Precision medicine has led to a paradigm shift allowing the development of targeted drugs that are agnostic to the tumor location. In this context, basket trials aim to identify which tumor types - or baskets - would benefit from the targeted therapy among patients with the same molecular marker or mutation. We propose the implementation of continuous monitoring for basket trials to increase the likelihood of early identification of non-promising baskets. Although the current Bayesian trial designs available in the literature can incorporate more than one interim analysis, most of them have high computational cost, and none of them handle delayed outcomes that are expected for targeted treatments such as immunotherapies. We leverage the Bayesian empirical approach proposed by Fujiwara et al., which has low computational cost. We also extend ideas of Cai et al to address the practical challenge of performing interim analysis with delayed outcomes using multiple imputation. Operating characteristics of four different strategies to handle delayed outcomes in basket trials are compared in an extensive simulation study with the benchmark strategy where trial accrual is put on hold until complete data is observed to make a decision. The optimal handling of missing data at interim analyses is trial-dependent. With slow accrual, missingness is minimal even with continuous monitoring, favoring simpler approaches over computationally intensive methods. Although individual sample-size savings are small, multiple imputation becomes more appealing when sample size savings scale with the number of baskets and agents tested.

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e19549 Background: Achievement of deep responses in multiple myeloma (MM) has been associated with improved outcome and the regular assessment of minimal residual disease (MRD) has become the new standard in patient care. With the improvement of molecular assays, the sensitivity of MRD assessment has deepened to levels of 10 -6 and the achievement of MRD negativity at such levels has shown to predict for excellent long-term outcome. Yet, follow up in most studies has been short and there is a lack of understanding if and how other factors could influence the prognostic impact of MRD negativity. This retrospective, single-center study investigates the long-term outcome of MM patients that achieved MRD negativity at 10 -6 and investigate the impact of other meaningful clinical parameters, such as age, high-risk (HR) disease and treatment lines. Methods: Negative test results from any MM patient who had at least one negative MRD test at 10 -6 by Clonoseq between 2018-2024 were included, yielding a total of 726 samples from 271 patients. HR status was defined according to the IMS/IMWG consensus. Time to progression (TTP) and overall survival (OS) were calculated with Kaplan Meier Curves; multivariate analysis (MVA) was performed using Cox regression with a counting-process model. Results: Median age of the patient cohort was 64 years with 136 (51%) being male and 84 patients (31.5%) had HR disease. For 222 patients (83.2%), the MRD test was done during first line therapy, while in 27 patients (10%) the test was performed during second line and in the remaining 18 (7%) patients, MRD was performed in 3 rd or later lines. Median TTP for the whole cohort from time point of initial negative MRD test was not reached, but the 5-year TTP was 72.8%. 5-year OS was 82.1%. Median time from MM diagnosis to initial MRD negative test was 43 months for the whole cohort and 37 months for patients in 1 st line. For patients that progressed, the median time from last negative MRD test to next treatment was 18.5 months. 5-year TTP was significantly worse for HR patients (60% vs 79%) compared to non-HR patient, p=0.01. Furthermore, in MVA, a higher age (>60 years) and more than one treatment line were significantly associated with shorter progression free survival. Conclusions: Here we show that MM patients who achieve MRD negativity at 10 -6 have an excellent long-term outcome, yet MRD negative results should always be interpreted in the context of other clinical factors. Even among patients that achieve MRD-negativity at 10 -6 , IMWG risk, age and number of prior lines continue to have prognostic impact.

Abstract Castration-resistant prostate cancer (CRPC) is incurable and fatal, making prostate cancer the second leading cancer-related cause of death for American men. CRPC results from therapeutic resistance to standard-of-care androgen deprivation (AD) treatments, through incompletely understood molecular mechanisms, and lacks durable therapeutic options. In this study, we identified enhanced soluble guanylyl cyclase (sGC) signaling as a mechanism that restrains CRPC initiation and growth. Patients with aggressive, fatal CRPC exhibited significantly lower serum levels of the sGC catalytic product cyclic GMP (cGMP) compared with the castration-sensitive stage. In emergent castration-resistant cells isolated from castration-sensitive prostate cancer populations, the obligate sGC heterodimer was repressed via methylation of its β subunit. Genetically abrogating sGC complex formation in castration-sensitive prostate cancer cells promoted evasion of AD-induced senescence and concomitant castration-resistant tumor growth. In established castration-resistant cells, the sGC complex was present but in a reversibly oxidized and inactive state. Subjecting CRPC cells to AD regenerated the functional complex, and cotreatment with riociguat, an FDA-approved sGC agonist, evoked redox stress-induced apoptosis. Riociguat decreased castration-resistant tumor growth and increased apoptotic markers, with elevated cGMP levels correlating significantly with lower tumor burden. Riociguat treatment reorganized the tumor vasculature and eliminated hypoxic tumor niches, decreasing CD44+ tumor progenitor cells and increasing the radiosensitivity of castration-resistant tumors. Thus, this study showed that enhancing sGC activity can inhibit CRPC emergence and progression through tumor cell–intrinsic and extrinsic effects. Riociguat can be repurposed to overcome CRPC, with noninvasive monitoring of cGMP levels as a marker for on-target efficacy. Significance: Soluble guanylyl cyclase signaling inhibits castration-resistant prostate cancer emergence and can be stimulated with FDA-approved riociguat to resensitize resistant tumors to androgen deprivation, providing a strategy to prevent and treat castration resistance.

