trial procedures and/or statistical procedures of on-going clinical trials. Modifications to trial procedures are usually documented by protocol amendments. However, the actual patient population after protocol amendments could deviate from the originally targeted patient population. In addition, protocol amendments made based on accrued data of the on-going trial may distort the sampling distribution of the statistic designed for the case of no protocol change. In this article, we model the population deviations due to protocol amendments using some covariates and study how to develop a valid statistical inference procedure. An example concerning an asthma trial is presented for illustration.     

Inference for Clinical Trials with Some Protocol Amendments

ABSTRACT

The use of adaptive methods in clinical development has become very popular in recent years due to its flexibility in modifying trial procedures and/or statistical procedures of on-going clinical trials. Modifications to trial procedures are usually documented by protocol amendments. However, the actual patient population after protocol amendments could deviate from the originally targeted patient population. In addition, protocol amendments made based on accrued data of the on-going trial may distort the sampling distribution of the statistic designed for the case of no protocol change. In this article, we model the population deviations due to protocol amendments using some covariates and study how to develop a valid statistical inference procedure. An example concerning an asthma trial is presented for illustration.     

Operational Protocol Models in the Drug Development Process

Why do clinical trials that appear to be straightforward yield lower data quality and have longer timelines and higher costs than expected? Why do clinical development programs have difficulty combining studies when creating an integrated regulatory submission? Although the way these problems manifest themselves varies greatly, evidence continually points to the clinical trial protocol(s) as the root cause of operational errors, missed milestones, sponsor-site miscommunications, and incorrect data. Therefore, an up-front investment in analyzing the design features of a protocol for operational clarity and completeness could result in a substantial return from improved trial execution and regulatory reporting. A protocol design methodology, based on an innovative concept called the ‘operational protocol model’, has been developed to address operational problems. The operational protocol model replaces the current unstructured protocol document with a highly structured formal protocol model. A wide range of automated computer-based protocol quality analysis programs can utilize the operational protocol model to provide insight into protocol design from various perspectives. The operational protocol model is independent of the scientific, clinical, or statistical details of a study. Therefore, the operational protocol modeling methodology can be applied to markedly different protocol designs in widely differing clinical domains. Based on an empirical analysis of operational errors across a wide range of therapeutic areas, we have defined the following five key goals of a successful protocol design: coherency, completeness, consistency, comparability, and collaboration. The use of an operational protocol model can ensure that every clinical trial protocol designed within the operational protocol model meets all five protocol design quality goals.     

Clinical trial simulation: a tool for understanding study failures and preventing them

Execution models describe protocol deviations from a specified study design. When a clinical trial is planned, it is generally supposed that it will be executed according to a specific protocol that defines all aspects of the experimental design, from its beginning to its completion. Adherence to the protocol will allow estimation of the treatment outcome (safety and efficacy) with sufficient statistical power, or at least that is what is assumed. In reality, however, deviations from the protocol may lead to failure of the study to achieve its stated aims. In anticipation of protocol deviations that contribute to inflated residual variability and decreased study statistical power, trial designers tend to overpower studies in a rather arbitrary way. It is difficult to estimate quantitatively the consequences of one protocol deviation on statistical study power and, a fortiori, it is almost impossible to do it for a combination of protocol deviations. One way to study the consequences of model deviations is by using modelling and simulation techniques, and more specifically longitudinal stochastic models that can describe individual behaviours. Thus, execution models are powerful tools for identifying weaknesses or limitations in a proposed study design, which may be anticipated, avoided or resolved in order to increase robustness of the study design prior to implementation of the actual clinical study. As such, they are an integral component of clinical trial simulation and an essential tool in clinical trial design.     

治疗药物监测类体外诊断试剂临床研究浅析

目的 旨在阐述治疗药物监测类体外诊断试剂的临床研究要点.方法 结合体外诊断试剂现行法规和此类试剂自身特点,分析其临床研究的特殊要求.结果 对该类试剂的管理分类、研究方法、临床试验方案设置、临床试验样本选择、结果的统计学分析以及临床试验报告撰写等方面进行了详细解析.结论 治疗药物监测类体外诊断试剂的临床研究应从试剂本身的临床预期用途出发,科学、合理、完整地对试剂进行临床验证.     

