Semiparametric marginal regression for clustered competing risks data with missing cause of failure.

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.

Semiparametric regression on cumulative incidence function with interval-censored competing risks data and missing event types.

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.

Estimating optimal individualized treatment rules with multistate processes.

Multistate process data are common in studies of chronic diseases such as cancer. These data are ideal for precision medicine purposes as they can be leveraged to improve more refined health outcomes, compared to standard survival outcomes, as well as incorporate patient preferences regarding quantity versus quality of life. However, there are currently no methods for the estimation of optimal individualized treatment rules with such data. In this paper, we propose a nonparametric outcome weighted learning approach for this problem in randomized clinical trial settings. The theoretical properties of the proposed methods, including Fisher consistency and asymptotic normality of the estimated expected outcome under the estimated optimal individualized treatment rule, are rigorously established. A consistent closed-form variance estimator is provided and methodology for the calculation of simultaneous confidence intervals is proposed. Simulation studies show that the proposed methodology and inference procedures work well even with small-sample sizes and high rates of right censoring. The methodology is illustrated using data from a randomized clinical trial on the treatment of metastatic squamous-cell carcinoma of the head and neck.

Sieve estimation of a class of partially linear transformation models with interval-censored competing risks data.

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.

Assessment of ST2 for risk of death following graft-versus-host disease in pediatric and adult age groups.

Assessment of prognostic biomarkers of nonrelapse mortality (NRM) after allogeneic hematopoietic cell transplantation (HCT) in the pediatric age group is lacking. To address this need, we conducted a prospective cohort study with 415 patients at 6 centers: 170 were children age 10 years or younger and 245 were patients older than age 10 years (both children and adults were accrued from 2013 to 2018). The following 4 plasma biomarkers were assessed pre-HCT and at days +7, +14, and +21 post-HCT: stimulation-2 (ST2), tumor necrosis factor receptor 1 (TNFR1), regenerating islet-derived protein 3α (REG3α), and interleukin-6 (IL-6). We performed landmark analyses for NRM, dichotomizing the cohort at age 10 years or younger and using each biomarker median as a cutoff for high- and low-risk groups. Post-HCT biomarker analysis showed that ST2 (>26 ng/mL), TNFR1 (>3441 pg/mL), and REG3α (>25 ng/mL) are associated with NRM in children age 10 years or younger (ST2: hazard ratio [HR], 9.13; 95% confidence interval [CI], 2.74-30.38; P = .0003; TNFR1: HR, 4.29; 95% CI, 1.48-12.48; P = .0073; REG3α: HR, 7.28; 95% CI, 2.05-25.93; P = .0022); and in children and adults older than age 10 years (ST2: HR, 2.60; 95% CI, 1.15-5.86; P = .021; TNFR1: HR, 2.09; 95% CI, 0.96-4.58; P = .06; and REG3α: HR, 2.57; 95% CI, 1.19-5.55; P = .016). When pre-HCT biomarkers were included, only ST2 remained significant in both cohorts. After adjustment for significant covariates (race/ethnicity, malignant disease, graft, and graft-versus-host-disease prophylaxis), ST2 remained associated with NRM only in recipients age 10 years or younger (HR, 4.82; 95% CI, 1.89-14.66; P = .0056). Assays of ST2, TNFR1, and REG3α in the first 3 weeks after HCT have prognostic value for NRM in both children and adults. The presence of ST2 before HCT is a prognostic biomarker for NRM in children age 10 years or younger allowing for additional stratification. This trial was registered at www.clinicaltrials.gov as #NCT02194439.

Retention in care and viral suppression in the PMTCT continuum at a large referral facility in western Kenya.

Medical records of pregnant and postpartum women living with HIV and their infants attending a large referral facility in Kenya from 2015 to 2019 were analyzed to identify characteristics associated with retention in care and viral suppression. Women were stratified based on the timing of HIV care enrollment: known HIV-positive (KHP; enrolled pre-pregnancy) and newly HIV-positive (NHP; enrolled during pregnancy). Associations with retention at 18 months postpartum and viral suppression (< 1000 copies/mL) were determined. Among 856 women (20% NHP), retention was 83% for KHPs and 53% for NHPs. Viral suppression was 88% for KHPs and 93% for NHPs, but 19% of women were missing viral load results. In a competing risk model, viral suppression increased by 18% for each additional year of age but was not associated with other factors. Overall, 1.9% of 698 infants with ≥ 1 HIV test result were HIV-positive. Tailored interventions are needed to promote retention and viral load testing, particularly for NHPs, in the PMTCT continuum.

Nonparametric tests for multistate processes with clustered data.

