Browse
Submit Data
Databases
API
Help

Dataset Information

10 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A Rescue Strategy for Handling Unevaluable Patients in Simon's Two Stage Design.

ABSTRACT: For phase II oncology trials, Simon's two-stage design is the most commonly used strategy. However, when clinically unevaluable patients occur, the total number of patients included at each stage differs from what was initially planned. Such situations raise concerns about the operating characteristics of the trial design. This paper evaluates three classical ad hoc strategies and a novel one proposed in this work for handling unevaluable patients. This latter is called the rescue strategy which adapts the critical stopping rules to the number of unevaluable patients at each stage without modifying the planned sample size. blue Simulations show that none of these strategies perfectly match the original target constraints for type I and II error rates. Our rescue strategy is nevertheless the one which best approaches the target error rates requirement. A re-analysis of one real phase II clinical trials on metastatic cancer illustrates the use of the proposed strategy.

SUBMITTER: Belin L

PROVIDER: S-EPMC4569274 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

A Rescue Strategy for Handling Unevaluable Patients in Simon's Two Stage Design.

Belin Lisa L Broët Philippe P De Rycke Yann Y

PloS one 20150914 9

For phase II oncology trials, Simon's two-stage design is the most commonly used strategy. However, when clinically unevaluable patients occur, the total number of patients included at each stage differs from what was initially planned. Such situations raise concerns about the operating characteristics of the trial design. This paper evaluates three classical ad hoc strategies and a novel one proposed in this work for handling unevaluable patients. This latter is called the rescue strategy which ...[more]

PMID: 26368810

Similar Datasets

A batch-effect adjusted Simon's two-stage design for cancer vaccine clinical studies.

Project description:In the development of cancer treatment vaccines, phase II clinical studies are conducted to examine the efficacy of a vaccine in order to screen out vaccines with minimal activity. Immune responses are commonly used as the primary endpoint for assessing vaccine efficacy. With respect to study design, Simon's two-stage design is a popular format for phase II cancer clinical studies because of its simplicity and ethical considerations. Nonetheless, it is not straightforward to apply Simon's two-stage design to cancer vaccine studies when performing immune assays in batches, as outcomes from multiple patients may be correlated with each other in the presence of batch effects. This violates the independence assumption of Simon's two-stage design. In this paper, we numerically explore the impact of batch effects on Simon's two-stage design, propose a batch-effect adjusted Simon's two-stage design, demonstrate the proposed design by both a simulation study and a therapeutic human papillomavirus vaccine trial, and briefly introduce a software that implements the proposed design.

| S-EPMC8713137 | biostudies-literature

Inclusion of non-inferiority analysis in superiority-based clinical trials with single-arm, two-stage Simon's design.

Project description:IntroductionNon-inferiority (NI) analysis is not usually considered in the early phases of clinical development. In some negative phase II trials, a post-hoc NI analysis justified additional phase III trials that were successful. However, the risk of false positive achievements was not controlled in these early phase analyses. We propose to preplan NI analyses in superiority-based Simon's two-stage designs to control type I and II error rates.MethodsSimulations have been proposed to assess the control of type I and II errors rates with this method. A total of 12,768 two-stage Simon's design trials were constructed based on different assumptions of rejection response probability, desired response probability, type I and II errors, and NI margins. P-value and type II error were calculated with stochastic ordering using Uniformly Minimum Variance Unbiased Estimator. Type I and II errors were simulated using the Monte Carlo method. The agreement between calculated and simulated values was analyzed with Bland-Altman plots.ResultsWe observed the same level of agreement between calculated and simulated type I and II errors from both two-stage Simon's superiority designs and designs in which NI analysis was allowed. Different examples has been proposed to explain the utility of this method.ConclusionInclusion of NI analysis in superiority-based single-arm clinical trials may be useful for weighing additional factors such as safety, pharmacokinetics, pharmacodynamic, and biomarker data while assessing early efficacy. Implementation of this strategy can be achieved through simple adaptations to existing designs for one-arm phase II clinical trials.

| S-EPMC7733004 | biostudies-literature

A Simon's two-stage design trial evaluating the potential role of a kind of honey in preventing chemotherapy-hematopoietic toxicities.

