An evaluation of phase I clinical trial designs in the continuous dose-response setting

Both traditional phase I designs and the increasingly popular continual reassessment method (CRM) designs select an estimate of maximum tolerable dose (MTD) from among a set of prespecified dose levels. Although CRM designs use an implied dose-response model to select the next dose level, in general it is neither assumed nor necessary that this model be tied to the actual dose of a drug. In contrast, in our two-stage design the fitting of a dose-response model after data have been collected is a necessary feature of the design, and the MTD is not constrained to be one of the prespecified dose levels. We conducted a simulation study to evaluate the performance of the two-stage design, two likelihood-based CRM designs, and two traditional designs in estimating the MTD in situations where one assumes that an explicit dose-response model exists. Under a wide variety of dose-response settings, we examined the bias and precision of estimates, and the fraction of estimates that were extremely high or low. We also studied the effect of adding a model fitting step at the end of a traditional design or a CRM design. The best performance was achieved using the two-stage and CRM designs. Although the CRM designs generally had smaller bias, the two-stage design yielded equal or somewhat smaller precision in some cases. The addition of a model-fitting step slightly improved the precision of the CRM estimates and decreased the percentage of extreme estimates. Allowing interpolation between doses for updating during CRM did not improve overall performance.     

A comparison of two phase I trial designs

Phase I cancer chemotherapy trials are designed to determine rapidly the maximum tolerated dose of a new agent for further study. A recently proposed Bayesian method, the continual reassessment method, has been suggested to offer an improvement over the standard design of such trials. We find the previous comparisons did not completely address the relative performance of the designs as they would be used in practice. Our results indicate that with the continual reassessment method, more patients will be treated at very high doses and the trials will take longer to complete. We offer some suggested improvements to both the standard design and the Bayesian method.     

Frequentist evaluation of group sequential clinical trial designs

Group sequential stopping rules are often used as guidelines in the monitoring of clinical trials in order to address the ethical and efficiency issues inherent in human testing of a new treatment or preventive agent for disease. Such stopping rules have been proposed based on a variety of different criteria, both scientific (e.g. estimates of treatment effect) and statistical (e.g. frequentist type I error, Bayesian posterior probabilities, stochastic curtailment). It is easily shown, however, that a stopping rule based on one of these criteria induces a stopping rule on all other criteria. Thus, the basis used to initially define a stopping rule is relatively unimportant so long as the operating characteristics of the stopping rule are fully investigated. In this paper we describe how the frequentist operating characteristics of a particular stopping rule might be evaluated to ensure that the selected clinical trial design satisfies the constraints imposed by the many different disciplines represented by the clinical trial collaborators.     

Bayesian evaluation of group sequential clinical trial designs

Clinical trial designs often incorporate a sequential stopping rule to serve as a guide in the early termination of a study. When choosing a particular stopping rule, it is most common to examine frequentist operating characteristics such as type I error, statistical power, and precision of confidence intervals (Statist. Med. 2005, in revision). Increasingly, however, clinical trials are designed and analysed in the Bayesian paradigm. In this paper, we describe how the Bayesian operating characteristics of a particular stopping rule might be evaluated and communicated to the scientific community. In particular, we consider a choice of probability models and a family of prior distributions that allows concise presentation of Bayesian properties for a specified sampling plan.     

Three-dose-cohort designs in cancer phase I trials

Traditional designs for phase I clinical trials assign the same dose to patients in the same cohort. In this paper, we present a new class of designs for cancer phase I trials which initially rapidly escalate by allowing multiple doses (usually 3) to be assigned to each cohort of patients. The class of designs, called the LMH-CRM (an extension of the continual reassessment method (CRM) by administering different percentiles of the maximum tolerated dose (MTD), denoted 'low', 'medium', 'high'), is proven to be consistent and coherent (a commonsense property of phase I trials for dose escalation and de-escalation). Three designs (slow, moderate and fast) are derived based on different dose-escalation restrictions. Simulation results show that moderate and fast LMH-CRM combine the advantages of the CRM with one patient per cohort and three patients per cohort: it accurately estimates the MTD; controls overall toxicity rates; and is time efficient.     

Optimal phase I dose-escalation trial designs in oncology--a simulation study

In phase I oncology trials conducted over the past few decades, the maximum tolerated dose (MTD) has usually been estimated by the traditional escalation rule (TER), which traces back to 1973. In the meantime, new methods have been proposed which hope to estimate the true MTD more precisely than the TER while using less patients. In this simulation study, TER is compared with the accelerated titration dose design (ATD), two up-and-down designs (biased coin design, r-in-a-row (RIAR)), the maximum likelihood version of the continual reassessment method (CRML), and a Bayesian method that is implemented in the software Bayesian ADEPT (assisted decision-making in early phase trials). Each design was applied to 50,000 simulated studies. The designs were then compared for accuracy in detecting the true MTD (which is known here), while taking into account the average number of patients and toxicities per run. In terms of accuracy, ADEPT outperformed the other methods in the scenario with medium toxicity and was close to the best methods in the low and high toxic scenarios. The average number of patients needed per run was the lowest for TER in the scenario with low toxicity and for ADEPT in the remaining scenarios. The longer the escalation path to the target region of the MTD, the more the difference in the average number of patients per run pronounced between TER and ADEPT. TER induced least toxicities in all scenarios. ADEPT turned out to be quick and accurate in determining the MTD, while TER was the safest but least accurate method. CRML was as accurate as TER, and the up-and-down designs did not excel. Bayesian ADEPT is considered a valuable tool for the conduct of phase I dose-escalation trials in oncology, but careful preparation is indispensable before its practical use.     

