Two-stage Adaptive Design Research Articles

Lenalidomide and Low Dose Dexamethasone As First Line Therapy for Newly Diagnosed Patients with Primary Plasma Cell Leukemia

AbstractAbstract 729 Introduction:The prognosis of primary plasma cell leukemia (PPCL) remains poor. The “novel agents” have recently shown promising results in PPCL patients in case reports or small retrospective series. Here we describe the first prospective, multicenter, phase II clinical trial of PPCL, where lenalidomide in combination with low dose dexamethasone (Ld) was tested as initial therapy in newly diagnosed patients fulfilling the IMWG diagnostic criteria of PPCL. Patients and methods:Ld regimen consisted of lenalidomide 25 mg/d for 21 days and oral dexamethasone 40 mg on days 1, 8, 15, and 22 for each 28-day cycle. After 4 cycles, responding patients not eligible for stem cell transplantation (SCT) continued until 8 cycles of full-dose Ld, followed by a maintenance dose of single agent lenalidomide equal to 10 mg/d on days 1–21 of each 28-day cycle. Patients responding after 4 cycles and eligible for SCT proceeded according to the treatment centre’s transplant policy. Patients not responding after 4 cycles or progressing during this treatment were considered off-study. Appropriate dose reductions (in particular for patients with reduced renal function at baseline), double contraception methods and anti-thrombotic/anti-infective prophylaxis were recommended. The primary end-point was early response rate according to IMWG uniform response criteria. Secondary end-points were PFS, OS, feasibility and efficacy of SCT, safety. Results:According to the Simon optimal two-stage adaptive design, twenty-three patients were enrolled in between March 2009 and May 2011. The male/female ratio was 1.1, and median age was 60 years (range 44–80). The median absolute number and percentage of circulating plasma cells were 4.280/μl (range 1.500–114.660) and 34% (range 21–90) respectively. Fifteen patients (65.2%) had abnormal renal function at presentation. Twenty-one patients (91.3%) were tested by FISH analysis and cytogenetic abnormalities were detected in all of them, del13q being the most frequently found (16 patients). Seventeen patients showed multiple chromosomal lesions. Involvement of chromosome 14 was observed in 18 patients, three of whom showed t(4;14). Chromosome 1q gain and del17p were detected in 10 and 7 patients, respectively. In the intention-to-treat (ITT) population, overall response rate (ORR) after at least one Ld cycle was 73.9% (17/23), with 8 patients (34.7%) achieving partial remission (PR), 5 (21.7%) very good PR (VGPR), 3 (13%) complete response (CR), and 1(4.3%) near CR (nCR) (VGPR or better: 39%). In the efficacy-evaluable (EE) population, 14 out of 15 patients who received the initially planned 4 Ld cycles (65.2% of the ITT population) responded (ORR 93.3%), achieving 5 PR (33.3%), 5 VGPR (33.3%), 1 near-CR (6.6%) and 3 CR (20%) (VGPR or better: 59.9%). The maintenance phase was reached and safely performed in 4 responding patients not eligible for SCT, 3 of whom relapsed after 2, 8 and 22 months, respectively. After Ld induction therapy, 8 patients received single (n. 4) or double (n. 4) autologous SCT (ASCT); another patient underwent a sequence of ASCT followed by non-myeloablative allogeneic SCT (AlloSCT). Six eligible patients did not receive ASCT frontline, due to initial Ld failure or adverse events; three of them underwent single ASCT (n. 2) or tandem ASCT/non-myeloablative AlloSCT (n. 1) after a bortezomib-based salvage therapy, achieving 2 CR and 1 PR. After a median follow-up of 23 months, median OS and PFS in ITT population were not reached and 22 months, respectively. All transplanted patients remained alive, although three of them relapsed and started salvage treatments; OS was 12 months in the 11 patients who did not receive ASCT (p < 0.001). The correspondent figures for PFS were 29 and 16 months, respectively (p < 0.01). Considering ITT population, multivariate analyses showed that SCT was positively correlated to both OS and PFS. There were 17 episodes of grade 3/4 non hematological toxicity, which occurred in 13 patients (5 infections, 3 renal, 3 metabolic, 2 gastro-intestinal, 2 skin, 1 fatigue, 1 thromboembolic), causing early interruption of Ld treatment in 4 patients. Grade 3/4 hematological toxicity (mainly neutropenia) occurred in 11 patients (47.8%). Conclusions:Ld may be a feasible and effective initial therapeutic option for PPCL, particularly in patients who receive ASCT after a short course of induction treatment. Disclosures:Musto:Celgene: Honoraria, Research Funding. Off Label Use: Lenalidomide as first line therapy of plasma cell leukemia. Petrucci:Celgene: Honoraria. Cascavilla:Celgene: Honoraria. Di Raimondo:Celgene: Honoraria. Caravita:Celgene: Honoraria. Morabito:Celgene: Honoraria. Offidani:Celgene: Honoraria. Bringhen:Celgene: Honoraria. Boccadoro:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Palumbo:Celgene: Consultancy, Honoraria.

