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Nivolumab is a fully human monoclonal antibody that inhibits programmed death‐1 activation. The clinical pharmacology profile of nivolumab was analyzed by a population pharmacokinetics model that assessed covariate effects on nivolumab concentrations in 1,895 patients who received 0.3–10.0 mg/kg nivolumab in 11 clinical trials. Nivolumab pharmacokinetics is linear with a time‐varying clearance. A full covariate model was developed to assess covariate effects on pharmacokinetic parameters. Nivolumab clearance and volume of distribution increase with body weight. The final model included the effects of baseline performance status (PS), baseline body weight, and baseline estimated glomerular filtration rate (eGFR), sex, and race on clearance, and effects of baseline body weight and sex on volume of distribution in the central compartment. Sex, PS, baseline eGFR, age, race, baseline lactate dehydrogenase, mild hepatic impairment, tumor type, tumor burden, and programmed death ligand‐1 expression had a significant but not clinically relevant (<20%) effect on nivolumab clearance.

This paper proposes a two-stage phase I-II clinical trial design to optimize doseschedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decisionmaking is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood-based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy-toxicity trade-off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the posterior predictive distributions of utilities. A simulation study is presented to evaluate the design’s performance under a variety of scenarios, and to assess its sensitivity to the amount of missing data, the prior, and model misspecification.

Abstract Background The INTEGRATE phase 3 trial in advanced gastric and esophagogastric junction cancer involved pooling overall survival data with its preceding phase 2 trial, raising concerns about misalignment due to treatment switching in phase 2, or the “winner’s curse.” We evaluated phase 2 results, adjusted for these opposing effects, against phase 3 according to the prespecified statistical analysis plan. Methods Overall survival estimates were adjusted for treatment switching using the rank-preserving structural failure time model (RPSFTM) and inverse probability of censoring weights (IPCW) method. A novel shrinkage approach mitigated overestimation from the winner’s curse, and Bayesian prediction methods predicted phase 3 outcomes from phase 2 estimates. A simulation study modeled 10 000 seamless phase 2/3 trials to quantify bias in the pooled estimate. Results The observed phase 3 hazard ratio (HR = 0.71, 95% CI = 0.54 to 0.93) for overall survival was more conservative than the adjusted phase 2 estimates (RPSFTM and novel shrinkage approach: HR = 0.61, 95% CI = 0.29 to 1.29; RPSFTM and Bayesian prediction: HR = 0.59, 95% CI = 0.48 to 0.73; IPCW and novel shrinkage approach: HR = 0.55, 95% CI = 0.31 to 0.99; IPCW and Bayesian prediction: HR = 0.58, 95% CI = 0.46 to 0.72). Simulations indicated negligible bias in the pooled log hazard ratio of ‒0.011 and 0.005 under the null and alternative hypotheses, respectively. Conclusion Adjusting phase 2 estimates for both treatment switching and the winner’s curse produced point estimates similar to the unadjusted phase 3 results. A prospective plan to pool trial data under a closed testing procedure may be a reasonable strategy when a recruitment shortfall in phase 3 is anticipated, provided that potential sources of misalignment are thoroughly assessed. Clinical trial information ACTRN12612000239864 (INTEGRATE I) NCT02773524 (INTEGRATE IIA)

Comprehensive data understanding is a key success driver for data analytics projects. Knowing the characteristics of the data helps a lot in selecting the appropriate data analysis techniques. Especially in data-driven product planning, knowledge about the data is a necessary prerequisite because data of the use phase is very heterogeneous. However, companies often do not have the necessary know-how or time to build up solid data understanding in connection with data analysis. In this paper, we develop a methodology to organize and categorize and thus understand use phase data in a way that makes it accessible to general data analytics workflows, following a design science research approach. We first present a knowledge base that lists typical use phase data from a product planning view. Second, we develop a taxonomy based on standard literature and real data objects, which covers the diversity of the data considered. The taxonomy provides 8 dimensions that support classification of use phase data and allows to capture data characteristics from a data analytics view. Finally, we combine both views by clustering the objects of the knowledge base according to the taxonomy. Each of the resulting clusters covers a typical combination of analytics relevant characteristics occurring in practice. By abstracting from the diversity of use phase data into artifacts with manageable complexity, our approach provides guidance to choose appropriate data analysis and AI techniques.

