Browsing by Subject "Competing risks"

Now showing 1 - 8 of 8
  • Thumbnail Image
    Item
    Impact of covariate omission and categorization from the Fine–Gray model in randomized-controlled trials
    (Springer, 2021-12) Bakoyannis, Giorgos; Chu, Fang-I.; Babiker, Abdel G. A.; Touloumi, Giota; Biostatistics, School of Public Health
    In this paper, we study the statistical issues related to the omission and categorization of important covariates in the context of the Fine–Gray model in randomized-controlled trials with competing risks. We show that the omission of an important covariate from the Fine–Gray model leads to attenuated estimates for treatment effect and loss of proportionality in general. Our simulation studies reveal substantial attenuation in the estimate for treatment effect and the loss of statistical power, while dichotomizing a continuous covariate leads to similar but less pronounced impact. Our results are illustrated using data from a randomized clinical trial of HIV-infected individuals. The relative merits of conducting an adjusted versus an unadjusted analysis of treatment effect in light of both statistical and practical considerations are discussed.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    A pseudo-likelihood method for estimating misclassification probabilities in competing-risks settings when true event data are partially observed
    (Wiley, 2020) Mpofu, Philani B.; Bakoyannis, Giorgos; Yiannoutsos, Constantin T.; Mwangi, Ann W.; Mburu, Margaret; Biostatistics, School of Public Health
    Outcome misclassification occurs frequently in binary-outcome studies and can result in biased estimation of quantities such as the incidence, prevalence, cause-specific hazards, cumulative incidence functions etc. A number of remedies have been proposed to address the potential misclassification of the outcomes in such data. The majority of these remedies lie in the estimation of misclassification probabilities, which are in turn used to adjust analyses for outcome misclassification. A number of authors advocate using a gold-standard procedure on a sample internal to the study to learn about the extent of the misclassification. With this type of internal validation, the problem of quantifying the misclassification also becomes a missing data problem as, by design, the true outcomes are only ascertained on a subset of the entire study sample. Although, the process of estimating misclassification probabilities appears simple conceptually, the estimation methods proposed so far have several methodological and practical shortcomings. Most methods rely on missing outcome data to be missing completely at random (MCAR), a rather stringent assumption which is unlikely to hold in practice. Some of the existing methods also tend to be computationally-intensive. To address these issues, we propose a computationally-efficient, easy-to-implement, pseudo-likelihood estimator of the misclassification probabilities under a missing at random (MAR) assumption, in studies with an available internal validation sample. We present the estimator through the lens of studies with competing-risks outcomes, though the estimator extends beyond this setting. We describe the consistency and asymptotic distributional properties of the resulting estimator, and derive a closed-form estimator of its variance. The finite-sample performance of this estimator is evaluated via simulations. Using data from a real-world study with competing risks outcomes, we illustrate how the proposed method can be used to estimate misclassification probabilities. We also show how the estimated misclassification probabilities can be used in an external study to adjust for possible misclassification bias when modeling cumulative incidence functions.
  • Thumbnail Image
    Item
    Semiparametric Competing Risks Regression Under Interval Censoring Using the R Package intccr
    (Elsevier, 2019-05) Park, Jun; Bakoyannis, Giorgos; Yiannoutsos, Constantin T.; Biostatistics, School of Public Health
    Background and objective: Competing risk data are frequently interval-censored in real-world applications, that is, the exact event time is not precisely observed but is only known to lie between two time points such as clinic visits. This type of data requires special handling because the actual event times are unknown. To deal with this problem we have developed an easy-to-use open-source statistical software. Methods: An approach to perform semiparametric regression analysis of the cumulative incidence function with interval-censored competing risks data is the sieve maximum likelihood method based on B-splines. An important feature of this approach is that it does not impose restrictive parametric assumptions. Also, this methodology provides semiparametrically efficient estimates. Implementation of this methodology can be easily performed using our new R package intccr. Results: The R package intccr performs semiparametric regression analysis of the cumulative incidence function based on interval-censored competing risks data. It supports a large class of models including the proportional odds and the Fine-Gray proportional subdistribution hazards model as special cases. It also provides the estimated cumulative incidence functions for a particular combination of covariate values. The package also provides some data management functionality to handle data sets which are in a long format involving multiple lines of data per subject. Conclusions: The R package intccr provides a convenient and flexible software for the analysis of the cumulative incidence function based on interval-censored competing risks data.
