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Regression methods, including the proportional rates model and additive rates model, have been proposed to evaluate the effect of covariates on the risk of recurrent events. These two models have different assumptions on the form of the covariate effects. A more flexible model, the additive-multiplicative rates model, is considered to allow the covariates to have both additive and multiplicative effects on the marginal rate of recurrent event process. However, its use is limited to the cases where the time-dependent covariates are monitored continuously throughout the follow-up time. In practice, time-dependent covariates are often only measured intermittently, which renders the current estimation method for the additive-multiplicative rates model inapplicable. In this paper, we propose a semiparametric estimator for the regression coefficients of the additive-multiplicative rates model to allow intermittently observed time-dependent covariates. We present the simulation results for the comparison between the proposed method and the simple methods, including last covariate carried forward and linear interpolation, and apply the proposed method to an epidemiologic study aiming to evaluate the effect of time-varying streptococcal infections on the risk of pharyngitis among school children. The R package implementing the proposed method is available at www.github.com/TianmengL/rectime.

Chronic spontaneous urticaria (CSU) is characterized by the spontaneous development of wheals, itching, and/or angioedema, for ≥6 weeks. In China, non-sedating H1-antihistamines (H1AH) are the recommended first-line treatment, with escalation up to 4× the standard dose in symptomatic patients to achieve control. Treatment options for Chinese patients who remain symptomatic on H1AH treatment are limited. This 20-week randomized, double blind, placebo-controlled, parallel-group study investigated the efficacy and safety of omalizumab as an add-on therapy for the treatment of patients with CSU who remained symptomatic despite H1AH treatment in China. Adult patients (N = 418) diagnosed with refractory CSU for ≥6 months were randomized (2:2:1) to receive omalizumab 300 mg (OMA300), omalizumab 150 mg (OMA150) or placebo, subcutaneously, every 4 weeks. Primary outcome was change from baseline to week 12 in weekly itch severity score (ISS7). Safety was assessed by rates of adverse events (AEs). Demographic and disease characteristics at baseline were comparable across treatment groups. At week 12, statistically significant greater decreases from baseline were observed in ISS7 with OMA300 (least square mean difference [LSM]: -4.23; 95% confidence interval [CI]: -5.70, -2.77; p 0.001) and OMA150 (LSM: -3.79; 95% CI: -5.24, -2.33; p 0.001) versus placebo. Incidence of treatment-emergent AEs over 20 weeks was slightly higher with OMA300 (71.3%) compared to OMA150 and placebo groups (64.7% and 63.9%, respectively). The incidences of serious AEs were balanced between groups. This study demonstrated the efficacy and safety of omalizumab in Chinese adult patients with CSU who remained symptomatic despite H1AH therapy.

Various regression methods have been proposed for analyzing recurrent event data. Among them, the semiparametric additive rates model is particularly appealing because the regression coefficients quantify the absolute difference in the occurrence rate of the recurrent events between different groups. Estimation of the additive rates model requires the values of time-dependent covariates being observed throughout the entire follow-up period. In practice, however, the time-dependent covariates are usually only measured at intermittent follow-up visits. In this paper, we propose to kernel smooth functions involving time-dependent covariates across subjects in the estimating function, as opposed to imputing individual covariate trajectories. Simulation studies show that the proposed method outperforms simple imputation methods. The proposed method is illustrated with data from an epidemiologic study of the effect of streptococcal infections on recurrent pharyngitis episodes.

