Accrual strategies for phase I trials with delayed patient outcome

Subgroup-specific dose finding in phase I clinical trials based on time to toxicity allowing adaptive subgroup combination

A Bayesian design is presented that does precision dose finding based on time to toxicity in a phase I clinical trial with two or more patient subgroups. The design, called Sub-TITE, makes sequentially adaptive subgroup-specific decisions while possibly combining subgroups that have similar estimated dose-toxicity curves. Decisions are based on posterior quantities computed under a logistic regression model for the probability of toxicity within a fixed follow-up period, as a function of dose and subgroup. Similarly to the time-to-event continual reassessment method (TITE-CRM, Cheung and Chappell), the Sub-TITE design downweights each patient's likelihood contribution using a function of follow-up time. Spike-and-slab priors are assumed for subgroup parameters, with latent subgroup combination variables included in the logistic model to allow different subgroups to be combined for dose finding if they are homogeneous. This framework can be used in trials where clinicians have identified patient subgroups but are not certain whether they will have different dose-toxicity curves. A simulation study shows that, when the dose-toxicity curves differ between all subgroups, Sub-TITE has superior performance compared with applying the TITE-CRM while ignoring subgroups and has slightly better performance than applying the TITE-CRM separately within subgroups or using the two-group maximum likelihood approach of Salter et al that borrows strength among the two groups. When two or more subgroups are truly homogeneous but differ from other subgroups, the Sub-TITE design is substantially superior to either ignoring subgroups, running separate trials within all subgroups, or the maximum likelihood approach of Salter et al. Practical guidelines and computer software are provided to facilitate application.

Adaptive dose-finding studies: a review of model-guided phase I clinical trials

PURPOSE

We provide a comprehensive review of adaptive phase I clinical trials in oncology that used a statistical model to guide dose escalation to identify the maximum-tolerated dose (MTD). We describe the clinical setting, practical implications, and safety of such applications, with the aim of understanding how these designs work in practice.

METHODS

We identified 53 phase I trials published between January 2003 and September 2013 that used the continual reassessment method (CRM), CRM using escalation with overdose control, or time-to-event CRM for late-onset toxicities. Study characteristics, design parameters, dose-limiting toxicity (DLT) definition, DLT rate, patient-dose allocation, overdose, underdose, sample size, and trial duration were abstracted from each study. In addition, we examined all studies in terms of safety, and we outlined the reasons why escalations occur and under what circumstances.

RESULTS

On average, trials accrued 25 to 35 patients over a 2-year period and tested five dose levels. The average DLT rate was 18%, which is lower than in previous reports, whereas all levels above the MTD had an average DLT rate of 36%. On average, 39% of patients were treated at the MTD, and 74% were treated at either the MTD or an adjacent level (one level above or below).

CONCLUSION

This review of completed phase I studies confirms the safety and generalizability of model-guided, adaptive dose-escalation designs, and it provides an approach for using, interpreting, and understanding such designs to guide dose escalation in phase I trials.

Using Data Augmentation to Facilitate Conduct of Phase I-II Clinical Trials with Delayed Outcomes

A practical impediment in adaptive clinical trials is that outcomes must be observed soon enough to apply decision rules to choose treatments for new patients. For example, if outcomes take up to six weeks to evaluate and the accrual rate is one patient per week, on average three new patients will be accrued while waiting to evaluate the outcomes of the previous three patients. The question is how to treat the new patients. This logistical problem persists throughout the trial. Various ad hoc practical solutions are used, none entirely satisfactory. We focus on this problem in phase I-II clinical trials that use binary toxicity and efficacy, defined in terms of event times, to choose doses adaptively for successive cohorts. We propose a general approach to this problem that treats late-onset outcomes as missing data, uses data augmentation to impute missing outcomes from posterior predictive distributions computed from partial follow-up times and complete outcome data, and applies the design's decision rules using the completed data. We illustrate the method with two cancer trials conducted using a phase I-II design based on efficacy-toxicity trade-offs, including a computer stimulation study.

