Bayesian Group Sequential Clinical Trial Design using Total Toxicity Burden and Progression-Free Survival

Dose optimization for cancer treatments with considerations for late-onset toxicities

Given that novel anticancer therapies have different toxicity profiles and mechanisms of action, it is important to reconsider the current approaches for dose selection. In an effort to move away from considering the maximum tolerated dose as the optimal dose, the Food and Drug Administration Project Optimus points to the need of incorporating long-term toxicity evaluation, given that many of these novel agents lead to late-onset or cumulative toxicities and there are no guidelines on how to handle them. Numerous methods have been proposed to handle late-onset toxicities in dose-finding clinical trials. A summary and comparison of these methods are provided. Moreover, using PI3K inhibitors as a case study, we show how late-onset toxicity can be integrated into the dose-optimization strategy using current available approaches. We illustrate a re-design of this trial to compare the approach to those that only consider early toxicity outcomes and disregard late-onset toxicities. We also provide proposals going forward for dose optimization in early development of novel anticancer agents with considerations for late-onset toxicities.

Co-occurrence of symptoms after radiochemotherapy in locally advanced cervix cancer patients: a cluster analysis

BACKGROUND

State of the art combined radiochemotherapy and image-guided brachytherapy for locally advanced cervical cancer (LACC) has shown improved disease control and survival as well as a significant reduction of organ related morbidity. However, LACC cancer survivors are still experiencing a spectrum of symptoms. The aim of this study was to identify co-occurring symptoms in cervix cancer survivors by using patient-reported outcome and physician assessed morbidity.

MATERIALS AND METHOD

EMBRACE I is a multicenter prospective observational study with 1416 LACC patients (2008-2015). Information on physician-assessed morbidity and patient-reported outcome was assessed at baseline and at regular follow-ups up with the CTCAE v.3 and EORTC-C30/CX24, respectively. Patients with at least 2 years of follow-up were included and data from 3 months to 2 years was used in the analysis. Factor analysis was used on both EORTC and CTCAE data with symptoms and follow-ups as observations. The extracted factors represent clusters of symptoms. Subsequently, regression models were built to investigate associations between the symptom clusters and QOL.

RESULTS

The analysis included 742 patients. Despite the differences in the definition of physician-assessed and patient-reported symptoms, similar clusters are identified by the two assessment methods. Three main organ-related clusters are recognized for urinary, gastro-intestinal and vaginal morbidity. Furthermore, a general symptoms cluster where fatigue, pain, insomnia, neuropathy, and hot flashes have large weights is found. Lastly, a cluster with nausea, vomit and lack of appetite is also identified. The general, gastrointestinal and nausea clusters show significant associations with general QOL.

CONCLUSIONS

This analysis on both PRO and physician-assessed morbidity found a cluster associated with general symptoms and organ-related symptom clusters (urinary, gastrointestinal, vaginal). This shows that LACC survivors experience a variety of co-occurring symptoms. Our analysis also shows that the cluster of general symptoms is associated with a decrease in QOL.

Advances and Challenges in Conducting Clinical Trials With Proton Beam Therapy

Advances in proton therapy have garnered much attention and speculation in recent years as the indications for proton therapy have grown beyond pediatric, prostate, spine, and ocular tumors. To achieve and maintain consistent access to this cancer treatment and to ensure the future viability and availability of proton centers in the United States, a call for evidence has been heard and answered by proton radiation oncologists. Answers provided in this review include the evolution of proton therapy research, rationale for proton clinical trial design, challenges in and barriers to the conduct of proton therapy research, and other unique considerations for the study of proton therapy.

