Mobile testing services provide opportunities for active surveillance of infectious diseases for hard-to-reach and/or high-risk individuals who do not know their disease status. Identifying as many infected individuals as possible is important for mitigating disease transmission. Recently, multi-armed bandit sampling approaches have been adapted and applied in this setting to maximize the cumulative number of positive tests collected over time. However, these algorithms have not considered the possibility of variability in the number of tests administered across testing sites. What impact this variability has on the ability of these approaches to maximize yield is currently unknown. Therefore, we investigate this question by extending existing sampling frameworks to directly account for variability in testing volume while also maintaining the computational tractability of the previous methods. Through a simulation study based on human immunodeficiency virus infection characteristics in the Republic of the Congo (Congo-Brazzaville) as well as an application to COVID-19 testing data in Connecticut, we find improved long- and short-term performances of the new methods compared to several existing approaches. Based on these findings and the ease of computation, we recommend use of the newly developed methods for active surveillance of infectious diseases when variability in testing volume may be present.

Although some pollutants emitted in vehicle exhaust, such as benzene, are known to cause leukaemia in adults with high exposure levels, less is known about the relationship between traffic-related air pollution (TRAP) and childhood haematologic cancer. In the 1990s, the US EPA enacted the reformulated gasoline program in select areas of the U.S., which drastically reduced ambient TRAP in affected areas. This created an ideal quasi-experiment to study the effects of TRAP on childhood haematologic cancers. However, existing methods for quasi-experimental analyses can perform poorly when outcomes are rare and unstable, as with childhood cancer incidence. We develop Bayesian spatio-temporal matrix completion methods to conduct causal inference in quasi-experimental settings with rare outcomes. Selective information sharing across space and time enables stable estimation, and the Bayesian approach facilitates uncertainty quantification. We evaluate the methods through simulations and apply them to estimate the causal effects of TRAP on childhood leukaemia and lymphoma.

With the precipitous decline in response rates, researchers and pollsters have been left with highly nonrepresentative samples, relying on constructed weights to make these samples representative of the desired target population. Though practitioners employ valuable expert knowledge to choose what variables must be adjusted for, they rarely defend particular functional forms relating these variables to the response process or the outcome. Unfortunately, commonly used calibration weights-which make the weighted mean of in the sample equal that of the population-only ensure correct adjustment when the portion of the outcome and the response process left unexplained by linear functions of are independent. To alleviate this functional form dependency, we describe kernel balancing for population weighting (). This approach replaces the design matrix with a kernel matrix, encoding high-order information about . Weights are then found to make the weighted average row of among sampled units approximately equal to that of the target population. This produces good calibration on a wide range of smooth functions of , without relying on the user to decide which or what functions of them to include. We describe the method and illustrate it by application to polling data from the 2016 US presidential election.

Disease surveillance data are used for monitoring and understanding disease burden, which provides valuable information in allocating health programme resources. Statistical methods play an important role in estimating disease burden since disease surveillance systems are prone to undercounting. This paper is motivated by the challenge of estimating mortality associated with respiratory infections (e.g. influenza and COVID-19) that are not ascertained from death certificates. We propose a Bayesian spatial-temporal model incorporating measures of infection activity to estimate excess deaths. Particularly, the inclusion of time-varying coefficients allows us to better characterize associations between infection activity and mortality counts time series. Software to implement this method is available in the R package NBRegAD. Applying our modelling framework to weekly state-wide COVID-19 data in the US from 8 March 2020 to 3 July 2022, we identified temporal and spatial differences in excess deaths between different age groups. We estimated the total number of COVID-19 deaths in the US to be 1,168,481 (95% CI: 1,148,953 1,187,187) compared to the 1,022,147 from using only death certificate information. The analysis also suggests that the most severe undercounting was in the 18-49 years age group with an estimated underascertainment rate of 0.21 (95% CI: 0.16, 0.25).

We propose a Bayesian hierarchical model to estimate a socio-economic status (SES) index based on mixed dichotomous and continuous variables. In particular, we extend Quinn's ([2004]. Bayesian factor analysis for mixed ordinal and continuous responses. (4), 338-353. https://doi.org/10.1093/pan/mph022) and Schliep and Hoeting's ([2013]. Multilevel latent Gaussian process model for mixed discrete and continuous multivariate response data. (4), 492-513. https://doi.org/10.1007/s13253-013-0136-z) factor analysis models for mixed dichotomous and continuous variables by allowing a spatial hierarchical structure of key parameters of the model. Unlike most SES assessment models proposed in the literature, the hierarchical nature of this model enables the use of census observations at the household level without needing to aggregate any information . Therefore, it better accommodates the variability of the SES between census tracts and the number of households per area. The proposed model is used in the estimation of a socio-economic index using 10% of the 2010 Ghana census in the Greater Accra Metropolitan area. Out of the 20 observed variables, the number of people per room, access to water piping and flushable toilets differentiated high and low SES areas the best.

