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Trivariate joint modeling for longitudinal count data, recurrent events, and a terminal event for family data has increased interest in medical studies. For example, families with Lynch syndrome (LS) are at high risk of developing colorectal cancer (CRC), where the number of polyps and the frequency of colonoscopy screening visits are highly associated with the risk of CRC among individuals and families. To assess how screening visits influence polyp detection, which in turn influences time to CRC, we propose a clustered trivariate joint model. The proposed model facilitates longitudinal count data that are zero‐inflated and over‐dispersed and invokes individual‐specific and family‐specific random effects to account for dependence among individuals and families. We formulate our proposed model as a latent Gaussian model to use the Bayesian estimation approach with the integrated nested Laplace approximation algorithm and evaluate its performance using simulation studies. Our trivariate joint model is applied to a series of 18 families from Newfoundland, with the occurrence of CRC taken as the terminal event, the colonoscopy screening visits as recurrent events, and the number of polyps detected at each visit as zero‐inflated count data with overdispersion. We showed that our trivariate model fits better than alternative bivariate models and that the cluster effects should not be ignored when analyzing family data. Finally, the proposed model enables us to quantify heterogeneity across families and individuals in polyp detection and CRC risk, thus helping to identify individuals and families who would benefit from more intensive screening visits. [ABSTRACT FROM AUTHOR]

In causal inference, the estimation of the average treatment effect is often of interest. For example, in cancer research, an interesting question is to assess the effects of the chemotherapy treatment on cancer, with the information of gene expressions taken into account. Two crucial challenges in this analysis involve addressing measurement error in gene expressions and handling noninformative gene expressions. While analytical methods have been developed to address those challenges, no user-friendly computational software packages seem to be available to implement those methods. To close this gap, we develop an R package, called AteMeVs, to estimate the average treatment effect using the inverse-probability-weighting estimation method to handle data with both measurement error and spurious variables. This developed package accommodates the method proposed by Yi and Chen (2023) as a special case, and further extends its application to a broader scope. The usage of the developed R package is illustrated by applying it to analyze a cancer dataset with information of gene expressions. [ABSTRACT FROM AUTHOR]

Modelling multivariate survival data is complicated by the complex association structure among the responses. To balance model flexibility and interpretability, we propose a semiparametric copula model to modulate multivariate survival data, with the marginal distributions of the response components described by semiparametric linear transformation models. To conduct inference about the model parameters, we develop a two‐stage maximum likelihood method and a three‐stage pseudo‐likelihood estimation procedure. We investigate the impact of model misspecification on the estimation of covariate effects and identify a scenario in which consistent estimation of the marginal parameters is retained even when the copula model is misspecified. The proposed methods are justified both theoretically and empirically. An application to a real dataset is provided to demonstrate the utility of the proposed method. [ABSTRACT FROM AUTHOR]

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Research on dynamic treatment regimes has enticed extensive interest. Many methods have been proposed in the literature, which, however, are vulnerable to the presence of misclassification in covariates. In particular, although Q‐learning has received considerable attention, its applicability to data with misclassified covariates is unclear. In this article, we investigate how ignoring misclassification in binary covariates can impact the determination of optimal decision rules in randomized treatment settings, and demonstrate its deleterious effects on Q‐learning through empirical studies. We present two correction methods to address misclassification effects on Q‐learning. Numerical studies reveal that misclassification in covariates induces non‐negligible estimation bias and that the correction methods successfully ameliorate bias in parameter estimation. [ABSTRACT FROM AUTHOR]

