Shrinkage priors for isotonic probability vectors and binary data modeling, with applications to dose-response modeling.

Motivated by the need to model dose-response or dose-toxicity curves in clinical trials, we develop a new horseshoe-based prior for Bayesian isotonic regression modeling a binary outcome against an ordered categorical predictor, where the probability of the outcome is assumed to be monotonically non-decreasing with the predictor. The set of differences between outcome probabilities in consecutive categories of the predictor is equipped with a multivariate prior having support over simplex. The Dirichlet distribution, which can be derived from a normalized sum of independent gamma-distributed random variables, is a natural choice of prior, but using mathematical and simulation-based arguments, we show that the resulting posterior is prone to underflow and other numerical instabilities, even under simple data configurations. We propose an alternative prior based on horseshoe-type shrinkage that is numerically more stable. We show that this horseshoe-based prior is not subject to the numerical instability seen in the Dirichlet/gamma-based prior and that the horseshoe-based posterior can estimate the underlying true curve more efficiently than the Dirichlet-based one. We demonstrate the use of this prior in a model predicting the occurrence of radiation-induced lung toxicity in lung cancer patients as a function of dose delivered to normal lung tissue. Our methodology is implemented in the R package isotonicBayes and therefore suitable for use in the design of dose-finding studies or other dose-response modeling contexts.

Extracorporeal Life Support Organization Registry International Report 2022: 100,000 Survivors.

The Extracorporeal Life Support Organization (ELSO) maintains the world's largest extracorporeal membrane oxygenation (ECMO) registry by volume, center participation, and international scope. This 2022 ELSO Registry Report describes the program characteristics of ECMO centers, processes of ECMO care, and reported outcomes. Neonates (0-28 days), children (29 days-17 years), and adults (≥18 years) supported with ECMO from 2009 through 2022 and reported to the ELSO Registry were included. This report describes adjunctive therapies, support modes, treatments, complications, and survival outcomes. Data are presented descriptively as counts and percent or median and interquartile range (IQR) by year, group, or level. Missing values were excluded before calculating descriptive statistics. Complications are reported per 1,000 ECMO hours. From 2009 to 2022, 154,568 ECMO runs were entered into the ELSO Registry. Seven hundred and eighty centers submitted data during this time (557 in 2022). Since 2009, the median annual number of adult ECMO runs per center per year increased from 4 to 15, whereas for pediatric and neonatal runs, the rate decreased from 12 to 7. Over 50% of patients were transferred to the reporting ECMO center; 20% of these patients were transported with ECMO. The use of prone positioning before respiratory ECMO increased from 15% (2019) to 44% (2021) for adults during the coronavirus disease-2019 (COVID-19) pandemic. Survival to hospital discharge was greatest at 68.5% for neonatal respiratory support and lowest at 29.5% for ECPR delivered to adults. By 2022, the Registry had enrolled its 200,000th ECMO patient and 100,000th patient discharged alive. Since its inception, the ELSO Registry has helped centers measure and compare outcomes across its member centers and strategies of care. Continued growth and development of the Registry will aim to bolster its utility to patients and centers.

Modeling basal body temperature data using horseshoe process regression.

Biomedical data often exhibit jumps or abrupt changes. For example, women's basal body temperature may jump at ovulation, menstruation, implantation, and miscarriage. These sudden changes make these data challenging to model: many methods will oversmooth the sharp changes or overfit in response to measurement error. We develop horseshoe process regression (HPR) to address this problem. We define a horseshoe process as a stochastic process in which each increment is horseshoe-distributed. We use the horseshoe process as a nonparametric Bayesian prior for modeling a potentially nonlinear association between an outcome and its continuous predictor, which we implement via Stan and in the R package HPR. We provide guidance and extensions to advance HPR's use in applied practice: we introduce a Bayesian imputation scheme to allow for interpolation at unobserved values of the predictor within the HPR; include additional covariates via a partial linear model framework; and allow for monotonicity constraints. We find that HPR performs well when fitting functions that have sharp changes. We apply HPR to model women's basal body temperatures over the course of the menstrual cycle.

Real-world outcomes with immunosuppressive therapy for aplastic anemia in patients treated at the University of Michigan.