Background: The FOLFOXIRI regimen produces a high rate of radiological and histopathological responses. Bevacizumab added to chemotherapy showed an improvement in pathological response and necrosis of colorectal liver metastases (CLMs). FOLFOXIRI plus bevacizumab produced promising early clinical results and is under investigation in several randomised trials, although no data are currently available on its effects on response of CLMs and on liver toxicities. Methods: Starting from 499 patients enrolled in first-line phase II/III trials, we selected on the basis of tissue sample availability 18 patients treated with FOLFOXIRI/XELOXIRI and 24 patients treated with FOLFOXIRI plus bevacizumab who underwent secondary resection of CLMs. The 28 untreated patients who underwent primary resection of CLMs were included as control group. Responses of CLMs and chemotherapy-induced toxicities were assessed. Results: Among the patients, 63% of those treated with FOLFOXIRI plus bevacizumab, as compared with 28% of those treated with only FOLFOXIRI/XELOXIRI, showed a histopathological response ( P= 0.033). In the two groups, 52% and 12.5%, respectively, showed necrosis ⩾50% ( P =0.017). The incidence of liver toxicities was not significantly increased in patients treated with FOLFOXIRI plus bevacizumab. Conclusion: The addition of bevacizumab to FOLFOXIRI produces high rates of pathologic responses and necrosis of CLM without increasing liver toxicity.

Of the sources of noise affecting blood oxygen level‐dependent functional magnetic resonance imaging (fMRI), respiration and cardiac fluctuations are responsible for the largest part of the variance, particularly at high and ultrahigh field. Existing approaches to removing physiological noise either use external recordings, which can be unwieldy and unreliable, or attempt to identify physiological noise from the magnitude fMRI data. Data‐driven approaches are limited by sensitivity, temporal aliasing, and the need for user interaction. In the light of the sensitivity of the phase of the MR signal to local changes in the field stemming from physiological processes, we have developed an unsupervised physiological noise correction method using the information carried in the phase and the magnitude of echo‐planar imaging data. Our technique, Physiological Regressor Estimation from Phase and mAgnItude, sub‐tR (PREPAIR) derives time series signals sampled at the slice TR from both phase and magnitude images. It allows physiological noise to be captured without aliasing, and efficiently removes other sources of signal fluctuations not related to physiology, prior to regressor estimation. We demonstrate that the physiological signal time courses identified with PREPAIR agree well with those from external devices and retrieve challenging cardiac dynamics. The removal of physiological noise was as effective as that achieved with the most used approach based on external recordings, RETROICOR. In comparison with widely used recording‐free physiological noise correction tools—PESTICA and FIX, both performed in unsupervised mode—PREPAIR removed significantly more respiratory and cardiac noise than PESTICA, and achieved a larger increase in temporal signal‐to‐noise‐ratio at both 3 and 7 T. We have developed an unsupervised physiological noise correction method using the information carried in the phase and the magnitude of echo‐planar imaging data. We demonstrate that the physiological signal time courses identified with Physiological Regressor Estimation from Phase and mAgnItude, sub‐tR (PREPAIR) not only agree well with those from external devices, but also retrieve challenging cardiac dynamics.

The successful planning of future product generations requires reliable insights into the actual products’ problems and potentials for improvement. A valuable source for these insights is the product use phase. In practice, product planners are often forced to work with assumptions and speculations as insights from the use phase are insufficiently identified and documented. A new opportunity to address this problem arises from the ongoing digitalization that enables products to generate and collect data during their utilization. Analyzing these data could enable their manufacturers to generate and exploit insights concerning product performance and user behavior, revealing problems and potentials for improvement. However, research on analyzing use phase data in product planning of manufacturing companies is scarce. Therefore, we conducted an exploratory interview study with decision-makers of eight manufacturing companies. The result of this paper is a detailed description of the potentials and challenges that the interviewees associated with analyzing use phase data in product planning. The potentials explain the intended purpose and generic application examples. The challenges concern the products, the data, the customers, the implementation, and the employees. By gathering the potentials and challenges through expert interviews, our study structures the topic from the perspective of the potential users and shows the needs for future research.