The cystic fibrosis therapeutics development network (CF TDN): a paradigm of a clinical trials network for genetic and orphan diseases

Clinical trials have become critical to the advancement of medical science and to the evolution of patient care in medicine. The science of clinical research has advanced from early studies in which treatment was assessed without controls to sophisticated multinational collaborative randomized, double-blind, placebo controlled trials of therapeutic interventions. To facilitate the advancement of clinical research, clinical trials networks have been developed to conduct multicenter studies. This review describes the history of clinical trials, clinical trials networks, and the goals of such networks in the United States. The Cystic Fibrosis Therapeutics Development Network, a network that represents the paradigm for genetic and orphan diseases, is described in detail. This network has been extremely successful in its first 3.5 years of existence conducting 18 different clinical trials in patients with Cystic Fibrosis. Unique aspects of the network include the use of internet applications for study conduct and communication, the development of statistical methodology to enhance the efficiency of clinical trial design, the development of outcome measures specific to Cystic Fibrosis, and the development of infrastructure necessary for expediting protocol development. In the current environment, clinical research faces significant challenges related to ensuring the safe and ethical conduct of clinical research while promoting fast and efficient clinical trials. To succeed and move forward to provide treatments and find cures for diseases, clinical trials networks must continue to evolve. The Cystic Fibrosis Therapeutics Development Network represents a network that has met this challenge and will continue to provide a venue for the safe and efficient conduct of clinical trials in Cystic Fibrosis.     

乙型肝炎病毒核酸定量检测试剂临床研究要点

目的阐述乙型肝炎病毒核酸定量检测试剂的临床研究要点。方法结合体外诊断试剂现行法规和此类试剂自身的特点,对其管理分类、研究方法、临床试验方案设计、临床试验样本选择、结果的统计学分析以及临床试验报告撰写等方面进行了详细解析。结果与结论乙型肝炎病毒核酸定量检测试剂的临床研究应从试剂本身的临床预期用途出发,重点针对试剂定量准确性、特异性进行科学的临床考察。     

On the utility of the Dirichlet distribution for meta-analysis of clinical studies.

Recently, there has been an increasing interest in combining efficacy (or safety) results from several clinical trials to draw an overall conclusion about the efficacy (or safety) of new investigational drugs. In a two-armed clinical trial, these efficacy outcomes are often expressed in terms of the two treatment effect means or the proportions of treatment success. For several clinical trials, efficacy assessment could be based on different types of clinical outcomes. These outcomes might be both categorical and noncategorical. Therefore, it is desirable to have a statistical model that is flexible enough for combining clinical trial data of both outcomes across several trials or studies. We discuss a simple statistical model with a potential for such flexibility. We illustrate the use of the model by providing a couple of numerical examples on the meta-analyses of efficacy data from prospective clinical trials/studies.     

Statistical Consideration of Adaptive Methods in Clinical Development

ABSTRACT

In recent years, the use of adaptive methods in clinical development based on accrued data has become very popular due to its flexibility in modifying trial procedures and/or statistical procedures of on-going clinical trials. However, it is a concern that the actual patient population after the modifications could deviate from the originally targeted patient population. Major modifications of trial procedures and/or statistical procedures of on-going trials may result in a totally different trial, which is unable to address the scientific/medical questions that the trial intends to answer.

In this article, the impact on the target patient population, statistical inference, and power analysis for sample size adjustment after changes or modifications made to an on-going clinical trial is studied.     

Statistical consideration of adaptive methods in clinical development

In recent years, the use of adaptive methods in clinical development based on accrued data has become very popular due to its flexibility in modifying trial procedures and/or statistical procedures of on-going clinical trials. However, it is a concern that the actual patient population after the modifications could deviate from the originally targeted patient population. Major modifications of trial procedures and/or statistical procedures of on-going trials may result in a totally different trial, which is unable to address the scientific/medical questions that the trial intends to answer.     

The problem of protocol driven costs in pharmacoeconomic analysis.