A Bayesian adaptive Phase I-II clinical trial for evaluating efficacy and toxicity with delayed outcomes

BACKGROUND

In traditional Phase-I oncology trials, the safety of a new chemotherapeutic agent is tested in a dose escalation study to identify the maximum tolerated dose, which is defined as the highest dose with acceptable toxicity. An alternate approach is to jointly model toxicity and efficacy and allow dose finding to be directed by a prespecified trade-off between efficacy and toxicity. With this goal in mind, several designs have been proposed to jointly model toxicity and efficacy in a Phase I-II dose escalation study. A factor limiting the use of these designs is that toxicity and efficacy must be observed in a timely manner.

PURPOSE

One approach to overcoming this problem is to model toxicity and efficacy as time-to-event outcomes. This would allow new subjects to be enrolled before full information is available for previous subjects while incorporating partial information when adaptively assigning new subjects to a dose level.

METHODS

We propose a Phase I-II dose escalation study for evaluating toxicity and efficacy with delayed outcomes by jointly modeling toxicity and efficacy as time-to-event outcomes. We apply our proposed design to a Phase I-II clinical trial of a novel targeted toxin for canine hemangiosarcoma.

RESULTS

Our simulation results show that our design identifies the optimal dose at a similar rate to dose finding that treats toxicity and efficacy as binary outcomes, but with substantial savings in study duration.

LIMITATIONS

Our proposed design has acceptable operating characteristics and dramatically reduces the trial duration compared to a design that considers toxicity and efficacy as binary outcomes, but comes at the cost of enrolling additional subjects when all dose levels are unacceptable.

CONCLUSIONS

We developed a novel Phase I-II design that accounts for delayed outcomes by modeling toxicity and efficacy as time-to-event outcomes. Our design has similar operating characteristics to efficacy/toxicity trade-off designs that consider efficacy and toxicity as binary outcomes, but with a dramatically shorter study duration.

Adaptive phase I study of OROS methylphenidate treatment of attention deficit hyperactivity disorder with epilepsy

OBJECTIVE

The goal of this study was to pilot a randomized controlled trial of OROS methylphenidate (OROS-MPH) to treat attention deficit hyperactivity disorder (ADHD) plus epilepsy.

METHODS

Thirty-three patients, 6-18years of age, taking antiepileptic drugs and with a last seizure 1-60months prior were assigned to a maximum daily dose of 18, 36, or 54mg of OROS-MPH in a double-blind placebo-controlled crossover trial.

RESULTS

There were no serious adverse events and no carryover effects in the crossover trial. OROS-MPH reduced ADHD symptoms more than did placebo treatment. There were too few seizures during the active (5) and placebo arms (3) to confidently assess seizure risk; however, considering exposure time, we observed an increased daily risk of seizures with increasing dose of OROS-MPH, suggesting that potential safety concerns require further study.

CONCLUSION

A larger study to assess the effect of OROS-MPH on seizure risk is needed. A crossover design including subjects with frequent seizures could maximize power and address high patient heterogeneity and recruitment difficulties.

Bayesian Models and Decision Algorithms for Complex Early Phase Clinical Trials

An early phase clinical trial is the first step in evaluating the effects in humans of a potential new anti-disease agent or combination of agents. Usually called "phase I" or "phase I/II" trials, these experiments typically have the nominal scientific goal of determining an acceptable dose, most often based on adverse event probabilities. This arose from a tradition of phase I trials to evaluate cytotoxic agents for treating cancer, although some methods may be applied in other medical settings, such as treatment of stroke or immunological diseases. Most modern statistical designs for early phase trials include model-based, outcome-adaptive decision rules that choose doses for successive patient cohorts based on data from previous patients in the trial. Such designs have seen limited use in clinical practice, however, due to their complexity, the requirement of intensive, computer-based data monitoring, and the medical community's resistance to change. Still, many actual applications of model-based outcome-adaptive designs have been remarkably successful in terms of both patient benefit and scientific outcome. In this paper, I will review several Bayesian early phase trial designs that were tailored to accommodate specific complexities of the treatment regime and patient outcomes in particular clinical settings.