On the efficiency of two‐stage response‐adaptive designs

In this paper, we investigate the efficiency of response-adaptive locally optimum designs. We focus on two-stage adaptive designs, where after the first stage the accrued data are used to determine a locally optimum design for the second stage. On the basis of an explicit expansion of the information matrix, we compare the variance of the maximum likelihood estimates obtained from a two-stage adaptive design and a fixed design without adaptation. For several one-parameter models, we provide explicit expressions for the relative efficiency of these two designs, which is seen to depend sensitively on the statistical problem under investigation. In particular, we show that in non-linear regression models with moderate or large variances the first-stage sample size of an adaptive design should be chosen sufficiently large in order to address variability in the interim parameter estimates. These findings support the results of recent simulation studies conducted to compare adaptive designs in more complex situations. We finally present an application to a real clinical dose-finding trial aiming at the estimation of the smallest dose achieving a certain percentage of the maximum treatment effect by using a three-parameter Emax model.

Two‐stage designs for Phase 2 dose‐finding trials

We propose a Bayesian adaptive two-stage design for the efficient estimation of the maximum dose or the minimum effective dose in a dose-finding trial. The new design allocates subjects in stage two according to the posterior distribution of the target dose location. Simulations show that the proposed two-stage design is superior to equal allocation and to a two-stage strategy where only one dose is left in the second stage.

A new adaptive design based on Simon's two-stage optimal design for phase II clinical trials

Phase II clinical trials are conducted to determine whether a new agent or drug regimen has sufficient promise in treating cancer to merit further testing in larger groups of patients. Both ethical and practical considerations often require early termination of phase II trials if early results clearly indicate that the new regimen is not active or worthy of further investigation. Simon's two-stage designs (1989) are common methods for conducting phase II studies investigating new cancer therapies. Banerjee and Tsiatis (2006) proposed an adaptive two-stage design which allows the sample size at the second stage to depend on the results at the first stage. Their design is more flexible than Simon's, but it is somewhat counter-intuitive: as the response in the first stage increases, the second-stage sample size increases till a certain point and then abruptly becomes zero. In this paper, based on Simon's two-stage optimal design, we propose a new adaptive one which depends on the first stage results using the restrict conditions the conditional type I error and the conditional power. Comparisons are made between Banerjee and Tsiatis' results and our new adaptive designs.

Shrinkage estimation in two‐stage adaptive designs with midtrial treatment selection

We consider the problem of estimation in adaptive two-stage designs with selection of a single treatment arm at an interim analysis. It is well known that the standard maximum-likelihood estimator of the selected treatment is biased. We prove that selection bias of the maximum-likelihood estimator is maximal when all treatment effects are equal and the most-promising treatment is selected. Furthermore, we consider shrinkage estimation as a solution for the selection bias problem. We thereby extend previous work of Hwang on Lindley's estimator for single-stage multi-armed trials with four or more treatments and post-trial treatment selection. Following Hwang's ideas, we show that a simple two-stage version of Lindley's estimator has uniformly smaller Bayes risk than the maximum-likelihood estimator when assuming an empirical Bayesian framework with independent normal priors for the group means. For designs that start with two or three treatment groups, we suggest using a two-stage version of the common estimator of the best linear unbiased predicator of the corresponding random effects model. We show by an extensive simulation study that the shrinkage estimators perform well compared with maximum-likelihood and previously suggested bias-adjusted estimators in terms of selection bias and mean squared error.