Quasi-partial order continual reassessment method: Applying toxicity scores to cancer dose-finding drug combination trials

The primary endpoint of most dose-finding cancer trials is patient toxicity, and the primary goal is to identify the maximum tolerated dose (MTD), that is, the highest dose that falls below or within a pre-specified toxicity tolerability threshold. Conventionally, dose-finding methods have utilized a binary toxicity endpoint based on whether or not a patient experiences a dose limiting-toxicity (DLT). Improving upon this, in recent years several methods have been developed for modeling toxicity scores, a novel continuous endpoint designed to more precisely estimate patient toxicity burden. Separately, drug-combination trials have become increasingly prevalent, and due to added complexities regarding estimating 'true' dose ordering and potential for more complex patient toxicity profiles, provide an ideal setting which may benefit from the improved precision of toxicity scores. In this paper, we merge two frameworks based on the Continual Reassessment Method (CRM) - the Quasi-CRM and the Partial Order CRM (POCRM) - to propose a novel approach for modeling toxicity scores in a combination-trial setting. We demonstrate that utilizing toxicity scores has the potential to greatly improve correct dose-selection over a variety of trial scenarios. We further present a simple adaptation to the toxicity-score model to control for potential over-dosing issues such that it adheres to the conventional DLT definition and will, at worst, perform equivalently to that of the traditional binary DLT framework. We demonstrate that extending toxicity scores to the combination-trial setting offers potential for improvement over the conventional binary endpoint models.

Evaluation of toxicities for intravesical drugs in phase 1 bladder cancer trials

BACKGROUND

Improving clinical trial design is important for optimizing approval of safe and effective drugs. Phase 1 clinical trials seek to determine phase 2 doses by investigating predefined dose-limiting toxicities. Traditional definitions of dose-limiting toxicity may not be applicable to intravesical therapies for bladder cancer. This study compared the frequency of dose-limiting toxicities and serious adverse events in bladder cancer trials for intravesical therapies to other routes of administration.

METHODS

Studies were abstracted from ClinicalTrials.gov and reconciled with a PubMed search. Primary and secondary end points were predefined before data abstraction, and the primary end point was subject-level dose-limiting toxicity rate. Fisher exact tests were performed with p < .05 designated as significant.

RESULTS

Eighteen intravesical studies and 24 studies with other routes of administration (the per os/intravenous/intramuscular [PO/IV/IM] group) were identified. Dose-limiting toxicities were reported in 38.9% of intravesical studies, affecting 3.29% of subjects, compared with 30.0% of PO/IV/IM studies representing 4.19% of subjects (p = .52 for study-level and p = .60 for subject-level comparisons). Serious adverse events occurred in 53.9% of intravesical studies in 10.3% of subjects versus 91.0% of studies reporting serious adverse events affecting 41.4% of subjects in the PO/IV/IM group (p = .03 for subject-level and p < .0001 for study-level comparisons).

CONCLUSIONS

There was no difference in subject-level dose-limiting toxicity rate between intravesical and PO/IV/IM bladder cancer trials. The serious adverse event rate was lower in the intravesical group. Heterogeneity of dose-limiting toxicity definition may affect interpretation of toxicity in phase 1 bladder cancer clinical trials studying different routes of administration.

LAY SUMMARY

Bladder cancer is a common cancer type that may be treated with therapies that are instilled into the bladder and act locally, called intravesical therapies. This study used publicly available regulatory data from early phase clinical trials to determine whether measures of tolerability used in clinical trials are applicable to intravesical therapies for bladder cancer.

Basket Trials: Review of Current Practice and Innovations for Future Trials

Advances in biology and immunology have elucidated genetic and immunologic origins of cancer. Innovations in sequencing technologies revealed that distinct cancer histologies shared common genetic and immune phenotypic traits. Pharmacologic developments made it possible to target these alterations, yielding novel classes of targeted agents whose therapeutic potential span multiple tumor types. Basket trials, one type of master protocol, emerged as a tool for evaluating biomarker-targeted therapies among multiple tumor histologies. Conventionally conducted within the phase II setting and designed to estimate high and durable objective responses, basket trials pose challenges to statistical design and interpretation of results. This article reviews basket trials implemented in oncology studies and discusses issues related to their statistical design and analysis.