Summary We describe a novel collaboration between academia and industry, an in-house data science and artificial intelligence challenge held by Novartis to develop machine-learning models for predicting drug-development outcomes, building upon research at MIT using data from Informa as the starting point. With over 50 cross-functional teams from 25 Novartis offices around the world participating in the challenge, the domain expertise of these Novartis researchers was leveraged to create predictive models with greater sophistication. Ultimately, two winning teams developed models that outperformed the baseline MIT model—areas under the curve of 0.88 and 0.84 versus 0.78, respectively—through state-of-the-art machine-learning algorithms and the use of newly incorporated features and data. In addition to validating the variables shown to be associated with drug approval in the earlier MIT study, the challenge also provided new insights into the drivers of drug-development success and failure., Highlights • Data science challenge at Novartis in collaboration with MIT researchers • Dataset derived from 2 decades of drug development and clinical trial data • Winning teams outperformed the baseline MIT model by leveraging domain expertise • Predictive analytics can augment human judgment in drug-development risk management, The bigger picture The probability of success is a key parameter that clinical researchers, biopharma executives and investors, and portfolio managers focus on when making important scientific and business decisions about drug development. We describe an in-house data science and artificial intelligence challenge organized by Novartis in collaboration with MIT researchers. Using state-of-the-art machine-learning algorithms and extensive feature engineering augmented by domain expertise in drug development, two winning teams developed models that outperformed the baseline MIT model proposed in a prior study. These new predictive models can be used to augment human judgment to make more informed data-driven decisions in portfolio risk management and capital allocation. These results suggest the possibility of developing even more accurate models using more comprehensive and informative data, and a broader pool of challenge participants., In an in-house data science challenge, Novartis researchers developed machine-learning models for predicting drug-development outcomes, using 2 decades of clinical trial data and building upon previous work at MIT. By leveraging domain expertise, 2 winning teams, out of 50 that participated, developed models that outperformed the baseline MIT model through state-of-the-art algorithms and feature engineering. In addition to providing new insights into drug approvals, the models can augment human judgment to make more informed risk-management decisions.

Introduction: Using multi-sensor devices in large scale clinical trials may facilitate development of new digital health solutions for remote monitoring and care of patients. Choice of appropriate parameters and devices for use in such clinical trials poses significant technological and operational challenges. Aim: Conduct a technological, operational and clinical feasibility study to determine the most appropriate multi-sensor devices for use in large scale Asthma and COPD clinical trials Methods: Parameters of clinical relevance to monitor Asthma and COPD patients were shortlisted. Sensor devices were chosen based on required parameter coverage, regulatory status, scientific evidence and technological and operational feasibility. Sensor data was collected and analyzed from 29 patients (moderate to severe COPD or uncontrolled Asthma) in a 4-week observational clinical feasibility study. Results: Parameters with potential clinical relevance (inclusive of heart rate, oxygen saturation, respiratory rate, sleep, physical activity, lung function, FeNO, rescue medication use and environmental data) were assessed using the following devices: Philips Biosensor®, Biovotion®, ActiGraph®, GoSpiro®, NIOX VERO®, MDI Sensor (Propeller Health®) and Emfit® . Patient compliance, burden to patient, usability and data quality, varied between the sensor devices and between asthma and COPD patients. Conclusions: The key considerations for selection of digital sensors for large scale remote monitoring clinical trials are: type and severity of disease, clinically relevant parameters, burden to patient, usability and data-quality of devices.

OBJECTIVE To investigate whether early neurochemical abnormalities are detectable by high-field magnetic resonance spectroscopy (MRS) in individuals with spinocerebellar ataxias (SCAs) 1, 2, 3, and 6, including patients without manifestation of ataxia. METHODS A cohort of 100 subjects (N = 18-21 in each SCA group, including premanifest mutation carriers; mean score on the Scale for the Assessment and Rating of Ataxia [SARA]

Various semiparametric regression models have recently been proposed for the analysis of gap times between consecutive recurrent events. Among them, the semiparametric accelerated failure time (AFT) model is especially appealing owing to its direct interpretation of covariate effects on the gap times. In general, estimation of the semiparametric AFT model is challenging because the rank-based estimating function is a nonsmooth step function. As a result, solutions to the estimating equations do not necessarily exist. Moreover, the popular resampling-based variance estimation for the AFT model requires solving rank-based estimating equations repeatedly and hence can be computationally cumbersome and unstable. In this paper, we extend the induced smoothing approach to the AFT model for recurrent gap time data. Our proposed smooth estimating function permits the application of standard numerical methods for both the regression coefficients estimation and the standard error estimation. Large-sample properties and an asymptotic variance estimator are provided for the proposed method. Simulation studies show that the proposed method outperforms the existing nonsmooth rank-based estimating function methods in both point estimation and variance estimation. The proposed method is applied to the data analysis of repeated hospitalizations for patients in the Danish Psychiatric Center Register.