On Efficient Two-Stage Adaptive Designs for Clinical Trials with Sample Size Adjustment

Group sequential designs are rarely used for clinical trials with substantial over running due to fast enrollment or long duration of treatment and follow-up. Traditionally, such trials rely on fixed sample size designs. Recently, various two-stage adaptive designs have been introduced to allow sample size adjustment to increase statistical power or avoid unnecessarily large trials. However, these adaptive designs can be seriously inefficient. To address this infamous problem, we propose a likelihood-based two-stage adaptive design where sample size adjustment is derived from a pseudo group sequential design using cumulative conditional power. We show through numerical examples that this design cannot be improved by group sequential designs. In addition, the approach may uniformly improve any existing two-stage adaptive designs with sample size adjustment. For statistical inference, we provide methods for sequential p-values and confidence intervals, as well as median unbiased and minimum variance unbiased estimates. We show that the claim of inefficiency of adaptive designs by Tsiatis and Mehta (2003) is logically flawed, and thereby provide a strong defense of Cui et al. (1999).

Analysis of Time-to-Event Data with Nonuniform Patient Entry and Loss to Follow-Up under a Two-Stage Seamless Adaptive Design with Weibull Distribution

In the pharmaceutical industry, a two-stage seamless adaptive design that combines two separate independent clinical trials into a single clinical study is commonly employed in clinical research and development. In practice, in the interest of shortening the development process, it is not uncommon to consider study endpoints with different treatment durations at different stages (Chow and Chang, 2006; Maca et al., 2006). In this study, our attention is placed on the case where the study endpoints of interest are time-to-event data where the durations at the two stages are different with nonuniform patient entry and losses to follow-up or dropouts. Test statistics for the final analysis based on the combined data are developed under various hypotheses for testing equality, superiority, noninferiority, and equivalence. In addition, formulas for sample size calculation and allocation between the two stages based on the proposed test statistic are derived.

An Adaptive Group Sequential Phase II Design to Compare Treatments for Survival Endpoints in Rare Patient Entities

For rare diseases, standard treatments are often not available and essential study parameters are difficult or impossible to obtain. Therefore, designs of clinical trials for these diseases are often based on little information. Adaptive designs allow such trials to be started and to gain information during the study. Motivated by a trial for a rare subtype of renal-cell carcinoma, we present a two-stage adaptive design for right-censored time-to-event data and a two-sided test. After the first stage, one can stop for futility or continue with reestimated sample size. The properties of such designs are analyzed by simulation studies.

Efficiency of Two-Stage Adaptive Cluster Sampling Design in Estimating Fringe-Eared Oryx

Two-stage adaptive cluster sampling and two-stage conventional sampling designs were used to estimate population total of Fringe-Eared Oryx that are clustered and sparsely distributed. The study region was Amboseli-West Kilimanjaro and Magadi-Natron cross boarder landscape between Kenya and Tanzania. The study region was partitioned into different primary sampling units with different secondary sampling units that were of different sizes. Results show that two-stage adaptive cluster sampling design is efficient compared to simple random sampling and the conventional two- stage sampling design. The design is less variable compared to the conventional two-stage sampling design.

Two-Stage Adaptive Optimal Design with Fixed First-Stage Sample Size

In adaptive optimal procedures, the design at each stage is an estimate of the optimal design based on all previous data. Asymptotics for regular models with fixed number of stages are straightforward if one assumes the sample size of each stage goes to infinity with the overall sample size. However, it is not uncommon for a small pilot study of fixed size to be followed by a much larger experiment. We study the large sample behavior of such studies. For simplicity, we assume a nonlinear regression model with normal errors. We show that the distribution of the maximum likelihood estimates converges to a scale mixture family of normal random variables. Then, for a one parameter exponential mean function we derive the asymptotic distribution of the maximum likelihood estimate explicitly and present a simulation to compare the characteristics of this asymptotic distribution with some commonly used alternatives.