Accurately estimating the intra-class correlation coefficient (ICC) is crucial for adequately powering clustered randomized trials (CRTs). Challenges arise due to limited prior data on the specific outcome within the target population, making accurate ICC estimation difficult. Furthermore, ICC can vary significantly across studies, even for the same outcome, influenced by factors like study design, participant characteristics, and the specific intervention. Power calculations are extremely sensitive to ICC assumptions. Minor variation in the assumed ICC can lead to large differences in the number of clusters needed, potentially impacting trial feasibility and cost. This paper identifies a special class of CRTs aiming to detect the treatment effect heterogeneity, wherein the ICC can be completely disregarded in calculation of power and sample size. This result offers a solution for research projects lacking preliminary estimates of the ICC or facing challenges in their estimate. Moreover, this design facilitates power improvement through increasing the cluster sizes rather than the number of clusters, making it particular advantageous in the situations where expanding the number of clusters is difficult or costly. This paper provides a rigorous theoretical foundation for this class of ICC-ignorable CRTs, including mathematical proofs and practical guidance for implementation. We also present illustrative examples to demonstrate the practical implications of this approach in various research contexts in healthcare delivery.

Vocimagene amiretrorepvec (Toca 511) is a replicating retroviral vector (RRV) encoding cytosine deaminase that converts 5-fluorocytosine (5-FC) into 5-fluorouracil in the tumor microenvironment. This study investigated the safety and immunomodulatory effects of systemically administered Toca 511 and Toca FC in advanced cancer patients. Eligible subjects received three daily intravenous (i.v.) injections of Toca 511, followed by intratumoral (i.t.) injection of Toca 511, and a 7-day course of oral Toca FC given every 4-6 weeks. Among 21 enrolled patients, the most common treatment-related adverse events were diarrhea, nausea, vomiting, and fever. Correlative studies confirmed tumor homing after intravenous virus administration. Addition of Toca FC reduced tumor myeloid cells, Tregs, and exhausted T cells, and increased CD8 T cells. Systemically, a shift in T cells from naive to effector phenotypes, expansion of CD4 memory T cells, and increases in B cells were observed. Treatment was associated with a disease control rate of 61% and median overall survival of 9.6 months. The above results confirm feasibility of systemic administration of Toca 511 in combination with oral Toca FC, successful tumor targeting, and tumor and systemic immunomodulation. Further investigation of Toca 511/Toca FC in combination with checkpoint inhibitors and/or targeted agents is warranted.

