Browsing by Subject "Biostatistics"

Now showing 1 - 7 of 7
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    Best Practices for Biostatistical Consultation and Collaboration in Academic Health Centers
    (Informa UK Limited, 2016) Perkins, Susan M.; Bacchetti, Peter; Davey, Cynthia S.; Lindsell, Christopher J.; Mazumdar, Madhu; Oster, Robert A.; Rocke, David M.; Rudser, Kyle D.; Kim, Mimi; Biostatistics, School of Public Health
    Given the increasing level and scope of biostatistics expertise needed at academic health centers today, we developed best practices guidelines for biostatistics units to be more effective in providing biostatistical support to their institutions, and in fostering an environment in which unit members can thrive professionally. Our recommendations focus on the key areas of: 1) funding sources and mechanisms; 2) providing and prioritizing access to biostatistical resources; and 3) interacting with investigators. We recommend that the leadership of biostatistics units negotiate for sufficient long-term infrastructure support to ensure stability and continuity of funding for personnel, align project budgets closely with actual level of biostatistical effort, devise and consistently apply strategies for prioritizing and tracking effort on studies, and clearly stipulate with investigators prior to project initiation policies regarding funding, lead time, and authorship.
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    Multivariate semiparametric regression models for longitudinal data
    (2014) Li, Zhuokai; Tu, Wanzhu; Liu, Hai; Katz, Barry P.; Fortenberry, J. Dennis
    Multiple-outcome longitudinal data are abundant in clinical investigations. For example, infections with different pathogenic organisms are often tested concurrently, and assessments are usually taken repeatedly over time. It is therefore natural to consider a multivariate modeling approach to accommodate the underlying interrelationship among the multiple longitudinally measured outcomes. This dissertation proposes a multivariate semiparametric modeling framework for such data. Relevant estimation and inference procedures as well as model selection tools are discussed within this modeling framework. The first part of this research focuses on the analytical issues concerning binary data. The second part extends the binary model to a more general situation for data from the exponential family of distributions. The proposed model accounts for the correlations across the outcomes as well as the temporal dependency among the repeated measures of each outcome within an individual. An important feature of the proposed model is the addition of a bivariate smooth function for the depiction of concurrent nonlinear and possibly interacting influences of two independent variables on each outcome. For model implementation, a general approach for parameter estimation is developed by using the maximum penalized likelihood method. For statistical inference, a likelihood-based resampling procedure is proposed to compare the bivariate nonlinear effect surfaces across the outcomes. The final part of the dissertation presents a variable selection tool to facilitate model development in practical data analysis. Using the adaptive least absolute shrinkage and selection operator (LASSO) penalty, the variable selection tool simultaneously identifies important fixed effects and random effects, determines the correlation structure of the outcomes, and selects the interaction effects in the bivariate smooth functions. Model selection and estimation are performed through a two-stage procedure based on an expectation-maximization (EM) algorithm. Simulation studies are conducted to evaluate the performance of the proposed methods. The utility of the methods is demonstrated through several clinical applications.
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    Nonparametric analysis of nonhomogeneous multistate processes with clustered observations
    (Biometrics, 2020-06-24) Bakoyannis, Giorgos
    Frequently, clinical trials and observational studies involve complex event history data with multiple events. When the observations are independent, the analysis of such studies can be based on standard methods for multistate models. However, the independence assumption is often violated, such as in multicenter studies, which makes standard methods improper. This work addresses the issue of nonparametric estimation and two-sample testing for the population-averaged transition and state occupation probabilities under general multistate models with cluster-correlated, right-censored, and/or left-truncated observations. The proposed methods do not impose assumptions regarding the within-cluster dependence, allow for informative cluster size, and are applicable to both Markov and non-Markov processes. Using empirical process theory, the estimators are shown to be uniformly consistent and to converge weakly to tight Gaussian processes. Closed-form variance estimators are derived, rigorous methodology for the calculation of simultaneous confidence bands is proposed, and the asymptotic properties of the nonparametric tests are established. Furthermore, I provide theoretical arguments for the validity of the nonparametric cluster bootstrap, which can be readily implemented in practice regardless of how complex the underlying multistate model is. Simulation studies show that the performance of the proposed methods is good, and that methods that ignore the within-cluster dependence can lead to invalid inferences. Finally, the methods are illustrated using data from a multicenter randomized controlled trial.
