We analyze repeated cross‐sectional survey data collected by the Institute of Global Health Innovation, to characterize the perception and behavior of the Italian population during the Covid‐19 ...pandemic, focusing on the period that spans from April 2020 to July 2021. To accomplish this goal, we propose a Bayesian dynamic latent‐class regression model, that accounts for the effect of sampling bias including survey weights into the likelihood function. According to the proposed approach, attitudes towards covid‐19 are described via ideal behaviors that are fixed over time, corresponding to different degrees of compliance with spread‐preventive measures. The overall tendency toward a specific profile dynamically changes across survey waves via a latent Gaussian process regression, that adjusts for subject‐specific covariates. We illustrate the evolution of Italians' behaviors during the pandemic, providing insights on how the proportion of ideal behaviors has varied during the phases of the lockdown, while measuring the effect of age, sex, region and employment of the respondents on the attitude toward covid‐19.
In many fields, including medicine and biology, there has been in the last years an increasing diffusion and availability of complex data from different sources. Examples include biological ...experiments or data from health care providers. These data encompass information that can potentially enhance therapeutic advancement and constitute the core of modern system medicine. When analyzing these complex data, it is important to appropriately quantify uncertainty, avoiding using only algorithmic and automated approaches, which are not always appropriate. Improper application of algorithmic approaches, which ignore domain knowledge, could result in filling the literature with imprecise and/or misleading conclusions. In this chapter, we highlight the importance of statistical thinking when leveraging complex data and models to enhance science progress. In particular, we discuss the reproducibility and replicability issues, the importance of uncertainty quantification, and some common pitfalls in the analysis of big data.
There has been considerable recent interest in Bayesian modeling of high-dimensional networks via latent space approaches. When the number of nodes increases, estimation based on Markov chain Monte ...Carlo can be extremely slow and show poor mixing, thereby motivating research on alternative algorithms that scale well in high-dimensional settings. In this article, we focus on the latent factor model, a widely used approach for latent space modeling of network data. We develop scalable algorithms to conduct approximate Bayesian inference via stochastic optimization. Leveraging sparse representations of network data, the proposed algorithms show massive computational and storage benefits, and allow to conduct inference in settings with thousands of nodes. An R package with an efficient c++ implementation of the proposed algorithms is provided.
Purpose
We develop an open‐source R package to implement tree‐based scan statistics (TBSS) analyses.
Methods
TBSS are data mining methods used by the United States Food and Drug Administration and ...the Centers for Disease Control. They simultaneously screen thousands of hierarchically aggregated outcomes to identify unsuspected adverse effects of drugs or vaccines, accounting for multiple comparisons. The general structure of TBSS is highly adaptable, with four essential components: (1) a hierarchical outcome structure, (2) a test statistic to be computed for each element of the hierarchy, (3) an algorithm to generate data replicates under a null distribution, and (4) observed outcomes at the lower level of the hierarchy. We encode the general TBSS framework in a convenient R package that offers user‐friendly functions for the most used TBSS methods. To illustrate the performance of our software, we evaluated two examples of archetypical TBSS analyses previously analyzed using proprietary, closed‐source TreeScan™ software. The first considers the risk of congenital malformations associated with first‐trimester exposure to valproate, and the second compares exposure to newly prescribed canagliflozin with a dipeptidyl peptidase 4 inhibitor in adults affected by type 2 diabetes.
Results
The results of the original studies are replicated.
Conclusions
The diffusion of an open‐source implementation of TBSS can enhance innovation of TBSS methods and foster collaborations. We offer an intuitive R package implementing standard TBSS methods with accompanying tutorials. Our unified object‐oriented implementation allows expert users to extend the framework, introduce new features, or enhance existing ones.
A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients ...over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type‐I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response‐adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response‐adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study.