Continual reassessment method vs. traditional empirically based design: modifications motivated by Phase I trials in pediatric oncology by the Pediatric Brain Tumor Consortium

In this article we provide additional support for the use of a model-based design in pediatric Phase I trials and present our modifications to the continual reassessment method (CRM), which were largely motivated by specific challenges we encountered in the context of the Pediatric Brain Tumor Consortium trials. We also summarize the results of our extensive simulations studying the operating characteristics of our modified approach and contrasting it to the empirically based traditional method (TM). Compared to the TM, our simulations indicate that the modified version of CRM is more accurate, exposes fewer patients to potentially toxic doses, and tends to require fewer patients. Further, the CRM-based MTD has a consistent definition across trials, which is important, especially in a consortium setting where multiple agents are being tested in studies that are often running simultaneously and accruing from the same patient population.

Discussion on "A Hybrid Selection and Testing Procedure with Curtailment for Comparative Clinical Trials" by Elena M. Buzaianu and Pinyuen Chen

Buzaianu and Chen apply strong curtailment to modify the two-stage select-and-test clinical trial design proposed by Thall et al. (1988). The modification reduces the expected sample size while maintaining overall power but requires continuous monitoring in stage 1. I will review the history of this type of design and discuss practical issues related to the use of strong curtailment that arise in trial conduct.

Monitoring late-onset toxicities in phase I trials using predicted risks

Late-onset (LO) toxicities are a serious concern in many phase I trials. Since most dose-limiting toxicities occur soon after therapy begins, most dose-finding methods use a binary indicator of toxicity occurring within a short initial time period. If an agent causes LO toxicities, however, an undesirably large number of patients may be treated at toxic doses before any toxicities are observed. A method addressing this problem is the time-to-event continual reassessment method (TITE-CRM, Cheung and Chappell, 2000). We propose a Bayesian dose-finding method similar to the TITE-CRM in which doses are chosen using time-to-toxicity data. The new aspect of our method is a set of rules, based on predictive probabilities, that temporarily suspend accrual if the risk of toxicity at prospective doses for future patients is unacceptably high. If additional follow-up data reduce the predicted risk of toxicity to an acceptable level, then accrual is restarted, and this process may be repeated several times during the trial. A simulation study shows that the proposed method provides a greater degree of safety than the TITE-CRM, while still reliably choosing the preferred dose. This advantage increases with accrual rate, but the price of this additional safety is that the trial takes longer to complete on average.

np1: a computer program for dose escalation strategies in phase I clinical trials

The continual reassessment method is a recommended dose escalation design for including patients in phase I clinical trials designed to estimate the maximum tolerated dose. However, for a particular trial, the implementation of these methods requires extensive computer programming. Standard 3+3 designs do not require this, but have shown to possess poor statistical properties. np1 is a user friendly computer program which has implemented two continual reassessment methods for simulating and conducting a phase I clinical trial. Several options allow the user to investigate operating characteristics under various scenarios.

A phase I and pharmacokinetic study of LAF389 administered to patients with advanced cancer