Adaptive Patient Population Selection Design in Clinical Trials

For the success of a new drug development, it is crucial to select the sensitive patient populations. To potentially reduce timeline and cost, we may apply a two-stage adaptive patient population selection design to a therapeutic trial. In such a design, based on early results of the trial, patient population(s) will be selected/determined for the final stage and analysis. Because of this adaptive nature and the multiple between-treatment comparisons for multiple populations, an alpha adjustment is necessary. In this article, we propose a closed step-down testing procedure to assess treatment effects on multiple populations and a weighted combination test to combine data from the two stages after sample size adaptation. Computation/simulation is used to compare the performances of the proposed procedure and the other multiplicity adjustment procedures. A trial simulation is presented to illustrate the application of the methods.

Blinded sample size re-estimation in clinical trials comparing several treatments

An important question that arises in clinical trials is how many additional observations, if any, are required beyond those originally planned. This has satisfactorily been answered in the case of two-treatment double-blind clinical experiments. However, one may be interested in comparing a new treatment with its competitors, which may be more than one. This problem is addressed in this investigation involving responses from arbitrary distributions, in which the mean and the variance are not functionally related. First, a solution in determining the initial sample size for specified level of significance and power at a specified alternative is obtained. Then it is shown that when the initial sample size is large, the nominal level of significance and the power at the pre-specified alternative are fairly robust for the proposed sample size re-estimation procedure. An application of the results is made to the blood coagulation functionality problem considered by Kropf et al. [Multiple comparisons of treatments with stable multivariate tests in a two-stage adaptive design, including a test for non-inferiority, Biom. J. 42(8) (2000), pp. 951–965].

Final Results of a Phase II Study Evaluating Lenalidomide in Combination with Low Dose Dexamethasone As First Line Therapy for Primary Plasma Cell Leukemia

Abstract 2925Primary plasma cell leukemia (PPCL) is an aggressive variant of multiple myeloma, accounting for 0.5–4% of all newly diagnosed myeloma cases and characterized by a short survival (generally less than 1 year), which is only moderately improved by transplant procedures. Novel agents seem to be able to ameliorate the poor clinical outcome of both primary and secondary leukemic phases of myeloma; however, no data are currently available on the use of lenalidomide as first line therapy in PPCL.On March, 2009, we started a multicenter, phase II trial aiming to evaluate safety and antitumor activity of lenalidomide in combination with dexamethasone (LD) in previously untreated PPCL. Here we report the final results of this study. Newly diagnosed PPCL patients received lenalidomide at a dose of 25 mg/d for 21 days and oral dexamethasone at a dose of 40 mg on days 1, 8, 15, and 22 for each 28-day cycle. After 4 cycles, responding patients not eligible for stem cell transplantation (SCT) continued until 8 cycles of full-dose LD, if tolerated, followed by a maintenance dose of single agent lenalidomide equal to 10 mg/d on days 1–21 of each 28-day cycle. Patients responding after 4 cycles and eligible for SCT proceeded according to single Centre transplant policy. Patients not responding after 4 cycles or progressing during this treatment were considered off-study. The primary endpoint was early response rate according to International Uniform Criteria. The secondary endpoints were PFS, OS, safety and percentage of eligible patients able to undergo autologous or allogeneic SCT. Appropriate dose reductions, contraception methods and anti-thrombotic prophylaxis were applied.Twenty-three patients, as requested by the Simon Optimal Two-Stage Adaptive Design adopted, were enrolled. The trial was therefore closed on May, 31, 2011. M/F ratio was 0.7, mean age was 62 years (range 44–80). Circulating plasma cells ranged from 2.1 to 115 × 10e9/l. Moderate renal failure, increased LDH and extramedullary disease occurred in 39.1%, 43.5% and 13 % of patients, respectively. Hb was < 10 g/dl in 19 patients (82.6%), while platelet count was < 50 × 10e9/l in 5 patients (21.7%). Karyotype abnormalities were detected by FISH in 21 out of 22 tested patients; in particular, 1p loss was found in 9 patients, 1q gain in 10 patients, del(13q) in 16 patients, del(17p13) in 7 patients, t(11;14) in 7 patients, t(4;14) in 3 patients and MAF translocations, including t(14;20) and t(14;16), in 8 patients. Seventeen patients had a combination of two (n. 5) or more (n. 12) cytogenetic lesions.On intention-to-treat analysis, 14 patients completed the initial four planned cycles and all of them responded. In particular, 6 PR (26.1%), 4 VGPR (17.4%), 1 near-CR (4.3%) and 3 CR (13%) were achieved (ORR 60.8%, VGPR or better 34.7%). Causes of early treatment discontinuation were: a) progressive disease (4 patients, after an initial, brief response in 2 cases); b) severe adverse events (4 patients: one acute renal failure, one Stevens-Johnson’s syndrome, one pneumonia suspected for Pneumocystis carinii etiology, one multi-organ failure); c) death in PR due to causes unrelated to treatment or disease (one patient). Other relevant non-hematological toxicities included four episodes of pneumonia and one case of DVT. Grade 3–4 hematological toxicities occurred in about half of cases, requiring Lenalidomide dose adjustments. So far, among subjects achieving a response after 4 LD cycles, 8 eligible patients have successfully collected peripheral blood stem cells: 5 of them have completed single or double autologous SCT, one patient received tandem autologous-allogeneic non myeloablative SCT from a MUD donor. All patients transplanted after LD are currently alive and in remission phase. The maintenance phase has been reached in 3 responding patients not eligible for SCT, 2 of whom have relapsed after 2 and 8 months, respectively. With a mean follow-up of 15 months, OS and PFS are 65.2% and 52.1%, respectively.LD is a possible initial therapeutic option for PPCL, particularly in patients who receive SCT after a short course of induction treatment. Caution is required to prevent and to manage renal and hematological toxicities, as well as infectious complications. Considering some previous results obtained with other novel agents, the combination of lenalidomide and bortezomib might be an appealing approach to investigate prospectively in PPCL patients. Disclosures:Musto:Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Petrucci:Celgene: Honoraria. Cascavilla:Celgene: Honoraria. Di Raimondo:Celgene: Honoraria. Caravita:Celgene: Honoraria. Morabito:Celgene: Honoraria. Offidani:Celgene: Honoraria. Bringhen:Celgene: Honoraria. Boccadoro:Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Palumbo:Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.

