Search

In the framework of causal inference, the inverse probability weighting estimation method and its variants have been commonly employed to estimate the average treatment effect. Such methods, however, are challenged by the presence of irrelevant pre-treatment variables and measurement error. Ignoring these features and naively applying the usual inverse probability weighting estimation procedures may typically yield biased inference results. In this article, we develop an inference method for estimating the average treatment effect with those features taken into account. We establish theoretical properties for the resulting estimator and carry out numerical studies to assess the finite sample performance of the proposed estimator.

Matrix-variate logistic regression is useful in facilitating the relationship between the binary response and complex-featured matrix-variates arising commonly from medical imaging research. However, standard inference procedures based on such a model are impaired by the presence of the response misclassification as well as inactive covariates. It is imperative to account for the misclassification effects and select active covariates when employing matrix-variate logistic regression to analyze such data. In this paper, we develop penalized unbiased estimating functions using the smoothly clipped absolute deviation (SCAD) penalty to address the sparsity of matrix-variate data as well as the response misclassification effects. The proposed methods are justified both theoretically and numerically. We analyze the Breast Cancer Wisconsin data with the proposed methods.

To confine the spread of an infectious disease, setting a sensible quarantine time is crucial. To this end, it is imperative to well understand the distribution of incubation times of the disease. Regarding the ongoing COVID-19 pandemic, 14-days is commonly taken as a quarantine time to curb the virus spread in balancing the impacts of COVID-19 on diverse aspects of the society, including public health, economy, and humanity perspectives, etc. However, setting a sensible quarantine time is not trivial and it depends on various underlying factors. In this article, we take an angle of examining the distribution of the COVID-19 incubation time using likelihood-based methods. Our study is carried out on a dataset of 178 COVID-19 cases dated from January 20, 2020 to February 29, 2020, with the information of exposure periods and dates of symptom onset collected. To gain a good understanding of possible scenarios, we employ different models to describe incubation times of COVID-19. Our findings suggest that statistically, the 14-day quarantine time may not be long enough to control the probability of an early release of infected individuals to be small. While the size of the study data is not large enough to offer us a definitely acceptable quarantine time, and further in practice, the decision-makers may take account of other factors related to social and economic concerns to set up a practically acceptable quarantine time, our study demonstrates useful methods to determine a reasonable quarantine time from a statistical standpoint. Further, it reveals some associated complexity for fully understanding the COVID-19 incubation time distribution. Supplementary Information The online version contains supplementary material available at 10.1007/s12561-021-09320-8.

Providing sensible estimates of the mean incubation time for COVID-19 is important yet complex. This study aims to provide synthetic estimates of the mean incubation time of COVID-19 by capitalizing on available estimates reported in the literature and exploring different ways to accommodate heterogeneity involved in the reported studies. Online databases between January 1, 2020 and May 20, 2021 are first searched to obtain estimates of the mean incubation time of COVID-19, and meta-analyses are then conducted to generate synthetic estimates. Heterogeneity of the studies is examined via the use of Cochran's

COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV- 2) that was reported to spread in people in December 2019. Understanding epidemiological features of COVID-19 is important for the ongoing global efforts to contain the virus. As a complement to the available work, in this article we analyze the Kaggle novel coronavirus dataset of 3397 patients dated from January 22, 2020 to March 29, 2020. We employ semiparametric and nonparametric survival models as well as text mining and data visualization techniques to examine the clinical manifestations and epidemiological features of COVID-19. Our analysis shows that: (i) the median incubation time is about 5 days and older people tend to have a longer incubation period; (ii) the median time for infected people to recover is about 20 days, and the recovery time is significantly associated with age but not gender; (iii) the fatality rate is higher for older infected patients than for younger patients.

Summary Measurement error in covariates has been extensively studied in many conventional regression settings where covariate information is typically expressed in a vector form. However, there has been little work on error-prone matrix-variate data, which commonly arise from studies with imaging, spatial-temporal structures, etc. We consider analysis of error-contaminated matrix-variate data. We particularly focus on matrix-variate logistic measurement error models. We examine the biases induced from naive analysis which ignores measurement error in matrix-variate data. Two measurement error correction methods are developed to adjust for measurement error effects. The proposed methods are justified both theoretically and empirically. We analyse an electroencephalography dataset with the proposed methods.