Nancy Reid was born in September 1952 in Niagara Falls, Canada. She graduated from the University of Waterloo with a Bachelor in Mathematics and a Major in Statistics in 1974. She studied statistics at the University of British Columbia (UBC) where she obtained a Master's in Applied Mathematics in 1976, and at Stanford University where she graduated with a PhD in Statistics in 1979. After spending one year at Imperial College London visiting Sir David Cox, she joined UBC as an Assistant Professor in the Department of Mathematics, and in 1986 she moved to the University of Toronto as a faculty member in the Department of Statistics (now Statistical Sciences) where she has been ever since including serving as Chair between 1997 and 2002. At the time of writing, Nancy has authored over 100 papers and 5 books, including seminal developments in conditional inference, higher‐order asymptotics, composite likelihood, and Bayesian inference. Her outstanding contributions to statistics have been recognized nationally and internationally with many awards, including the President's Award of the Committee of Presidents of Statistical Societies (COPSS), the Gold Medal awarded by the Statistical Society of Canada (SSC), and being elected Foreign Associate of the National Academy of Sciences. In 2017, the International Statistical Review published Nancy's conversation with Ana Maria Staicu [Staicu, A. M. (2017). Interview with Nancy Reid. International Statistical Review, 85(3), 381‐403.], which had a biographical emphasis. Since then, Nancy has continued to support the discipline of statistics in important ways, such as by serving as Director of the Canadian Statistical Sciences Institute (CANSSI) (2015–2019) and Co‐chair of the Institute of Mathematical Statistics' Committee on Ethics (2018–2020). Her research activity continues to be celebrated with important awards such as Fellowship of the Royal Society of London (2018), the inaugural Hollander Distinguished Lectureship at Florida State University (2020), the Distinguished Achievement Award (and Lectureship) from COPSS (2022), and the Guy Medal in Gold from the Royal Statistical Society (2022). In May 2022, the Department of Statistical Sciences at the University of Toronto, in collaboration with CANSSI and the SSC, organized a one‐day conference, "Statistics at Its Best", in honour of Nancy's 70th birthday. This conversation took place in Toronto around the time of the event. Its focus is on Nancy's views on building a career in statistics, and the challenges and opportunities statisticians encounter within the rapidly evolving data science ecosystem. [ABSTRACT FROM AUTHOR]

Zero‐inflated count data arise frequently from genomics studies. Analysis of such data is often based on a mixture model which facilitates excess zeros in combination with a Poisson distribution, and various inference methods have been proposed under such a model. Those analysis procedures, however, are challenged by the presence of measurement error in count responses. In this article, we propose a new measurement error model to describe error‐contaminated count data. We show that ignoring the measurement error effects in the analysis may generally lead to invalid inference results, and meanwhile, we identify situations where ignoring measurement error can still yield consistent estimators. Furthermore, we propose a Bayesian method to address the effects of measurement error under the zero‐inflated Poisson model and discuss the identifiability issues. We develop a data‐augmentation algorithm that is easy to implement. Simulation studies are conducted to evaluate the performance of the proposed method. We apply our method to analyze the data arising from a prostate adenocarcinoma genomic study. [ABSTRACT FROM AUTHOR]

Research of complex associations between a gene network and multiple responses has attracted increasing attention. A great challenge in analyzing genetic data is posited by the presence of the genetic network that is typically unknown. Moreover, mismeasurement of responses introduces additional complexity to distort usual inferential procedures. In this paper, we consider the problem with mixed binary and continuous responses that are subject to mismeasurement and associated with complex structured covariates. We first start with the case where data are precisely measured. We propose a generalized network structured model and develop a two‐step inferential procedure. In the first step, we employ a Gaussian graphical model to facilitate the covariates network structure, and in the second step, we incorporate the estimated graphical structure of covariates and develop an estimating equation method. Furthermore, we extend the development to accommodating mismeasured responses. We consider two cases where the information on mismeasurement is either known or estimated from a validation sample. Theoretical results are established and numerical studies are conducted to evaluate the finite sample performance of the proposed methods. We apply the proposed method to analyze the outbred Carworth Farms White mice data arising from a genome‐wide association study. [ABSTRACT FROM AUTHOR]

Autoregressive (AR) models are useful in time series analysis. Inferences under such models are distorted in the presence of measurement error, a common feature in applications. In this article, we establish analytical results for quantifying the biases of the parameter estimation in AR models if the measurement error effects are neglected. We consider two measurement error models to describe different data contamination scenarios. We propose an estimating equation approach to estimate the AR model parameters with measurement error effects accounted for. We further discuss forecasting using the proposed method. Our work is inspired by COVID-19 data, which are error-contaminated due to multiple reasons including those related to asymptomatic cases and varying incubation periods. We implement the proposed method by conducting sensitivity analyses and forecasting the fatality rate of COVID-19 over time for the four most populated provinces in Canada. The results suggest that incorporating or not incorporating measurement error effects may yield rather different results for parameter estimation and forecasting. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]