Patient specific dosing in a cancer phase I clinical trial.

Recent improvements in our understanding of drug metabolism have led to the development of anticancer therapies that accommodate patient differences in drug tolerance. Such methods adjust the dose level according to measurable patient characteristics in order to obtain a target drug exposure. This paper describes the utilization of a patient specific dosing scheme in the statistical design of a phase I clinical trial involving patients with advanced adenocarcinomas of gastrointestinal origin. During the trial, dose levels were adjusted according to each patient's pretreatment concentration of an antibody that was shown in preclinical testing to moderate the effect of the agent under investigation. The design of the trial permitted a continual adjustment of the model used to tailor the dose to each patient's individual needs.     

Admissible two-stage designs for phase II cancer clinical trials

In a typical two-stage design for a phase II cancer clinical trial for efficacy screening of cytotoxic agents, a fixed number of patients are initially enrolled and treated. The trial may be terminated for lack of efficacy if the observed number of tumour responses after the first stage is too small, thus avoiding treatment of patient with inefficacious regimen. Otherwise, an additional fixed number of patients are enrolled and treated to accumulate additional information on efficacy as well as safety. The minimax and the so-called 'optimal' designs by Simon have been widely used, and other designs have largely been ignored in the past for such two-stage cancer clinical trials. Recently Jung et al. proposed a graphical method to search for compromise designs with features more favourable than either the minimax or the optimal design. In this paper, we develop a family of two-stage designs that are admissible according to a Bayesian decision-theoretic criterion based on an ethically justifiable loss function. We show that the admissible designs include as special cases the Simon's minimax and the optimal designs as well as the compromise designs introduced by Jung et al. We also present a Java program to search for admissible designs that are compromises between the minimax and the optimal designs.     

Competing designs for phase I clinical trials: a review

Phase I clinical trials are typically small, uncontrolled studies designed to determine a maximum tolerated dose of a drug which will be used in further testing. Two divergent schools have developed in designing phase I clinical trials. The first defines the maximum tolerated dose as a statistic computed from data, and hence it is identified, rather than estimated. The second defines the maximum tolerated dose as a parameter of a monotonic dose-response curve, and hence is estimated. We review techniques from both philosophies. The goal is to present these methods in a single package, to compare them from philosophical and statistical grounds, to hopefully clear up some common misconceptions, and to make a few recommendations. This paper is not a review of simulation studies of these designs, nor does it present any new simulations comparing these designs.     

An exact method for analysis following a two-stage phase II cancer clinical trial

This paper presents an exact method for the analysis of a phase II cancer clinical trial conducted using a two-stage design in which early stopping may be allowed for either futility or efficacy. The method provides a point and interval estimate of the response probability associated with the treatment under investigation and a p-value for testing whether this exceeds some standard null value. Two-stage designs are often used in phase II trials in oncology for reasons of ethics and efficiency, but this design feature is seldom taken into account when the results are analyzed. After any two-stage design or multi-stage design, the method for analysis should take into account the previous interim analyses performed, otherwise the results will be biased. In this paper, an approximate approach based on a log-odds ratio parameterisation will be compared with an exact method through the calculation of the precise coverage probabilities of each approach.     

Improved up-and-down designs for phase I trials

We consider several designs from the family of up-and-down rules for the sequential allocation of dose levels to subjects in a dose-response study. We show that an up-and-down design can be improved by using more information than the most recent response. For example, the k-in-a-row rule uses up to the k most recent responses. We introduce a new design, the Narayana rule, which uses a local estimate of the probability of toxicity calculated from all previous responses. For the Narayana rule, as the sample size gets large, the probability of assignment goes to zero for dose levels not among the two (or three) closest to the target. Different estimators of the target dose are compared. We find that the isotonic regression estimator is superior to other estimators for small to moderate sample sizes.     

Comparative review of novel model‐assisted designs for phase I clinical trials

A number of novel phase I trial designs have been proposed that aim to combine the simplicity of algorithm‐based designs with the superior performance of model‐based designs, including the modified toxicity probability interval, Bayesian optimal interval, and Keyboard designs. In this article, we review these “model‐assisted” designs, contrast their statistical foundations and pros and cons, and compare their operating characteristics with the continual reassessment method. To provide unbiased and reliable results, our comparison is based on 10 000 dose‐toxicity scenarios randomly generated using the pseudo‐uniform algorithm recently proposed in the literature. The results showed that the continual reassessment method, Bayesian optimal interval, and Keyboard designs provide comparable, superior operating characteristics, and each outperforms the modified toxicity probability interval design. These designs are more likely to correctly select the maximum tolerated dose and less likely to overdose patients.     