BackgroundIdentifying potential placebo responders among apparent drug responders is critical to dissect drug-specific and nonspecific effects in depression.ObjectiveThis project aimed to develop and test a prediction model for the probability of responding to placebo in antidepressant trials. Such a model will allow us to estimate the probability of placebo response among drug responders in antidepressants trials.MethodsWe identified all placebo-controlled, double-blind randomised controlled trials (RCTs) of second generation antidepressants for major depressive disorder conducted in Japan and requested their individual patient data (IPD) to pharmaceutical companies. We obtained IPD (n=1493) from four phase II/III RCTs comparing mirtazapine, escitalopram, duloxetine, paroxetine and placebo. Out of 1493 participants in the four clinical trials, 440 participants allocated to placebo were included in the analyses. Our primary outcome was response, defined as 50% or greater reduction on Hamilton Rating Scale for Depression at study endpoint. We used multivariable logistic regression to develop a prediction model. All available candidate of predictor variables were tested through a backward variable selection and covariates were selected for the prediction model. The performance of the model was assessed by using Hosmer-Lemeshow test for calibration and the area under the ROC curve for discrimination.FindingsPlacebo response rates differed between 31% and 59% (grand average: 43%) among four trials. Four variables were selected from all candidate variables and included in the final model: age at onset, age at baseline, bodily symptoms, and study-level difference. The final model performed satisfactorily in terms of calibration (Hosmer-Lemeshow p=0.92) and discrimination (the area under the ROC curve (AUC): 0.70).ConclusionsOur model is expected to help researchers discriminate individuals who are more likely to respond to placebo from those who are less likely so.Clinical implicationsA larger sample and more precise individual participant information should be collected for better performance. Examination of external validity in independent datasets is warranted.Trial registration numberCRD42017055912.

To develop a method to reconstruct quantitative susceptibility mapping (QSM) from multi-echo, multi-flip angle data collected using strategically acquired gradient echo (STAGE) imaging. The proposed QSM reconstruction algorithm, referred to as \"structurally constrained Susceptibility Weighted Imaging and Mapping\" scSWIM, performs an and regularization-based reconstruction in a single step. The unique contrast of the T1 weighted enhanced (T1WE) image derived from STAGE imaging was used to extract reliable geometry constraints to protect the basal ganglia from over-smoothing. The multi-echo multi-flip angle data were used for improving the contrast-to-noise ratio in QSM through a weighted averaging scheme. The measured susceptibility values from scSWIM for both simulated and data were compared to the: original susceptibility model (for simulated data only), the multi orientation COSMOS (for data only), truncated k-space division (TKD), iterative susceptibility weighted imaging and mapping (iSWIM), and morphology enabled dipole inversion (MEDI) algorithms. Goodness of fit was quantified by measuring the root mean squared error (RMSE) and structural similarity index (SSIM). Additionally, scSWIM was assessed in ten healthy subjects. The unique contrast and tissue boundaries from T1WE and iSWIM enable the accurate definition of edges of high susceptibility regions. For the simulated brain model without the addition of microbleeds and calcium, the RMSE was best at 5.21ppb for scSWIM and 8.74ppb for MEDI thanks to the reduced streaking artifacts. However, by adding the microbleeds and calcium, MEDI's performance dropped to 47.53ppb while scSWIM performance remained the same. The SSIM was highest for scSWIM (0.90) and then MEDI (0.80). The deviation from the expected susceptibility in deep gray matter structures for simulated data relative to the model (and for the data relative to COSMOS) as measured by the slope was lowest for scSWIM + 1%(-1%); MEDI + 2%(-11%) and then iSWIM -5%(-10%). Finally, scSWIM measurements in the basal ganglia of healthy subjects were in agreement with literature. This study shows that using a data fidelity term and structural constraints results in reduced noise and streaking artifacts while preserving structural details. Furthermore, the use of STAGE imaging with multi-echo and multi-flip data helps to improve the signal-to-noise ratio in QSM data and yields less artifacts.