Aplastic anemia (AA) is a rare bone marrow failure disorder that is treated with either allogeneic stem cell transplant or immunosuppressive therapy (IST) consisting of antithymocyte globulin (ATG), cyclosporine (CSA), and eltrombopag. While outcomes are favorable in younger patients, older patients (>60) have significantly worse long-term survival. The dose of ATG is often reduced in older patients and those with multiple comorbidities given concerns for tolerability. The efficacy and safety of dose-attenuated IST in this population is largely undescribed. We performed a retrospective review of patients with AA treated with IST. Our analysis was confounded by changes in practice patterns and the introduction of eltrombopag. We identified 53 patients >60 years old, of which, 20 received dose-attenuated IST, with no statistically significant difference in overall survival between full and attenuated dose cohorts. Overall response rates in both cohorts were similar at 6 months at 71% and 68%. There were more documented infectious complications in the full dose cohort (13 vs. 3). This supports the consideration of dose-attenuated IST in older patients with concerns about tolerance of IST. Lastly, our data confirmed favorable outcomes of younger patients receiving IST, especially in combination with eltrombopag.

Optimizing the doses of cancer drugs after usual dose finding.

Since the middle of the 20th century, oncology's dose-finding paradigm has been oriented toward identifying a drug's maximum tolerated dose, which is then carried forward into phase 2 and 3 trials and clinical practice. For most modern precision medicines, however, maximum tolerated dose is far greater than the minimum dose needed to achieve maximal benefit, leading to unnecessary side effects. Regulatory change may decrease maximum tolerated dose's predominance by enforcing dose optimization of new drugs. Dozens of already approved cancer drugs require re-evaluation, however, introducing a new methodologic and ethical challenge in cancer clinical trials. In this article, we assess the history and current landscape of cancer drug dose finding. We provide a set of strategic priorities for postapproval dose optimization trials of the future. We discuss ethical considerations for postapproval dose optimization trial design and review three major design strategies for these unique trials that would both adhere to ethical standards and benefit patients and funders. We close with a discussion of financial and reporting considerations in the realm of dose optimization. Taken together, we provide a comprehensive, bird's eye view of the postapproval dose optimization trial landscape and offer our thoughts on the next steps required of methodologies and regulatory and funding regimes.

Targeted randomization dose optimization trials enable fractional dosing of scarce drugs.

Administering drug at a dose lower than that used in pivotal clinical trials, known as fractional dosing, can stretch scarce resources. Implementing fractional dosing with confidence requires understanding a drug's dose-response relationship. Clinical trials aimed at describing dose-response in scarce, efficacious drugs risk underdosing, leading dose-finding trials to not be pursued despite their obvious potential benefit. We developed a new set of response-adaptive randomized dose-finding trials and demonstrate, in a series of simulated trials across diverse dose-response curves, these designs' efficiency in identifying the minimum dose that achieves satisfactory efficacy. Compared to conventional designs, these trials have higher probabilities of identifying lower doses while reducing the risks of both population- and subject-level underdosing. We strongly recommend that, upon demonstration of a drug's efficacy, pandemic drug development swiftly proceeds with response-adaptive dose-finding trials. This unified strategy ensures that scarce effective drugs produce maximum social benefits.

CBK model composition using paired web services and executable functions: A demonstration for individualizing preventive services.

Introduction: Learning health systems are challenged to combine computable biomedical knowledge (CBK) models. Using common technical capabilities of the World Wide Web (WWW), digital objects called Knowledge Objects, and a new pattern of activating CBK models brought forth here, we aim to show that it is possible to compose CBK models in more highly standardized and potentially easier, more useful ways. Methods: Using previously specified compound digital objects called Knowledge Objects, CBK models are packaged with metadata, API descriptions, and runtime requirements. Using open-source runtimes and a tool we developed called the KGrid Activator, CBK models can be instantiated inside runtimes and made accessible via RESTful APIs by the KGrid Activator. The KGrid Activator then serves as a gateway and provides a means to interconnect CBK model outputs and inputs, thereby establishing a CBK model composition method. Results: To demonstrate our model composition method, we developed a complex composite CBK model from 42 CBK submodels. The resulting model called CM-IPP is used to compute life-gain estimates for individuals based their personal characteristics. Our result is an externalized, highly modularized CM-IPP implementation that can be distributed and made runnable in any common server environment. Discussion: CBK model composition using compound digital objects and the distributed computing technologies is feasible. Our method of model composition might be usefully extended to bring about large ecosystems of distinct CBK models that can be fitted and re-fitted in various ways to form new composites. Remaining challenges related to the design of composite models include identifying appropriate model boundaries and organizing submodels to separate computational concerns while optimizing reuse potential. Conclusion: Learning health systems need methods for combining CBK models from a variety of sources to create more complex and useful composite models. It is feasible to leverage Knowledge Objects and common API methods in combination to compose CBK models into complex composite models.