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    Nonparametric tests for transition probabilities in nonhomogeneous Markov processes
    (Journal of Nonparametric Statistics, 2020) Bakoyannis, Giorgos
    This paper proposes nonparametric two-sample tests for the direct comparison of the probabilities of a particular transition between states of a continuous time non-homogeneous Markov process with a finite state space. The proposed tests are a linear nonparametric test, an L2-norm-based test and a Kolmogorov–Smirnov-type test. Significance level assessment is based on rigorous procedures, which are justified through the use of modern empirical process theory. Moreover, the L2-norm and the Kolmogorov–Smirnov-type tests are shown to be consistent for every fixed alternative hypothesis. The proposed tests are also extended to more complex situations such as cases with incompletely observed absorbing states and non-Markov processes. Simulation studies show that the test statistics perform well even with small sample sizes. Finally, the proposed tests are applied to data on the treatment of early breast cancer from the European Organization for Research and Treatment of Cancer (EORTC) trial 10854, under an illness-death model.
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    Relationship between African-American Race and Delirium in the Intensive Care Unit
    (Wolters Kluwer, 2016-09) Khan, Babar A.; Perkins, Anthony; Hui, Siu L.; Gao, Sujuan; Campbell, Noll L.; Farber, Mark O.; Boustani, Malaz A.; Medicine, School of Medicine
    Objective Delirium is a highly prevalent syndrome of acute brain dysfunction among critically ill patients that has been linked to multiple risk factors such as age, pre-existing cognitive impairment, and use of sedatives; but to date the relationship between race and delirium is unclear. We conducted this study to identify whether African-American race is a risk factor for developing ICU delirium. Design A prospective cohort study. Setting Medical and Surgical ICUs of a university affiliated, safety-net hospital in Indianapolis, Indiana. Patients 2087 consecutive admissions with 1008 African-Americans admitted to the ICU services from May 2009 to August 2012. Interventions None Measurements and Main Results Incident delirium defined as first positive Confusion Assessment Method for the ICU (CAM-ICU) result after an initial negative CAM-ICU; and prevalent delirium defined as positive CAM-ICU on first CAM-ICU assessment. The overall incident delirium rate in African-Americans was 8.7% compared to 10.4% in Caucasians (P: 0.26). The prevalent delirium rate was 14% in both African-Americans and Caucasians (P: 0.95). Significant age and race interactions were detected for incident delirium (P: 0.02), but not for prevalent delirium (P: 0.3). The hazard ratio for incident delirium for African-Americans in the 18–49 years age group compared to Caucasians of similar age was 0.4 (0.1– 0.9). The hazard and odds ratios for incident and prevalent delirium in other groups were not different. Conclusions African-American race does not confer any additional risk for developing incident or prevalent delirium in the ICU. Instead younger African-Americans tend to have lower rates of incident delirium compared to similar age Caucasians.
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    Semiparametric regression on cumulative incidence function with interval-censored competing risks data and missing event types
    (Biostatistics, 2021) Park, Jun; Bakoyannis, Giorgos; Zhang, Ying; Yiannoutsos, Constantin T.
    Competing risk data are frequently interval-censored, that is, the exact event time is not observed but only known to lie between two examination time points such as clinic visits. In addition to interval censoring, another common complication is that the event type is missing for some study participants. In this article, we propose an augmented inverse probability weighted sieve maximum likelihood estimator for the analysis of interval-censored competing risk data in the presence of missing event types. The estimator imposes weaker than usual missing at random assumptions by allowing for the inclusion of auxiliary variables that are potentially associated with the probability of missingness. The proposed estimator is shown to be doubly robust, in the sense that it is consistent even if either the model for the probability of missingness or the model for the probability of the event type is misspecified. Extensive Monte Carlo simulation studies show good performance of the proposed method even under a large amount of missing event types. The method is illustrated using data from an HIV cohort study in sub-Saharan Africa, where a significant portion of events types is missing. The proposed method can be readily implemented using the new function ciregic_aipw in the R package intccr.