The increasing number of economic evaluations of healthcare interventions and of drug therapies in particular has been well documented. Surveys of the quality of studies have demonstrated that standards of conduct of such studies have not similarly increased. Concerns over the standards have led to increased calls that economic analyses be more closely linked to randomised controlled clinical trials (RCT). Seven potential threats to the external validity of results limit the generalisability of studies based on RCTs. One such threat is the existence of protocol driven costs. There are two main types of protocol driven costs. Protocol prescribed costs arise as a result of resource use mandated by the clinical trial design. Protocol derived costs occur when increased clinical investigations mandated by trial protocols lead to atypical disease management. Methods to control for protocol driven costs within pharmacoeconomic study designs are available. Modelling studies can be based on data within clinical trials combined with observational data representing more typical resource use. The adoption of pragmatic clinical trial designs provide greater external validity though reduced internal validity. Refinements to explanatory clinical trials can also lead to reduced protocol driven costs. The extent that current studies control for such costs is unclear due to the lack of transparency in the reporting of study methods. A review of published studies found little consideration of protocol driven costs although in several studies there was evidence of their existence. Future studies conducted alongside RCTs should explicitly address how the issue of protocol driven costs was handled within the study framework.     

Simulation for clinical repeated-dose pharmacokinetic trials applying a peak-and-trough sampling design to estimate oral clearance

We performed a simulation for the clinical pharmacokinetic study, in which blood was sampled at two time points corresponding to the peak concentration (C(peak)) and trough concentration (C(trough)) following repetitive oral drug administration to subjects. We estimated the approximate oral clearance (CL/F(approx)) as 2.D/(C(peak).tau+C(trough).tau), where D is the dose, and tau is the dosing interval. The CL/F(approx) value was accurate for drugs with a long-elimination half-life, and the estimation error of the CL/F value was slightly increased for drugs with a shorter elimination half-life. The accuracy of CL/F(approx) in each subject was not affected by the magnitude of the interindividual pharmacokinetic variability, but was significantly decreased by the larger measurement error of drug concentrations (or intraindividual pharmacokinetic variability). We further performed several computer simulations to mimic statistical hypothesis testing following the clinical repeated-dose pharmacokinetic trials. The statistical power to detect the difference of oral clearance between two groups was marginally dependent on the measurement error of drug concentration, but was highly dependent on the interindividual pharmacokinetic variability. These findings suggested that the peak-and-trough sampling design to estimate the CL/F(approx) value is useful for clinical repeated-dose pharmacokinetic trials, and that the study design and protocol should be evaluated carefully by computer simulation prior to a real clinical trial.     

On the Commonly Used Design and Statistical Considerations in Double Blind,Potentially Unblind,and Open‐Label Clinical Trials

In clinical trials, parallel group designs, such as placebo‐controlled trials or active‐controlled trials, are commonly used. The study design in comparative clinical trials to address the intended objective is vital to the interpretation of the trial results. If inappropriate study design are employed, they not only cannot answer the intended clinical hypothesis, but they can also impose adverse effects on the collection of efficacy and safety data that may further be compounded by observable and unobservable baseline data. Documentation and interpretation of the outcomes need to be carefully considered. It is well known that various sources of biases can arise during the course of the trial, for instance, design bias, operational bias, analytical bias, methodological bias, interpretation bias, and documentation bias. As a result of such biases, the accuracy and precision of the treatment effect estimate may be severely compromised, depending on the design considered. In this article, commonly used study designs for comparative clinical trials are described. Statistical considerations with various designs, the concept of robustness of statistical findings, and statistical evidence will be discussed in light of the blinding, potential unblinding, and open‐label designs.     

Clinical and statistical issues in therapeutic equivalence trials

Summary Absolute proof of efficacy can only be given by placebo controlled trials. It is, however, important to classify a drug within the spectrum of existing therapeutic alternatives and, where effective treatment is available, it may be imperative due to ethical considerations to demonstrate that one drug is as effective as another.The issue of therapeutic equivalence trials is discussed along the lines of the important items which should be defined in the protocol: a) the target parameter, which is the primary endpoint of the trial, b) the reference drug, which should be selected with respect to efficacy (superior to others), and safety (largest amount of data), c) the acceptance range, which depends on the primary endpoint, and its implication for the clinical endpoints of morbidity and mortality (the conventional acceptance range for bioequivalence trials does not apply), and d) the statistical procedures, which must take into consideration the unsuitability of the conventional power approach for confirming equivalence.In an equivalence trial, compared to those that are placebo-controlled, the proof that one drug is as effective as another relies much more upon the quality of conduct of the study according to Good Clinical Practice.     

