Control arm heterogeneity in trials for unselected advanced triple-negative breast cancer (aTNBC).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1082-1082
Author(s):  
Kinisha Gala ◽  
Ankit Kalucha ◽  
Samuel Martinet ◽  
Anushri Goel ◽  
Kalpana Devi Narisetty ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Single Agent ◽  
Progression Free Survival ◽  
Sample Sizes ◽  
Time To Event ◽  
Sample Size Calculations ◽  
Subgroup Analyses ◽  
Combination Regimens

1082 Background: Primary endpoints of clinical trials frequently include subgroup-analyses. Several solid cancers such as aTNBC are heterogeneous, which can lead to unpredictable control arm performance impairing accurate assumptions for sample size calculations. We explore the value of a comprehensive clinical trial results repository in assessing control arm heterogeneity with aTNBC as the pilot. Methods: We identified P2/3 trials reporting median overall survival (mOS) and/or median progression-free survival (mPFS) in unselected aTNBC through a systematic search of PubMed, clinical trials databases and conference proceedings. Trial arms with sample sizes ≤25 or evaluating drugs no longer in development were excluded. Due to inconsistency among PD-L1 assays, PD-L1 subgroup analyses were not assessed separately. The primary aim was a descriptive analysis of control arm mOS and mPFS across all randomized trials in first line (1L) aTNBC. Secondary aims were to investigate time-to-event outcomes in control arms in later lines and to assess time-trends in aTNBC experimental and control arm outcomes. Results: We included 33 trials published between June 2013-Feb 2021. The mOS of control arms in 1L was 18.7mo (range 12.6-22.8) across 5 trials with single agent (nab-) paclitaxel [(n)P], and 18.1mo (similar range) for 7 trials including combination regimens (Table). The mPFS of control arms in 1L was 4.9mo (range 3.8-5.6) across 5 trials with single-agent (n)P, and 5.6mo (range 3.8-6.1) across 8 trials including combination regimens. Control arm mOS was 13.1mo (range 9.4-17.4) for 3 trials in first and second line (1/2L) and 8.7mo (range 6.7-10.8) across 5 trials in 2L and beyond. R2 for the mOS best-fit lines across control and experimental arms over time was 0.09, 0.01 and 0.04 for 1L, 1/2L and 2L and beyond, respectively. Conclusions: Median time-to-event outcomes of control arms in 1L aTNBC show considerable heterogeneity, even among trials with comparable regimens and large sample sizes. Disregarding important prognostic factors at stratification can lead to imbalances between arms, which may jeopardize accurate sample size calculations, trial results and interpretation. Optimizing stratification and assumptions for power calculations is of utmost importance in aTNBC and beyond. A digitized trial results repository with precisely defined patient populations and treatment settings could improve accuracy of assumptions during clinical trial design.[Table: see text]

scholarly journals Genetic variability and potential effects on clinical trial outcomes: perspectives in Parkinson’s disease

2018 ◽  
Author(s):  
Hampton Leonard ◽  
Cornelis Blauwendraat ◽  
Lynne Krohn ◽  
Faraz Faghri ◽  
Hirotaka Iwaki ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Clinical Trial ◽  
Parkinson's Disease ◽  
Clinical Trials ◽  
Sample Size ◽  
Risk Score ◽  
Genetic Risk ◽  
False Negative ◽  
Sample Sizes ◽  
Percent Difference

