Competing Risk Outcomes Research Articles

A pseudo-likelihood method for estimating misclassification probabilities in competing-risks settings when true-event data are partially observed.

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, and so forth. 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 incidencefunctions.

Competing risk modeling and testing for X-chromosome genetic association

The complexity of X-chromosome inactivation arouses the X-linked genetic association being overlooked in most of the genetic studies, especially for genetic association analysis on time to event outcomes. To fill this gap, we propose novel methods to analyze the X-linked genetic association for competing risk failure time data based on a subdistribution hazard function. Specifically, we consider two mechanisms for a single genetic variant on X-chromosome: (1) all the subjects in a population undergo the same inactivation process; (2) the subjects randomly undergo different inactivation processes. According to the assumptions, one of the proposed methods can be used to infer the unknown biological process under scenario (1), while another method can be used to estimate the proportion of a certain biological process in the population under scenario (2). Both of the two methods can infer the direction of skewness for skewed X-chromosome inactivation and derive asymptotically unbiased estimates of the model parameters. The asymptotic distributions for the parameter estimates and constructed score tests with nuisance parameters only presented under the alternative hypothesis are illustrated under both assumptions. Finite sample performance of these novel methods is examined via extensive simulation studies. An application is illustrated with implementation on a cancer genetic study with competing risk outcomes.

Stratified proportional subdistribution hazards model with covariate‐adjusted censoring weight for case‐cohort studies

AbstractThe case‐cohort study design is widely used to reduce cost when collecting expensive covariates in large cohort studies with survival or competing risks outcomes. A case‐cohort study dataset consists of two parts: (a) a random sample and (b) all cases or failures from a specific cause of interest. Clinicians often assess covariate effects on competing risks outcomes. The proportional subdistribution hazards model directly evaluates the effect of a covariate on the cumulative incidence function under the non‐covariate‐dependent censoring assumption for the full cohort study. However, the non‐covariate‐dependent censoring assumption is often violated in many biomedical studies. In this article, we propose a proportional subdistribution hazards model for case‐cohort studies with stratified data with covariate‐adjusted censoring weight. We further propose an efficient estimator when extra information from the other causes is available under case‐cohort studies. The proposed estimators are shown to be consistent and asymptotically normal. Simulation studies show (a) the proposed estimator is unbiased when the censoring distribution depends on covariates and (b) the proposed efficient estimator gains estimation efficiency when using extra information from the other causes. We analyze a bone marrow transplant dataset and a coronary heart disease dataset using the proposed method.

Quantifying diagnostic accuracy improvement of new biomarkers for competing risk outcomes.

The net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) were originally proposed to characterize accuracy improvement in predicting a binary outcome, when new biomarkers are added to regression models. These two indices have been extended from binary outcomes to multi-categorical and survival outcomes. Working on an AIDS study where the onset of cognitive impairment is competing risk censored by death, we extend the NRI and the IDI to competing risk outcomes, by using cumulative incidence functions to quantify cumulative risks of competing events, and adopting the definitions of the two indices for multi-category outcomes. The "missing" category due to independent censoring is handled through inverse probability weighting. Various competing risk models are considered, such as the Fine and Gray, multistate, and multinomial logistic models. Estimation methods for the NRI and the IDI from competing risk data are presented. The inference for the NRI is constructed based on asymptotic normality of its estimator, and the bias-corrected and accelerated bootstrap procedure is used for the IDI. Simulations demonstrate that the proposed inferential procedures perform very well. The Multicenter AIDS Cohort Study is used to illustrate the practical utility of the extended NRI and IDI for competing risk outcomes.

Helicobacter pylori eradication treatment for gastric carcinoma prevention in asymptomatic or dyspeptic adults: systematic review and Bayesian meta-analysis of randomised controlled trials

ObjectivesRecent meta-analyses of eradication therapy in Helicobacter pylori-infected adults reported significant reductions in gastric carcinoma risk. However, concerns about supporting unfocused screening and eradication programme in healthy, asymptomatic populations have...