A phase I clinical trial of mefloquine in Brazilian male subjects

A double-blind, randomized phase I clinical trial was carried out to compare mefloquine with sulfadoxine—pyrimethamine for safety and tolerance. Twenty adult male Brazilian subjects from areas endemic for malaria were studied for a period of 66 days, which included 2 days of basal studies and a 63-day follow-up after drug administration. Subjects received either mefloquine, given as a single oral dose of 1000 mg (4 × 250-mg tablets) or sulfadoxine—pyrimethamine (2 tablets, each containing 500 mg of sulfadoxine plus 25 mg of pyrimethamine). Clinical examination, electrocardiogram, chest X-ray, and haematological, biochemical, stool, and urine analyses were carried out before drug administration and at various intervals afterwards. Peripheral blood smears were examined for malarial parasites.     

Competing designs for drug combination in phase I dose‐finding clinical trials

The aim of phase I combination dose‐finding studies in oncology is to estimate one or several maximum tolerated doses (MTDs) from a set of available dose levels of two or more agents. Combining several agents can indeed increase the overall anti‐tumor action but at the same time also increase the toxicity. It is, however, unreasonable to assume the same dose–toxicity relationship for the combination as for the simple addition of each single agent because of a potential antagonist or synergistic effect. Therefore, using single‐agent dose‐finding methods for combination therapies is not appropriate. In recent years, several authors have proposed novel dose‐finding designs for combination studies, which use either algorithm‐based or model‐based methods. The aim of our work was to compare, via a simulation study, six dose‐finding methods for combinations proposed in recent years. We chose eight scenarios that differ in terms of the number and location of the true MTD(s) in the combination space. We then compared the performance of each design in terms of correct combination selection, patient allocation, and mean number of observed toxicities during the trials. Our results showed that the model‐based methods performed better than the algorithm‐based ones. However, none of the compared model‐based designs gave consistently better results than the others. Copyright © 2014 John Wiley & Sons, Ltd.     

A phase I clinical trial of a new 5-valent rotavirus vaccine

We conducted a phase I, double-blind, placebo-controlled trial to evaluate a new 5-valent oral rotavirus vaccine's safety and immunogenicity profiles. Subjects were randomly assigned to receive 3 orally administered doses of a live-attenuated human-bovine (UK) reassortant rotavirus vaccine, containing five viral antigens (G1, G2, G3, G4 and G9), or a placebo. The frequency and severity of adverse events were assessed. Immunogenicity was evaluated by the titers of anti-rotavirus IgA and the presence of neutralizing antibodies anti-rotavirus. No severe adverse events were observed. There was no difference in the frequency of mild adverse events between experimental and control groups. The proportion of seroconversion was consistently higher in the vaccine group, for all serotypes, after each one of the doses. The 5-valent vaccine has shown a good profile of safety and immunogenicity in this small sample of adult volunteers.     

Optimal flexible designs in phase II clinical trials

In the conduct of a phase II cancer clinical trial, patients usually enter in two stages. If the response rate from the first stage is low, then the study terminates. Within various two-stage designs, Simon proposed the optimal and minimax criteria. In the co-operative group setting, practical considerations make it difficult to arrive at the planned sample size exactly. Green and Dahlberg proposed and compared several flexible designs. In this paper, we explicitly define a flexible design as a collection of two-stage designs where the first stage size is in a set of consecutive values (n₁,…,n_k) and the second stage size is also in another set of consecutive values (N₁,…,N_k), and each of k² possible designs has the same probability of occurrence. We apply Simon's optimal and minimax criteria to flexible designs for phase II trials in order to minimize the number of patients tested on an ineffective drug. © 1998 John Wiley & Sons, Ltd. This paper was produced under the auspices of the US Government and is therefore not subject to copyright in the US.     

Issues in the use of adaptive clinical trial designs

Sequential sampling plans are often used in the monitoring of clinical trials in order to address the ethical and efficiency issues inherent in human testing of a new treatment or preventive agent for disease. Group sequential stopping rules are perhaps the most commonly used approaches, but in recent years, a number of authors have proposed adaptive methods of choosing a stopping rule. In general, such adaptive approaches come at a price of inefficiency (almost always) and clouding of the scientific question (sometimes). In this paper, I review the degree of adaptation possible within the largely prespecified group sequential stopping rules, and discuss the operating characteristics that can be characterized fully prior to collection of the data. I then discuss the greater flexibility possible when using several of the adaptive approaches receiving the greatest attention in the statistical literature and conclude with a discussion of the scientific and statistical issues raised by their use.