Adding venetoclax to lenalidomide and rituximab is safe and effective in patients with untreated mantle cell lymphoma.

Mantle cell lymphoma (MCL) is a rare, incurable hematological malignancy with a heterogeneous presentation and clinical course. A wide variety of chemotherapy-based regimens are currently used in patients who are untreated. Over the last several years, several targeted or small-molecule therapies have shown efficacy in the relapsed/refractory setting and have since been explored in the frontline setting. Lenalidomide plus rituximab was explored in a phase 2 study of 38 patients with MCL who were untreated and ineligible to receive transplantation, in which the combination produced durable remissions. We looked to build upon this regimen by adding venetoclax to the combination. We conducted a multicenter, open-label, nonrandomized, single-arm study to evaluate this combination. We enrolled 28 unselected patients with untreated disease irrespective of age, fitness, or risk factors. Lenalidomide was dosed at 20 mg daily from days 1 to 21 of each 28-day cycle. The dose of venetoclax was determined using the time-to-event continual reassessment method. Rituximab was dosed at 375 mg/m2 weekly, starting on cycle 1, day 1 until cycle 2, day 1. No dose-limiting toxicities were noted. All patients were treated with venetoclax at the maximum tolerated dose of 400 mg daily. The most common adverse events were neutropenia and thrombocytopenia. The overall and complete response rates were 96% and 86%, respectively. In total, 86% of patients achieved minimal residual disease undetectability via next-generation sequencing. The median overall and progression-free survivals were not reached. The combination of lenalidomide, rituximab, and venetoclax is a safe and effective regimen in patients with untreated MCL. This trial was registered at www.clinicaltrials.gov as #NCT03523975.

Evaluating feasibility of a novel mobile emergency medical dispatch tool for lay first responder prehospital response coordination in Sierra Leone: A simulation-based study.

INTRODUCTION: The global injury burden, driven by road traffic injuries, disproportionately affects low- and middle-income countries, which lack robust emergency medical services (EMS) to address injury. The WHO recommends training lay first responders (LFRs) as the first step toward formal EMS development. Emergency medical dispatch (EMD) systems are the recognized next step but whether small groups of LFRs equipped with mobile dispatch infrastructure can efficiently respond to geographically-dispersed emergencies in a timely fashion and the quality of prehospital care provided is unknown. MATERIALS AND METHODS: We piloted an EMD system utilizing a mobile phone application in Sierra Leone. Ten LFRs were randomly selected from a pool of 61 highly-active LFRs trained in 2019 and recruited to participate in an emergency simulation-based study. Ten simulation scenarios were created matching proportions of injury conditions across 1,850 previous incidents (June-December 2019). Fifty total simulations were launched in randomized order over 3 months, randomized along 10 km of highway in Makeni. Replicating real-world conditions, highly-active LFR participants were blinded to randomized dispatch timing/scenario to assess response time and skill performance under direct observation with a checklist using standardized patient actors. We used novel cost data tracked during EMD pilot implementation to inform the calculation of a new cost-effectiveness ratio ($USD cost per disability-adjusted life year averted (DALY)) for LFR programs equipped with dispatch, following WHOCHOICE guidelines, which state cost-effectiveness ratios less than gross domestic product (GDP) per capita are considered "very cost-effective." RESULTS: Median total response interval (notification to arrival) was 5 min 39 s (IQR:0:03:51, 0:09:18). LFRs initially trained with a 5-hour curriculum and refresher training provide high-quality prehospital care during simulated emergencies. Median first aid skill checklist completion was 89% (IQR: 78%, 90%). Cost-effectiveness equals $179.02USD per DALY averted per 100,000 people, less than Sierra Leonean GDP per capita ($484.52USD). CONCLUSION: LFRs equipped with mobile dispatch demonstrate appropriate response times and effective basic initial management of simulated emergencies. Training smaller cohorts of highly-active LFRs equipped with mobile dispatch appears highly cost-effective and may be a feasible model to facilitate efficient dispatch to expand emergency coverage while conserving valuable training resources in resource-limited settings.