Association of Racial Disparities With Access to Kidney Transplant After the Implementation of the New Kidney Allocation System

Inactive patients on the kidney transplant wait-list have a higher mortality. The implications of this status change on transplant outcomes between racial/ethnic groups are unknown. To determine if activity status changes differ among races/ethnicities and levels of sensitization, and if these differences are associated with transplant probability after implementation of the Kidney Allocation System. A multistate model was constructed from the Organ Procurement and Transplantation Network kidney transplant database (December 4, 2014, to September 8, 2016). The time interval followed Kidney Allocation System implementation and provided at least 1-year follow-up for all patients. The model calculated probabilities between active and inactive status and the following competing risk outcomes: living donor transplant, deceased donor transplant, and death/other. This retrospective cohort study included 42 558 patients on the Organ Procurement and Transplantation Network kidney transplant wait-list following Kidney Allocation System implementation. To rule out time-varying confounding from relisting, analysis was limited to first-time registrants. Owing to variations in listing practices, primary center listing data were used for dually listed patients. Individuals listed for another organ or pancreatic islets were excluded. Analysis began July 2017. Probabilities were determined for transitions between active and inactive status and the following outcome states: active to living donor transplant, active to deceased donor transplant, active to death/other, inactive to living donor transplant, inactive to deceased donor transplant, and inactive to death/other. The median (interquartile range) age at listing was 55.0 (18.0-89.0) years, and 26 535 of 42 558 (62.4%) were men. White individuals were 43.3% (n = 18 417) of wait-listed patients, while black and Hispanic individuals made up 27.8% (n = 11 837) and 19.5% (n = 8296), respectively. Patients in the calculated plasma reactive antibody categories of 0% or 1% to 79% showed no statistically significant difference in transplant probability among races/ethnicities. White individuals had an advantage in transplant probability over black individuals in calculated plasma reactive antibody categories of 80% to 89% (hazard ratio [HR], 1.8 [95% CI, 1.4-2.2]) and 90% or higher (HR, 2.4 [95% CI, 2.1-2.6]), while Hispanic individuals had an advantage over black individuals in the calculated plasma reactive antibody group of 90% or higher (HR, 2.5 [95% CI, 2.1-2.8]). Once on the inactive list, white individuals were more likely than Hispanic individuals (HR, 1.2 [95% CI, 1.17-1.3]) or black individuals (HR, 1.4 [95% CI, 1.3-1.4]) to resolve issues for inactivity resulting in activation. For patients who are highly sensitized, there continues to be less access to kidney transplant in the black population after the implementation of the Kidney Allocation System. Health disparities continue after listing where individuals from minority groups have greater difficulty in resolving issues of inactivity.

A joint model for dynamic prediction in uveitis.

Uveitis is characterised as a recurrent inflammation of the eye and an ongoing inflammation can have severe impact on the visual acuity of the patient. The Rotterdam Eye Hospital has been collecting data on every uveitis patient visiting the hospital since 2000. We propose a joint model for the inflammation and visual acuity with the purpose of making dynamic predictions. Dynamic prediction models allow predictions to be updated during the follow-up of the patient based on the patient's disease history. The joint model consists of a submodel for the inflammation, the event history outcome, and one for the visual acuity, the longitudinal outcome. The inflammation process is described with a two-state reversible multistate model, where transition times are interval censored. Correlated log-normal frailties are included in the multistate model to account for the within eye and within patient correlation. A linear mixed model is used for the visual acuity. The joint model is fitted in a two-stage procedure and we illustrate how the model can be used to make dynamic predictions. The performance of the method was investigated in a simulation study. The novelty of the proposed model includes the extension to a multistate outcome, whereas, previously, the standard has been to consider survival or competing risk outcomes. Furthermore, it is usually the case that the longitudinal outcome affects the event history outcome, but in this model, the relation is reversed.

Cumulative incidence regression for dynamic treatment regimens.

Recently dynamic treatment regimens (DTRs) have drawn considerable attention, as an effective tool for personalizing medicine. Sequential Multiple Assignment Randomized Trials (SMARTs) are often used to gather data for making inference on DTRs. In this article, we focus on regression analysis of DTRs from a two-stage SMART for competing risk outcomes based on cumulative incidence functions (CIFs). Even though there are extensive works on the regression problem for DTRs, no research has been done on modeling the CIF for SMART trials. We extend existing CIF regression models to handle covariate effects for DTRs. Asymptotic properties are established for our proposed estimators. The models can be implemented using existing software by an augmented-data approximation. We show the improvement provided by our proposed methods by simulation and illustrate its practical utility through an analysis of a SMART neuroblastoma study, where disease progression cannot be observed after death.