Background The stepped-wedge cluster randomized trial (SW-CRT) design has become popular in healthcare research. It is an appealing alternative to traditional cluster randomized trials (CRTs) since the burden of logistical issues and ethical problems can be reduced. Several approaches for sample size determination for the overall treatment effect in the SW-CRT have been proposed. However, in certain situations we are interested in examining the heterogeneity in treatment effect (HTE) between groups instead. This is equivalent to testing the interaction effect. An important example includes the aim to reduce racial disparities through healthcare delivery interventions, where the focus is the interaction between the intervention and race. Sample size determination and power calculation for detecting an interaction effect between the intervention status variable and a key covariate in the SW-CRT study has not been proposed yet for binary outcomes. Methods We utilize the generalized estimating equation (GEE) method for detecting the heterogeneity in treatment effect (HTE). The variance of the estimated interaction effect is approximated based on the GEE method for the marginal models. The power is calculated based on the two-sided Wald test. The Kauermann and Carroll (KC) and the Mancl and DeRouen (MD) methods along with GEE (GEE-KC and GEE-MD) are considered as bias-correction methods. Results Among three approaches, GEE has the largest simulated power and GEE-MD has the smallest simulated power. Given cluster size of 120, GEE has over 80% statistical power. When we have a balanced binary covariate (50%), simulated power increases compared to an unbalanced binary covariate (30%). With intermediate effect size of HTE, only cluster sizes of 100 and 120 have more than 80% power using GEE for both correlation structures. With large effect size of HTE, when cluster size is at least 60, all three approaches have more than 80% power. When we compare an increase in cluster size and increase in the number of clusters based on simulated power, the latter has a slight gain in power. When the cluster size changes from 20 to 40 with 20 clusters, power increases from 53.1% to 82.1% for GEE; 50.6% to 79.7% for GEE-KC; and 48.1% to 77.1% for GEE-MD. When the number of clusters changes from 20 to 40 with cluster size of 20, power increases from 53.1% to 82.1% for GEE; 50.6% to 81% for GEE-KC; and 48.1% to 79.8% for GEE-MD. Conclusions We propose three approaches for cluster size determination given the number of clusters for detecting the interaction effect in SW-CRT. GEE and GEE-KC have reasonable operating characteristics for both intermediate and large effect size of HTE.

Introduction: Timely incorporation of palliative care (PC) during treatment of patients with metastatic cancers can improve symptom management and quality of life. Older age has been associated with lower PC use in patients with cancer. The frequency by which older patients with metastatic breast cancer (MBC) receive PC is unknown. The goal of this study was to use the National Cancer Database (NCDB) to describe national patterns in PC use in older adults over 75 years of age with MBC. Materials and Methods: Females with a diagnosis of MBC at age >= 75 years from 2010 to 2019 were identified from the NCDB. The NCDB defined PC as any surgery, radiation, systemic therapy, and/or pain management that was administered with noncurative intent. Multivariable logistic regression models were performed to assess associations between PC receipt and study covariates. Results: Of 17,325 eligible participants included in the final analysis, 39.4% were 75-79, 30.1% 80-84, and 30.4% >= 85 years of age. Overall, 22.1% (N = 3824) of patients utilized PC, of whom 14.3% received pain management, while the remainder received palliative intent surgery, radiation, and/or systemic therapy. Patients who were Hispanic were less likely to receive PC (AOR: 0.62, 95% CI: 0.48-0.79), p < 0.001). In the overall population, the use of PC increased from 19.2% in 2010 to 25.3% in 2019, though this was primarily driven by the statistically significant increase in the 75-79 age group (19.9% to 28.1%, p = 0.001). Discussion: In this patient population from the NCDB, we observed an increase in PC utilization over the last decade in older adults with MBC, though the increase was lowest in patients who were 85 years and older. Barriers to PC in older adults with cancer need to be further explored.

BACKGROUND:The use of preoperative magnetic resonance imaging (MRI) for early-stage breast cancer (ESBC) is increasing, but its utility in detecting additional malignancy is unclear and delays surgical management (Jatoi and Benson in Future Oncol 9:347-353, 2013. https://doi.org/10.2217/fon.12.186 , Bleicher et al. J Am Coll Surg 209:180-187, 2009. https://doi.org/10.1016/j.jamcollsurg.2009.04.010 , Borowsky et al. J Surg Res 280:114-122, 2022. https://doi.org/10.1016/j.jss.2022.06.066 ). The present study sought to identify ESBC patients most likely to benefit from preoperative MRI by assessing the positive predictive values (PPVs) of ipsilateral and contralateral biopsies.METHODS:A retrospective cohort study included patients with cTis-T2N0-N1 breast cancer from two institutions during 2016-2021. A "positive" biopsy result was defined as additional cancer (PositiveCancer) or cancer with histology often excised (PositiveSurg). The PPV of MRI biopsies was calculated with respect to age, family history, breast density, and histology. Uni- and multivariate logistic regression determined whether combinations of age younger than 50 years, dense breasts, family history, and pure ductal carcinoma in situ (DCIS) histology led to higher biopsy yield.RESULTS:Of the included patients, 447 received preoperative MRI and 131 underwent 149 MRI-guided biopsies (96 ipsilateral, 53 contralateral [18 bilateral]). PositiveCancer for ipsilateral biopsy was 54.2%, and PositiveCancer for contralateral biopsy was 17.0%. PositiveSurg for ipsilateral biopsy was 62.5%, and PositiveSurg for contralateral biopsy was 24.5%. Among the contralateral MRI biopsies, patients younger than 50 years were less likely to have PositiveSurg (odds ratio, 0.02; 95% confidence interval, 0.00-0.84; p = 0.041). The combinations of age, density, family history, and histology did not lead to a higher biopsy yield.CONCLUSION:Historically accepted factors for recommending preoperative MRI did not appear to confer a higher MRI biopsy yield. To prevent delays to surgical management, MRI should be carefully selected for individual patients most likely to benefit from additional imaging.