A Two-Stage Adaptive Design in Phase 2 Clinical Trials for Acute Treatment of Migraine

In the development of acute migraine treatments, traditional phase 2 dose-finding trial designs are particularly challenging when a wide dose range needs to be investigated. In this article, an adaptive two-stage dose-finding design is introduced. The proposed design combines traditional phase 2a and 2b to serve the dual purpose of proof of concept and dose finding through the use of an unblinded interim analysis that provides an opportunity for adaptation. The design has a fixed total sample size but includes provisions for discontinuation of less effective doses at the interim analysis to allow for the allocation of patients into a more promising range of doses. The interim adaptation is designed to increase the amount of information collected on more effective doses and increase study power as compared to a traditional nonadaptive dose-finding design. On the other hand, despite the interim look and adaptation of the design, conventional statistical analyses of the final data can still be used for the final analysis and provide proper inferences with adequately controlled type I error and an ignorable amount of bias.

Targeting population entering phase III trials: A new stratified adaptive phase II design

The primary goal of phase II studies is to assess the efficacy of the new treatment in order to decide whether it has sufficient activity to warrant further evaluation in a phase III comparative trial. However, many adequately conducted phase II trials are negative leading to termination of drug development. Heterogeneity of the population is often considered to be a cause of treatment effect dilution. One approach to determine the sensitive subpopulation is to conduct several phase II trials, one in each specific subset of patients. This option might unethically increase the number of non-sensitive patients under evaluation. Adaptive two-stage designs have been recently proposed. London and Chang proposed a global one-sample test for response rates for stratified phase II clinical trials, whereas Jones and Holmgren proposed an adaptive design that allows preliminary determination of efficacy that may be restricted to a specific subpopulation defined by biomarker status. These two methods do not allow early termination for efficacy in one or several subgroups as they are extensions of the Simon design. The authors propose an alternative method to deal with stratification in phase II clinical trials and identification of the best target population. This method is based on the multiple-stage Fleming design allowing for early stopping rules for either efficacy or inefficacy. It also integrates a procedure testing whether treatment effects are similar or heterogeneous between the two groups. The operating characteristics of this method were compared with those of a standard Fleming design using exact binomial probabilities.