Joint Modeling of Multivariate Longitudinal Data and Competing Risks Using Multiphase Sub-models

In many clinical studies that involve follow-up, it is common to observe one or more sequences of longitudinal measurements, as well as one or more time to event outcomes. A competing risks situation arises when the probability of occurrence of one event is altered/hindered by another time to event. Recently, there has been much attention paid to the joint analysis of a single longitudinal response and a single time to event outcome, when the missing data mechanism in the longitudinal process is non-ignorable. We, in this paper, propose an extension where multiple longitudinal responses are jointly modeled with competing risks (multiple time to events). Our shared parameter joint model consists of a system of multiphase non-linear mixed effects sub-models for the multiple longitudinal responses, and a system of cause-specific non-proportional hazards frailty sub-models for competing risks, with associations among multiple longitudinal responses and competing risks modeled using latent parameters. The joint model is applied to a data set of patients who are on mechanical circulatory support and are awaiting heart transplant, using readily available software. While on the mechanical circulatory support, patient liver and renal functions may worsen and these in turn may influence one of the two possible competing outcomes: (i) death before transplant; (ii) transplant. In one application, we propose a system of multiphase cause-specific non-proportional hazard sub-model where frailty can be time varying. Performance under different scenarios was assessed using simulation studies. By using the proposed joint modeling of the multiphase sub-models, one can identify: (i) non-linear trends in multiple longitudinal outcomes; (ii) time-varying hazards and cumulative incidence functions of the competing risks; (iii) identify risk factors for the both types of outcomes, where the effect may or may not change with time; and (iv) assess the association between multiple longitudinal and competing risks outcomes, where the association may or may not change with time.

Testing for center effects on survival and competing risks outcomes using pseudo-value regression.

In multi-center studies, the presence of a cluster effect leads to correlation among outcomes within a center and requires different techniques to handle such correlation. Testing for a cluster effect can serve as a pre-screening step to help guide the researcher towards the appropriate analysis. With time to event data, score tests have been proposed which test for the presence of a center effect on the hazard function. However, sometimes researchers are interested in directly modeling other quantities such as survival probabilities or cumulative incidence at a fixed time. We propose a test for the presence of a center effect acting directly on the quantity of interest using pseudo-value regression, and derive the asymptotic properties of our proposed test statistic. We examine the performance of our proposed test through simulation studies in both survival and competing risks settings. The proposed test may be more powerful than tests based on the hazard function in settings where the center effect is time-varying. We illustrate the test using a multicenter registry study of survival and competing risks outcomes after hematopoietic cell transplantation.

Competing-risk outcomes after hematopoietic stem cell transplantation from the perspective of time-dependent effects.

The success of hematopoietic stem cell transplantation is determined by multiple factors. Additional complexity is conferred by covariables showing time-dependent effects. We evaluated the effect of predictors on competing-risk outcomes after hematopoietic stem cell transplantation in a time-dependent manner. We analyzed 14951 outcomes of adult patients with hematologic malignancies who underwent a first allogeneic transplant. We extended the combined endpoints of disease-free and overall survival to competing-risk settings: disease-free survival was split into relapse and non-relapse mortality. Overall survival was divided into transplant-related mortality, death from other causes and death from unknown causes. For time-dependent effects we computed estimators before and after a covariable-specific cut-point. Patients treated with reduced intensity conditioning had a constantly higher risk of relapse compared to patients treated with myeloablative conditioning. For non-relapse mortality, patients treated with reduced intensity conditioning had a reduced mortality risk but this effect was only seen in the first 4 months after transplantation (hazard ratio: 0.76, P<0.001) and not afterwards. Graft source exhibited a time-dependent effect on both transplant-related mortality (in first year: hazard ratio 0.70, P<0.001; after first year: hazard ratio 1.47, P=0.002) and non-relapse mortality (in first 8 months: hazard ratio 0.75, P<0.001; after first 8 months: hazard ratio 1.38, P<0.001). Patients with a poor Karnofsky performance score (<80) had a considerably higher risk of all endpoints in the first 4 months. The competing-risk analysis for overall survival and disease-free survival allows resolution of effects with different vectors early and later after stem cell transplantation, as shown for graft source. This information may be useful in risk assessment of treatment choices and for counseling patients on an individual basis.

The index of prediction accuracy: an intuitive measure useful for evaluating risk prediction models

BackgroundMany measures of prediction accuracy have been developed. However, the most popular ones in typical medical outcome prediction settings require additional investigation of calibration.MethodsWe show how rescaling the Brier score produces a measure that combines discrimination and calibration in one value and improves interpretability by adjusting for a benchmark model. We have called this measure the index of prediction accuracy (IPA). The IPA permits a common interpretation across binary, time to event, and competing risk outcomes. We illustrate this measure using example datasets.ResultsThe IPA is simple to compute, and example code is provided. The values of the IPA appear very interpretable.ConclusionsIPA should be a prominent measure reported in studies of medical prediction model performance. However, IPA is only a measure of average performance and, by default, does not measure the utility of a medical decision.