Direct incorporation of patient-specific efficacy and toxicity estimates in radiation therapy plan optimization

PURPOSE

Current radiation therapy (RT) treatment planning relies mainly on pre-defined dose-based objectives and constraints to develop plans that aim to control disease while limiting damage to normal tissues during treatment. These objectives and constraints are generally population-based, in that they are developed from the aggregate response of a broad patient population to radiation. However, correlations of new biologic markers and patient-specific factors to treatment efficacy and toxicity provide the opportunity to further stratify patient populations and develop a more individualized approach to RT planning. We introduce a novel intensity-modulated radiation therapy (IMRT) optimization strategy that directly incorporates patient-specific dose response models into the planning process. In this strategy, we integrate the concept of utility-based planning where the optimization objective is to maximize the predicted value of overall treatment utility, defined by the probability of efficacy (e.g., local control) minus the weighted sum of toxicity probabilities. To demonstrate the feasibility of the approach, we apply the strategy to treatment planning for non-small cell lung cancer (NSCLC) patients.

METHODS AND MATERIALS

We developed a prioritized approach to patient-specific IMRT planning. Using a commercial treatment planning system (TPS), we calculate dose based on an influence matrix of beamlet-dose contributions to regions-of-interest. Then, outside of the TPS, we hierarchically solve two optimization problems to generate optimal beamlet weights that can then be imported back to the TPS. The first optimization problem maximizes a patient's overall plan utility subject to typical clinical dose constraints. In this process, we facilitate direct optimization of efficacy and toxicity trade-off based on individualized dose-response models. After optimal utility is determined, we solve a secondary optimization problem that minimizes a conventional dose-based objective subject to the same clinical dose constraints as the first stage but with the addition of a constraint to maintain the optimal utility from the first optimization solution. We tested this method by retrospectively generating plans for five previously treated NSCLC patients and comparing the prioritized utility plans to conventional plans optimized with only dose metric objectives. To define a plan utility function for each patient, we utilized previously published correlations of dose to local control and grade 3-5 toxicities that include patient age, stage, microRNA levels, and cytokine levels, among other clinical factors.

RESULTS

The proposed optimization approach successfully generated RT plans for five NSCLC patients that improve overall plan utility based on personalized efficacy and toxicity models while accounting for clinical dose constraints. Prioritized utility plans demonstrated the largest average improvement in local control (16.6%) when compared to plans generated with conventional planning objectives. However, for some patients, the utility-based plans resulted in similar local control estimates with decreased estimated toxicity.

CONCLUSION

The proposed optimization approach, where the maximization of a patient's RT plan utility is prioritized over the minimization of standardized dose metrics, has the potential to improve treatment outcomes by directly accounting for variability within a patient population. The implementation of the utility-based objective function offers an intuitive, humanized approach to biological optimization in which planning trade-offs are explicitly optimized.

Assessing the effect of blood type on death and a novel scoring system to assess clinical course in patients with COVID-19

Background

Coronavirus disease (COVID-19) continues to lead to worldwide morbidity and mortality. This study examined the association between blood type and clinical outcomes in patients with COVID-19 measured by a calculated morbidity score and mortality rates. The secondary aim was to investigate the relationship between patient characteristics and COVID-19 associated clinical outcomes and mortality.

Methods

Logistic regression was used to determine what factors were associated with death. A total morbidity score was constructed based on overall patient's COVID-19 clinical course. This score was modeled using Quasi-Poisson regression. Bayesian variable selection was used for the logistic regression to obtain a posterior probability that blood type is important in predicting worsened clinical outcomes and death.

Results

Neither blood type nor Rh+ status was a significant moderator of death or morbidity score in regression analyses. Increased age (adjusted Odds Ratio=3.37, 95% CI=2.44–4.67), male gender (aOR=1.35, 95% CI=1.08-1.69), and number of comorbid conditions (aOR=1.28, 95% CI=1.01-1.63) were significantly associated with death. Significant factors in predicting total morbidity score were age (adjusted Multiplicative Effect=1.45; 95% CI=1.349-1.555) and gender (aME=1.17; 95% CI=1.109-1.243). The posterior probability that blood type influenced death was only 10%.

Conclusions

There is strong evidence that blood type was not a significant predictor of clinical course or death in patients hospitalized with COVID-19. Older age and male gender led to worse clinical outcomes and higher rates of death; older age, male gender, and comorbidities predicted a worse clinical course and higher morbidity score. Race was not a significant predictor of death in our population and was associated with an increased, albeit not significant, morbidity score.