In this work, we introduce a personalized and age-specific net benefit function, composed of benefits and costs, to recommend optimal timing of risk assessments for cardiovascular disease (CVD) prevention. We extend the 2-stage landmarking model to estimate patient-specific CVD risk profiles, adjusting for time-varying covariates. We apply our model to data from the Clinical Practice Research Datalink, comprising primary care electronic health records from the UK. We find that people at lower risk could be recommended an optimal risk-assessment interval of 5 years or more. Time-varying risk factors are required to discriminate between more frequent schedules for high-risk people.

Different methods for describing health disparities in the distributions of continuous measured health-related variables among groups provide more insight into the nature and impact of the disparities than comparing measures of central tendency. Transformations of the Lorenz curve and analogues of the Gini index used in the analysis of income inequality are adapted to provide graphical and analytical measures of health disparities. Akin to the classical Peters-Belson regression method for partitioning a disparity into a component explained by group differences in a set of covariates and an unexplained component, a new modified Lorenz curve is proposed. The estimation of these curves/measures is adapted for data obtained from surveys with complex sample weighted designs. The statistical properties of sample weighted estimators of the proposed measures and their bootstrap variances are explored through simulation studies. Applications are demonstrated using BMI and blood lead levels among race/ethnic groups of adult females and children, respectively, from the 2013-2018 and 1988-1994 US National Health and Nutrition Examination Surveys. Another application examines disparities in distance to nearest acute care hospital among census blocks in the US state of New York grouped by their level of urbanicity using US census data and the American Hospital Association survey.

Cardiometabolic risk factors (CRFs) during pregnancy are early indicators of maternal diseases, such as stroke and type 2 diabetes. The total number of CRFs typically takes the form of binomial counts that exhibit overdispersion and zero inflation due to correlations among the underlying CRFs. Motivated by an examination of spatiotemporal trends in five CRFs among pregnant women in the U.S. state of South Carolina during the COVID-19 pandemic, we developed a zero-inflated beta-binomial model within a spatiotemporal framework. This model combines a point mass at zero to account for zero inflation and a beta-binomial distribution to model the remaining CRF counts. Given the notable racial disparities in CRFs during pregnancy that vary across the state over time, we incorporate a spatially varying coefficient model to explore the complex relationships between CRFs and geographic and temporal disparities among non-Hispanic White and non-Hispanic Black women. For posterior inference, we developed an efficient hybrid Markov Chain Monte Carlo algorithm that relies on easily sampled Gibbs and Metropolis-Hastings steps. Our analysis of CRFs in South Carolina reveals that certain counties, such as Chesterfield and Clarendon, exhibit gaps in racial health disparities, making them prime candidates for community-level interventions aimed at reducing these disparities.

Healthcare quality metrics refer to a variety of measures used to characterize what should have been done or not done for a patient or the health consequences of what was or was not done. When estimating healthcare quality, many metrics are measured and combined to provide an overall estimate either at the patient level or at higher levels, such as the provider organization or insurer. Racial and ethnic disparities are defined as the mean difference in quality between minorities and Whites not justified by underlying health conditions or patient preferences. Several statistical features of healthcare quality data have been ignored: quality is a theoretical construct not directly observable; quality metrics are measured on different scales or, if measured on the same scale, have different baseline rates; the construct may be multidimensional; and metrics are correlated within-individuals. Balancing health differences across race and ethnicity groups is challenging due to confounding. We provide an approach addressing these features, utilizing exploratory multidimensional item response theory (IRT) models and latent class IRT models to estimate quality, and optimization-based matching to adjust for confounding among the race and ethnicity groups. Quality metrics measured on 93,000 adults with schizophrenia residing in five US states illustrate approaches.

Studies intended to estimate the effect of a treatment, like randomized trials, may not be sampled from the desired target population. To correct for this discrepancy, estimates can be transported to the target population. Methods for transporting between populations are often premised on a positivity assumption, such that all relevant covariate patterns in one population are also present in the other. However, eligibility criteria, particularly in the case of trials, can result in violations of positivity when transporting to external populations. To address nonpositivity, a synthesis of statistical and mathematical models can be considered. This approach integrates multiple data sources (e.g. trials, observational, pharmacokinetic studies) to estimate treatment effects, leveraging mathematical models to handle positivity violations. This approach was previously demonstrated for positivity violations by a single binary covariate. Here, we extend the synthesis approach for positivity violations with a continuous covariate. For estimation, two novel augmented inverse probability weighting estimators are proposed. Both estimators are contrasted with other common approaches for addressing nonpositivity. Empirical performance is compared via Monte Carlo simulation. Finally, the competing approaches are illustrated with an example in the context of two-drug vs. one-drug antiretroviral therapy on CD4 T cell counts among women with HIV.

