Bayesian inference for a principal stratum estimand on recurrent events truncated by death.

Recurrent events are often important endpoints in randomized clinical trials. For example, the number of recurrent disease-related hospitalizations may be considered as a clinically meaningful endpoint in cardiovascular studies. In some settings, the recurrent event process may be terminated by an event such as death, which makes it more challenging to define and estimate a causal treatment effect on recurrent event endpoints. In this paper, we focus on the principal stratum estimand, where the treatment effect of interest on recurrent events is defined among subjects who would be alive regardless of the assigned treatment. For the estimation of the principal stratum effect in randomized clinical trials, we propose a Bayesian approach based on a joint model of the recurrent event and death processes with a frailty term accounting for within-subject correlation. We also present Bayesian posterior predictive check procedures for assessing the model fit. The proposed approaches are demonstrated in the randomized Phase III chronic heart failure trial PARAGON-HF (NCT01920711).

Efficacy and safety of omalizumab in Chinese patients with anti-histamine refractory chronic spontaneous urticaria.

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.

Neurochemical abnormalities in premanifest and early spinocerebellar ataxias.

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] <10 for all genotypes, and 22 matched controls) was scanned at 7 Tesla to obtain neurochemical profiles of the cerebellum and brainstem. A novel multivariate approach (distance-weighted discrimination) was used to combine regional profiles into an "MRS score." RESULTS: MRS scores robustly distinguished individuals with SCA from controls, with misclassification rates of 0% (SCA2), 2% (SCA3), 5% (SCA1), and 17% (SCA6). Premanifest mutation carriers with estimated disease onset within 10 years had MRS scores in the range of early-manifest SCA subjects. Levels of neuronal and glial markers significantly correlated with SARA and an Activities of Daily Living score in subjects with SCA. Regional neurochemical alterations were different between SCAs at comparable disease severity, with SCA2 displaying the most extensive neurochemical abnormalities, followed by SCA1, SCA3, and SCA6. INTERPRETATION: Neurochemical abnormalities are detectable in individuals before manifest disease, which may allow premanifest enrollment in future SCA trials. Correlations with ataxia and quality-of-life scores show that neurochemical levels can serve as clinically meaningful endpoints in trials. Ranking of SCA types by degree of neurochemical abnormalities indicates that the neurochemistry may reflect synaptic function or density. Ann Neurol 2018;83:816-829.

Discriminating sample groups with multi-way data.

High-dimensional linear classifiers, such as distance weighted discrimination (DWD) and versions of the support vector machine (SVM), 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 from single vector to multi-way features, 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 magnetic resonance spectroscopy metabolite data over multiple brain regions to compare participants with and without spinocerebellar ataxia; the second uses publicly available gene expression time-course data to compare degrees of treatment response among patients with multiple sclerosis. Our multi-way method can improve performance and simplify interpretation over naive applications of full rank linear and non-linear classification to multi-way data. The R package is available at https://github.com/lockEF/MultiwayClassification.

Induced smoothing for rank-based regression with recurrent gap time data.

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.

Design considerations in clinical trials with cure rate survival data: A case study in oncology.

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.

Test-retest reproducibility of neurochemical profiles with short-echo, single-voxel MR spectroscopy at 3T and 7T.

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 Cramér-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 < 0.05), between-session coefficients of variance (CVs) were comparable at the two fields with 64 transients. Five metabolites were quantified with between-session CVs ≤5% at both fields. Analysis of subspectra showed that a minimum achievable CV was reached with a lower number of transients at 7T for multiple metabolites and that between-session CVs were lower at 7T than at 3T with fewer than 64 transients. CONCLUSION: State-of-the-art MRS methodology allows excellent reproducibility for many metabolites with 5-min data averaging on clinical 3T hardware. Sensitivity and resolution advantages at 7T are important for weakly represented metabolites, short acquisitions, and small volumes of interest. Magn Reson Med 76:1083-1091, 2016. © 2015 Wiley Periodicals, Inc.

Additive-Multiplicative Rates Model for Recurrent Event Data with Intermittently Observed Time-Dependent Covariates.

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.

Additive rates model for recurrent event data with intermittently observed time-dependent covariates.

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.

Predicting drug approvals: The Novartis data science and artificial intelligence challenge.

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.

Sensitivity of Volumetric Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy to Progression of Spinocerebellar Ataxia Type 1.

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. T1-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 < 0.02), and the pontine volume was the most sensitive MRI measure to change (-2.6 ± 1.2%/yr in SCA1 vs. -0.1 ± 1.2 in controls; P < 0.02). Effect size (mean percent change/standard deviation of percent change) of pontine volume was highest (-2.13) followed by pontine N-acetylaspartate-to-myo-inositol ratio (-0.84) and SARA (+0.60). The pontine N-acetylaspartate-to-myo-inositol ratio was abnormal for 1 premanifest patient at all visits and predicted study withdrawal as a result of disease progression in 3 patients. CONCLUSION: Both MRI and MRS were more sensitive to disease progression than SARA in SCA1. Pontine volume was most sensitive to change, whereas MRS may have more sensitivity at the premanifest stage and predictive value for disease progression.

The lung tissue microbiota of mild and moderate chronic obstructive pulmonary disease.

BACKGROUND: Oral taxa are often found in the chronic obstructive pulmonary disease (COPD) lung microbiota, but it is not clear if this is due to a physiologic process such as aspiration or experimental contamination at the time of specimen collection. METHODS: Microbiota samples were obtained from nine subjects with mild or moderate COPD by swabbing lung tissue and upper airway sites during lung lobectomy. Lung specimens were not contaminated with upper airway taxa since they were obtained surgically. The microbiota were analyzed with 16S rRNA gene qPCR and 16S rRNA gene hypervariable region 3 (V3) sequencing. Data analyses were performed using QIIME, SourceTracker, and R. RESULTS: Streptococcus was the most common genus in the oral, bronchial, and lung tissue samples, and multiple other taxa were present in both the upper and lower airways. Each subject's own bronchial and lung tissue microbiota were more similar to each other than were the bronchial and lung tissue microbiota of two different subjects (permutation test, p = 0.0139), indicating more within-subject similarity than between-subject similarity at these two lung sites. Principal coordinate analysis of all subject samples revealed clustering by anatomic sampling site (PERMANOVA, p = 0.001), but not by subject. SourceTracker analysis found that the sources of the lung tissue microbiota were 21.1% (mean) oral microbiota, 8.7% nasal microbiota, and 70.1% unknown. An analysis using the neutral theory of community ecology revealed that the lung tissue microbiota closely reflects the bronchial, oral, and nasal microbiota (immigration parameter estimates 0.69, 0.62, and 0.74, respectively), with some evidence of ecologic drift occurring in the lung tissue. CONCLUSION: This is the first study to evaluate the mild-moderate COPD lung tissue microbiota without potential for upper airway contamination of the lung samples. In our small study of subjects with COPD, we found oral and nasal bacteria in the lung tissue microbiota, confirming that aspiration is a source of the COPD lung microbiota.