Project description:Background and aimHematopoietic toxicities are a serious consequence of myelosuppressive CT that may result in dose reductions, delays or even discontinuation of CT, which, in turn, may compromise patient outcomes. Concerns about tolerability and costs of CSFs are still ongoing, therefore the potential use of supportive therapeutics agents are still of interest.Experimental procedureWe performed a monocentric, phase II study using Simon's two-stage design. The primary endpoint was the evaluation of the potential clinical benefit of a special kind of honey (Life-Mel Honey) administered prophylactically to reduce the incidence of hematopoietic toxicities following chemotherapy. We have enrolled patients undergoing adjuvant or first-line chemotherapy.Results and conclusionFrom November 2013 to May 2014 (First stage) and from November 2014 to April 2016 (Second stage), 39 patients were enrolled at our Institution. The majority of patients was male (24/39, 61.5%), medium age was 60.4 years (range 34-77 years). The median follow up was 74.5 days (SD +/- 28.5). Overall, the majority of patients could underwent their chemoterapy with a regular schedule (25/39, 64.1%), while 9/39 patients (23.1%) need to delay chemotherapy due to hematological adverse events of various grade. Ten/39 patients (25.6%) had a grade 1 neutrophils count decreased, 56.4% a grade 1 platelets count decrease and 64.1% a grade 1 hemoglobin decrease. Therefore, Life-Mel Honey showed an interesting profile to reduce hematological toxicities. The proportion of responses is sufficiently high to recommend this honey to go to a next step in the clinical trial phase.

| S-EPMC8427476 | biostudies-literature

Modified Simon's minimax and optimal two-stage designs for single-arm phase II cancer clinical trials.

Project description:Simon's two-stage design and the admissible two-stage design have been commonly used in practice for single-arm phase II clinical trials when the primary endpoint is binary. The ethical benefit of the two-stage design over the single-stage design is attained by the early termination of the trial when the treatment seems to be inactive. While Simon's optimal design is the two-stage design that minimizes the expected number of subjects under the null hypothesis, the probability of falsely declaring futility after the first stage frequently seems undesirably high. In Simon's minimax design, however, it is often the case that a high proportion of the total planned subjects are evaluated in the first stage, and thus the ethical benefit may not be achieved. In this paper, we propose modified minimax and optimal two-stage designs which guarantee not only type I and II error rates but also reasonable sample size proportions in the first stage, while maintaining the probability of falsely declaring futility under a pre-selected level. The characteristics of the modified two-stage design will be compared with those of Simon's and the admissible two-stage design. The modified minimax design yields a design that requires modest increase in 29% of cases, while the modified optimal design saves 1 to 13 subjects in 81% of cases for β = 0.2. The modified design approach provides investigators with an alternative when the sample sizes of Simon's designs are severely unbalanced or the Type II error is unacceptably high after the first stage.

| S-EPMC6611508 | biostudies-literature

Statistical inference for extended or shortened phase II studies based on Simon's two-stage designs.

Project description:Simon's two-stage designs are popular choices for conducting phase II clinical trials, especially in the oncology trials to reduce the number of patients placed on ineffective experimental therapies. Recently Koyama and Chen (2008) discussed how to conduct proper inference for such studies because they found that inference procedures used with Simon's designs almost always ignore the actual sampling plan used. In particular, they proposed an inference method for studies when the actual second stage sample sizes differ from planned ones.We consider an alternative inference method based on likelihood ratio. In particular, we order permissible sample paths under Simon's two-stage designs using their corresponding conditional likelihood. In this way, we can calculate p-values using the common definition: the probability of obtaining a test statistic value at least as extreme as that observed under the null hypothesis.In addition to providing inference for a couple of scenarios where Koyama and Chen's method can be difficult to apply, the resulting estimate based on our method appears to have certain advantage in terms of inference properties in many numerical simulations. It generally led to smaller biases and narrower confidence intervals while maintaining similar coverages. We also illustrated the two methods in a real data setting.Inference procedures used with Simon's designs almost always ignore the actual sampling plan. Reported P-values, point estimates and confidence intervals for the response rate are not usually adjusted for the design's adaptiveness. Proper statistical inference procedures should be used.

| S-EPMC4535394 | biostudies-other

A two-stage strategy to accommodate general patterns of confounding in the design of observational studies.