Statistical inference for response adaptive randomization procedures with adjusted optimal allocation proportions

Seamless phase II/III clinical trials have attracted increasing attention recently. They mainly use Bayesian response adaptive randomization (RAR) designs. There has been little research into seamless clinical trials using frequentist RAR designs because of the difficulty in performing valid statistical inference following this procedure. The well-designed frequentist RAR designs can target theoretically optimal allocation proportions, and they have explicit asymptotic results. In this paper, we study the asymptotic properties of frequentist RAR designs with adjusted target allocation proportions, and investigate statistical inference for this procedure. The properties of the proposed design provide an important theoretical foundation for advanced seamless clinical trials. Our numerical studies demonstrate that the design is ethical and efficient.     

Ginkgo biloba extract in peripheral arterial diseases. Meta-analysis of controlled clinical studies]

In the first part the statistical methods of meta-analysis are discussed. Meta-analysis is considered as a statistical tool for quantitatively summarizing the results of clinical trials with comparable aims (treatments) and designs. Meta-analysis can be based on the significance probabilities or effect values. The last procedure is preferable as it gives an estimate (and confidence interval) for the global effect of the treatment of interest, if homogeneity of the effects between the trials can be assumed. Such a homogeneity can be often achieved by a suitable standardization of the effect variables within the trials. In the second part the methods of meta-analysis are applied to controlled clinical trials with Ginkgo biloba extract EGb 761 in patients with peripheral arterial disease. Included were 5 placebo-controlled clinical trials with similar design and inclusion criteria. In all studies treatment effect was quantified by the increase of walking distance (measured in standardized treadmill exercise). The effect value of EGb 761 treatment was expressed by the standardized mean difference in walking distance increase between EGb 761 and placebo, standardized by the standard deviation. It could be shown that this effect value is homogeneous in all trials. The global effect size was estimated as 0.75. This means that the mean increase in walking distance achieved by EGb 761 is 0.75 times of the standard deviation higher than that achieved by placebo. This value is highly significant different from zero. So the meta-analysis revealed a highly significant therapeutic effect of EGb 761 for the treatment of peripheral arterial disease.     

Stopping rules for phase II studies

This paper, the second in a series of three papers concerned with the statistical aspects of interim analyses in clinical trials, is concerned with stopping rules in phase II clinical trials. Phase II trials are generally small-scale studies, and may include one or more experimental treatments with or without a control. A common feature is that the results primarily determine the course of further clinical evaluation of a treatment rather than providing definitive evidence of treatment efficacy. This means that there is more flexibility available in the design and analysis of such studies than in phase III trials. This has led to a range of different approaches being taken to the statistical design of stopping rules for such trials. This paper briefly describes and compares the different approaches. In most cases the stopping rules can be described and implemented easily without knowledge of the detailed statistical and computational methods used to obtain the rules.     

Design, monitoring, and analysis issues relative to adverse events.

In the clinical development of new drugs for market approval, it is frequently impossible to design trials to provide definitive information about safety--particularly about adverse events. It is possible, however, to design most trials to provide definitive information about efficacy. Efficacy trials with new drugs should therefore be monitored for safety, and the safety profile described within and across trials. Confidence intervals are recommended as the appropriate statistical methodology for doing this. Such intervals provide an interval estimate on the unknown incidences of adverse experiences among patients who could be treated with each regimen, as well as permit a conclusion that two regimens are different.     

新药研发中药物临床试验方案的风险管理

本文为新药临床试验风险管理提供参考。在临床试验方案中引入风险管理方法，从风险管理的基本程序：风险识别、风险评估、风险控制等方面展开论证。充分认识和把握临床试验方案中存在的风险因素：临床前研究资料不足的风险、入选与排除标准制订的风险、有效性与安全性评价的风险、统计设计的风险、研究者和受试者依从性的风险等。根据风险性质的不同，可采用风险减轻、风险规避、风险转移、风险接受等控制措施，以有效减少后期临床试验过程中风险发生和降低损失。     

Regulatory Issues of Propensity Score Methodology Application to Drug and Device Safety Studies

While randomized, well-controlled, clinical trials have been viewed as the gold standard in the evaluation of medical products, including drugs, biological products, and medical devices, it is not uncommon for safety assessment to be performed using observational studies, for ethical or practical reasons. In observational studies, various biases could be introduced in every stage and aspect of study, and consequently the resulting statistical inference may carry a lower level of scientific assurance, compared to randomized trials. To ensure the objectivity of study design and interpretability of the results, it is critical to address the challenges of such studies. In this paper, we share regulatory considerations on the prospective design of observational studies to address safety issues using propensity score methodology.