The overall response rate (ORR) 28 days (D28) after treatment has been adopted as the primary endpoint for clinical trials of acute graft versus host disease (GVHD). ORR combines complete response (CR) and partial response (PR) because CR and PR have very similar non relapse mortality (NRM). D28 ORR has some disadvantages: first, physicians usually decide to modify immunosuppression much earlier than D28, and second, NRM does not always correlate well with ORR at D28, particularly for patients who present with intermediate risk or low risk disease. MAGIC serum biomarkers at day 14 (D14) of GVHD treatment have been shown to correlate better with NRM than clinical symptom response (Srinagesh et al, Blood Advances, 2019). We therefore hypothesized that a combination of clinical symptom severity and biomarkers at D14 of treatment would predict NRM at least as well and possibly better than clinical response at D28. We studied 1144 patients from 23 MAGIC sites who were systemically treated with at least 0.25 mg/kg/day of systemic steroids (prednisone equivalents) and had clinical data and serum samples available in the MAGIC database and biorepository. We divided the patients into a training set (n=764) and a validation set (n=380). We used 12-month NRM from the time of acute GVHD onset as the primary outcome of interest. We first trained a recursive partitioning algorithm in the training set to create a response model (MAGIC D14 clinical response) according to overall GVHD grade at both onset and D14 that predicted 12-month NRM and then applied it to the validation set. Patients that either progressed to grade III/IV GVHD, continued having grade III/IV GVHD, or only improved to grade II at D14 from grade III/IV at D0 had high NRM and were classified as non-responders. Patients with grade I/II GVHD at day 0 who had grade 0-II GVHD at day 14 and patients who improved to grade 0/I at day 14 from grade III/IV at day 0 had low NRM and were classified as responders. Although counterintuitive, responders included patients whose GVHD remained at grade II because the 12 month NRM of this group was only 20%. The new clinical model predicted NRM as well as the D28 standard response model in the validation set. When we integrated the D14 MAP biomarker score (low: ≤ 0.290 vs high: >0.290) with the new D14 clinical response (D14 MAGIC integrated response), we observed three response groups of integrated response with strikingly different 12 month NRM (8%, 35%, 76%, p<0.001), in contrast to the D28 standard response (Figure 1A, 1B). D14 complete response required MAGIC clinical response with low MAP; D14 partial response required either MAGIC clinical response with high MAP or MAGIC clinical non-response with low MAP; and D14 non-response required MAGIC clinical non-response with high MAP. The D14 response groups also displayed significant differences in 12 month OS (82%, 58%, 14% respectively, p<0.001) We then compared the D14 integrated response with the D28 standard response for 12 month NRM prediction. This D14 integrated response model was superior for time-dependent AUC (0.78 vs 0.71, P=0.03), sensitivity (0.71 vs 0.54), positive predictive value (0.52 vs 0.48) and negative predictive value (0.92 vs 0.89), with minimal loss in specificity (0.85 vs 0.87) in the prediction of 12-month NRM compared to the D28 standard response model. Using decision curve analysis, the D14 integrated response displayed higher net benefit (correct identification of patients that will experience NRM by 12 months) over the whole range of threshold probabilities/preferences for changing immunosuppression when 12-month NRM was used as the outcome. We conclude first, that the definition of response that uses onset and D14 grades is as accurate as D28 ORR model; and second, that a model using clinical grades and biomarkers on D14 more accurately predicts long term NRM than the standard D28 definition and may be useful as a clinical trial endpoint.