SummaryBackgroundImproper randomization in clinical trials can result in the failure of the trial to meet its primary end-point. The last ∼10 years have revealed that common and rare genetic variants are an important disease factor and sometimes account for a substantial portion of disease risk variance. However, the burden of common genetic risk variants is not often considered in the randomization of clinical trials and can therefore lead to additional unwanted variance between trial arms. We simulated clinical trials to estimate false negative and false positive rates and investigated differences in single variants and mean genetic risk scores (GRS) between trial arms to investigate the potential effect of genetic variance on clinical trial outcomes at different sample sizes.MethodsSingle variant and genetic risk score analyses were conducted in a clinical trial simulation environment using data from 5851 Parkinson’s Disease patients as well as two simulated virtual cohorts based on public data. The virtual cohorts included a GBA variant cohort and a two variant interaction cohort. Data was resampled at different sizes (n = 200-5000 for the Parkinson’s Disease cohort) and (n = 50-800 and n = 50-2000 for virtual cohorts) for 1000 iterations and randomly assigned to the two arms of a trial. False negative and false positive rates were estimated using simulated clinical trials, and percent difference in genetic risk score and allele frequency was calculated to quantify disparity between arms.FindingsSignificant genetic differences between the two arms of a trial are found at all sample sizes. Approximately 90% of the iterations had at least one statistically significant difference in individual risk SNPs between each trial arm. Approximately 10% of iterations had a statistically significant difference between trial arms in polygenic risk score mean or variance. For significant iterations at sample size 200, the average percent difference for mean GRS between trial arms was 130.87%, decreasing to 29.87% as sample size reached 5000. In the GBA only simulations we see an average 18.86% difference in GRS scores between trial arms at n = 50, decreasing to 3.09% as sample size reaches 2000. Balancing patients by genotype reduced mean percent difference in GRS between arms to 36.71% for the main cohort and 2.00% for the GBA cohort at n = 200. When adding a drug effect to the simulations, we found that unbalanced genetics with an effect on the chosen measurable clinical outcome can result in high false negative rates among trials, especially at small sample sizes. At a sample size of n = 50 and a targeted drug effect of −0.5 points in UPDRS per year, we discovered 33.9% of trials resulted in false negatives.InterpretationsOur data support the hypothesis that within genetically unmatched clinical trials, particularly those below 1000 participants, heterogeneity could confound true therapeutic effects as expected. This is particularly important in the changing environment of drug approvals. Clinical trials should undergo pre-trial genetic adjustment or, at the minimum, post-trial adjustment and analysis for failed trials. Clinical trial arms should be balanced on genetic risk variants, as well as cumulative variant distributions represented by GRS, in order to ensure the maximum reduction in trial arm disparities. The reduction in variance after balancing allows smaller sample sizes to be utilized without risking the large disparities between trial arms witnessed in typical randomized trials. As the cost of genotyping will likely be far less than greatly increasing sample size, genetically balancing trial arms can lead to more cost-effective clinical trials as well as better outcomes.

Planning the Size of Clinical Trials with Allowance for Patient Noncompliance

1990 ◽  
Vol 29 (03) ◽  
pp. 243-246 ◽  
Author(s):  
M. A. A. Moussa
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Event Rate ◽  
Survival Functions ◽  
Time Intervals ◽  
Patient Noncompliance ◽  
Event Rates ◽  
Larger Sample

AbstractVarious approaches are considered for adjustment of clinical trial size for patient noncompliance. Such approaches either model the effect of noncompliance through comparison of two survival distributions or two simple proportions. Models that allow for variation of noncompliance and event rates between time intervals are also considered. The approach that models the noncompliance adjustment on the basis of survival functions is conservative and hence requires larger sample size. The model to be selected for noncompliance adjustment depends upon available estimates of noncompliance and event rate patterns.

scholarly journals Geographic Disparities In Access To Cancer Clinical Trials In India

2020 ◽  
Author(s):  
Santam Chakraborty ◽  
Indranil Mallick ◽  
Hung N Luu ◽  
Tapesh Bhattacharyya ◽  
Arunsingh Moses ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
The State ◽  
Cancer Site ◽  
Cancer Clinical Trials ◽  
Clinical Trial Registry ◽  
Geographic Disparities ◽  
Incident Cancer ◽  
Incident Cases