Many population surveys do not provide information on respondents' residential addresses, instead offering coarse geographies like zip code or higher aggregations. However, fine resolution geography can be beneficial for characterizing neighbourhoods, especially for relatively rare populations such as immigrants. One way to obtain such information is to link survey records to records in auxiliary databases that include residential addresses by matching on variables common to both files. We present an approach based on probabilistic record linkage that enables matching survey participants in the Chinese Immigrants in Raleigh-Durham Study to records from InfoUSA, an information provider of residential records. The two files use different Chinese name romanization practices, which we address through a novel and generalizable strategy for constructing records' pairwise comparison vectors for romanized names. Using a fully Bayesian record linkage model, we characterize the geospatial distribution of Chinese immigrants in the Raleigh-Durham area of North Carolina.

Model integration refers to the process of incorporating a fitted historical model into the estimation of a current study to increase statistical efficiency. Integration can be challenging when the current model includes new covariates, leading to potential model misspecification. We present and evaluate seven existing and novel model integration techniques, which employ both likelihood constraints and Bayesian informative priors. Using a simulation study of logistic regression, we quantify how efficiency-assessed by bias and variance-changes with the sample sizes of both historical and current studies and in response to violations to transportability assumptions. We also apply these methods to a case study in which the goal is to use novel predictors to update a risk prediction model for in-hospital mortality among pediatric extracorporeal membrane oxygenation patients. Our simulation study and case study suggest that (i) when historical sample size is small, accounting for this statistical uncertainty is more efficient; (ii) all methods lose efficiency when there exist differences between the historical and current data-generating mechanisms; (iii) additional shrinkage to zero can improve efficiency in higher-dimensional settings but at the cost of bias in estimation.

Accurate cancer risk estimation is crucial to clinical decision-making, such as identifying high-risk people for screening. However, most existing cancer risk models incorporate data from epidemiologic studies, which usually cannot represent the target population. While population-based health surveys are ideal for making inference to the target population, they typically do not collect time-to-cancer incidence data. Instead, time-to-cancer specific mortality is often readily available on surveys via linkage to vital statistics. We develop calibrated pseudoweighting methods that integrate individual-level data from a cohort and a survey, and summary statistics of cancer incidence from national cancer registries. By leveraging individual-level cancer mortality data in the survey, the proposed methods impute time-to-cancer incidence for survey sample individuals and use survey calibration with auxiliary variables of influence functions generated from Cox regression to improve robustness and efficiency of the inverse-propensity pseudoweighting method in estimating pure risks. We develop a lung cancer incidence pure risk model from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial using our proposed methods by integrating data from the National Health Interview Survey and cancer registries.

Exposure to fine particulate matter (PM) poses significant health risks and accurately determining the shape of the relationship between PM and health outcomes has crucial policy implications. Although various statistical methods exist to estimate this exposure-response curve (ERC), few studies have compared their performance under plausible data-generating scenarios. This study compares seven commonly used ERC estimators across 72 exposure-response and confounding scenarios via simulation. Additionally, we apply these methods to estimate the ERC between long-term PM exposure and all-cause mortality using data from over 68 million Medicare beneficiaries in the United States. Our simulation indicates that regression methods not placed within a causal inference framework are unsuitable when anticipating heterogeneous exposure effects. Under the setting of a large sample size and unknown ERC functional form, we recommend utilizing causal inference methods that allow for nonlinear ERCs. In our data application, we observe a nonlinear relationship between annual average PM and all-cause mortality in the Medicare population, with a sharp increase in relative mortality at low PM concentrations. Our findings suggest that stricter limits on PM could avert numerous premature deaths. To facilitate the utilization of our results, we provide publicly available, reproducible code on Github for every step of the analysis.

Recent years have seen increasing efforts to forecast infectious disease burdens, with a primary goal being to help public health workers make informed policy decisions. However, there has been only limited discussion of how predominant forecast evaluation metrics might indicate the success of policies based in part on those forecasts. We explore one possible tether between forecasts and policy: the allocation of limited medical resources so as to minimize unmet need. We use probabilistic forecasts of disease burden in each of several regions to determine optimal resource allocations, and then we score forecasts according to how much unmet need their associated allocations would have allowed. We illustrate with forecasts of COVID-19 hospitalizations in the U.S., and we find that the forecast skill ranking given by this allocation scoring rule can vary substantially from the ranking given by the weighted interval score. We see this as evidence that the allocation scoring rule detects forecast value that is missed by traditional accuracy measures and that the general strategy of designing scoring rules that are directly linked to policy performance is a promising direction for epidemic forecast evaluation.

