arXiv subjects

Kristin A. Linn

Publications and source records attributed to Kristin A. Linn.

7 recordsLinked to original sources

arXiv2026

Quantifying Time-Varying Physical Activity Intervention Effects via Functional Regression

Physical activity (PA) intervention studies often collect repeated intensity measurements over long observation periods. Quantifying the variation in intervention effects over the study period is critical to evaluating and improving intervention strategies, yet many analyses reduce PA data into scalar summary measures, resulting in limited insights. We propose a functional regression framework, which captures time-varying intervention effects by modeling the entire PA trajectory as a functional observation. From both methodological and practical perspectives, we demonstrate the advantages of function-on-scalar regression (FoSR) over the traditional two-step approach of applying functional principal components analysis (FPCA) followed by regressing scores on covariates. The FoSR is further extended to a function-on-function regression (FoFR) for studying the association of PA across time periods. Methods are applied to daily step counts from the Social incentives to Encourage Physical Activity and Understand Predictors (STEP UP) study, revealing distinct and highly interpretable time-varying effects of three intervention strategies on PA and differences in their sustainability. Our case study highlights the feasibility of functional data analysis techniques for uncovering novel insights in intervention studies with high-dimensional endpoints.

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arXiv2025

Estimating the average treatment effect in cluster-randomized trials with misclassified outcomes and non-random validation subsets

Randomized trials are viewed as the benchmark for assessing causal effects of treatments on outcomes of interest. Nonetheless, challenges such as measurement error can undermine the standard causal assumptions for randomized trials. In ASPIRE, a cluster-randomized trial, pediatric primary care clinics were assigned to one of two treatments aimed at promoting clinician delivery of a secure firearm program to parents during well-child visits. A key outcome of interest is thus parent receipt of the program at each visit. Clinicians documented program delivery in patients' electronic health records for all visits, but their reporting is a proxy measure for the parent receipt outcome. Parents were also surveyed to report directly on program receipt after their child's visit; however, only a small subset of them completed the survey. Here, we develop a causal inference framework for a binary outcome that is subject to misclassification through silver-standard measures (clinician reports), but gold-standard measures (parent reports) are only available for a non-random internal validation subset. We propose a method for identifying the average treatment effect (ATE) that addresses the risk of bias due to misclassification and non-random validation selection, even when the outcome (parent receipt) may directly impact selection propensity (survey responsiveness). We show that ATE estimation relies on specifying the relationship between the gold- and silver-standard outcome measures in the validation subset, which may depend on treatment and covariates. Additionally, the clustered design is reflected in our causal assumptions and in our cluster-robust approach to estimation of the ATE. Simulation studies demonstrate acceptable finite-sample operating characteristics of our ATE estimator, supporting its application to ASPIRE.

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arXiv2025

Comparative Evaluation of Difference in Differences Methods for Staggered Adoption Interventions

Staggered adoption is a common approach for implementing healthcare interventions, where different units adopt the program at different times. Difference-in-differences (DiD) methods are frequently used to evaluate the effects of such interventions. Nonetheless, recent research has shown that classical DiD approaches designed for a single treatment start date can produce biased estimates in staggered adoption settings, particularly due to treatment effect heterogeneity across adoption and calendar time. Several alternative methods have been developed to address these limitations. However, these methods have not been fully systematically compared, and their practical utility remains unclear. Motivated by a payment program implemented by a healthcare provider in Hawaii, we provide a comprehensive review of the staggered adoption setting and a selection of DiD methods suitable for this context. We begin with a theoretical overview of these methods, followed by a simulation study designed to resemble the characteristics of our application, where the intervention is implemented at the cluster level. Our results show that the current methods tend to under-perform when the number of clusters is small, but improve as the number of clusters increases. We then apply the methods to evaluate the real-world payment program intervention and offer practical recommendations for researchers implementing DiD methods for staggered adoption settings. Finally, we translate our findings into practical guidance for applied researchers choosing among DiD methods for staggered adoption settings.

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arXiv2025

Integrating Misclassified EHR Outcomes with Validated Outcomes from a Non-probability Sample

Although increasingly used for research, electronic health records (EHR) often lack gold-standard assessment of key data elements. Linking EHRs to other data sources with higher-quality measurements can improve statistical inference, but such analyses must account for selection bias if the linked data source arises from a non-probability sample. We propose a set of novel estimators targeting the average treatment effect (ATE) that combine information from binary outcomes measured with error in a large, population-representative EHR database with gold-standard outcomes obtained from a smaller validation sample subject to selection bias. We evaluate our approach in extensive simulations and an analysis of data from the Adult Changes in Thought (ACT) study, a longitudinal study of incident dementia in a cohort of Kaiser Permanente Washington members with linked EHR data. For a subset of deceased ACT participants who consented to brain autopsy prior to death, gold-standard measures of Alzheimer's disease neuropathology are available. Our proposed estimators reduced bias and improved efficiency for the ATE, facilitating valid inference with EHR data when key data elements are ascertained with error.

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arXiv2022

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Alzheimer's disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. The volume of the hippocampus is often used in diagnosis and monitoring of the disease. Measuring this volume via neuroimaging is difficult since each hippocampus must either be manually identified or automatically delineated, a task referred to as segmentation. Automatic hippocampal segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each hippocampus is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms employ voting procedures with voting weights assigned directly or estimated via optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. Our results suggest that incorporating tissue classification (e.g, gray matter) into the label fusion procedure can greatly improve segmentation when relatively homogeneous, healthy brains are used as atlases for diseased brains. The fully Bayesian approach also produces meaningful uncertainty measures about hippocampal volumes, information which can be leveraged to detect significant, scientifically meaningful differences between healthy and diseased populations, improving the potential for early detection and tracking of the disease.

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arXiv2020

The extent and drivers of gender imbalance in neuroscience reference lists

Like many scientific disciplines, neuroscience has increasingly attempted to confront pervasive gender imbalances within the field. While much of the conversation has centered around publishing and conference participation, recent research in other fields has called attention to the prevalence of gender bias in citation practices. Because of the downstream effects that citations can have on visibility and career advancement, understanding and eliminating gender bias in citation practices is vital for addressing inequity in a scientific community. In this study, we sought to determine whether there is evidence of gender bias in the citation practices of neuroscientists. Using data from five top neuroscience journals, we find that reference lists tend to include more papers with men as first and last author than would be expected if gender were not a factor in referencing. Importantly, we show that this overcitation of men and undercitation of women is driven largely by the citation practices of men, and is increasing over time as the field becomes more diverse. We develop a co-authorship network to assess homophily in researchers' social networks, and we find that men tend to overcite men even when their social networks are representative. We discuss possible mechanisms and consider how individual researchers might address these findings in their own practices.

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arXiv2015

Interactive Q-learning for Probabilities and Quantiles

A dynamic treatment regime is a sequence of decision rules in which each decision rule recommends treatment based on features of patient medical history such as past treatments and outcomes. Existing methods for estimating optimal dynamic treatment regimes from data optimize the mean of a response variable. However, the mean may not always be the most appropriate summary of performance. We derive estimators of decision rules for optimizing probabilities and quantiles computed with respect to the response distribution for two-stage, binary treatment settings. This enables estimation of dynamic treatment regimes that optimize the cumulative distribution function of the response at a prespecified point or a prespecified quantile of the response distribution such as the median. The proposed methods perform favorably in simulation experiments. We illustrate our approach with data from a sequentially randomized trial where the primary outcome is remission of depression symptoms.

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