No abstract
There is much interest in translating neuroimaging findings into meaningful clinical diagnostics. The goal of scientific discoveries differs from clinical diagnostics. Scientific discoveries must replicate under a specific set of conditions; to translate to the clinic we must show that findings using purpose-built scientific instruments will be observable in clinical populations and instruments. Here we describe and evaluate data and computational methods designed to translate a scientific observation to a clinical setting. Using diffusion weighted imaging (DWI), Wahl et al., (2010) observed that across subjects the mean fractional anisotropy (FA) of homologous pairs of tracts is highly correlated. We hypothesize that this is a fundamental biological trait that should be present in most healthy participants, and deviations from this assessment may be a useful diagnostic metric. Using this metric as an illustration of our methods, we analyzed six pairs of homologous white matter tracts in nine different DWI datasets with 44 subjects each. Considering the original FA measurement as a baseline, we show that the new metric is between 2 and 4 times more precise when used in a clinical context. Our framework to translate research findings into clinical practice can be applied, in principle, to other neuroimaging results.
Learning optimal policies from historical data enables the gains from personalization to be realized in a wide variety of applications. The growing policy learning literature focuses on a setting where the treatment assignment policy does not adapt to the data. However, adaptive data collection is becoming more common in practice, from two primary sources: 1) data collected from adaptive experiments that are designed to improve inferential efficiency; 2) data collected from production systems that are adaptively evolving an operational policy to improve performance over time (e.g. contextual bandits). In this paper, we aim to address the challenge of learning the optimal policy with adaptively collected data and provide one of the first theoretical inquiries into this problem. We propose an algorithm based on generalized augmented inverse propensity weighted estimators and establish its finite-sample regret bound. We complement this regret upper bound with a lower bound that characterizes the fundamental difficulty of policy learning with adaptive data. Finally, we demonstrate our algorithm's effectiveness using both synthetic data and public benchmark datasets.
We consider the problem of assessing the importance of multiple variables or factors from a dataset when side information is available. In principle, using side information can allow the statistician to pay attention to variables with a greater potential, which in turn, may lead to more discoveries. We introduce an adaptive knockoff filter, which generalizes the knockoff procedure (Barber and Candès, 2015;Candès et al., 2018) in that it uses both the data at hand and side information to adaptively order the variables under study and focus on those that are most promising. Adaptive knockoffs controls the finite-sample false discovery rate (FDR) and we demonstrate its power by comparing it with other structured multiple testing methods. We also apply our methodology to real genetic data in order to find associations between genetic variants and various phenotypes such as Crohn's disease and lipid levels. Here, adaptive knockoffs makes more discoveries than reported in previous studies on the same datasets.
We propose a model-free framework for sensitivity analysis of individual treatment effects (ITEs), building upon ideas from conformal inference. For any unit, our procedure reports the Γ-value, a number which quantifies the minimum strength of confounding needed to explain away the evidence for ITE. Our approach rests on the reliable predictive inference of counterfactuals and ITEs in situations where the training data are confounded. Under the marginal sensitivity model of [Z. Tan, J. Am. Stat. Assoc. 101, 1619-1637 (2006)], we characterize the shift between the distribution of the observations and that of the counterfactuals. We first develop a general method for predictive inference of test samples from a shifted distribution; we then leverage this to construct covariate-dependent prediction sets for counterfactuals. No matter the value of the shift, these prediction sets (resp. approximately) achieve marginal coverage if the propensity score is known exactly (resp. estimated). We describe a distinct procedure also attaining coverage, however, conditional on the training data. In the latter case, we prove a sharpness result showing that for certain classes of prediction problems, the prediction intervals cannot possibly be tightened. We verify the validity and performance of the methods via simulation studies and apply them to analyze real datasets.
We study the problem of dynamic batch learning in high-dimensional sparse linear contextual bandits, where a decision maker, under a given maximum-number-of-batch constraint and only able to observe rewards at the end of each batch, can dynamically decide how many individuals to include in the next batch (at the end of the current batch) and what personalized action-selection scheme to adopt within each batch. Such batch constraints are ubiquitous in a variety of practical contexts, including personalized product offerings in marketing and medical treatment selection in clinical trials. We characterize the fundamental learning limit in this problem via a regret lower bound and provide a matching upper bound (up to log factors), thus prescribing an optimal scheme for this problem. To the best of our knowledge, our work provides the first inroad into a theoretical understanding of dynamic batch learning in high-dimensional sparse linear contextual bandits. Notably, even a special case of our result (when no batch constraint is present) yields the first minimax optimal Õ( √ s0T ) regret bound for standard online learning in high-dimensional linear contextual bandits (for the no-margin case), where s0 is the sparsity parameter (or an upper bound thereof) and T is the learning horizon. This result-both that Õ( √ s0T ) is achievable and that Ω( √ s0T ) is a lower bound-appears to be unknown in the emerging literature of high-dimensional contextual bandits.
Lay Summary The COVID-19 pandemic has had unprecedented and potentially irreversible impacts on health and health care globally. Many complex issues and factors therefore need to be accounted for as we look at the long-term impact of COVID-19 on humanity and society and how this ongoing crisis continues to affect health and health care outcomes for different populations. There are significant health disparities and sex inequalities associated with the COVID-19 pandemic. Sex-responsive interventions are imperative to address widening sex gaps resulting from the pandemic. Although many researchers have studied the impact of the COVID-19 pandemic on health disparities, there is a lack of research on the sole and joint association of the pandemic or sex differences and their specific impact on health and health care. The study examined the association of the COVID-19 pandemic, the association of sex, and the joint association of sex and the COVID-19 pandemic with health communication, physical activity, mental health, and behavioral health. We also highlighted the application of Bonferroni corrections when conducting multiple hypotheses testing simultaneously. Objective To examine the association of the COVID-19 pandemic, the association of sex, and the joint association of sex and the COVID-19 pandemic with health communication, physical activity, mental health, and behavioral health. Methods We drew data from the National Cancer Institute's 2020 Health Information National Trends Survey (HINTS). We described and compared the characteristics of social determinants of health, physical activity, mental health, alcohol use, patterns of social networking service use, and health information data sharing. Analyses were weighted to provide nationally representative estimates. Multivariate models (multiple linear regression, multiple logistic regression, and multinomial logistic model) were used to assess the sole and joint association with sex and pandemic. In addition, we applied the Bonferroni correction to adjust p-values to decrease the risks of type I errors when making multiple statistical tests. Results Females were more likely to use mobile health and health communication technologies than males, and the difference increased after the pandemic. The association between sex and mental health was significant after the COVID-19 pandemic. Females were more likely to experience depression or anxiety disorders. Both males and females had a slight decrease in terms of the quantity and intensity of physical activity and females were less likely to perform moderate exercise and strength training regularly. Males were likely to drink more alcohol than females. Conclusion The COVID-19 pandemic amplifies the differences between males and females in health communication, physical activity, mental health, and behavioral health. Intersectional analyses of sex are integral to addressing issues that arise and mitigating the exacerbation of inequities. Responses to the pandemic should consider diverse perspectives, including sex.
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