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Simulation Modeling of Cancer Clinical Trials: Application to Omitting Radiotherapy in Low-risk Breast Cancer.

ABSTRACT: Background:We used two models to simulate a proposed noninferiority trial of radiotherapy (RT) omission in low-risk invasive breast cancer to illustrate how modeling could be used to predict the trial's outcomes, inform trial design, and contribute to practice debates. Methods:The proposed trial was a prospective randomized trial of no-RT vs RT in women age 40 to 74 years undergoing lumpectomy and endocrine therapy for hormone receptor-positive, human epidermal growth factor receptor 2-negative, stage I breast cancer with an Oncotype DX score of 18 or lower. The primary endpoint was recurrence-free interval (RFI), including locoregional recurrence, distant recurrence, and breast cancer death. Noninferiority required the two-sided 90% confidence interval of the RFI hazard ratio (HR) for no-RT vs RT to be entirely below 1.7. Model inputs included published data. The trial was simulated 1000 times, and results were summarized as percent concluding noninferiority and mean (standard deviation) of hazard ratios for Model GE and Model M, respectively. Results:Noninferiority was demonstrated in 18.0% and 3.7% for the two models. The respective means (SD) of the RFI hazard ratios were 1.8 (0.7) and 2.4 (0.9); most were locoregional recurrences. The mean five-year RFI rates for no-RT vs RT (SD) were 92.7% (2.9%) vs 95.5% (2.2%) and 88.4% (2.0%) vs 94.5% (1.6%). Both models showed little or no difference in breast cancer-specific or overall survival. Alternative definitions of low risk based on combinations of age and grade produced similar results. Conclusions:The proposed trial was unlikely to show noninferiority of omitting radiotherapy even using alternative definitions of low-risk, as the endpoint included local recurrence. Future trials regarding radiotherapy should address absolute reduction in recurrence and impact of type of recurrence on the patient.

SUBMITTER: Jayasekera J

PROVIDER: S-EPMC6292816 | biostudies-other | 2018 Dec

REPOSITORIES: biostudies-other

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Simulation Modeling of Cancer Clinical Trials: Application to Omitting Radiotherapy in Low-risk Breast Cancer.

Jayasekera Jinani J Li Yisheng Y Schechter Clyde B CB Jagsi Reshma R Song Juhee J White Julia J Luta George G Chapman Judith-Anne W JW Feuer Eric J EJ Zellars Richard C RC Stout Natasha N Julian Thomas B TB Whelan Timothy T Huang Xuelin X Shelley Hwang E E Hopkins Judith O JO Sparano Joseph A JA Anderson Stewart J SJ Fyles Anthony W AW Gray Robert R Sauerbrei Willi W Mandelblatt Jeanne J Berry Donald A DA

Journal of the National Cancer Institute 20181201 12

<h4>Background</h4>We used two models to simulate a proposed noninferiority trial of radiotherapy (RT) omission in low-risk invasive breast cancer to illustrate how modeling could be used to predict the trial's outcomes, inform trial design, and contribute to practice debates.<h4>Methods</h4>The proposed trial was a prospective randomized trial of no-RT vs RT in women age 40 to 74 years undergoing lumpectomy and endocrine therapy for hormone receptor-positive, human epidermal growth factor recep ...[more]

PMID: 29718314

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Project description:BACKGROUND:Fatigue is one of the most common and distressing side-effects of breast cancer radiotherapy. According to current guidelines, accelerated partial breast irradiation (APBI) may be considered as an alternative treatment option for women with early-stage low-risk breast cancer. One method for APBI is single-dose intraoperative radiotherapy (IORT) applied directly to the tumor bed during breast conserving surgery (BCS). The COSMOPOLITAN trial therefore aims to analyze the intensity of fatigue following single-shot IORT with electrons (IOERT) compared to conventional hypofractionated whole breast irradiation (WBI) in low risk early breast cancer patients. METHODS:This trial is conducted as a multicenter, prospective, randomized, two-arm phase II study comparing the intensity of fatigue in early-stage breast cancer (cT1cN0cM0, tumor size <?2,5?cm, ER pos. Her2neu neg., age?>?50?years) treated either with WBI or APBI after BCS. Secondary outcomes investigated are tumor control, overall survival (OS), disease-free survival (DFS), acute and chronic toxicity, quality of life (QoL) and cosmesis. A total of 202 patients will be randomized into two arms: Patients in arm A will receive WBI (40.05?Gy, 15 fractions) after surgical resection, while patients in arm B will receive IOERT (21?Gy to the 90%-isodose) during BCS. Fatigue will be assessed 12?weeks post surgery with the help of the Functional Assessment of Chronic Illness Therapy (FACIT) Fatigue Scale. DISCUSSION:The present trial aims to evaluate treatment response to compare single-shot intraoperative electron APBI to conventional WBI following BCS in early-stage low risk breast cancer patients. Fatigue is selected as the primary, patient-reported endpoint due its major clinical relevance. TRIAL REGISTRATION:The study is prospectively registered on February 12th, 2019: Clinicaltrials.gov, NCT03838419. "Intraoperative Electron Radiotherapy for Low-risk Early Breast Cancer (COSMOPOLITAN)". STUDY STATUS:Ongoing study. Start of recruitment was December 2019.