Tracheostomy Practices and Outcomes in Patients With COVID-19 Supported by Extracorporeal Membrane Oxygenation: An Analysis of the Extracorporeal Life Support Organization Registry.

OBJECTIVES: The use of extracorporeal membrane oxygenation (ECMO) in patients with COVID-19 has been supported by major healthcare organizations, yet the role of specific management strategies during ECMO requires further study. We sought to characterize tracheostomy practices, complications, and outcomes in ECMO-supported patients with acute respiratory failure related to COVID-19. DESIGN: Retrospective cohort study. SETTING: ECMO centers contributing to the Extracorporeal Life Support Organization Registry. PATIENTS: Patients 16 years or older receiving venovenous ECMO for respiratory support for: 1) COVID-19 in 2020 and 2021 (through October 2021) and 2) pre-COVID-19 viral pneumonia in 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We identified 7,047 patients who received ECMO support for acute respiratory failure related to COVID-19. A total of 32% of patients were recorded as having a tracheostomy procedure during ECMO, and 51% had a tracheostomy at some point during hospitalization. The frequency of tracheostomy was similar in pre-COVID-19 viral pneumonia, but tracheostomies were performed 3 days earlier compared with patients with COVID-19 (median 6.7 d [interquartile range [IQR], 3.0-12.0 d] vs 10.0 d [IQR, 5.0-16.5 d]; p < 0.001). More patients were mobilized with pre-COVID-19 viral pneumonia, but receipt of a tracheostomy during ECMO was associated with increased mobilization in both cohorts. More bleeding complications occurred in patients who received a tracheostomy, with 9% of patients with COVID-19 who received a tracheostomy reported as having surgical site bleeding. CONCLUSIONS: Tracheostomies are performed in COVID-19 patients receiving ECMO at rates similar to practices in pre-COVID-19 viral pneumonia, although later during the course of ECMO. Receipt of a tracheostomy was associated with increased patient mobilization. Overall mortality was similar between those who did and did not receive a tracheostomy.

Improved prediction of radiation pneumonitis by combining biological and radiobiological parameters using a data-driven Bayesian network analysis.

Grade 2 and higher radiation pneumonitis (RP2) is a potentially fatal toxicity that limits efficacy of radiation therapy (RT). We wished to identify a combined biomarker signature of circulating miRNAs and cytokines which, along with radiobiological and clinical parameters, may better predict a targetable RP2 pathway. In a prospective clinical trial of response-adapted RT for patients (n = 39) with locally advanced non-small cell lung cancer, we analyzed patients' plasma, collected pre- and during RT, for microRNAs (miRNAs) and cytokines using array and multiplex enzyme linked immunosorbent assay (ELISA), respectively. Interactions between candidate biomarkers, radiobiological, and clinical parameters were analyzed using data-driven Bayesian network (DD-BN) analysis. We identified alterations in specific miRNAs (miR-532, -99b and -495, let-7c, -451 and -139-3p) correlating with lung toxicity. High levels of soluble tumor necrosis factor alpha receptor 1 (sTNFR1) were detected in a majority of lung cancer patients. However, among RP patients, within 2 weeks of RT initiation, we noted a trend of temporary decline in sTNFR1 (a physiological scavenger of TNFα) and ADAM17 (a shedding protease that cleaves both membrane-bound TNFα and TNFR1) levels. Cytokine signature identified activation of inflammatory pathway. Using DD-BN we combined miRNA and cytokine data along with generalized equivalent uniform dose (gEUD) to identify pathways with better accuracy of predicting RP2 as compared to either miRNA or cytokines alone. This signature suggests that activation of the TNFα-NFκB inflammatory pathway plays a key role in RP which could be specifically ameliorated by etanercept rather than current therapy of non-specific leukotoxic corticosteroids.

Utility based approach in individualized optimal dose selection using machine learning methods.