Current status and application of proton therapy for esophageal cancer

Esophageal cancer remains one of the leading causes of death from cancer across the world despite advances in multimodality therapy. Although early-stage disease can often be treated surgically, the current state of the art for locally advanced disease is concurrent chemoradiation, followed by surgery whenever possible. The uniform midline tumor location puts a strong importance on the need for precise delivery of radiation that would minimize dose to the heart and lungs, and the biophysical properties of proton beam makes this modality potential ideal for esophageal cancer treatment. This review covers the current state of knowledge of proton therapy for esophageal cancer, focusing on published retrospective single- and multi-institutional clinical studies, and emerging data from prospective clinical trials, that support the benefit of protons vs photon-based radiation in reducing postoperative complications, cardiac toxicity, and severe radiation induced immune suppression, which may improve survival outcomes for patients. In addition, we discuss the incorporation of immunotherapy to the curative management of esophageal cancers in the not-too-distant future. However, there is still a lack of high-level evidence to support proton therapy in the treatment of esophageal cancer, and proton therapy has its limitations in clinical application. It is expected to see the results of future large-scale randomized clinical trials and the continuous improvement of proton radiotherapy technology.

Activity-Based Costing of Intensity-Modulated Proton versus Photon Therapy for Oropharyngeal Cancer

PURPOSE

In value-based health care delivery, radiation oncologists need to compare empiric costs of care delivery with advanced technologies, such as intensity-modulated proton therapy (IMPT) and intensity-modulated radiation therapy (IMRT). We used time-driven activity-based costing (TDABC) to compare the costs of delivering IMPT and IMRT in a case-matched pilot study of patients with newly diagnosed oropharyngeal (OPC) cancer.

MATERIALS AND METHODS

We used clinicopathologic factors to match 25 patients with OPC who received IMPT in 2011-12 with 25 patients with OPC treated with IMRT in 2000-09. Process maps were created for each multidisciplinary clinical activity (including chemotherapy and ancillary services) from initial consultation through 1 month of follow-up. Resource costs and times were determined for each activity. Each patient-specific activity was linked with a process map and TDABC over the full cycle of care. All calculated costs were normalized to the lowest-cost IMRT patient.

RESULTS

TDABC costs for IMRT were 1.00 to 3.33 times that of the lowest-cost IMRT patient (mean ± SD: 1.65 ± 0.56), while costs for IMPT were 1.88 to 4.32 times that of the lowest-cost IMRT patient (2.58 ± 0.39) (P < .05). Although single-fraction costs were 2.79 times higher for IMPT than for IMRT (owing to higher equipment costs), average full cycle cost of IMPT was 1.53 times higher than IMRT, suggesting that the initial cost increase is partly mitigated by reductions in costs for other, non-RT supportive health care services.

CONCLUSIONS

In this matched sample, although IMPT was on average more costly than IMRT primarily owing to higher equipment costs, a subset of IMRT patients had similar costs to IMPT patients, owing to greater use of supportive care resources. Multidimensional patient outcomes and TDABC provide vital methodology for defining the value of radiation therapy modalities.

Making Patient-Specific Treatment Decisions Using Prognostic Variables and Utilities of Clinical Outcomes

We argue that well-informed patient-specific decision-making may be carried out as three consecutive tasks: (1) estimating key parameters of a statistical model, (2) using prognostic information to convert these parameters into clinically interpretable values, and (3) specifying joint utility functions to quantify risk-benefit trade-offs between clinical outcomes. Using the management of metastatic clear cell renal cell carcinoma as our motivating example, we explain the role of prognostic covariates that characterize between-patient heterogeneity in clinical outcomes. We show that explicitly specifying the joint utility of clinical outcomes provides a coherent basis for patient-specific decision-making.

The Promise of Proton Beam Therapy for Oesophageal Cancer: A Systematic Review of Dosimetric and Clinical Outcomes

AIMS

Due to its physical advantages over photon radiotherapy, proton beam therapy (PBT) has the potential to improve outcomes from oesophageal cancer. However, for many tumour sites, high-quality evidence supporting PBT use is limited. We carried out a systematic review of published literature of PBT in oesophageal cancer to ascertain potential benefits of this technology and to gauge the current state-of-the-art. We considered if further evaluation of this technology in oesophageal cancer is desirable.