Many methods have been developed for analyzing survival data which are commonly right-censored. These methods, however, are challenged by complex features pertinent to the data collection as well as the nature of data themselves. Typically, biased samples caused by left-truncation (or length-biased sampling) and measurement error often accompany survival analysis. While such data frequently arise in practice, little work has been available to simultaneously address these features. In this paper, we explore valid inference methods for handling left-truncated and right-censored survival data with measurement error under the widely used Cox model. We first exploit a flexible estimator for the survival model parameters which does not require specification of the baseline hazard function. To improve the efficiency, we further develop an augmented nonparametric maximum likelihood estimator. We establish asymptotic results and examine the efficiency and robustness issues for the proposed estimators. The proposed methods enjoy appealing features that the distributions of the covariates and of the truncation times are left unspecified. Numerical studies are reported to assess the finite sample performance of the proposed methods.

Causal inference has been widely conducted in various fields and many methods have been proposed for different settings. However, for noisy data with both mismeasurements and missing observations, those methods often break down. In this paper, we consider a problem that binary outcomes are subject to both missingness and misclassification, when the interest is in estimation of the average treatment effects (ATE). We examine the asymptotic biases caused by ignoring missingness and/or misclassification and establish the intrinsic connections between missingness effects and misclassification effects on the estimation of ATE. We develop valid weighted estimation methods to simultaneously correct for missingness and misclassification effects. To provide protection against model misspecification, we further propose a doubly robust correction method which yields consistent estimators when either the treatment model or the outcome model is misspecified. Simulation studies are conducted to assess the performance of the proposed methods. An application to smoking cessation data is reported to illustrate the use of the proposed methods.

Measurement error and misclassification arise commonly in various data collection processes. It is well-known that ignoring these features in the data analysis usually leads to biased inference. With the generalized linear model setting, Yi et al. [Functional and structural methods with mixed measurement error and misclassification in covariates. J Am Stat Assoc. 2015;110:681–696] developed inference methods to adjust for the effects of measurement error in continuous covariates and misclassification in discrete covariates simultaneously for the scenario where validation data are available. The augmented simulation-extrapolation (SIMEX) approach they developed generalizes the usual SIMEX method which is only applicable to handle continuous error-prone covariates. To implement this method, we develop an R package, augSIMEX, for public use. Simulation studies are conducted to illustrate the use of the algorithm. This package is available at CRAN.

Bivariate responses with mixed continuous and binary variables arise commonly in applications such as clinical trials and genetic studies. Statistical methods based on jointly modeling continuous and binary variables have been available. However, such methods ignore the effects of response mismeasurement, a ubiquitous feature in applications. It has been well studied that in many settings, ignorance of mismeasurement in variables usually results in biased estimation. In this paper, we consider the setting with a bivariate outcome vector which contains a continuous component and a binary component both subject to mismeasurement. We propose estimating equation approaches to handle measurement error in the continuous response and misclassification in the binary response simultaneously. The proposed estimators are consistent and robust to certain model misspecification, provided regularity conditions. Extensive simulation studies confirm that the proposed methods successfully correct the biases resulting from the error-in-variables under various settings. The proposed methods are applied to analyze the outbred Carworth Farms White mice data arising from a genome-wide association study.

The COVID-19 pandemic has posed significant influence on the public mental health in a stealthy manner. Current efforts focus on alleviating the impacts of the disease on public health and economy, with the psychological effects due to COVID-19 largely ignored. In this paper, we analyze a mental health related dataset from the US to enhance our understanding of human reactions to the pandemic. We are particularly interested in providing quantitative characterization of the pandemic impact on the public mental health, on top of qualitative explorations. We employ the multiple imputation by chained equations (MICE) method to deal with missing values and take the logistic regression with least absolute shrinkage and selection operator (Lasso) method to identify risk factors for mental health. The analyses are conducted to a large scale of online survey data from 12 consecutive weeks, so that the longitudinal trend of the risk factors can be investigated. Our analysis results unveil evidence-based findings to identify the groups who are psychologically vulnerable to COVID-19. This study is useful to assist healthcare providers and policy makers to take steps for mitigating the pandemic effects on public mental health.