BACKGROUND: Spinocerebellar ataxia type 1 (SCA1) causes progressive degeneration of the cerebellum and brainstem. Volumetric magnetic resonance imaging (MRI) was shown to be more sensitive to disease progression than the most sensitive clinical measure, the Scale for the Assessment and Rating of Ataxia (SARA), in longitudinal studies, and magnetic resonance spectroscopy (MRS) was shown to detect neurochemical abnormalities with high sensitivity cross‐sectionally in SCA1. OBJECTIVES: The objectives of this study were to compare the sensitivities to change of volumetric MRI, MRS, and SARA in a 3‐year longitudinal study in SCA1. METHODS: A total of 16 early‐to‐moderate stage patients with SCA1 (SARA 0‐14) and 21 matched healthy participants were scanned up to 3 times with 1.5‐year intervals. Ataxia severity was assessed with SARA. T(1)‐weighted images and magnetic resonance spectra from the cerebellar vermis, cerebellar white matter, and pons were acquired at 3T. RESULTS: The pontine total N‐acetylaspartate‐to‐myo‐inositol ratio was the most sensitive MRS measure to change (−3.9 ± 4.6%/yr in SCA1 vs. −0.3 ± 3.5%/yr in controls; P

Purpose To determine the test-retest reproducibility of neurochemical concentrations obtained with a highly optimized, short-echo, single-voxel proton MR spectroscopy (MRS) pulse sequence at 3T and 7T using state-of-the-art hardware. Methods A semi-LASER sequence (echo time = 26–28 ms) was used to acquire spectra from the posterior cingulate and cerebellum at 3T and 7T from six healthy volunteers who were scanned four times weekly on both scanners. Spectra were quantified with LCModel. Results More neurochemicals were quantified with mean Cramer-Rao lower bounds (CRLBs) ≤20% at 7T than at 3T despite comparable frequency-domain signal-to-noise ratio. Whereas CRLBs were lower at 7T (P

OBJECTIVE: To investigate whether early neurochemical abnormalities are detectable by high field magnetic resonance spectroscopy (MRS) in individuals with spinocerebellar ataxias (SCAs) 1, 2, 3 and 6, including patients without manifestation of ataxia. METHODS: A cohort of 100 subjects (N=18–21 in each SCA group, including premanifest mutation carriers; mean score on the Scale for the Assessment and Rating of Ataxia (SARA)

For clinical trials with time-to-event as the primary endpoint, the clinical cutoff is often event-driven and the log-rank test is the most commonly used statistical method for evaluating treatment effect. However, this method relies on the proportional hazards assumption in that it has the maximal power in this circumstance. In certain disease areas or populations, some patients can be curable and never experience the events despite a long follow-up. The event accumulation may dry out after a certain period of follow-up and the treatment effect could be reflected as the combination of improvement of cure rate and the delay of events for those uncurable patients. Study power depends on both cure rate improvement and hazard reduction. In this paper, we illustrate these practical issues using simulation studies and explore sample size recommendations, alternative ways for clinical cutoffs, and efficient testing methods with the highest study power possible.

High-dimensional linear classifiers, such as the support vector machine (SVM) and distance weighted discrimination (DWD), are commonly used in biomedical research to distinguish groups of subjects based on a large number of features. However, their use is limited to applications where a single vector of features is measured for each subject. In practice data are often multi-way, or measured over multiple dimensions. For example, metabolite abundance may be measured over multiple regions or tissues, or gene expression may be measured over multiple time points, for the same subjects. We propose a framework for linear classification of high-dimensional multi-way data, in which coefficients can be factorized into weights that are specific to each dimension. More generally, the coefficients for each measurement in a multi-way dataset are assumed to have low-rank structure. This framework extends existing classification techniques, and we have implemented multi-way versions of SVM and DWD. We describe informative simulation results, and apply multi-way DWD to data for two very different clinical research studies. The first study uses metabolite magnetic resonance spectroscopy data over multiple brain regions to compare patients with and without spinocerebellar ataxia, the second uses publicly available gene expression time-course data to compare treatment responses for patients with multiple sclerosis. Our method improves performance and simplifies interpretation over naive applications of full rank linear classification to multi-way data. An R package is available at https://github.com/lockEF/MultiwayClassification ., Comment: 25 pages, 5 figures, 2 tables