MATERIALS AND METHODS

A systematic literature search of Medline, Embase, Cochrane Library and Web of Science using structured search terms was carried out. Inclusion criteria included non-metastatic cancer, full articles and English language studies only. Articles deliberating technical aspects of PBT planning or delivery were excluded to maintain a clinical focus. Studies were divided into two sections: dosimetric and clinical studies; qualitatively synthesised.

RESULTS

In total, 467 records were screened, with 32 included for final qualitative synthesis. This included two prospective studies with the rest based on retrospective data. There was heterogeneity in treatment protocols, including treatment intent (neoadjuvant or definitive), dose, fractionation and chemotherapy used. Compared with photon radiotherapy, PBT seemed to reduce dose to organs at risk, especially lung and heart, although not for all reported parameters. Toxicity outcomes, including postoperative complications, were reduced compared with photon radiotherapy. Survival outcomes were reported to be at least comparable with photon radiotherapy.

CONCLUSION

There is a paucity of high-quality evidence supporting PBT use in oesophageal cancer. Wide variation in intent and treatment protocols means that the role and 'gold-standard' treatment protocol are yet to be defined. Current literature suggests significant benefit in terms of toxicity reduction, especially in the postoperative period, with comparable survival outcomes. PBT in oesophageal cancer holds significant promise for improving patient outcomes but requires robust systematic evaluation in prospective studies.

Adverse Event Burden Score-A Versatile Summary Measure for Cancer Clinical Trials

Simple Summary

In cancer clinical trials, adverse event data are collected after every treatment cycle, using the Common Terminology Criteria for adverse events, which includes 837 terms. The vast number of potentially reportable adverse events over multiple treatment cycles makes summarizing and analyzing adverse event data challenging. The current standard reporting of adverse event data includes the frequency of the maximum (worst) grade of commonly occurring adverse events. In this article, we propose a single quantitative summary measure that incorporates both the frequency and the severity of multiple adverse events over time; the adverse event burden score. This score is a well-defined measure that enables statistical comparisons analogous to other quantitative endpoints in clinical trials. The adverse event burden score can readily accommodate different trial settings, diseases, and treatments, with diverse safety profiles.

Abstract

This article introduces the adverse event (AE) burden score. The AE burden by treatment cycle is a weighted sum of all grades and AEs that the patient experienced in a cycle. The overall AE burden score is the total AE burden the patient experienced across all treatment cycles. AE data from two completed Alliance multi-center randomized double-blind placebo-controlled trials, with different AE profiles (NCCTG 97-24-51: 176 patients, and A091105: 83 patients), were utilized for illustration. Results of the AE burden score analyses corroborated the trials’ primary results. In 97-24-51, the overall AE burden for patients on the treatment arm was 2.2 points higher than those on the placebo arm, with a higher AE burden for patients who went off treatment early due to AE. Similarly, in A091105, the overall AE burden was 1.6 points higher on the treatment arm. On the placebo arms, the AE burden in 97-24-51 remained constant over time; and increased in later cycles in A091105, likely attributable to the increase in disease morbidity. The AE burden score enables statistical comparisons analogous to other quantitative endpoints in clinical trials, and can readily accommodate different trial settings, diseases, and treatments, with diverse AE profiles.

Randomized Controlled Trials

Randomized controlled trials (RCTs) are considered the highest level of evidence to establish causal associations in clinical research. There are many RCT designs and features that can be selected to address a research hypothesis. Designs of RCTs have become increasingly diverse as new methods have been proposed to evaluate increasingly complex scientific hypotheses. This article reviews the principles and general concepts behind many common RCT designs and introduces newer designs that have been proposed, such as adaptive and cluster randomized trials. A focus on the many choices for randomization within an RCT is described, along with their potential tradeoffs. To illustrate their diversity, examples of RCTs from the literature are provided. Statistical considerations, such as power and type I error rates, are discussed with the intention of providing practical guidance about how to specify study hypotheses that address the scientific question while being statistically appropriate. Finally, the freely available Consolidated Standards of Reporting Trials guidelines and US Food and Drug Administration guidance documents are introduced, along with a set of guidelines one should consider when planning an RCT or reviewing RCTs submitted for publication in peer-reviewed academic journals.