Data with a huge size present great challenges in modeling, inferences, and computation. In handling big data, much attention has been directed to settings with "large p small n", and relatively less work has been done to address problems with p and n being both large, though data with such a feature have now become more accessible than before, where p represents the number of variables and n stands for the sample size. The big volume of data does not automatically ensure good quality of inferences because a large number of unimportant variables may be collected in the process of gathering informative variables. To carry out valid statistical analysis, it is imperative to screen out noisy variables that have no predictive value for explaining the outcome variable. In this paper, we develop a screening method for handling large-sized survival data, where the sample size n is large and the dimension p of covariates is of non-polynomial order of the sample size n, or the so-called NP-dimension. We rigorously establish theoretical results for the proposed method and conduct numerical studies to assess its performance. Our research offers multiple extensions of existing work and enlarges the scope of high-dimensional data analysis. The proposed method capitalizes on the connections among useful regression settings and offers a computationally efficient screening procedure. Our method can be applied to different situations with large-scale data including genomic data.

The support vector machine (SVM) and its extensions have been widely used in various areas. However, these methods cannot effectively handle imbalanced data with spatial association. The ensembling imbalanced-spatial-structured support vector machine (EISS-SVM) method is proposed to handle such data. Not only the proposed method accommodates the relationship between the response and predictors, but also accounts for the spatial correlation existing in data which may be imbalanced. The EISS-SVM classifier embraces the usual SVM as a special case. Numerical studies show satisfactory performance of the proposed method, and the analysis results are reported for the application of the proposed method to handling the imaging data from an ongoing prostate cancer research conducted in Canada.

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread stealthily and presented a tremendous threat to the public. It is important to investigate the transmission dynamic of COVID-19 to help understand the impact of the disease on public health and economy. While a number of epidemic models have been available to study infectious diseases, they are in-adequate to describe the dynamic of COVID-19. In this paper, we develop a new epidemic model which utilizes a set of ordinary differential equations with unknown parameters to delineate the transmission process of COVID-19. Different from the traditional epidemic models, this model accounts for asymptomatic infections as well the lag between symptoms onset and the confirmation date of infection. We describe an estimation procedure for the unknown parameters in the proposed model by adapting the iterated filter-ensemble adjustment Kalman filter (IF-EAKF) algorithm to the reported number of confirmed cases. To assess the performance of our proposed model, we examine COVID-19 data in Quebec for the period of April 2, 2020 to May 10, 2020 and carry out sensitivity studies under a variety of assumptions. To reflect the transmission potential of an infected case, we derive the basic reproduction number from the proposed model. The estimated basic reproduction number suggests that the pandemic situation in Quebec for the period of April 2, 2020 to May 10, 2020 is not under control.

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread stealthily and presented a tremendous threat to the public. It is important to investigate the transmission dynamics of COVID-19 to help understand the impact of the disease on public health and the economy. In this article, we develop a new epidemic model that utilizes a set of ordinary differential equations with unknown parameters to delineate the transmission process of COVID-19. The model accounts for asymptomatic infections as well as the lag between symptom onset and the confirmation date of infection. To reflect the transmission potential of an infected case, we derive theLa maladie à coronavirus 2019 (COVID‐19), causée par le coronavirus 2 du syndrome respiratoire aigu sévère (SARS‐CoV‐2), s'est rapidement propagée et représente une grande menace pour le public. Pour mieux comprendre l'impact de cette maladie sur la santé publique et l'économie, il est important d'étudier la dynamique de sa transmission. A cette fin, les auteurs de cet article proposent un nouveau modèle épidémiologique basé sur un ensemble d'équations différentielles ordinaires avec des paramètres inconnus et qui tient compte des infections asymptomatiques ainsi que du décalage entre l'apparition des symptômes et la date de confirmation de l'infection. Ils en déduisent le

In survival data analysis, the Cox proportional hazards (PH) model is perhaps the most widely used model to feature the dependence of survival times on covariates. While many inference methods have been developed under such a model or its variants, those models are not adequate for handling data with complex structured covariates. High-dimensional survival data often entail several features: (1) many covariates are inactive in explaining the survival information, (2) active covariates are associated in a network structure, and (3) some covariates are error-contaminated. To hand such kinds of survival data, we propose graphical PH measurement error models and develop inferential procedures for the parameters of interest. Our proposed models significantly enlarge the scope of the usual Cox PH model and have great flexibility in characterizing survival data. Theoretical results are established to justify the proposed methods. Numerical studies are conducted to assess the performance of the proposed methods.