LAF389 is a synthetic analogue of bengamide B, a natural product isolated from Jaspidae sponges. LAF389 has both antiproliferative and antiangiogenetic properties, and preclinical investigations showed a broad antitumour activity. This clinical trial aimed to determine the safety and pharmacokinetic profile of LAF389 administered as a slow intravenous injection for 3 consecutive days every 3 weeks in patients with advanced solid tumours. Eight dose levels were tested: 1, 2.5, 5, 10, 15, 30, 25 and 20 mg/day. A total of 33 patients, median age 52 years (range 33-72), with refractory solid tumours were enroled, 19 men and 14 women with a median World Health Organization performance status of 1 (0-4). Seventy-eight cycles of treatment have been administered (mean 2.5, range 1-10). Four cardiovascular dose-limiting toxicities were reported at 30 mg (2/2 patients) and 25 mg (2/9 patients), eight additional patients at various dose levels had (cardio)vascular toxicity, probably drug related, and one patient died owing to pulmonary embolism at the 5 mg dose. No objective responses were recorded. Pharmacokinetic parameters were variable, although linear and without obvious accumulation from cycle I to cycle II. LAF389 dose escalation was terminated owing to occurrence of unpredictable cardiovascular events. This, associated with the lack of clinical activity, did not warrant further investigation of this agent.

The Accelerated Biased Coin Up-and-Down Design in Phase I Trials

The biased coin up-and-down design (BCD) is used to allocate doses in phase I clinical trials. The BCD requires that the treatment response or the toxicity evaluation is observed quickly. In trials with a long treatment evaluation, the BCD will lead to long trial duration because a new patient cannot be enrolled until the preceding patient has completed the evaluation period. We propose a simple method to modify the BCD that will reduce the trial duration without significantly affecting the estimate of the target dose. The idea is to allocate a dose to each patient as he or she arrives based on the toxicity information of the last completed subject. This allows multiple patients to be concurrently under evaluation. A simulation study shows that this modification does not adversely affect the precision of the recommended dose, as estimated by isotonic regression, but it does significantly reduce the total time to complete the study.

Phase I Studies of Chemotherapeutic Agents in Cancer Patients: A Review of the Designs

I review the designs available for Phase I dose-finding studies of chemotherapeutic agents in cancer patients. The designs are based on the assumption that both efficacy and toxicity increase with dose, and thus attempt to minimize the number of patients treated at low doses, and also to minimize the chance that patients will be treated at excessively toxic or lethal doses. The designs fall into two classes: rule-based and model-guided. Rule-based designs can always determine a reasonable maximum tolerable dose based on observed toxicity, but when model assumptions are not satisfied, many model-guided designs will not.

Bayesian statistics in medicine: a 25 year review

This review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

Using the continual reassessment method: lessons learned from an EORTC phase I dose finding study

Many clinicians often do not feel comfortable with the Continual Reassessment Method (CRM). This article reviews its implementation, showing the characteristics, advantages and limitations of this method in Phase I studies as an alternative to the classical 'Fibonacci' escalation schema. A two center, dose escalation phase I study of rViscumin was carried out. Thirty-seven patients were included at 14 different dose-levels (10 to 6400 ng/kg). The complete clinical results are presented elsewhere. A 2-step CRM design enables one to speed-up the study and most importantly to obtain an accurate estimate of the maximum tolerated dose (MTD). Different management issues related to a multicenter study are illustrated and we show how the method can go wrong when severe toxicity, or dose limiting toxicity (DLT), is not considered by the clinician as being sufficient to limit dose escalation (here a grade 3 asthenia related to the drug). This would have affected any dose finding methods. We believe that CRM is a good alternative to the standard method from both a statistical and a practical point of view but further methodological research is necessary to address the issues related to the composite nature of the endpoint.

Dose-finding study of ibuprofen in patent ductus arteriosus using the continual reassessment method

OBJECTIVE

Intravenous ibuprofen (IBU) has been found to be as effective as indomethacin for the treatment of patent ductus arteriosus (PDA) in preterm infants and has been associated with fewer adverse effects in comparative phase III studies. The dose regimen used (10-5-5 mg/kg/day) was based on limited pharmacokinetic data and no phase II study was available to determine the optimal dose of IBU for this indication. The present study was designed to determine the minimum effective dose regimen (MEDR) of IBU (one course) required to close ductus arteriosus in preterm infants.