Reprioritizing Risk and Benefit: The Future of Study Design in Early-Phase Cancer Research

The scientific purpose of phase I trials is to determine the maximum tolerated dose and/or optimal biological dose of experimental agents. Yet most participants in phase I oncology trials enroll hoping for direct medical benefit. The most common phase I trial designs use low starting doses and escalate cautiously in a "risk-escalation" model focused on minimizing risk for each participant. This approach ensures that a proportion of subjects will likely not receive any benefit, even if the intervention proves to be successful at appropriate doses. In this article, we propose that trial designs should employ dosing strategies that increase chances of providing benefit if the investigational agent should prove to be successful while limiting risk to reasonable levels. We then describe how adaptive trial designs can facilitate refined dose optimization based on both therapeutic benefit and toxicity, which can simultaneously decrease the risk of harm while increasing the chances of benefit.

Randomized Phase IIB Trial of Proton Beam Therapy Versus Intensity-Modulated Radiation Therapy for Locally Advanced Esophageal Cancer

PURPOSE

Whether dosimetric advantages of proton beam therapy (PBT) translate to improved clinical outcomes compared with intensity-modulated radiation therapy (IMRT) remains unclear. This randomized trial compared total toxicity burden (TTB) and progression-free survival (PFS) between these modalities for esophageal cancer.

METHODS

This phase IIB trial randomly assigned patients to PBT or IMRT (50.4 Gy), stratified for histology, resectability, induction chemotherapy, and stage. The prespecified coprimary end points were TTB and PFS. TTB, a composite score of 11 distinct adverse events (AEs), including common toxicities as well as postoperative complications (POCs) in operated patients, quantified the extent of AE severity experienced over the duration of 1 year following treatment. The trial was conducted using Bayesian group sequential design with three planned interim analyses at 33%, 50%, and 67% of expected accrual (adjusted for follow-up).

RESULTS

This trial (commenced April 2012) was approved for closure and analysis upon activation of NRG-GI006 in March 2019, which occurred immediately prior to the planned 67% interim analysis. Altogether, 145 patients were randomly assigned (72 IMRT, 73 PBT), and 107 patients (61 IMRT, 46 PBT) were evaluable. Median follow-up was 44.1 months. Fifty-one patients (30 IMRT, 21 PBT) underwent esophagectomy; 80% of PBT was passive scattering. The posterior mean TTB was 2.3 times higher for IMRT (39.9; 95% highest posterior density interval, 26.2-54.9) than PBT (17.4; 10.5-25.0). The mean POC score was 7.6 times higher for IMRT (19.1; 7.3-32.3) versus PBT (2.5; 0.3-5.2). The posterior probability that mean TTB was lower for PBT compared with IMRT was 0.9989, which exceeded the trial's stopping boundary of 0.9942 at the 67% interim analysis. The 3-year PFS rate (50.8% v 51.2%) and 3-year overall survival rates (44.5% v 44.5%) were similar.

CONCLUSION

For locally advanced esophageal cancer, PBT reduced the risk and severity of AEs compared with IMRT while maintaining similar PFS.

Bayesian Utility-Based Designs for Subgroup-Specific Treatment Comparison and Early-Phase Dose Optimization in Oncology Clinical Trials

PURPOSE

Despite the fact that almost any sample of patients with a particular disease is heterogeneous, most clinical trial designs ignore the possibility that treatment or dose effects may differ between prognostic or biologically defined subgroups. This article reviews two clinical trial designs that make subgroup-specific decisions and compares each to a simpler design that ignores patient heterogeneity. The purpose is to illustrate the benefits of accounting prospectively for treatment-subgroup interactions and how utilities may be used to quantify risk-benefit trade-offs.

