Statistical methods for recurrent event data in the presence of a terminal event and incomplete covariate information

In many clinical and epidemiological studies, recurrent events such as infections in immunocompromised patients or injuries in athletes often occur. It is of interest to examine the relationship between covariates and recurrent events, however in many situations, some of the covariates collected involve missing information due to various reasons. Under such missingness, a commonly practiced method is to analyze complete cases; this method may be inefficient or result in biased estimates for parameters. In this dissertation, we develop methods to analyze recurrent events data with missing covariate information. These will be useful in reducing the bias and improving the efficiency of parameter estimates. This method is motivated by the need for analyzing recurrent infections in a renal transplant cohort from India in which approximately 19% of patients died and over 13% had missing covariate information. Literature shows that opportunistic infections times and death time may be correlated and need to be adjusted in the estimation process. First, we studied this problem by developing methods using marginal rate models for both recurrent events and terminal events with missing data. We adopted a weighted estimating equation approach with missing data assumed to be missing at random (MAR) for estimating the parameters. Second, we considered a marginal rate model for multiple type recurrent events in the presence of a terminal event. We proposed a weighted estimating equation approach assuming that terminal events preclude further recurrent events. We adjusted for the terminal events via inverse probability survival weights. The asymptotic properties of the proposed estimators were derived using empirical process theory. Third, we extended the marginal rate model for analyzing multiple type recurrent events in the presence of a terminal event to handle missing covariates. The main goal was to examine the relationship between covariates and multiple type recurrent infections broadly classified into bacterial, fungal and viral origin from the aforementioned data. We considered a weighted estimating equation approach to estimate the parameters. Through simulations, we examined the finite sample properties of the estimators and then applied the method to the India renal transplant data for illustration in all three papers

Differentiable Turbulence

Deep learning is increasingly becoming a promising pathway to improving the accuracy of sub-grid scale (SGS) turbulence closure models for large eddy simulations (LES). We leverage the concept of differentiable turbulence, whereby an end-to-end differentiable solver is used in combination with physics-inspired choices of deep learning architectures to learn highly effective and versatile SGS models for two-dimensional turbulent flow. We perform an in-depth analysis of the inductive biases in the chosen architectures, finding that the inclusion of small-scale non-local features is most critical to effective SGS modeling, while large-scale features can improve pointwise accuracy of the a-posteriori solution field. The filtered velocity gradient tensor can be mapped directly to the SGS stress via decomposition of the inputs and outputs into isotropic, deviatoric, and anti-symmetric components. We see that the model can generalize to a variety of flow configurations, including higher and lower Reynolds numbers and different forcing conditions. We show that the differentiable physics paradigm is more successful than offline, a-priori learning, and that hybrid solver-in-the-loop approaches to deep learning offer an ideal balance between computational efficiency, accuracy, and generalization. Our experiments provide physics-based recommendations for deep-learning based SGS modeling for generalizable closure modeling of turbulence

The agreement chart

Abstract Background When assessing the concordance between two methods of measurement of ordinal categorical data, summary measures such as Cohen’s (1960) kappa or Bangdiwala’s (1985) B-statistic are used. However, a picture conveys more information than a single summary measure. Methods We describe how to construct and interpret Bangdiwala’s (1985) agreement chart and illustrate its use in visually assessing concordance in several example clinical applications. Results The agreement charts provide a visual impression that no summary statistic can convey, and summary statistics reduce the information to a single characteristic of the data. However, the visual impression is personal and subjective, and not usually reproducible from one reader to another. Conclusions The agreement chart should be used to complement the summary kappa or B-statistics, not to replace them. The graphs can be very helpful to researchers as an early step to understand relationships in their data when assessing concordance

The agreement chart

Abstract Background When assessing the concordance between two methods of measurement of ordinal categorical data, summary measures such as Cohen’s (1960) kappa or Bangdiwala’s (1985) B-statistic are used. However, a picture conveys more information than a single summary measure. Methods We describe how to construct and interpret Bangdiwala’s (1985) agreement chart and illustrate its use in visually assessing concordance in several example clinical applications. Results The agreement charts provide a visual impression that no summary statistic can convey, and summary statistics reduce the information to a single characteristic of the data. However, the visual impression is personal and subjective, and not usually reproducible from one reader to another. Conclusions The agreement chart should be used to complement the summary kappa or B-statistics, not to replace them. The graphs can be very helpful to researchers as an early step to understand relationships in their data when assessing concordance