Transition probabilities between changing sensitization levels, waitlist activity status and competing-risk kidney transplant outcomes using multi-state modeling.

BackgroundSensitization and activity status are associated with kidney transplant waitlist mortality. Unknown is how changes in these covariates after listing impact transplant outcomes.MethodsTwo cohorts were created from the OPTN (Organ Procurement and Transplantation Network) database, one pre-KAS (new kidney allocation system) (10/01/2009-12/04/2013, n = 97,793) and one post-KAS (12/04/2014-06/17/2015, n = 13,113). Multi-state modeling provides transition probabilities between intermediate states (CPRA category/activity status combinations) and competing-risk outcomes: transplant (living), transplant (deceased), death, or other/well.ResultsTransition probabilities show chances of converting between intermediate states prior to a competing-risk outcome. One year transplant probabilities for post-KAS candidates with a CPRA of 0%(P, 0.123[95% CI, 0.117,0.129]), 1–79%(P, 0.125 [95% CI, 0.112,0.139]), 95–98%(P, 0.242[95% CI, 0.188, 0.295]) and 99–100%(P, 0.252 [95% CI, 0.195, 0.308]) were significantly higher than the pre-KAS cohort; they were lower for CPRA 80–89%(P, 0.152 [95% CI, 0.116,0.189]) and not statistically different for CPRA 90–94%(P, 0.180 [95% CI, 0.137,0.223]) candidates. Post-KAS, Whites had a statistically higher transplant probability only at a CPRA of 99–100%.ConclusionMulti-state modeling provides transition probabilities between CPRA/activity status combinations, giving estimates on how changing patient characteristic’s after listing impact outcomes. Preliminarily, across most CPRA categories, there was no statistical difference in transplant probabilities between Whites, Blacks and Hispanics following KAS implementation, however, this finding requires longer follow-up for validation.

Partially linear mixed-effects joint models for skewed and missing longitudinal competing risks outcomes

ABSTRACTLongitudinal competing risks data frequently arise in clinical studies. Skewness and missingness are commonly observed for these data in practice. However, most joint models do not account for these data features. In this article, we propose partially linear mixed-effects joint models to analyze skew longitudinal competing risks data with missingness. In particular, to account for skewness, we replace the commonly assumed symmetric distributions by asymmetric distribution for model errors. To deal with missingness, we employ an informative missing data model. The joint models that couple the partially linear mixed-effects model for the longitudinal process, the cause-specific proportional hazard model for competing risks process and missing data process are developed. To estimate the parameters in the joint models, we propose a fully Bayesian approach based on the joint likelihood. To illustrate the proposed model and method, we implement them to an AIDS clinical study. Some interesting findings are reported. We also conduct simulation studies to validate the proposed method.

Long-acting injectable antipsychotics in patients with schizophrenia: systematic review and mixed treatment meta-analysis

Long-acting injectable antipsychotics (LAIs) are recommended for schizophrenic patients who cannot adhere to oral medication. We systematically reviewed randomized controlled trials of 6 LAIs available in Thailand including fluphenazine, flupentixol, haloperidol, zuclopenthixol, paliperidone, and risperidone in PubMed/ MEDLINE and the Cochrane library (1955–Nov 2013). Inclusion was limited to studies of schizophrenia ?24 weeks duration published in English. We selected 17 of 1,245 articles including 1,904 patients. The number of patients ranged from 19 to 747 per study (median 54). Mean study duration was 52.3 weeks (24?96 weeks) and median quality score using a Jadad scoring method was 4 (2–5). We applied a Bayesian model with a mixed treatment comparison approach for 3 competing risk outcomes including relapse, and discontinuation because of adverse events or other reasons. Based on the random effect model preferred by a goodness of fit analysis, risperidone had the lowest 52 week probability of relapse (mean ± SD, 0.26 ± 0.321) followed by paliperidone (0.30 ± 0.314). Zuclopenthixol had the lowest probability of discontinuation because of an adverse event (0.07 ± 0.159) or other reasons (0.26 ± 0.295). Risperidone had the highest probability of preventing relapse (0.35 ± 0.476) or discontinuation for other reasons (0.31 ± 0.461). Zuclopenthixol had the highest probability of preventing discontinuation because of adverse effects (0.31 ± 0.464). All 6 LAIs tended to have a lower risk of relapse compared with placebo. Differences between LAIs preventing any treatment discontinuation or relapse were seen, but limited in our analysis. Keywords: Competing risk outcomes, long acting injectable antipsychotics, meta-analysis, mixed treatment comparison

Evaluating predictors of competing risk outcomes when censoring depends on time-dependent covariates, with application to safety and efficacy of HIV treatment.

