A batch-effect adjusted Simon's two-stage design for cancer vaccine clinical studies.
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ABSTRACT:
SUBMITTER: Wang C
PROVIDER: S-EPMC8713137 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC4569274 | biostudies-literature
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
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
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
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
Project description:When testing genotype-phenotype associations using linear regression, departure of the trait distribution from normality can impact both Type I error rate control and statistical power, with worse consequences for rarer variants. Because genotypes are expected to have small effects (if any) investigators now routinely use a two-stage method, in which they first regress the trait on covariates, obtain residuals, rank-normalize them, and then use the rank-normalized residuals in association analysis with the genotypes. Potential confounding signals are assumed to be removed at the first stage, so in practice, no further adjustment is done in the second stage. Here, we show that this widely used approach can lead to tests with undesirable statistical properties, due to both combination of a mis-specified mean-variance relationship and remaining covariate associations between the rank-normalized residuals and genotypes. We demonstrate these properties theoretically, and also in applications to genome-wide and whole-genome sequencing association studies. We further propose and evaluate an alternative fully adjusted two-stage approach that adjusts for covariates both when residuals are obtained and in the subsequent association test. This method can reduce excess Type I errors and improve statistical power.
| S-EPMC6416071 | biostudies-literature
Project description:BackgroundAnchored covariate-adjusted indirect comparisons inform reimbursement decisions where there are no head-to-head trials between the treatments of interest, there is a common comparator arm shared by the studies, and there are patient-level data limitations. Matching-adjusted indirect comparison (MAIC), based on propensity score weighting, is the most widely used covariate-adjusted indirect comparison method in health technology assessment. MAIC has poor precision and is inefficient when the effective sample size after weighting is small.MethodsA modular extension to MAIC, termed two-stage matching-adjusted indirect comparison (2SMAIC), is proposed. This uses two parametric models. One estimates the treatment assignment mechanism in the study with individual patient data (IPD), the other estimates the trial assignment mechanism. The first model produces inverse probability weights that are combined with the odds weights produced by the second model. The resulting weights seek to balance covariates between treatment arms and across studies. A simulation study provides proof-of-principle in an indirect comparison performed across two randomized trials. Nevertheless, 2SMAIC can be applied in situations where the IPD trial is observational, by including potential confounders in the treatment assignment model. The simulation study also explores the use of weight truncation in combination with MAIC for the first time.ResultsDespite enforcing randomization and knowing the true treatment assignment mechanism in the IPD trial, 2SMAIC yields improved precision and efficiency with respect to MAIC in all scenarios, while maintaining similarly low levels of bias. The two-stage approach is effective when sample sizes in the IPD trial are low, as it controls for chance imbalances in prognostic baseline covariates between study arms. It is not as effective when overlap between the trials' target populations is poor and the extremity of the weights is high. In these scenarios, truncation leads to substantial precision and efficiency gains but induces considerable bias. The combination of a two-stage approach with truncation produces the highest precision and efficiency improvements.ConclusionsTwo-stage approaches to MAIC can increase precision and efficiency with respect to the standard approach by adjusting for empirical imbalances in prognostic covariates in the IPD trial. Further modules could be incorporated for additional variance reduction or to account for missingness and non-compliance in the IPD trial.
| S-EPMC9358807 | biostudies-literature
Project description:Single cell RNA sequencing (scRNA-seq) can be used to characterize variation in gene expression levels at high resolution. However, the sources of experimental noise in scRNA-seq are not yet well understood. We investigated the technical variation associated with sample processing using the single cell Fluidigm C1 platform. To do so, we processed three C1 replicates from three human induced pluripotent stem cell (iPSC) lines. We added unique molecular identifiers (UMIs) to all samples, to account for amplification bias. We found that the major source of variation in the gene expression data was driven by genotype, but we also observed substantial variation between the technical replicates. We observed that the conversion of reads to molecules using the UMIs was impacted by both biological and technical variation, indicating that UMI counts are not an unbiased estimator of gene expression levels. Based on our results, we suggest a framework for effective scRNA-seq studies.
2016-07-08 | GSE77288 | GEO
Project description:Finding a genetic marker associated with a trait is a classic problem in human genetics. Recently, two-stage approaches have gained popularity in marker-trait association studies, in part because researchers hope to reduce the multiple testing problem by testing fewer markers in the final stage. We compared one two-stage family-based approach to an analogous single-stage method, calculating the empirical type I error rates and power for both methods using fully simulated data sets modeled on nuclear families with rheumatoid arthritis, and data sets of real single-nucleotide polymorphism genotypes from Centre d'Etude du Polymorphisme Humain pedigrees with simulated traits. In these analyses performed in the absence of population stratification, the single-stage method was consistently more powerful than the two-stage method for a given type I error rate. To explore the sources of this difference, we performed a case study comparing the individual steps of two-stage designs, the two-stage design itself, and the analogous one-stage design.
| S-EPMC2367501 | biostudies-literature
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