After market exclusivity ends for biologic drugs, biosimilars—follow‐on versions made by other manufacturers—can compete with lower prices. Biosimilars have modestly reduced prescription drug ...spending for US payers, but it is unclear whether patients have directly experienced any savings. In this study we assessed whether availability of biosimilar infliximab was associated with lower out‐of‐pocket (OOP) costs, using claims from a national data set of commercially insured patients from 2014 to 2018. We used two‐part models, adjusting for patient demographics, clinical characteristics, insurance plan type, and calendar month. Compared with the reference biologic, there was no difference in the percentage of biosimilar claims with OOP costs (30.1% vs. 30.8%; adjusted odds ratio (aOR) 0.98, 95% confidence interval (CI), 0.84–1.15, P = 0.84) or the average nonzero OOP cost (median $378 vs. $538, adjusted mean ratio (aMR) 0.97, 95% CI, 0.80–1.18, P = 0.77). The percentage of claims with OOP costs was lower after biosimilar competition (30.7% vs. 35.0%, aOR 0.96, 95% CI, 0.94–0.99, P = 0.003), but average nonzero costs increased (median $534 vs. $520, aMR 1.04, 95% CI, 1.01–1.07, P = 0.004). Thus, early biosimilar infliximab competition did not improve affordability for patients. Policymakers need to better assure that competition in the biosimilar market translates to lower costs for patients using these medications.
Multivariate categorical data are common in many fields. An illustrative example is provided by election polls studies assessing evidence of changes in voters’ opinions with their candidates ...preferences in the 2016 United States Presidential primaries or caucuses. Similar goals arise in routine applications, but current literature lacks a general methodology which combines flexibility, efficiency, and tractability in testing for group differences in multivariate categorical data at different – potentially complex – scales. This contribution addresses such goal by leveraging a Bayesian representation, which factorizes the joint probability mass function for the group variable and the multivariate categorical data as the product of the marginal probabilities for the groups and the conditional probability mass function of the multivariate categorical data, given the group membership. To enhance flexibility, the conditional probability mass function of the multivariate categorical data is defined via a group-dependent mixture of tensor factorizations which facilitates dimensionality reduction and borrowing of information, while providing tractable procedures for computation, and accurate tests assessing global and local group differences. The proposed methods are compared with popular competitors, and the improved performance is outlined in simulations and in American election polls studies.
We illustrate a method for stratum assignment in small cohort studies that avoids modeling assumptions.
Off-the-shelf software ( rgenoud ) made stratum assignments to minimize a loss function built ...on within-stratum and population-adjusted Euclidean distances.
In 100 trials using simulated data of 300 records with a binary treatment and four dissimilar covariate treatment predictors, minimizing a loss based on Euclidean distance reduced covariate imbalance by a median of 99%. Stratification by propensity score and weighting records by the inverse of their probability of treatment reduced imbalance by 76%-89% and 83%-94%, respectively. Loss minimization applied to a cohort of 361 children undergoing immunotherapy achieved nearly complete elimination of covariate differences for important treatment predictors.
With the availability of semiparametric stratum-assignment algorithms, analysts can tailor loss functions to meet design goals. Here, a loss function that emphasized covariate balance performed well under limited testing.
In 2019, the U.S. Food and Drug Administration (FDA) approved the first generic maintenance inhaler for asthma and chronic obstructive pulmonary disease (COPD). The inhaler, Wixela Inhub ...(fluticasone-salmeterol; Viatris), is a substitutable version of the dry powder inhaler Advair Diskus (fluticasone-salmeterol; GlaxoSmithKline). When approving complex generic products like inhalers, the FDA applies a special "weight-of-evidence" approach. In this case, manufacturers were required to perform a randomized controlled trial in patients with asthma but not COPD, although the product received approval for both indications.
To compare the effectiveness and safety of generic (Wixela Inhub) and brand-name (Advair Diskus) fluticasone-salmeterol among patients with COPD treated in routine care.
A 1:1 propensity score-matched cohort study.
A large, longitudinal health care database.
Adults older than 40 years with a diagnosis of COPD.
Incidence of first moderate or severe COPD exacerbation (effectiveness outcome) and incidence of first pneumonia hospitalization (safety outcome) in the 365 days after cohort entry.
Among 45 369 patients (27 305 Advair Diskus users and 18 064 Wixela Inhub users), 10 012 matched pairs were identified for the primary analysis. Compared with Advair Diskus use, Wixela Inhub use was associated with a nearly identical incidence of first moderate or severe COPD exacerbation (hazard ratio HR, 0.97 95% CI, 0.90 to 1.04) and first pneumonia hospitalization (HR, 0.99 CI, 0.86 to 1.15).
Follow-up times were short, reflecting real-world clinical practice. The possibility of residual confounding cannot be completely excluded.
Use of generic and brand-name fluticasone-salmeterol was associated with similar outcomes among patients with COPD treated in routine practice.
National Heart, Lung, and Blood Institute.
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