  • Thumbnail Image
    Item
    Semiparametric marginal regression for clustered competing risks data with missing cause of failure
    (Oxford University Press, 2023) Zhou, Wenxian; Bakoyannis, Giorgos; Zhang, Ying; Yiannoutsos, Constantin T.; Biostatistics and Health Data Science, School of Medicine
    Clustered competing risks data are commonly encountered in multicenter studies. The analysis of such data is often complicated due to informative cluster size (ICS), a situation where the outcomes under study are associated with the size of the cluster. In addition, the cause of failure is frequently incompletely observed in real-world settings. To the best of our knowledge, there is no methodology for population-averaged analysis with clustered competing risks data with an ICS and missing causes of failure. To address this problem, we consider the semiparametric marginal proportional cause-specific hazards model and propose a maximum partial pseudolikelihood estimator under a missing at random assumption. To make the latter assumption more plausible in practice, we allow for auxiliary variables that may be related to the probability of missingness. The proposed method does not impose assumptions regarding the within-cluster dependence and allows for ICS. The asymptotic properties of the proposed estimators for both regression coefficients and infinite-dimensional parameters, such as the marginal cumulative incidence functions, are rigorously established. Simulation studies show that the proposed method performs well and that methods that ignore the within-cluster dependence and the ICS lead to invalid inferences. The proposed method is applied to competing risks data from a large multicenter HIV study in sub-Saharan Africa where a significant portion of causes of failure is missing.
  • Thumbnail Image
    Item
    A semiparametric method for the analysis of outcomes during a gap in HIV care under incomplete outcome ascertainment
    (De Gruyter, 2020-09) Bakoyannis, Giorgos; Diero, Lameck; Mwangi, Ann; Wools-Kaloustian, Kara K.; Yiannoutsos, Constantin T.; Biostatistics, School of Public Health
    Objectives: Estimation of the cascade of HIV care is essential for evaluating care and treatment programs, informing policy makers and assessing targets such as 90-90-90. A challenge to estimating the cascade based on electronic health record concerns patients "churning" in and out of care. Correctly estimating this dynamic phenomenon in resource-limited settings, such as those found in sub-Saharan Africa, is challenging because of the significant death under-reporting. An approach to partially recover information on the unobserved deaths is a double-sampling design, where a small subset of individuals with a missed clinic visit is intensively outreached in the community to actively ascertain their vital status. This approach has been adopted in several programs within the East Africa regional IeDEA consortium, the context of our motivating study. The objective of this paper is to propose a semiparametric method for the analysis of competing risks data with incomplete outcome ascertainment. Methods: Based on data from double-sampling designs, we propose a semiparametric inverse probability weighted estimator of key outcomes during a gap in care, which are crucial pieces of the care cascade puzzle. Results: Simulation studies suggest that the proposed estimators provide valid estimates in settings with incomplete outcome ascertainment under a set of realistic assumptions. These studies also illustrate that a naïve complete-case analysis can provide seriously biased estimates. The methodology is applied to electronic health record data from the East Africa IeDEA Consortium to estimate death and return to care during a gap in care. Conclusions: The proposed methodology provides a robust approach for valid inferences about return to care and death during a gap in care, in settings with death under-reporting. Ultimately, the resulting estimates will have significant consequences on program construction, resource allocation, policy and decision making at the highest levels.
  • Thumbnail Image
    Item
    Semiparametric regression on cumulative incidence function with interval-censored competing risks data
    (Wiley, 2017-10-15) Bakoyannis, Giorgos; Yu, Menggang; Yiannoutsos, Constantin T.; Biostatistics, School of Public Health
    Many biomedical and clinical studies with time-to-event outcomes involve competing risks data. These data are frequently subject to interval censoring. This means that the failure time is not precisely observed but is only known to lie between two observation times such as clinical visits in a cohort study. Not taking into account the interval censoring may result in biased estimation of the cause-specific cumulative incidence function, an important quantity in the competing risks framework, used for evaluating interventions in populations, for studying the prognosis of various diseases, and for prediction and implementation science purposes. In this work, we consider the class of semiparametric generalized odds rate transformation models in the context of sieve maximum likelihood estimation based on B-splines. This large class of models includes both the proportional odds and the proportional subdistribution hazard models (i.e., the Fine-Gray model) as special cases. The estimator for the regression parameter is shown to be consistent, asymptotically normal and semiparametrically efficient. Simulation studies suggest that the method performs well even with small sample sizes. As an illustration, we use the proposed method to analyze data from HIV-infected individuals obtained from a large cohort study in sub-Saharan Africa. We also provide the R function ciregic that implements the proposed method and present an illustrative example.
  • Thumbnail Image
    Item
    Sieve estimation of a class of partially linear transformation models with interval-censored competing risks data
    (Academia Sinica, 2023) Lu, Xuewen; Wang, Yan; Bandyopadhyay, Dipankar; Bakoyannis, Giorgos; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    In this paper, we consider a class of partially linear transformation models with interval-censored competing risks data. Under a semiparametric generalized odds rate specification for the cause-specific cumulative incidence function, we obtain optimal estimators of the large number of parametric and nonparametric model components via maximizing the likelihood function over a joint B-spline and Bernstein polynomial spanned sieve space. Our specification considers a relatively simpler finite-dimensional parameter space, approximating the infinite-dimensional parameter space as n → ∞, thereby allowing us to study the almost sure consistency, and rate of convergence for all parameters, and the asymptotic distributions and efficiency of the finite-dimensional components. We study the finite sample performance of our method through simulation studies under a variety of scenarios. Furthermore, we illustrate our methodology via application to a dataset on HIV-infected individuals from sub-Saharan Africa.