We propose a prediction model for the cumulative incidence functions of competing risks, based on a logit link. Because of a concern about censoring potentially depending on time-varying covariates in our motivating human immunodeficiency virus (HIV) application, we describe an approach for estimating the parameters in the prediction models using inverse probability of censoring weighting under a missingness at random assumption. We then illustrate the application of this methodology to identify predictors of the competing outcomes of virologic failure, an efficacy outcome, and treatment limiting adverse event, a safety outcome, among human immunodeficiency virus-infected patients first starting antiretroviral treatment. Copyright © 2016 John Wiley & Sons, Ltd.

Recursive Partitioning Method on Competing Risk Outcomes.

In some cancer clinical studies, researchers have interests to explore the risk factors associated with competing risk outcomes such as recurrence-free survival. We develop a novel recursive partitioning framework on competing risk data for both prognostic and predictive model constructions. We define specific splitting rules, pruning algorithm, and final tree selection algorithm for the competing risk tree models. This methodology is quite flexible that it can corporate both semiparametric method using Cox proportional hazards model and parametric competing risk model. Both prognostic and predictive tree models are developed to adjust for potential confounding factors. Extensive simulations show that our methods have well-controlled type I error and robust power performance. Finally, we apply both Cox proportional hazards model and flexible parametric model for prognostic tree development on a retrospective clinical study on oropharyngeal cancer patients.

Estimating sample size in the presence of competing risks - Cause-specific hazard or cumulative incidence approach?

In designing randomised clinical trials involving competing risks endpoints, it is important to consider competing events to ensure appropriate determination of sample size. We conduct a simulation study to compare sample sizes obtained from the cause-specific hazard and cumulative incidence (CMI) approaches, by first assuming exponential event times. As the proportional subdistribution hazard assumption does not hold for the CMI exponential (CMIExponential) model, we further investigate the impact of violation of such an assumption by comparing the results obtained from the CMI exponential model with those of a CMI model assuming a Gompertz distribution (CMIGompertz) where the proportional assumption is tenable. The simulation suggests that the CMIExponential approach requires a considerably larger sample size when treatment reduces the hazards of both the main event, A, and the competing risk, B. When treatment has a beneficial effect on A but no effect on B, the sample sizes required by both methods are largely similar, especially for large reduction in the main risk. If treatment has a protective effect on A but adversely affects B, then the sample size required by CMIExponential is notably smaller than cause-specific hazard for small to moderate reduction in the main risk. Further, a smaller sample size is required for CMIGompertz as compared with CMIExponential. The choice between a cause-specific hazard or CMI model in competing risks outcomes has implications on the study design. This should be made on the basis of the clinical question of interest and the validity of the associated model assumption.

Modelling competing risks in nephrology research: an example in peritoneal dialysis

BackgroundModelling competing risks is an essential issue in Nephrology Research. In peritoneal dialysis studies, sometimes inappropriate methods (i.e. Kaplan-Meier method) have been used to estimate probabilities for an event of interest in the presence of competing risks. In this situation a competing risk analysis should be preferable. The objectives of this study are to describe the bias resulting from the application of standard survival analysis to estimate peritonitis-free patient survival and to provide alternative statistical approaches taking competing risks into account.MethodsThe sample comprises patients included in a university hospital peritoneal dialysis program between October 1985 and June 2011 (n = 449). Cumulative incidence function and competing risk regression models based on cause-specific and subdistribution hazards were discussed.ResultsThe probability of occurrence of the first peritonitis is wrongly overestimated using Kaplan-Meier method. The cause-specific hazard model showed that factors associated with shorter time to first peritonitis were age (≥55 years) and previous treatment (haemodialysis). Taking competing risks into account in the subdistribution hazard model, age remained significant while gender (female) but not previous treatment was identified as a factor associated with a higher probability of first peritonitis event.ConclusionsIn the presence of competing risks outcomes, Kaplan-Meier estimates are biased as they overestimated the probability of the occurrence of an event of interest. Methods which take competing risks into account provide unbiased estimates of cumulative incidence for each specific outcome experienced by patients. Multivariable regression models such as those based on cause-specific hazard and on subdistribution hazard should be used in this competing risk setting.

Comparing competing risk outcomes within principal strata, with application to studies of mother-to-child transmission of HIV

Comparing competing risk outcomes within principal strata, with application to studies of mother-to-child transmission of HIV