METHOD

A double-blind dose-finding study was conducted using the continual reassessment method, a Bayesian sequential design. Two distinct target closure rates were initially chosen according to postmenstrual age (PMA) at birth: 80% in infants with a PMA of 27-29 weeks, and 50% in infants with a PMA < 27 weeks. Forty neonates (20 in each PMA group) with PDA were treated between days 3 and 5 of life. Four different dose regimens were tested: loading doses of 5, 10, 15 or 20 mg/kg, followed by two doses (1/2 loading dose) at 24-h intervals. Efficacy was evaluated by echocardiography 24 h after the third infusion.

RESULTS

In infants with a PMA of 27-29 weeks, the estimated MEDR was 10-5-5 mg/kg with a final estimated probability of success of 77% (95% credibility interval: 56-92%). The 15-7.5-7.5 mg/kg dose regimen had a better estimated probability of success (88%, 95% credibility interval: 68-97%), but resulted in more minor renal adverse effects. In contrast, in infants with a PMA < 27 weeks, the estimated MEDR was 20-10-10 mg/kg with an estimated probability of success of 54.8% (95% credibility interval: 22-84%), whereas the conventional dose regimen resulted in a low estimated probability of success (30.6%, 95% credibility interval: 13-56%). In these infants, compared with those with a PMA of 27-29 weeks, minor renal adverse effects were more frequent from the 10-5-5 mg/kg/day dose regimen and did not appear to be clearly dose related.

CONCLUSION

This study confirms that the currently recommended dose regimen (10-5-5 mg/kg) of IBU is associated with a high closure rate (80%) and few adverse effects in premature infants with a PMA of 27-29 weeks. The failure rate was much higher below 27 weeks. A higher dose regimen (20-10-10 mg/kg) might achieve a higher closure rate. However, tolerability and safety of this dose regimen should be assessed in a larger population before considering the use of these doses for ductus arteriosus closure.

Phase I Study of Bortezomib in Refractory or Relapsed Acute Leukemias

Bortezomib (Velcade, formerly PS-341) is proteasome inhibitor with documented antitumor activity in multiple myeloma and other lymphoid malignancies. We performed a Phase I study to investigate the maximum tolerated dose and dose-limiting toxicity of bortezomib in patients with acute leukemias refractory to or relapsing after prior therapy. Fifteen patients were treated with 0.75 (n = 3), 1.25 (n = 7), or 1.5 (n = 5) mg/m(2) bortezomib administered twice weekly for 4 weeks every 6 weeks. Dose-limiting toxicity included orthostatic hypotension (n = 2), nausea (n = 2), diarrhea (n = 1), and fluid retention (n = 1), all at 1.5 mg/m(2) bortezomib. Proteasome inhibition was dose dependent and reached 68% at 1.5 mg/m(2) bortezomib. Peak inhibition was observed 1 h after treatment and returned to near baseline levels by 72 h after treatment. Incubation of blast cells with bortezomib in vitro showed induction of apoptosis in three of five patients investigated. We conclude that the maximum tolerated dose of bortezomib in patients with acute leukemia is 1.25 mg/m(2), using a twice-weekly for 4 weeks every 6 weeks schedule. The in vitro evidence of antileukemia and transient hematological improvements observed in some patients warrants further investigation of bortezomib in acute leukemias, probably in combination with other agents.