The goal in personalized medicine is to individualize treatment using patient characteristics and improve health outcomes. Selection of optimal dose must balance the effect of dose on both treatment efficacy and toxicity outcomes. We consider a setting with one binary efficacy and one binary toxicity outcome. The goal is to find the optimal dose for each patient using clinical features and biomarkers from available dataset. We propose to use flexible machine learning methods such as random forest and Gaussian process models to build models for efficacy and toxicity depending on dose and biomarkers. A copula is used to model the joint distribution of the two outcomes and the estimates are constrained to have non-decreasing dose-efficacy and dose-toxicity relationships. Numerical utilities are elicited from clinicians for each potential bivariate outcome. For each patient, the optimal dose is chosen to maximize the posterior mean of the utility function. We also propose alternative approaches to optimal dose selection by adding additional toxicity based constraints and an approach taking into account the uncertainty in the estimation of the utility function. The proposed methods are evaluated in a simulation study to compare expected utility outcomes under various estimated optimal dose rules. Gaussian process models tended to have better performance than random forest. Enforcing monotonicity during modeling provided small benefits. Whether and how, correlation between efficacy and toxicity, was modeled, had little effect on performance. The proposed methods are illustrated with a study of patients with liver cancer treated with stereotactic body radiation therapy.

Extracorporeal membrane oxygenation for COVID-19: evolving outcomes from the international Extracorporeal Life Support Organization Registry.

BACKGROUND: Over the course of the COVID-19 pandemic, the care of patients with COVID-19 has changed and the use of extracorporeal membrane oxygenation (ECMO) has increased. We aimed to examine patient selection, treatments, outcomes, and ECMO centre characteristics over the course of the pandemic to date. METHODS: We retrospectively analysed the Extracorporeal Life Support Organization Registry and COVID-19 Addendum to compare three groups of ECMO-supported patients with COVID-19 (aged ≥16 years). At early-adopting centres-ie, those using ECMO support for COVID-19 throughout 2020-we compared patients who started ECMO on or before May 1, 2020 (group A1), and between May 2 and Dec 31, 2020 (group A2). Late-adopting centres were those that provided ECMO for COVID-19 only after May 1, 2020 (group B). The primary outcome was in-hospital mortality in a time-to-event analysis assessed 90 days after ECMO initiation. A Cox proportional hazards model was fit to compare the patient and centre-level adjusted relative risk of mortality among the groups. FINDINGS: In 2020, 4812 patients with COVID-19 received ECMO across 349 centres within 41 countries. For early-adopting centres, the cumulative incidence of in-hospital mortality 90 days after ECMO initiation was 36·9% (95% CI 34·1-39·7) in patients who started ECMO on or before May 1 (group A1) versus 51·9% (50·0-53·8) after May 1 (group A2); at late-adopting centres (group B), it was 58·9% (55·4-62·3). Relative to patients in group A2, group A1 patients had a lower adjusted relative risk of in-hospital mortality 90 days after ECMO (hazard ratio 0·82 [0·70-0·96]), whereas group B patients had a higher adjusted relative risk (1·42 [1·17-1·73]). INTERPRETATION: Mortality after ECMO for patients with COVID-19 worsened during 2020. These findings inform the role of ECMO in COVID-19 for patients, clinicians, and policy makers. FUNDING: None.

A simulation study of diagnostics for selection bias.

A non-probability sampling mechanism arising from non-response or non-selection is likely to bias estimates of parameters with respect to a target population of interest. This bias poses a unique challenge when selection is 'non-ignorable', i.e. dependent upon the unobserved outcome of interest, since it is then undetectable and thus cannot be ameliorated. We extend a simulation study by Nishimura et al. [International Statistical Review, 84, 43-62 (2016)], adding two recently published statistics: the so-called 'standardized measure of unadjusted bias (SMUB)' and 'standardized measure of adjusted bias (SMAB)', which explicitly quantify the extent of bias (in the case of SMUB) or non-ignorable bias (in the case of SMAB) under the assumption that a specified amount of non-ignorable selection exists. Our findings suggest that this new sensitivity diagnostic is more correlated with, and more predictive of, the true, unknown extent of selection bias than other diagnostics, even when the underlying assumed level of non-ignorability is incorrect.

Investigating the SPECT Dose-Function Metrics Associated With Radiation-Induced Lung Toxicity Risk in Patients With Non-small Cell Lung Cancer Undergoing Radiation Therapy.