METHODS

Two Bayesian clinical trial designs that perform subgroup-specific decision making and inference based on elicited utilities of patient outcomes are reviewed. The first is a randomized comparative trial of nutritional prehabilitation for patients undergoing esophageal resection that has two prognostic subgroups and is based on postoperative morbidity score. The second is a sequentially adaptive trial of natural killer cells for treating hematologic malignancies that is based on five time-to-event outcomes and that performs safety monitoring and optimizes cell dose within six disease subgroups. Computer simulations under a range of different scenarios are presented for each design to establish its operating characteristics and compare it to a more conventional design that ignores patient heterogeneity.

RESULTS

Each design has attractive operating characteristics, is greatly superior to a simplified design that ignores patient subgroups, is robust to deviations from its assumed statistical model, and is feasible to use for conducting trials.

CONCLUSION

Bayesian designs that make subgroup-specific decisions in randomized comparative trials or sequentially adaptive early-phase dose-finding trials are superior to designs that ignore patient heterogeneity. Using elicited utilities of complex patient outcomes to quantify risk-benefit trade-offs provides a practical and ethical basis for decision making and treatment evaluation in clinical trials.

Bayesian personalized treatment selection strategies that integrate predictive with prognostic determinants

The evolution of "informatics" technologies has the potential to generate massive databases, but the extent to which personalized medicine may be effectuated depends on the extent to which these rich databases may be utilized to advance understanding of the disease molecular profiles and ultimately integrated for treatment selection, necessitating robust methodology for dimension reduction. Yet, statistical methods proposed to address challenges arising with the high-dimensionality of omics-type data predominately rely on linear models and emphasize associations deriving from prognostic biomarkers. Existing methods are often limited for discovering predictive biomarkers that interact with treatment and fail to elucidate the predictive power of their resultant selection rules. In this article, we present a Bayesian predictive method for personalized treatment selection that is devised to integrate both the treatment predictive and disease prognostic characteristics of a particular patient's disease. The method appropriately characterizes the structural constraints inherent to prognostic and predictive biomarkers, and hence properly utilizes these complementary sources of information for treatment selection. The methodology is illustrated through a case study of lower grade glioma. Theoretical considerations are explored to demonstrate the manner in which treatment selection is impacted by prognostic features. Additionally, simulations based on an actual leukemia study are provided to ascertain the method's performance with respect to selection rules derived from competing methods.

Proton beam therapy for gastrointestinal cancers: past, present, and future

Despite the conformality of modern X-ray therapy limiting high dose received by normal tissues the physical properties of X-rays make it impossible to avoid dose being delivered distal to the target. This "exit dose" is likely clinically significant especially for patients with gastrointestinal (GI) cancers when considering that even low dose received by the heart, lungs, bowel, and other radiosensitive structures can lead to morbidity and even may affect long-term tumor control. In contrast, proton beam therapy (PBT) delivers no "exit dose" and a growing body of literature suggests that this may improve clinical outcomes by reducing toxicity and even allowing for safe dose intensification to enhance tumor control. While there are not yet robust prospective data demonstrating the role of PBT for GI cancers, emerging retrospective data provide a strong rationale for continued study of how PBT may improve the therapeutic ratio for these patients. Here we review these data as well as discuss ongoing clinical trials of PBT for GI cancers.

Proton Therapy for Head and Neck Cancers

Because of its sharp lateral penumbra and steep distal fall-off, proton therapy offers dosimetric advantages over photon therapy. In head and neck cancer, proton therapy has been used for decades in the treatment of skull-base tumors. In recent years the use of proton therapy has been extended to numerous other disease sites, including nasopharynx, oropharynx, nasal cavity and paranasal sinuses, periorbital tumors, skin, and salivary gland, or to reirradiation. The aim of this review is to present the physical properties and dosimetric benefit of proton therapy over advanced photon therapy; to summarize the clinical benefit described for each disease site; and to discuss issues of patient selection and cost-effectiveness.

Bayesian adaptive trials for rare cardiovascular conditions

Escalating costs of cardiovascular trials are limiting medical innovations, prompting the development of more efficient and flexible study designs. The Bayesian paradigm offers a framework conducive to adaptive trial methodologies and is well suited for the study of small populations. Bayesian adaptive trials provide a statistical structure for combining prior information with accumulating data to compute probabilities of unknown quantities of interest. Adaptive design features are useful in modifying randomization schemes, adjusting sample sizes and providing continuous surveillance to guide decisions on dropping study arms or premature trial interruption. Advantages include greater efficiency, minimization of risks, inclusion of knowledge as it is generated, cost savings and more intuitive interpretability. Extensive high-level computations are facilitated by an expanding armamentarium of available tools.