Project description:Accommodating general patterns of confounding in sample size/power calculations for observational studies is extremely challenging, both technically and scientifically. While employing previously implemented sample size/power tools is appealing, they typically ignore important aspects of the design/data structure. In this paper, we show that sample size/power calculations that ignore confounding can be much more unreliable than is conventionally thought; using real data from the US state of North Carolina, naive calculations yield sample size estimates that are half those obtained when confounding is appropriately acknowledged. Unfortunately, eliciting realistic design parameters for confounding mechanisms is difficult. To overcome this, we propose a novel two-stage strategy for observational study design that can accommodate arbitrary patterns of confounding. At the first stage, researchers establish bounds for power that facilitate the decision of whether or not to initiate the study. At the second stage, internal pilot data are used to estimate key scientific inputs that can be used to obtain realistic sample size/power. Our results indicate that the strategy is effective at replicating gold standard calculations based on knowing the true confounding mechanism. Finally, we show that consideration of the nature of confounding is a crucial aspect of the elicitation process; depending on whether the confounder is positively or negatively associated with the exposure of interest and outcome, naive power calculations can either under or overestimate the required sample size. Throughout, simulation is advocated as the only general means to obtain realistic estimates of statistical power; we describe, and provide in an R package, a simple algorithm for estimating power for a case-control study.

| S-EPMC3297823 | biostudies-other

Two-stage microbial community experimental design.

Project description:Microbial community samples can be efficiently surveyed in high throughput by sequencing markers such as the 16S ribosomal RNA gene. Often, a collection of samples is then selected for subsequent metagenomic, metabolomic or other follow-up. Two-stage study design has long been used in ecology but has not yet been studied in-depth for high-throughput microbial community investigations. To avoid ad hoc sample selection, we developed and validated several purposive sample selection methods for two-stage studies (that is, biological criteria) targeting differing types of microbial communities. These methods select follow-up samples from large community surveys, with criteria including samples typical of the initially surveyed population, targeting specific microbial clades or rare species, maximizing diversity, representing extreme or deviant communities, or identifying communities distinct or discriminating among environment or host phenotypes. The accuracies of each sampling technique and their influences on the characteristics of the resulting selected microbial community were evaluated using both simulated and experimental data. Specifically, all criteria were able to identify samples whose properties were accurately retained in 318 paired 16S amplicon and whole-community metagenomic (follow-up) samples from the Human Microbiome Project. Some selection criteria resulted in follow-up samples that were strongly non-representative of the original survey population; diversity maximization particularly undersampled community configurations. Only selection of intentionally representative samples minimized differences in the selected sample set from the original microbial survey. An implementation is provided as the microPITA (Microbiomes: Picking Interesting Taxa for Analysis) software for two-stage study design of microbial communities.

| S-EPMC3834858 | biostudies-other

An optimal stratified Simon two-stage design.

Project description:In Phase II oncology trials, therapies are increasingly being evaluated for their effectiveness in specific populations of interest. Such targeted trials require designs that allow for stratification based on the participants' molecular characterisation. A targeted design proposed by Jones and Holmgren (JH) Jones CL, Holmgren E: 'An adaptive Simon two-stage design for phase 2 studies of targeted therapies', Contemporary Clinical Trials 28 (2007) 654-661.determines whether a drug only has activity in a disease sub-population or in the wider disease population. Their adaptive design uses results from a single interim analysis to decide whether to enrich the study population with a subgroup or not; it is based on two parallel Simon two-stage designs. We study the JH design in detail and extend it by providing a few alternative ways to control the familywise error rate, in the weak sense as well as the strong sense. We also introduce a novel optimal design by minimising the expected sample size. Our extended design contributes to the much needed framework for conducting Phase II trials in stratified medicine. © 2016 The Authors Pharmaceutical Statistics Published by John Wiley & Sons Ltd.

| S-EPMC5405342 | biostudies-literature

Expression data from early stage CRC patients' tumors [NanoString-Design]

Project description:This study was conducted in order to identify biomarkers for a prognostic gene expression signature for metastases in early stage CRC.

2017-08-02 | GSE81983 | GEO

A two-stage experimental design for dilution assays.

Project description:Dilution assays to determine solute concentration have found wide use in biomedical research. Many dilution assays return imprecise concentration estimates because they are only done to orders of magnitude. Previous statistical work has focused on how to design efficient experiments that can return more precise estimates, however this work has not considered the practical difficulties of implementing these designs in the laboratory. We developed a two-stage experiment with a first stage that obtains an order of magnitude estimate and a second stage that concentrates effort on the most informative dilution to increase estimator precision. We show using simulations and an empirical example that the best two-stage experimental designs yield estimates that are remarkably more accurate than standard methods with equivalent effort. This work demonstrates how to utilize previous advances in experimental design in a manner consistent with current laboratory practice. We expect that multi-stage designs will prove to be useful for obtaining precise estimates with minimal experimental effort.

| S-EPMC7199778 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data