Comparative effectiveness research frequently employs the instrumental variable design since randomized trials can be infeasible for many reasons. In this study, we investigate treatments for emergency -inflammation of the gallbladder. A standard treatment for cholecystitis is surgical removal of the gallbladder, while alternative non-surgical treatments include managed care and pharmaceutical options. As randomized trials are judged to violate the principle of equipoise, we consider an instrument for operative care: the surgeon's tendency to operate. Standard instrumental variable estimation methods, however, often rely on parametric models that are prone to bias from model misspecification. Thus, we outline instrumental variable methods based on the doubly robust machine learning framework. These methods enable us to employ various machine learning techniques, delivering consistent estimates, and permitting valid inference on various estimands. We use these methods to estimate the primary target estimand in an instrumental variable design. Additionally, we expand these methods to develop new estimators for heterogeneous causal effects, profiling principal strata, and sensitivity analyses for a key instrumental variable assumption. We conduct a simulation study to demonstrate scenarios where more flexible estimation methods outperform standard methods. Our findings indicate that operative care is generally more effective for cholecystitis patients, although the benefits of surgery can be less pronounced for key patient subgroups.

Claiming causal inferences in network settings necessitates careful consideration of the often complex dependency between outcomes for actors. Of particular importance are treatment spillover or outcome interference effects. We consider causal inference when the actors are connected via an underlying network structure. Our key contribution is a model for causality when the underlying network is endogenous; where the ties between actors and the actor covariates are statistically dependent. We develop a joint model for the relational and covariate generating process that avoids restrictive separability and fixed network assumptions, as these rarely hold in realistic social settings. While our framework can be used with general models, we develop the highly expressive class of Exponential-family Random Network models (ERNM) of which Markov random fields and Exponential-family Random Graph models are special cases. We present potential outcome-based inference within a Bayesian framework and propose a modification to the exchange algorithm to allow for sampling from ERNM posteriors. We present results of a simulation study demonstrating the validity of the approach. Finally, we demonstrate the value of the framework in a case study of smoking in the context of adolescent friendship networks.

Using administrative patient-care data such as Electronic Health Records (EHR) and medical/pharmaceutical claims for population-based scientific research has become increasingly common. With vast sample sizes leading to very small standard errors, researchers need to pay more attention to potential biases in the estimates of association parameters of interest, specifically to biases that do not diminish with increasing sample size. Of these multiple sources of biases, in this paper, we focus on understanding selection bias. We present an analytic framework using directed acyclic graphs for guiding applied researchers to dissect how different sources of selection bias may affect estimates of the association between a binary outcome and an exposure (continuous or categorical) of interest. We consider four easy-to-implement weighting approaches to reduce selection bias with accompanying variance formulae. We demonstrate through a simulation study when they can rescue us in practice with analysis of real-world data. We compare these methods using a data example where our goal is to estimate the well-known association of cancer and biological sex, using EHR from a longitudinal biorepository at the University of Michigan Healthcare system. We provide annotated R codes to implement these weighted methods with associated inference.

Researchers are often interested in estimating the effect of sustained use of a treatment on a health outcome. However, adherence to strict treatment protocols can be challenging for individuals in practice and, when non-adherence is expected, estimates of the effect of sustained use may not be useful for decision making. As an alternative, more relaxed treatment protocols which allow for periods of time off treatment (i.e. grace periods) have been considered in pragmatic randomized trials and observational studies. In this article, we consider the interpretation, identification, and estimation of treatment strategies which include grace periods. We contrast grace period strategies which allow individuals the flexibility to take treatment as they would naturally do, with grace period strategies in which the investigator specifies the distribution of treatment utilization. We estimate the effect of initiation of a thiazide diuretic or an angiotensin-converting enzyme inhibitor in hypertensive individuals under various strategies which include grace periods.

Social relations models allow the identification of cluster, actor, partner, and relationship effects when analysing clustered dyadic data on interactions between individuals or other units of analysis. We propose an extension of this model which handles longitudinal data and incorporates dynamic structure, where the response may be continuous, binary, or ordinal. This allows the disentangling of the relationship effects from temporal fluctuation and measurement error and the investigation of whether individuals respond to their partner's behaviour at the previous observation. We motivate and illustrate the model with an application to Canadian data on pairs of individuals within families observed working together on a conflict discussion task.