Abstract Introduction The current study was aimed at quantifying the disparity in geographic access to cancer clinical trials in India. Methods We collated data of cancer clinical trials from the clinical trial registry of India (CTRI) and data on state-wise cancer incidence from the Global Burden of Disease Study. The total sample size for each clinical trial was divided by the trial duration to get the sample size per year. This was then divided by the number of states in which accrual was planned to get the sample size per year per state (SSY). For interventional trials investigating a therapy, the SSY was divided by the number of incident cancers in the state to get the SSY per 1,000 incident cancer cases. The SSY data was then mapped to visualise the geographical disparity.Results We identified 181 ongoing studies, of whom 132 were interventional studies. There was a substantial inter-state disparity - with a median SSY of 1.55 per 1000 incident cancer cases (range 0.00 - 296.81 per 1,000 incident cases) for therapeutic interventional studies. Disparities were starker when cancer site-wise SSY was considered. Even in the state with the highest SSY, only 29.7 % of the newly diagnosed cancer cases have an available slot in a therapeutic cancer clinical trial. Disparities in access were also apparent between academic (range: 0.21 - 226.60) and industry-sponsored trials (range: 0.17 - 70.21).Conclusion There are significant geographic disparities in access to cancer clinical trials in India. Future investigations should evaluate the reasons and mitigation approaches for such disparities.

scholarly journals Tool for filtering PubMed search results by sample size

2018 ◽  
Vol 25 (7) ◽  
pp. 774-779
Author(s):  
Carlos Baladrón ◽  
Alejandro Santos-Lozano ◽  
Javier M Aguiar ◽  
Alejandro Lucia ◽  
Juan Martín-Hernández
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Search Engine ◽  
Sample Sizes ◽  
Search Results ◽  
Estimate Sample Size ◽  
Search Tool ◽  
Pubmed Search ◽  
Meta Analyses ◽  
Validation Tests

Abstract Objective The most used search engine for scientific literature, PubMed, provides tools to filter results by several fields. When searching for reports on clinical trials, sample size can be among the most important factors to consider. However, PubMed does not currently provide any means of filtering search results by sample size. Such a filtering tool would be useful in a variety of situations, including meta-analyses or state-of-the-art analyses to support experimental therapies. In this work, a tool was developed to filter articles identified by PubMed based on their reported sample sizes. Materials and Methods A search engine was designed to send queries to PubMed, retrieve results, and compute estimates of reported sample sizes using a combination of syntactical and machine learning methods. The sample size search tool is publicly available for download at http://ihealth.uemc.es. Its accuracy was assessed against a manually annotated database of 750 random clinical trials returned by PubMed. Results Validation tests show that the sample size search tool is able to accurately (1) estimate sample size for 70% of abstracts and (2) classify 85% of abstracts into sample size quartiles. Conclusions The proposed tool was validated as useful for advanced PubMed searches of clinical trials when the user is interested in identifying trials of a given sample size.

scholarly journals A Review of Docetaxel: Its Use in the Treatment of Gastric Cancer

2010 ◽  
Vol 2 ◽  
pp. CMT.S5191
Author(s):  
Alessandro Inno ◽  
Michele Basso ◽  
Alessandra Cassano ◽  
Carlo Barone
Keyword(s):  
Gastric Cancer ◽  
Phase Ii ◽  
Performance Status ◽  
Single Agent ◽  
Progression Free Survival ◽  
Poor Performance ◽  
Phase Iii ◽  
Poor Performance Status ◽  
Phase Ii Studies ◽  
Combination Regimens

Docetaxel, a member of the taxane family, promotes cell death by binding β-tubulin and has demonstrated activity against several human malignancies, both as a single agent and in combination therapy. It has been approved in Europe and the US as front-line treatment for advanced gastric cancer in combination with cisplatin and fluorouracil (DCF regimen). This approval was based on the results of a pivotal study (V325) which demonstrated that the addition of docetaxel to the reference regimen of cisplatin and fluorouracil improves overall survival and progression-free survival with a better quality of life despite increased toxicity (mainly haematological). Modifications of DCF regimen have been successfully investigated as a means of making the treatment more tolerable and suitable also for elderly patients or patients with poor performance status. Emerging data from several phase II studies suggest that other docetaxel-based combination regimens with anthracyclines or irinotecan have interesting activity with acceptable toxicity profiles, but the true efficacy of these regimens needs to be assessed in large randomized phase III studies. Thus, the best docetaxel-containing regimen has yet to be identified. Docetaxel also represents a good candidate for combination with novel molecular target agents. In light of the high response rates observed in phase II-III studies, a docetaxel-based chemotherapy regimen might also be considered a treatment option as perioperative or adjuvant therapy in potentially curable gastric cancer and further studies with or without biological agents are eagerly awaited in this setting.

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