PURPOSE: Dose to normal lung has commonly been linked with radiation-induced lung toxicity (RILT) risk, but incorporating functional lung metrics in treatment planning may help further optimize dose delivery and reduce RILT incidence. The purpose of this study was to investigate the impact of the dose delivered to functional lung regions by analyzing perfusion (Q), ventilation (V), and combined V/Q single-photon-emission computed tomography (SPECT) dose-function metrics with regard to RILT risk in patients with non-small cell lung cancer (NSCLC) patients who received radiation therapy (RT). METHODS AND MATERIALS: SPECT images acquired from 88 patients with locally advanced NSCLC before undergoing conventionally fractionated RT were retrospectively analyzed. Dose was converted to the nominal dose equivalent per 2 Gy fraction, and SPECT intensities were normalized. Regional lung segments were defined, and the average dose delivered to each lung region was quantified. Three functional categorizations were defined to represent low-, normal-, and high-functioning lungs. The percent of functional lung category receiving ≥20 Gy and mean functional intensity receiving ≥20 Gy (iV20) were calculated. RILT was defined as grade 2+ radiation pneumonitis and/or clinical radiation fibrosis. A logistic regression was used to evaluate the association between dose-function metrics and risk of RILT. RESULTS: By analyzing V/Q normalized intensities and functional distributions across the population, a wide range in functional capability (especially in the ipsilateral lung) was observed in patients with NSCLC before RT. Through multivariable regression models, global lung average dose to the lower lung was found to be significantly associated with RILT, and Q and V iV20 were correlated with RILT when using ipsilateral lung metrics. Through a receiver operating characteristic analysis, combined V/Q low-function receiving ≥20 Gy (low-functioning V/Q20) in the ipsilateral lung was found to be the best predictor (area under the curce: 0.79) of RILT risk. CONCLUSIONS: Irradiation of the inferior lung appears to be a locational sensitivity for RILT risk. The multivariable correlation between ipsilateral lung iV20 and RILT, as well as the association of low-functioning V/Q20 and RILT, suggest that irradiating low-functioning regions in the lung may lead to higher toxicity rates.

Evaluating a Novel Prehospital Emergency Trauma Care Assessment Tool (PETCAT) for Low- and Middle-Income Countries in Sierra Leone.

BACKGROUND: WHO recommends training lay first responders (LFRs) as the first step toward formal emergency medical services development, yet no tool exists to evaluate LFR programs. METHODS: We developed Prehospital Emergency Trauma Care Assessment Tool (PETCAT), a seven-question survey administered to first-line hospital-based healthcare providers, to independently assess LFR prehospital intervention frequency and quality. PETCAT surveys were administered one month pre-LFR program launch (June 2019) in Makeni, Sierra Leone and again 14 months post-launch (August 2020). Using a difference-in-differences approach, PETCAT was also administered in a control city (Kenema) with no LFR training intervention during the study period at the same intervals to control for secular trends. PETCAT measured change in both the experimental and control locations. Cronbach's alpha, point bi-serial correlation, and inter-rater reliability using Cohen's Kappa assessed PETCAT reliability. RESULTS: PETCAT administration to 90 first-line, hospital-based healthcare providers found baseline prehospital intervention were rare in Makeni and Kenema prior to LFR program launch (1.2/10 vs. 1.8/10). Fourteen months post-LFR program implementation, PETCAT demonstrated prehospital interventions increased in Makeni with LFRs (5.2/10, p < 0.0001) and not in Kenema (1.2/10) by an adjusted difference of + 4.6 points/10 (p < 0.0001) ("never/rarely" to "half the time"), indicating negligible change due to secular trends. PETCAT demonstrated high reliability (Cronbach's α = 0.93, Cohen's K = 0.62). CONCLUSIONS: PETCAT measures changes in rates of prehospital care delivery by LFRs in a resource-limited African setting and may serve as a robust tool for independent EMS quality assessment.

A modular framework for early-phase seamless oncology trials.