Privacy Aware Experiments without Cookies

Consider two brands that want to jointly test alternate web experiences for their customers with an A/B test. Such collaborative tests are today enabled using \textit{third-party cookies}, where each brand has information on the identity of visitors to another website. With the imminent elimination of third-party cookies, such A/B tests will become untenable. We propose a two-stage experimental design, where the two brands only need to agree on high-level aggregate parameters of the experiment to test the alternate experiences. Our design respects the privacy of customers. We propose an estimater of the Average Treatment Effect (ATE), show that it is unbiased and theoretically compute its variance. Our demonstration describes how a marketer for a brand can design such an experiment and analyze the results. On real and simulated data, we show that the approach provides valid estimate of the ATE with low variance and is robust to the proportion of visitors overlapping across the brands.Comment: Technical repor

Predicting Recovery Patterns After Sport-Related Concussion

Clinicians sometimes treat concussed individuals who have amnesia, loss of consciousness (LOC), a concussion history, or certain symptom types more conservatively, but it is unclear whether recovery patterns differ in individuals with these characteristics

Semiparametric additive marginal regression models for multiple type recurrent events

Recurrent event data are often encountered in biomedical research, for example, recurrent infections or recurrent hospitalizations for patients after renal transplant. In many studies, there are more than one type of events of interest. Cai and Schaubel (2004) advocated a proportional marginal rate model for multiple type recurrent event data. In this paper, we propose a general additive marginal rate regression model. Estimating equations approach is used to obtain the estimators of regression coefficients and baseline rate function. We prove the consistency and asymptotic normality of the proposed estimators. The finite sample properties of our estimators are demonstrated by simulations. The proposed methods are applied to the India renal transplant study to examine risk factors for bacterial, fungal and viral infections

Health Care Use and Status Among Abused Young People

BackgroundChildhood abuse negatively affects young people's health. Little is known about its effect on health care usage patterns or on perception of health status during a life stage when learning to use care independently is a key developmental task.ObjectivesIn nonclinical study settings, abuse has been found to be associated with disorganized use of care and perceived poorer health. Our objective was to determine whether abused youth receiving health care had similar outcomes.MethodsThis observational study, conducted between December 5, 2005 and April 13, 2007, screened for childhood abuse in 532 young people seeking services at a primary care clinic. The setting was a New York City young people's free health clinic. Participants were aged 12-24 years, recruited during a visit, mostly female (86%), Latino or black (94%), and currently in school or college (79%). Exclusions included not being fluent in English or having difficulty understanding the study/consent process.ResultsHealth care use (routine vs urgent care) in the prior 12 months and perceived health status were measured using the Health Service Utilization Scale. Potential demographic covariates were controlled for, as was depression (using the Beck Depression Inventory for Primary Care—Fast Screen). A total of 54% disclosed abuse. Compared with those who were not abused, those reporting sexual abuse had 1.4 times greater odds of choosing both urgent and routine care over routine care only. Those reporting any type of abuse had lower odds of selecting urgent care only over routine care. No association was found between childhood abuse and perceived health status.ConclusionsIn contrast to studies conducted among youth in nonclinic settings, in this study childhood abuse was not associated with health care usage patterns or with poorer perception of health. Further research is needed regarding the impact receiving health care has on perceived health and health care use in abused youth. Annals of 'Global Health' 2017;0:000-00

Comparison of Modes of Administration of Screens to Identify a History of Childhood Physical Abuse in an Adolescent and Young Adult Population

Background: Childhood physical abuse is a major public health issue with negative consequences to health and well-being manifested in childhood and adolescence, and persisting into adulthood. Yet much childhood physical abuse is not identified when it occurs and little is known about how to screen for it. Methods: To address this gap, the effectiveness of 4 modes of administration of screens to identify childhood physical abuse were compared in a sample of 506 adolescents and young adults aged 12-24 years seeking general health services at a primary care clinic. Comparisons were made between paper and pencil screen, audio computer-assisted self-interview screen, face-to-face structured screen (all 3 using the same measure), and face-to-face unstructured interview. Findings: Overall, 44.5% of the sample disclosed that they had been physically abused. Compared to paper and pencil screen, the odds of reporting physical abuse were 1.5 (95% confidence interval [CI]: 0.92, 2.58) and 4.3 (95% CI: 2.49, 7.43) higher among participants using face-to-face structured screen and face-to-face unstructured interview methods, respectively. The face-to-face unstructured interview identified significantly more reports than the paper and pencil screen. Conclusions: Although the unstructured interview was the most effective mode for screening for childhood physical abuse, additional research is needed to confirm whether this holds true in other health care settings. Further research should examine how a health provider's training, experience, and comfort level might influence the identification of physical abuse disclosure in primary care settings using face-to-face unstructured interview