Acute myeloid leukemia

In this chapter, Drs. Keating and Willman review recent advances in our understanding of the pathophysiology of acute myeloid leukemia (AML) and allied conditions, including the advanced myelodysplastic syndromes (MDS), while Drs. Goldstone, Avivi, Giles, and Kantarjian focus on therapeutic data with an emphasis on current patient care and future research studies. In Section I, Dr. Armand Keating reviews the role of the hematopoietic microenvironment in the initiation and progression of leukemia. He also discusses recent data on the stromal, or nonhematopoietic, marrow mesenchymal cell population and its possible role in AML. In Section II, Drs. Anthony Goldstone and Irit Avivi review the current role of stem cell transplantation as therapy for AML and MDS. They focus on data generated on recent Medical Research Council studies and promising investigation approaches. In Section III, Dr. Cheryl Willman reviews the current role of molecular genetics and gene expression analysis as tools to assist in AML disease classification systems, modeling of gene expression profiles associated with response or resistance to various interventions, and identifying novel therapeutic targets. In Section IV, Drs. Hagop Kantarjian and Francis Giles review some promising agents and strategies under investigation in the therapy of AML and MDS with an emphasis on novel delivery systems for cytotoxic therapy and on targeted biologic agents.

Practical model-based dose-finding in phase I clinical trials: methods based on toxicity

We describe two practical, outcome-adaptive statistical methods for dose-finding in phase I clinical trials. One is the continual reassessment method and the other is based on a logistic regression model. Both methods use Bayesian probability models as a basis for learning from the accruing data during the trial, choosing doses for successive patient cohorts, and selecting a maximum tolerable dose (MTD). These methods are illustrated and compared to the conventional 3+3 algorithm by application to a particular trial in renal cell carcinoma. We also compare their average behavior by computer simulation under each of several hypothetical dose-toxicity curves. The comparisons show that the Bayesian methods are much more reliable than the conventional algorithm for selecting an MTD, and that they have a low risk of treating patients at unacceptably toxic doses.

Ethical issues in oncology biostatistics

A medical statistician's routine professional activities are likely to have important ethical consequences. This is due in part to the fact that good medical practice and scientifically valid medical research both require as precursors high quality statistical design and data analysis. In this paper I discuss various ethical issues that I have encountered while working as a biostatistician at M.D. Anderson Cancer Center. I describe particular experiences and the ethical issues involved. Topics include medical decision making, benefit-harm trade-offs, safety monitoring, adaptive randomization, informed consent, and publication bias.

Decision-theoretic designs for pre-phase II screening trials in oncology

A Bayesian decision-theoretic method is proposed for conducting small, randomized pre-phase II selection trials. The aim is to improve on the design of Thall and Estey (1993, Statistics in Medicine 12, 1197-1211). Designs are derived that optimize a gain function accounting for current and future patient gains, per-patient cost, and future treatment development cost. To reduce the computational burden associated with backward induction, myopic versions of the design that consider only one, two, or three future decisions at a time are also considered. The designs are compared in the context of a screening trial in acute myelogenous leukemia.

The continual reassessment method: comparison of Bayesian stopping rules for dose-ranging studies

The continual reassessment method (CRM) provides a Bayesian estimation of the maximum tolerated dose (MTD) in phase I clinical trials and is also used to estimate the minimal efficacy dose (MED) in phase II clinical trials. In this paper we propose Bayesian stopping rules for the CRM, based on either posterior or predictive probability distributions that can be applied sequentially during the trial. These rules aim at early detection of either the mis-choice of dose range or a prefixed gain in the point estimate or accuracy of estimated probability of response associated with the MTD (or MED). They were compared through a simulation study under six situations that could represent the underlying unknown dose-response (either toxicity or failure) relationship, in terms of sample size, probability of correct selection and bias of the response probability associated to the MTD (or MED). Our results show that the stopping rules act correctly, with early stopping by using the two first rules based on the posterior distribution when the actual underlying dose-response relationship is far from that initially supposed, while the rules based on predictive gain functions provide a discontinuation of inclusions whatever the actual dose-response curve after 20 patients on average, that is, depending mostly on the accumulated data. The stopping rules were then applied to a data set from a dose-ranging phase II clinical trial aiming at estimating the MED dose of midazolam in the sedation of infants during cardiac catheterization. All these findings suggest the early use of the two first rules to detect a mis-choice of dose range, while they confirm the requirement of including at least 20 patients at the same dose to reach an accurate estimate of MTD (MED). A two-stage design is under study.

