arXiv subjects

Maya B. Mathur

Publications and source records attributed to Maya B. Mathur.

8 recordsLinked to original sources

arXiv2025

Using directed acyclic graphs to determine whether multiple imputation or subsample multiple imputation estimates of an exposure-outcome association are unbiased

Missing data is a pervasive problem in epidemiology, with multiple imputation (MI) a commonly used analysis method. MI is valid when data are missing at random (MAR). However, definitions of MAR with multiple incomplete variables are not easily interpretable and graphical model-based conditions are not accessible to applied researchers. Previous literature shows that MI may be valid in subsamples, even if not in the full dataset. Practical guidance on applying MI with multiple incomplete variables is lacking. We present an algorithm using directed acyclic graphs to determine when MI will estimate an exposure-outcome coefficient without bias. We extend the algorithm to assess whether MI in a subsample of the data, in which some variables are complete, and the remaining are imputed, will be valid and unbiased for the same coefficient. We apply the algorithm to several simple exemplars, and in a more complex real-life example highlight that only subsample MI of the outcome would be valid. Our algorithm provides researchers with the tools to decide whether (and how) to use MI in practice when there are multiple incomplete variables. Further work could focus on the likely size and direction of biases, and the impact of different missing data patterns.

stat.ME↗

arXiv2025

How causal perspectives can inform neuroscience data analysis

Over the past two decades, considerable strides have been made in advancing neuroscientific techniques, yet challenges remain in attributing causality to observed associations. This review addresses a fundamental issue in observational neuroscience studies and advocates for incorporating causal inference frameworks into standard practice. We systematically introduce necessary definitions and concepts, emphasizing how causal assumptions underlie statistical analyses even when not explicitly stated. Through a running example on sleep quality and white matter integrity, we illustrate how persistent challenges, including confounding and selection biases, can be conceptualized and addressed using causal frameworks. We demonstrate practical approaches for making assumption violations transparent through hands-on examples: supplementary case studies using multi-site harmonization and head motion exclusion procedures provide step-by-step diagnostic techniques for checking covariate overlap and identifying selection bias through exclusion pattern analysis. We explore how these causal perspectives can inform both experimental design and analytical choices, particularly for observational studies where traditional randomization is infeasible. Together, we believe this framework offers concrete tools for strengthening causal interpretations and inspiring more robust approaches to problems in neuroscience.

q-bio.OT↗

arXiv2025

What can large language models do for sustainable food?

Food systems are responsible for a third of human-caused greenhouse gas emissions. We investigate what Large Language Models (LLMs) can contribute to reducing the environmental impacts of food production. We define a typology of design and prediction tasks based on the sustainable food literature and collaboration with domain experts, and evaluate six LLMs on four tasks in our typology. For example, for a sustainable protein design task, food science experts estimated that collaboration with an LLM can reduce time spent by 45% on average, compared to 22% for collaboration with another expert human food scientist. However, for a sustainable menu design task, LLMs produce suboptimal solutions when instructed to consider both human satisfaction and climate impacts. We propose a general framework for integrating LLMs with combinatorial optimization to improve reasoning capabilities. Our approach decreases emissions of food choices by 79% in a hypothetical restaurant while maintaining participants' satisfaction with their set of choices. Our results demonstrate LLMs' potential, supported by optimization techniques, to accelerate sustainable food development and adoption.

cs.CY↗

arXiv2020

Multiple-bias sensitivity analysis using bounds

Unmeasured confounding, selection bias, and measurement error are well-known sources of bias in epidemiologic research. Methods for assessing these biases have their own limitations. Many quantitative sensitivity analysis approaches consider each type of bias individually, while more complex approaches are harder to implement or require numerous assumptions. By failing to consider multiple biases at once, researchers can underestimate -- or overestimate -- their joint impact. We show that it is possible to bound the total composite bias due to these three sources, and to use that bound to assess the sensitivity of a risk ratio to any combination of these biases. We derive bounds for the total composite bias under a variety of scenarios, providing researchers with tools to assess their total potential impact. We apply this technique to a study where unmeasured confounding and selection bias are both concerns, and to another study in which possible differential exposure misclassification and unmeasured confounding are concerns. We also show that a "multi-bias E-value" can describe the minimal strength of joint bias-parameter association necessary for an observed risk ratio to be compatible with a null causal effect (or with other pre-specified effect sizes). This may provide intuition about the relative impacts of each type of bias. The approach we describe is easy to implement with minimal assumptions, and we provide R functions to do so.

