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Resampling phase III data to assess phase II trial designs and endpoints.

ABSTRACT: The best phase II design and endpoint for growth inhibitory agents is controversial. We simulated phase II trials by resampling patients from a positive (sorafenib vs. placebo; TARGET) and a negative (AE941 vs. placebo) phase III trial in metastatic renal cancer to compare the ability of various designs and endpoints to predict the known results.A total of 770 and 259 patients from TARGET and the AE 941 trial, respectively, were resampled (5,000 replicates) to simulate phase II trials with ? = 0.10 (one-sided). Designs/endpoints: single arm, two-stage with response rate (RR) by Response Evaluation Criteria in Solid Tumors (RECIST; 37 patients); and randomized, two arm (20-35 patients per arm) with RR by RECIST, mean log ratio of tumor sizes (log ratio), progression-free survival (PFS) rate at 90 days (PFS-90), and overall PFS.Single-arm trials were positive with RR by RECIST in 55% and 1% of replications for sorafenib and AE 941, respectively. Randomized trials versus placebo with 20 patients per arm were positive with RR by RECIST in 55% and 7%, log ratio in 88% and 25%, PFS-90 in 64% and 15%, and overall PFS in 69% and 9% of replications for sorafenib and AE 941, respectively.Compared with the single-arm design and the randomized design comparing PFS, the randomized phase II design with the log ratio endpoint has greater power to predict the positive phase III result of sorafenib in renal cancer, but a higher false positive rate for the negative phase III result of AE 941.

SUBMITTER: Sharma MR

PROVIDER: S-EPMC3328614 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Resampling phase III data to assess phase II trial designs and endpoints.

Sharma Manish R MR Karrison Theodore G TG Jin Yuyan Y Bies Robert R RR Maitland Michael L ML Stadler Walter M WM Ratain Mark J MJ

Clinical cancer research : an official journal of the American Association for Cancer Research 20120127 8

<h4>Purpose</h4>The best phase II design and endpoint for growth inhibitory agents is controversial. We simulated phase II trials by resampling patients from a positive (sorafenib vs. placebo; TARGET) and a negative (AE941 vs. placebo) phase III trial in metastatic renal cancer to compare the ability of various designs and endpoints to predict the known results.<h4>Experimental design</h4>A total of 770 and 259 patients from TARGET and the AE 941 trial, respectively, were resampled (5,000 replic ...[more]

PMID: 22287601

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Project description:BackgroundGo/no-go decisions after phase II and sample size chosen for phase III are usually based on phase II results (e.g., the treatment effect estimate of phase II). Due to the decision rule (only promising phase II results lead to phase III), treatment effect estimates from phase II that initiate a phase III trial commonly overestimate the true treatment effect. Underpowered phase III trials are the consequence. Optimistic findings may then not be reproduced, leading to the failure of potentially expensive drug development programs. For some disease areas these failure rates are described to be quite high: 62.5%.MethodsWe integrate the ideas of multiplicative and additive adjustment of treatment effect estimates after go decisions in a utility-based framework for optimizing drug development programs. The design of a phase II/III program, i.e., the "right amount of adjustment", the allocation of the resources to phase II and III in terms of sample size, and the rule applied to decide whether to stop or to proceed with phase III influences its success considerably. Given specific drug development program characteristics (e.g., fixed and variable per patient costs for phase II and III, probable gain in case of market launch), optimal designs with respect to the maximal expected utility can be identified by the proposed Bayesian-frequentist approach. The method will be illustrated by application to practical examples characteristic for oncological studies.ResultsIn general, our results show that the program set-ups with adjusted treatment effect estimate used for phase III planning are superior to the "naïve" program set-ups with respect to the maximal expected utility. Therefore, we recommend considering an adjusted phase II treatment effect estimate for the phase III sample size calculation. However, there is no one-fits-all design.ConclusionIndividual drug development planning for a specific program is necessary to find the optimal design. The optimal choice of the design parameters for a specific drug development program at hand can be found by our user friendly R Shiny application and package (both assessable open-source via [1]).

Project description:INTRODUCTION:Ceftazidime-avibactam combines the established anti-pseudomonal cephalosporin, ceftazidime, with the novel non-?-lactam ?-lactamase inhibitor, avibactam. OBJECTIVES:The aim of this study was to evaluate the safety of ceftazidime-avibactam in adults using pooled data from two phase II (NCT00690378, NCT00752219) and five phase III (NCT01499290, NCT01726023, NCT01644643, NCT01808093 and NCT01595438/NCT01599806) clinical studies. METHODS:Safety data from seven multicentre, randomised, active-comparator studies were pooled by study group at the patient level for descriptive analyses, comprising patients with complicated urinary tract infection (cUTI), including pyelonephritis, complicated intra-abdominal infection (cIAI), or nosocomial pneumonia (NP), including ventilator-associated pneumonia (VAP), treated with ceftazidime-avibactam?±?metronidazole or comparator. RESULTS:In total, 4050 patients (ceftazidime-avibactam?±?metronidazole, n?=?2024; comparator, n?=?2026) were included in the pooled analysis. Adverse events (AEs) up to the last study visit occurred in 996 (49.2%) and 965 (47.6%) patients treated with ceftazidime-avibactam?±?metronidazole and comparator, respectively. The most common AEs across treatment groups were diarrhoea, nausea, headache, vomiting and pyrexia. There were few discontinuations due to AEs (2.5% and 1.7% for ceftazidime-avibactam?±?metronidazole and comparators, respectively). Overall rates of serious AEs were 8.7% for ceftazidime-avibactam?±?metronidazole and 7.2% for comparators; respective rates of AEs with an outcome of death were 2.0% and 1.8%. AEs considered causally related to the study drug or procedures occurred in 10.7% and 9.6% of patients treated with ceftazidime-avibactam?±?metronidazole and comparators; the most common drug-related AEs in both groups were diarrhoea, headache, nausea and increased alanine aminotransferase. No impact to the safety profile of ceftazidime-avibactam?±?metronidazole was found with regard to intrinsic factors, such as age or renal function at baseline, or extrinsic factors, such as geographical origin. Potentially clinically significant changes in laboratory parameters were infrequent with no trends or safety concerns identified. CONCLUSION:The observed safety profile of ceftazidime-avibactam across infection types is consistent with the established safety profile of ceftazidime monotherapy and no new safety findings were identified. This analysis supports the use of ceftazidime-avibactam as a treatment option in adults with cUTI, cIAI and NP, including VAP.

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Resampling phase III data to assess phase II trial designs and endpoints.

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Resampling phase III data to assess phase II trial designs and endpoints.

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