The continual reassessment method and its applications: a Bayesian methodology for phase I cancer clinical trials

We discuss the continual reassessment method (CRM) and its extension with practical applications in phase I and I/II cancer clinical trials. The CRM has been proposed as an alternative design of a traditional cohort design and its essential features are the sequential (continual) selection of a dose level for the next patients based on the dose-toxicity relationship and the updating of the relationship based on patients' response data using Bayesian calculation. The original CRM has been criticized because it often tends to allocate too toxic doses to many patients and our proposal for overcoming this practical problem is to monitor a posterior density function of the occurrence of the dose limiting toxicity (DLT) at each dose level. A simulation study shows that strategies based on our proposal allocate a smaller number of patients to doses higher than the maximum tolerated dose (MTD) compared with the original method while the mean squared error of the probability of the DLT occurrence at the MTD is not inflated. We present a couple of extensions of the CRM with real prospective applications: (i) monitoring efficacy and toxicity simultaneously in a combination phase I/II trial; (ii) combining the idea of pharmacokinetically guided dose escalation (PKGDE) and utilization of animal toxicity data in determining the prior distribution. A stopping rule based on the idea of separation among the DLT density functions is discussed in the first example and a strategy for determining the model parameter of the dose-toxicity relationship is suggested in the second example.

Dose-finding based on feasibility and toxicity in T-cell infusion trials

A new modality for treatment of cancer involves the ex vivo growth of cancer-specific T-cells for subsequent infusion into the patient. The therapeutic aim is selective destruction of cancer cells by the activated infused cells. An important problem in the early phase of developing such a treatment is to determine a maximal tolerated dose (MTD) for use in a subsequent phase II clinical trial. Dose may be quantified by the number of cells infused per unit body weight, and determination of an MTD may be based on the probability of infusional toxicity as a function of dose. As in a phase I trial of a new chemotherapeutic agent, this may be done by treating successive cohorts of patients at different dose levels, with each new level chosen adaptively based on the toxicity data of the patients previously treated. Such a dose-finding strategy is inadequate in T-cell infusion trials because the number of cells grown ex vivo for a given patient may be insufficient for infusing the patient at the current targeted dose. To address this problem, we propose an algorithm for trial conduct that determines a feasible MTD based on the probabilities of both infusibility and toxicity as functions of dose. The method is illustrated by application to a dendritic cell activated lymphocyte infusion trial in the treatment of acute leukemia. A simulation study indicates that the proposed methodology is both safe and reliable.

A new statistical method for dose-finding based on efficacy and toxicity in early phase clinical trials

Most statistical methods for dose-finding in phase I clinical trials determine a maximum tolerable dose based on toxicity while ignoring efficacy. Most phase II designs assume that an acceptable dose has been determined and aim to estimate treatment efficacy, possibly with early stopping rules for safety monitoring. The purpose of this paper is to describe a new statistical strategy for dose-finding in single-arm clinical trials where patient outcome is characterized in terms of both response and toxicity. The strategy, which may be considered a phase I/II hybrid, was first proposed by Thall and Russell [1] and subsequently modified by Thall [2]. The underlying mathematical model expresses the probabilities of response and toxicity as interdependent functions of dose. The method is based on fixed standards for the minimum probability of response and the maximum probability of toxicity appropriate for the particular trial. The best acceptable dose is chosen for each successive patient cohort adaptively, based on the fixed standards and the dose-outcome data from patients treated previously in the trial. The scientific goals are to select one best acceptable dose for future patients and to estimate the response and toxicity probabilities at that dose, or to stop the trial early if it becomes sufficiently unlikely that any dose is both safe and efficacious. An application of the method to a trial of donor lymphocyte infusion as salvage therapy for chemo-refractory AML/MDS patients is described. To illustrate the method's flexibility and potential breadth of application, two additional examples are provided, including a hypothetical trial in which a 5% response rate is of interest.