Graft-versus-host disease (GVHD) is a major cause of nonrelapse mortality (NRM) after allogeneic hematopoietic cell transplantation. Algorithms containing either the gastrointestinal (GI) GVHD biomarker amphiregulin (AREG) or a combination of 2 GI GVHD biomarkers (suppressor of tumorigenicity-2 [ST2] + regenerating family member 3 alpha [REG3 alpha]) when measured at GVHD diagnosis are validated predictors of NRM risk but have never been assessed in the same patients using identical statistical methods. We measured the serum concentrations of ST2, REG3 alpha, and AREG by enzyme-linked immunosorbent assay at the time of GVHD diagnosis in 715 patients divided by the date of transplantation into training (2004-2015) and validation (2015-2017) cohorts. The training cohort (n = 341) was used to develop algorithms for predicting the probability of 12-month NRM that contained all possible combinations of 1 to 3 biomarkers and a threshold corresponding to the concordance probability was used to stratify patients for the risk of NRM. Algorithms were compared with each other based on several metrics, including the area under the receiver operating characteristics curve, proportion of patients correctly classi fied, sensitivity, and speci ficity using only the validation cohort (n = 374). All algorithms were strong discriminators of 12-month NRM, whether or not patients were systemically treated (n = 321). An algorithm containing only ST2 + REG3 alpha had the highest area under the receiver operating characteristics curve (0.757), correctly classi fied the most patients (75%), and more accurately risk -strati fied those who developed Minnesota standard -risk GVHD and for patients who received posttransplant cyclophosphamide-based prophylaxis. An algorithm containing only AREG more accurately risk -strati fied patients with Minnesota high -risk GVHD. Combining ST2, REG3 alpha, and AREG into a single algorithm did not improve performance.

Background Patients with prostate cancer undergoing radiation therapy (RT) need comfortably full bladders to reduce toxicities during treatment. Poor compliance is common with standard of care written or verbal instructions, leading to wasted patient value (PV) and clinic resources via poor throughput efficiency (TE). Objective Herein, we assessed the feasibility and acceptability of a smartphone-based behavioral intervention (SBI) to improve bladder-filling compliance and methods for quantifying PV and TE. Methods In total, 36 patients with prostate cancer were enrolled in a single-institution, closed-access, nonrandomized feasibility trial. The SBI consists of a fully automated smart water bottle and smartphone app. Both pieces alert the patient to empty his bladder and drink a personalized volume goal, based on simulation bladder volume, 1.25 hours before his scheduled RT. Patients were trained to adjust their volume goal and notification times to achieve comfortably full bladders. The primary end point was met if qualitative (QLC) and quantitative compliance (QNC) were >80%. For QLC, patients were asked if they prepared their bladders before daily RT. QNC was met if bladder volumes on daily cone-beam tomography were >75% of the simulation’s volume. The Service User Technology Acceptability Questionnaire (SUTAQ) was given in person pre- and post-SBI. Additional acceptability and engagement end points were met if >3 out of 5 across 4 domains on the SUTAQ and >80% (15/18) of patients used the device >50% of the time, respectively. Finally, the impact of SBI on PV and TE was measured by time spent in a clinic and on the linear accelerator (linac), respectively, and contrasted with matched controls. Results QLC was 100% in 375 out of 398 (94.2%) total treatments, while QNC was 88.9% in 341 out of 398 (85.7%) total treatments. Of a total score of 5, patients scored 4.33 on privacy concerns, 4 on belief in benefits, 4.56 on satisfaction, and 4.24 on usability via SUTAQ. Further, 83% (15/18) of patients used the SBI on >50% of treatments. Patients in the intervention arm spent less time in a clinic (53.24, SEM 1.71 minutes) compared to the control (75.01, SEM 2.26 minutes) group (P<.001). Similarly, the intervention arm spent less time on the linac (10.67, SEM 0.40 minutes) compared to the control (14.19, SEM 0.32 minutes) group (P<.001). Conclusions This digital intervention trial showed high rates of bladder-filling compliance and engagement. High patient value and TE were feasibly quantified by shortened clinic times and linac usage, respectively. Future studies are needed to evaluate clinical outcomes, patient experience, and cost-benefit. Trial Registration ClinicalTrials.gov NCT04946214; https://www.clinicaltrials.gov/study/NCT04946214