Impacts on type I error rate with inappropriate use of learn and confirm in confirmatory adaptive design trials

A two-stage adaptive design trial is a single trial that combines the learning data from stage 1 (or phase II) and the confirming data in stage 2 (or phase III) for formal statistical testing. We call it a "Learn and Confirm" trial. The studywise type I error rate remains to be at issue in a "Learn and Confirm" trial. For studying multiple doses or multiple enpdoints, a "Learn and Confirm" adaptive design can be more attractive than a fixed design approach. This is because intuitively the learning data in stage 1 should not be subjected to type I error scrutiny if there is no formal interim analysis performed and only an adaptive selection of design parameters is made at stage 1. In this work, we conclude from extensive simulation studies that the intuition is most often misleading. That is, regardless of whether or not there is a formal interim analysis for making an adaptive selection, the type I error rates are always at risk of inflation. Inappropriate use of any "Learn and Confirm" strategy should not be overlooked.

Adaptive confidence intervals of desired length and power for normal means

In all empirical or experimental sciences, it is a standard approach to present results, additionally to point estimates, in form of confidence intervals on the parameters of interest. The length of a confidence interval characterizes the accuracy of the whole findings. Consequently, confidence intervals should be constructed to hold a desired length. Basic ideas go back to Stein (1945) and Seelbinder (1953) who proposed a two-stage procedure for hypothesis testing about a normal mean. Tukey (1953) additionally considered the probability or power a confidence interval should possess to hold its length within a desired boundary. In this paper, an adaptive multi-stage approach is presented that can be considered as an extension of Stein's concept. Concrete rules for sample size updating are provided. Following an adaptive two-stage design of O’Brien and Fleming (1979) type, a real data example is worked out in detail.

Design of adaptive two-stage double-arm clinical trials for dichotomous variables

It is well known that flexibility is one of the major advantages of an adaptive two-stage design, and the intended adaptation should be as preplanned as possible to maintain the integrity of the clinical trial. The design of adaptive two-stage double-arm clinical trials for dichotomous variables was proposed by simulation and forecasting procedure at the planning stage. To further ensure the integrity of the clinical trial, the sample size scheme for each scenario, which was supposed to be based on the first stage, was provided in the protocol by Monte Carlo simulation. In addition, the study parameters were determined by comparing the assessment indexes such as total sample size, expected sample size and the test power at the first stage. Furthermore, Fisher's combination test and pooled data analysis were considered and compared through the simulation. The latter, which has the larger overall power and the better overall type I error control, with the same sample size was adopted for further simulation and statistical analysis in the clinical trial.

A Bayesian predictive strategy for an adaptive two‐stage design in phase II clinical trials

Phase II clinical trials are typically designed as two-stage studies, in order to ensure early termination of the trial if the interim results show that the treatment is ineffective. Most of two-stage designs, developed under both a frequentist and a Bayesian framework, select the second stage sample size before observing the first stage data. This may cause some paradoxical situations during the practical carrying out of the trial. To avoid these potential problems, we suggest a Bayesian predictive strategy to derive an adaptive two-stage design, where the second stage sample size is not selected in advance, but depends on the first stage result. The criterion we propose is based on a modification of a Bayesian predictive design recently presented in the literature (see (Statist. Med. 2008; 27:1199-1224)). The distinction between analysis and design priors is essential for the practical implementation of the procedure: some guidelines for choosing these prior distributions are discussed and their impact on the required sample size is examined.

A Bayesian adaptive design for two-stage clinical trials with survival data

A randomized two-stage adaptive Bayesian design is proposed and studied for allocation and comparison in a phase III clinical trial with survival time as treatment response. Several exact and limiting properties of the design and the follow-up inference are studied, both numerically and theoretically, and are compared with a single-stage randomized procedure. The applicability of the proposed methodology is illustrated by using some real data.