Robust treatment comparison based on utilities of semi-competing risks in non-small-cell lung cancer

A design is presented for a randomized clinical trial comparing two second-line treatments, chemotherapy versus chemotherapy plus reirradiation, for treatment of recurrent non-small-cell lung cancer. The central research question is whether the potential efficacy benefit that adding reirradiation to chemotherapy may provide justifies its potential for increasing the risk of toxicity. The design uses two co-primary outcomes: time to disease progression or death, and time to severe toxicity. Because patients may be given an active third-line treatment at disease progression that confounds second-line treatment effects on toxicity and survival following disease progression, for the purpose of this comparative study follow-up ends at disease progression or death. In contrast, follow-up for disease progression or death continues after severe toxicity, so these are semi-competing risks. A conditionally conjugate Bayesian model that is robust to misspecification is formulated using piecewise exponential distributions. A numerical utility function is elicited from the physicians that characterizes desirabilities of the possible co-primary outcome realizations. A comparative test based on posterior mean utilities is proposed. A simulation study is presented to evaluate test performance for a variety of treatment differences, and a sensitivity assessment to the elicited utility function is performed. General guidelines are given for constructing a design in similar settings, and a computer program for simulation and trial conduct is provided.

A Bayesian sequential design with binary outcome

Several researchers have proposed solutions to control type I error rate in sequential designs. The use of Bayesian sequential design becomes more common; however, these designs are subject to inflation of the type I error rate. We propose a Bayesian sequential design for binary outcome using an alpha-spending function to control the overall type I error rate. Algorithms are presented for calculating critical values and power for the proposed designs. We also propose a new stopping rule for futility. Sensitivity analysis is implemented for assessing the effects of varying the parameters of the prior distribution and maximum total sample size on critical values. Alpha-spending functions are compared using power and actual sample size through simulations. Further simulations show that, when total sample size is fixed, the proposed design has greater power than the traditional Bayesian sequential design, which sets equal stopping bounds at all interim analyses. We also find that the proposed design with the new stopping for futility rule results in greater power and can stop earlier with a smaller actual sample size, compared with the traditional stopping rule for futility when all other conditions are held constant. Finally, we apply the proposed method to a real data set and compare the results with traditional designs.

Contrast-associated acute kidney injury in the critically ill: systematic review and Bayesian meta-analysis

PURPOSE

Critically ill patients, among whom acute kidney injury is common, are often considered particularly vulnerable to iodinated contrast medium nephrotoxicity. However, the attributable incidence remains uncertain given the paucity of observational studies including a control group. This study assessed acute kidney injury incidence attributable to iodinated contrast media in critically ill patients based on new data accounting for sample and effect size and including a control group.

METHODS

Systematic review of studies measuring incidence of acute kidney injury in critically ill patients following contrast medium exposure compared to matched unexposed patients. Patient-level meta-analysis implementing a Bayesian nested mixed effects multiple logistic regression model.

RESULTS

Ten studies were identified; only four took into account the baseline acute kidney injury risk, three by patient matching (560 patients). Objective meta-analysis of these three studies (vague and impartial a priori hypothesis concerning attributable acute kidney injury risk) did not find that iodinated contrast media increased the incidence of acute kidney injury (odds ratio 0.95, 95% highest posterior density interval 0.45-1.62). Bayesian analysis demonstrated that, to conclude in favor of a statistically significant incidence of acute kidney injury attributable to contrast media despite this observed lack of association, one's a priori belief would have to be very strongly biased, assigning to previous uncontrolled reports 3-12 times the weight of evidence strength provided by the matched studies including a control group.

CONCLUSIONS

Meta-analysis of matched cohort studies of iodinated contrast medium exposure does not support a significant incidence of acute kidney injury attributable to iodinated contrast media in critically ill patients.