OBJECTIVES:There are 2 grading approaches to radical prostatectomy (RP) in multifocal cancer: Grade Group (GG) and percentage of Gleason pattern 4 (GP4%). We investigated whether RP GG and GP4% generated by global vs individual tumor grading correlate differently with biochemical recurrence. METHODS:We reviewed 531 RP specimens with GG2 or GG3 cancer. Each tumor was scored separately with assessment of tumor volume and GP4%. Global grade and GP4% were assigned by combining Gleason pattern 3 and 4 volumes for all tumors. Correlation of GG and GP4% generated by 2 methods with biochemical recurrence was assessed by Cox proportional hazard regression and receiver operating characteristic curves, with optimism adjustment using a bootstrap analysis. RESULTS:Median age was 63 (range, 42-79) years. Median prostate-specific antigen was 6.3 (range, 0.3-62.9) ng/mL. In total, the highest-grade tumor in 371 (36.9%) men was GG2 and in 160 (30.1%) men was GG3. Global grading was downgraded from GG3 to GG2 in 37 of 121 (30.6%) specimens with multifocal disease, and 145 of 404 (35.9%) specimens had GP4% decreased by at least 10%. Ninety-eight men experienced biochemical recurrence within a median of 13 (range, 3-119) months. Men without biochemical recurrence were followed up for a median of 47 (range, 12-205) months. Grade Group, GP4%, and margin status correlated with the risk of biochemical recurrence using highest-grade tumor and global grading, but the degrees of these correlations varied and were statistically significantly different between the 2 grading approaches. CONCLUSIONS:Grade Group, GP4%, and margin status derived by global vs individual tumor grading predict postoperative biochemical recurrence statistically significantly differently. This difference has important implications if results derived from cohorts graded using different methods are compared.

Objective: The aim of this study was to evaluate the association between neighborhood disadvantage and Oncotype DX score, a surrogate for tumor biology, among a national cohort.Background: Women living in disadvantaged neighborhoods have shorter breast cancer (BC) survival, even after accounting for individual-level, tumor, and treatment characteristics. This suggests unaccounted social and biological mechanisms by which neighborhood disadvantage may impact BC survival.Methods: This cross-sectional study included stage I and II, ER+/HER2- BC patients with Oncotype DX score data from the National Cancer Database (NCDB) from 2004 to 2019. Multivariate regression models tested the association of neighborhood-level income on Oncotype DX score controlling for age, race/ethnicity, insurance, clinical stage, and education. Cox regression assessed overall survival.Results: Of the 294,283 total BC patients selected, the majority were non-Hispanic White (n=237,197, 80.6%) with 7.6% non-Hispanic Black (n=22,495) and 4.5% other (n=13,383). 27.1% (n=797,254) of the population lived in the disadvantaged neighborhoods with an annual neighborhood-level income of <$48,000, while 59.62% (n=175,305) lived in advantaged neighborhoods with a neighborhood-level income of >$48,000. On multivariable analysis controlling for age, race/ethnicity, insurance status, neighborhood-level education, and pathologic stage, patients in disadvantaged neighborhoods had greater odds of high-risk versus low-risk Oncotype DX scores compared with those in advantaged neighborhoods [odds ratio=1.04 (1.01-1.07), P=0.0067].Conclusion and Relevance: This study takes a translational epidemiologic approach to identify that women living in the most disadvantaged neighborhoods have more aggressive tumor biology, as determined by the Oncotype DX score.

In epidemiologic studies, association estimates of an exposure with disease outcomes are often biased when the uncertainties of exposure are ignored. Consequently, corresponding confidence intervals (CIs) will not have correct coverage. This issue is particularly problematic when exposures must be reconstructed from physical measurements, for example, for environmental or occupational radiation doses that were received by a study population for which radiation doses cannot be measured directly. To incorporate complex uncertainties in reconstructed exposures, the two-dimensional Monte Carlo (2DMC) dose estimation method has been proposed and used in various dose reconstruction efforts. The 2DMC method generates multiple exposure realizations from dosimetry models that incorporate various sources of errors to reflect the uncertainty of the dose distribution as well as the uncertainties in individual doses in the exposed population. Traditional measurement-error model approaches, typically based on using mean doses in the dose-exposure analysis, do not fully account exposure uncertainties. A recently developed statistical approach that overcomes many of these limitations by analyzing multiple exposure realizations in relation to disease risk is Bayesian model averaging (BMA). The analytic advantage of the BMA is its ability to better accommodate complex exposure uncertainty in the risk estimation, but a practical. Drawback is its significant computational complexity. In this present paper, we propose a novel frequentist model averaging (FMA) approach which has all the analytical advantages of the BMA method but is much simpler to implement and computationally faster. We show in simulations that, like BMA, FMA yields 95% confidence intervals for association parameters that close to 95% coverage rate. In simulations, the FMA has shorter length of CIs than those of another frequentist approach, the corrected information matrix (CIM) method. We illustrate the similarities in performance of BMA and FMA from a study of exposures from radioactive fallout in Kazakhstan.

Supplementary Figure S2 shows cell apoptosis and cell cycle analysis after Uro A treatment in PDAC cells.

Weight of PKT mice during treatment regimen.

Supplementary Figure S5 shows densitometry analyses of pAKT and pP70S6K in HPNE and HPNE-KRAS cells.