BACKGROUND: As our understanding of the etiology and mechanisms of cancer becomes more sophisticated and the number of therapeutic options increases, phase I oncology trials today have multiple primary objectives. Many such designs are now "seamless," meaning that the trial estimates both the maximum tolerated dose and the efficacy at this dose level. Sponsors often proceed with further study only with this additional efficacy evidence. However, with this increasing complexity in trial design, it becomes challenging to articulate fundamental operating characteristics of these trials, such as (1) what is the probability that the design will identify an acceptable, that is., safe and efficacious, dose level? or (2) how many patients will be assigned to an acceptable dose level on average? METHODS: In this manuscript, we propose a new modular framework for designing and evaluating seamless oncology trials. Each module is comprised of either a dose assignment step or a dose-response evaluation, and multiple such modules can be implemented sequentially. We develop modules from existing phase I/II designs as well as a novel module for evaluating dose-response using a Bayesian isotonic regression scheme. RESULTS: We also demonstrate a freely available R package called seamlesssim to numerically estimate, by means of simulation, the operating characteristics of these modular trials. CONCLUSIONS: Together, this design framework and its accompanying simulator allow the clinical trialist to compare multiple different candidate designs, more rigorously assess performance, better justify sample sizes, and ultimately select a higher quality design.

ASSESSING SELECTION BIAS IN REGRESSION COEFFICIENTS ESTIMATED FROM NONPROBABILITY SAMPLES WITH APPLICATIONS TO GENETICS AND DEMOGRAPHIC SURVEYS.

Selection bias is a serious potential problem for inference about relationships of scientific interest based on samples without well-defined probability sampling mechanisms. Motivated by the potential for selection bias in: (a) estimated relationships of polygenic scores (PGSs) with phenotypes in genetic studies of volunteers and (b) estimated differences in subgroup means in surveys of smartphone users, we derive novel measures of selection bias for estimates of the coefficients in linear and probit regression models fitted to nonprobability samples, when aggregate-level auxiliary data are available for the selected sample and the target population. The measures arise from normal pattern-mixture models that allow analysts to examine the sensitivity of their inferences to assumptions about nonignorable selection in these samples. We examine the effectiveness of the proposed measures in a simulation study and then use them to quantify the selection bias in: (a) estimated PGS-phenotype relationships in a large study of volunteers recruited via Facebook and (b) estimated subgroup differences in mean past-year employment duration in a nonprobability sample of low-educated smartphone users. We evaluate the performance of the measures in these applications using benchmark estimates from large probability samples.

Serial Molecular Profiling of Low-grade Prostate Cancer to Assess Tumor Upgrading: A Longitudinal Cohort Study.

BACKGROUND: The potential for low-grade (grade group 1 [GG1]) prostate cancer (PCa) to progress to high-grade disease remains unclear. OBJECTIVE: To interrogate the molecular and biological features of low-grade PCa serially over time. DESIGN, SETTING, AND PARTICIPANTS: Nested longitudinal cohort study in an academic active surveillance (AS) program. Men were on AS for GG1 PCa from 2012 to 2017. INTERVENTION: Electronic tracking and resampling of PCa using magnetic resonance imaging/ultrasound fusion biopsy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: ERG immunohistochemistry (IHC) and targeted DNA/RNA next-generation sequencing were performed on initial and repeat biopsies. Tumor clonality was assessed. Molecular data were compared between men who upgraded and those who did not upgrade to GG ≥ 2 cancer. RESULTS AND LIMITATIONS: Sixty-six men with median age 64 yr (interquartile range [IQR], 59-69) and prostate-specific antigen 4.9 ng/mL (IQR, 3.3-6.4) underwent repeat sampling of a tracked tumor focus (median interval, 11 mo; IQR, 6-13). IHC-based ERG fusion status was concordant at initial and repeat biopsies in 63 men (95% vs expected 50%, p < 0.001), and RNAseq-based fusion and isoform expression were concordant in nine of 13 (69%) ERG+ patients, supporting focal resampling. Among 15 men who upgraded with complete data at both time points, integrated DNA/RNAseq analysis provided evidence of shared clonality in at least five cases. Such cases could reflect initial undersampling, but also support the possibility of clonal temporal progression of low-grade cancer. Our assessment was limited by sample size and use of targeted sequencing. CONCLUSIONS: Repeat molecular assessment of low-grade tumors suggests that clonal progression could be one mechanism of upgrading. These data underscore the importance of serial tumor assessment in men pursuing AS of low-grade PCa. PATIENT SUMMARY: We performed targeted rebiopsy and molecular testing of low-grade tumors on active surveillance. Our findings highlight the importance of periodic biopsy as a component of monitoring for cancer upgrading during surveillance.