stat.ME↗

arXiv2019

How to apply multiple imputation in propensity score matching with partially observed confounders: a simulation study and practical recommendations

Propensity score matching (PSM) has been widely used to mitigate confounding in observational studies, although complications arise when the covariates used to estimate the PS are only partially observed. Multiple imputation (MI) is a potential solution for handling missing covariates in the estimation of the PS. Unfortunately, it is not clear how to best apply MI strategies in the context of PSM. We conducted a simulation study to compare the performances of popular non-MI missing data methods and various MI-based strategies under different missing data mechanisms (MDMs). We found that commonly applied missing data methods resulted in biased and inefficient estimates, and we observed large variation in performance across MI-based strategies. Based on our findings, we recommend 1) deriving the PS after applying MI (referred to as MI-derPassive); 2) conducting PSM within each imputed data set followed by averaging the treatment effects to arrive at one summarized finding (INT-within) for mild MDMs and averaging the PSs across multiply imputed datasets before obtaining one treatment effect using PSM (INT-across) for more complex MDMs; 3) a bootstrapped-based variance to account for uncertainty of PS estimation, matching, and imputation; and 4) inclusion of key auxiliary variables in the imputation model.

stat.AP↗

arXiv2018

Outcome-wide longitudinal designs for causal inference: a new template for empirical studies

In this paper we propose a new template for empirical studies intended to assess causal effects: the outcome-wide longitudinal design. The approach is an extension of what is often done to assess the causal effects of a treatment or exposure using confounding control, but now, over numerous outcomes. We discuss the temporal and confounding control principles for such outcome-wide studies, metrics to evaluate robustness or sensitivity to potential unmeasured confounding for each outcome, and approaches to handle multiple testing. We argue that the outcome-wide longitudinal design has numerous advantages over more traditional studies of single exposure-outcome relationships including results that are less subject to investigator bias, greater potential to report null effects, greater capacity to compare effect sizes, a tremendous gain in the efficiency for the research community, a greater policy relevance, and a more rapid advancement of knowledge. We discuss both the practical and theoretical justification for the outcome-wide longitudinal design and also the pragmatic details of its implementation.

stat.ME↗

arXiv2017

Simulating realistically complex comparative effectiveness studies with time-varying covariates and right-censored outcomes

Simulation studies are useful for evaluating and developing statistical methods for the analyses of complex problems. Performance of methods may be affected by multiple complexities present in real scenarios. Generating sufficiently realistic data for this purpose, however, can be challenging. Our study of the comparative effectiveness of HIV protocols on the risk of cardiovascular disease -- involving the longitudinal assessment of HIV patients -- is such an example. The correlation structure across covariates and within subjects over time must be considered as well as right-censoring of the outcome of interest, time to myocardial infarction. A challenge in simulating the covariates is to incorporate a joint distribution for variables of mixed type -- continuous, binary or polytomous. An additional challenge is incorporating within-subject correlation where some variables may vary over time and others may remain static. To address these issues, we extend the work of Demirtas and Doganay (2012). Identifying a model from which to simulate the right-censored outcome as a function of these covariates builds on work developed by Sylvestre and Abrahamowicz (2007). In this paper, we describe a cohesive and user-friendly approach accompanied by R code to simulate comparative effectiveness studies with right-censored outcomes that are functions of time-varying covariates.

stat.AP↗

arXiv2017

Sensitivity Analysis for Unmeasured Confounding in Meta-Analyses

Random-effects meta-analyses of observational studies can produce biased estimates if the synthesized studies are subject to unmeasured confounding. We propose sensitivity analyses quantifying the extent to which unmeasured confounding of specified magnitude could reduce to below a certain threshold the proportion of true effect sizes that are scientifically meaningful. We also develop converse methods to estimate the strength of confounding capable of reducing the proportion of scientifically meaningful true effects to below a chosen threshold. These methods apply when a "bias factor" is assumed to be normally distributed across studies or is assessed across a range of fixed values. Our estimators are derived using recently proposed sharp bounds on confounding bias within a single study that do not make assumptions regarding the unmeasured confounders themselves or the functional form of their relationships to the exposure and outcome of interest. We provide an R package, ConfoundedMeta, and a freely available online graphical user interface that compute point estimates and inference and produce plots for conducting such sensitivity analyses. These methods facilitate principled use of random-effects meta-analyses of observational studies to assess the strength of causal evidence for a hypothesis.

stat.ME↗