Project description:BACKGROUND:The loss of large amounts of blood postpartum can lead to severe maternal morbidity and mortality. Understanding the nature of postpartum blood loss distribution is critical for the development of efficient analysis techniques when comparing treatments to prevent this event. When blood loss is measured, resulting in a continuous volume measure, often this variable is categorized in classes, and reduced to an indicator of volume greater than a cutoff point. This reduction of volume to classes entails a substantial loss of information. As a consequence, very large trials are needed to assess clinically important differences between treatments to prevent postpartum haemorrhage. METHODS:The authors explore the nature of postpartum blood loss distribution, assuming that the physical properties of blood loss lead to a lognormal distribution. Data from four clinical trials and one observational study are used to confirm this empirically. Estimates of probabilities of postpartum haemorrhage events 'blood loss greater than a cutoff point' and relative risks are obtained from the fitted lognormal distributions. Confidence intervals for relative risk are obtained by bootstrap techniques. RESULTS:A variant of the lognormal distribution, the three-parameter lognormal distribution, showed an excellent fit to postpartum blood loss data of the four trials and the observational study. A measurement quality assessment showed that problems of digit preference and lower limit of detection were well handled by the lognormal fit. The analysis of postpartum haemorrhage events based on a lognormal distribution improved the efficiency of the estimates. Sample size calculation for a hypothetical future trial showed that the application of this procedure permits a reduction of sample size for treatment comparison. CONCLUSION:A variant of the lognormal distribution fitted very well postpartum blood loss data from different geographical areas, suggesting that the lognormal distribution might fit postpartum blood loss universally. An approach of analysis of postpartum haemorrhage events based on the lognormal distribution improves efficiency of estimates of probabilities and relative risk, and permits a reduction of sample size for treatment comparison. TRIAL REGISTRATION:This paper reports secondary analyses for trials registered at Australian New Zealand Clinical Trials Registry (ACTRN 12608000434392 and ACTRN12614000870651); and at clinicaltrials.gov (NCT00781066).

Project description:Despite the strong prognostic stratification of circulating tumor cells (CTCs) enumeration in metastatic breast cancer (MBC), current clinical trials usually do not include a baseline CTCs in their design. This study aimed to generate a classifier for CTCs prognostic simulation in existing datasets for hypothesis generation in patients with MBC. A K-nearest neighbor machine learning algorithm was trained on a pooled dataset comprising 2436 individual MBC patients from the European Pooled Analysis Consortium and the MD Anderson Cancer Center to identify patients likely to have CTCs ≥ 5/7 mL blood (StageIVaggressive vs StageIVindolent). The model had a 65.1% accuracy and its prognostic impact resulted in a hazard ratio (HR) of 1.89 (Simulatedaggressive vs SimulatedindolentP < .001), similar to patients with actual CTCs enumeration (HR 2.76; P < .001). The classifier's performance was then tested on an independent retrospective database comprising 446 consecutive hormone receptor (HR)-positive HER2-negative MBC patients. The model further stratified clinical subgroups usually considered prognostically homogeneous such as patients with bone-only or liver metastases. Bone-only disease classified as Simulatedaggressive had a significantly worse overall survival (OS; P < .0001), while patients with liver metastases classified as Simulatedindolent had a significantly better prognosis (P < .0001). Consistent results were observed for patients who had undergone CTCs enumeration in the pooled population. The differential prognostic impact of endocrine- (ET) and chemotherapy (CT) was explored across the simulated subgroups. No significant differences were observed between ET and CT in the overall population, both in terms of progression-free survival (PFS) and OS. In contrast, a statistically significant difference, favoring CT over ET was observed among Simulatedaggressive patients (HR: 0.62; P = .030 and HR: 0.60; P = .037, respectively, for PFS and OS).

Project description:Background/aims The goal of this article is to illustrate the utility of multi-state models in cancer clinical trials. Our specific aims are to describe multi-state models and how they differ from standard survival methods, to illustrate how multi-state models can facilitate deeper understanding of the treatment effect on multiple paths along the disease process that patients could experience in cancer clinical trials, to explain the differences between multi-state models and time-dependent Cox models, and to briefly describe available software to conduct such analyses. Methods Data from 717 newly diagnosed acute myeloid leukemia patients who enrolled in the CALGB 10603 trial were used as an illustrative example. The current probability-in-state was estimated using the Aalen-Johansen estimator. The restricted mean time in state was calculated as the area under the probability-in-state curves. Cox-type regression was used to evaluate the effect of midostaurin on the various clinical paths. Simulation was conducted using a newly constructed shiny application. All analyses were performed using the R software. Results Multi-state model analyses of CALGB 10603 suggested that the overall improvement in survival with midostaurin seen in the primary analysis possibly resulted from a higher complete remission rate in combination with a lower risk of relapse and of death after complete remission in patients treated with midostaurin. Simulation results, in a three-state illness-death without recovery model, demonstrate that multi-state models and time-dependent Cox models evaluate treatment effects from different frameworks. Conclusion Multi-state models allow detailed evaluation of treatment effects in complex clinical trial settings where patients can experience multiple paths between study enrollment and the final outcome. Multi-state models can be used as a complementary tool to standard survival analyses to provide deeper insights to the effects of treatment in trial settings with complex disease process.

Dataset Information

Simulation Modeling of Cancer Clinical Trials: Application to Omitting Radiotherapy in Low-risk Breast Cancer.

Publications

Simulation Modeling of Cancer Clinical Trials: Application to Omitting Radiotherapy in Low-risk Breast Cancer.

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