In this work, we propose nonparametric two-sample tests for population-averaged transition and state occupation probabilities for continuous-time and finite state space processes with clustered, right-censored, and/or left-truncated data. We consider settings where the two groups under comparison are independent or dependent, with or without complete cluster structure. The proposed tests do not impose assumptions regarding the structure of the within-cluster dependence and are applicable to settings with informative cluster size and/or non-Markov processes. The asymptotic properties of the tests are rigorously established using empirical process theory. Simulation studies show that the proposed tests work well even with a small number of clusters, and that they can be substantially more powerful compared to the only, to the best of our knowledge, previously proposed test for this problem. The tests are illustrated using data from a multicenter randomized controlled trial on metastatic squamous-cell carcinoma of the head and neck.

Do Life-sustaining Treatment Orders Match Patient and Surrogate Preferences? The Role of POLST.

BACKGROUND: It is essential to high-quality medical care that life-sustaining treatment orders match the current, values-based preferences of patients or their surrogate decision-makers. It is unknown whether concordance between orders and current preferences is higher when a POLST form is used compared to standard documentation practices. OBJECTIVE: To assess concordance between existing orders and current preferences for nursing facility residents with and without POLST forms. DESIGN: Chart review and interviews. SETTING: Forty Indiana nursing facilities (29 where POLST is used and 11 where POLST is not in use). PARTICIPANTS: One hundred sixty-one residents able to provide consent and 197 surrogate decision-makers of incapacitated residents with and without POLST forms. MAIN MEASUREMENTS: Concordance was measured by comparing life-sustaining treatment orders in the medical record (e.g., orders about resuscitation, intubation, and hospitalization) with current preferences. Concordance was analyzed using population-averaged binary logistic regression. Inverse probability weighting techniques were used to account for non-response. We hypothesized that concordance would be higher in residents with POLST (n = 275) in comparison to residents without POLST (n = 83). KEY RESULTS: Concordance was higher for residents with POLST than without POLST (59.3% versus 34.9%). In a model adjusted for resident, surrogate, and facility characteristics, the odds were 3.05 times higher that residents with POLST had orders for life-sustaining treatment match current preferences in comparison to residents without POLST (OR 3.05 95% CI 1.67-5.58, p < 0.001). No other variables were significantly associated with concordance. CONCLUSIONS: Nursing facility residents with POLST are significantly more likely than residents without POLST to have concordance between orders in their medical records and current preferences for life-sustaining treatments, increasing the likelihood that their treatment preferences will be known and honored. However, findings indicate further systems change and clinical training are needed to improve POLST concordance.

Nonparametric analysis of nonhomogeneous multistate processes with clustered observations.

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.

On shared gamma-frailty conditional Markov model for semicompeting risks data.

Semicompeting risks data are a mixture of competing risks data and progressive state data. This type of data occurs when a nonterminal event is subject to truncation by a well-defined terminal event, but not vice versa. The shared gamma-frailty conditional Markov model (GFCMM) has been used to analyze semicompeting risks data because of its flexibility. There are two versions of this model: the restricted and the unrestricted model. Maximum likelihood estimation methodology has been proposed in the literature. However, we found through numerical experiments that the unrestricted model sometimes yields nonparametrically biased estimation. In this article, we provide a practical guideline for using the GFCMM in the analysis of semicompeting risk data that includes: (a) a score test to assess if the restricted model, which does not exhibit estimation problems, is reasonable under a proportional hazards assumption, and (b) a graphical illustration to justify whether the unrestricted model yields nonparametric estimation with substantial bias for cases where the test provides a statistical significant result against the restricted model. This guideline was applied to the Indianapolis-Ibadan Dementia Project data as an illustration to explore how dementia occurrence changes mortality risk.

Comparison of nonlinear curves and surfaces.

Estimation of nonlinear curves and surfaces has long been the focus of semiparametric and nonparametric regression analysis. What has been less studied is the comparison of nonlinear functions. In lower-dimensional situations, inference typically involves comparisons of curves and surfaces. The existing comparative procedures are subject to various limitations, and few computational tools have been made available for off-the-shelf use. To address these limitations, two modified testing procedures for nonlinear curve and surface comparisons are proposed. The proposed computational tools are implemented in an R package, with a syntax similar to that of the commonly used model fitting packages. An R Shiny application is provided with an interactive interface for analysts who do not use R. The new tests are consistent against fixed alternative hypotheses. Theoretical details are presented in an appendix. Operating characteristics of the proposed tests are assessed against the existing methods. Applications of the methods are illustrated through real data examples.

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 incidence functions.

Semiparametric regression and risk prediction with competing risks data under missing cause of failure.

The cause of failure in cohort studies that involve competing risks is frequently incompletely observed. To address this, several methods have been proposed for the semiparametric proportional cause-specific hazards model under a missing at random assumption. However, these proposals provide inference for the regression coefficients only, and do not consider the infinite dimensional parameters, such as the covariate-specific cumulative incidence function. Nevertheless, the latter quantity is essential for risk prediction in modern medicine. In this paper we propose a unified framework for inference about both the regression coefficients of the proportional cause-specific hazards model and the covariate-specific cumulative incidence functions under missing at random cause of failure. Our approach is based on a novel computationally efficient maximum pseudo-partial-likelihood estimation method for the semiparametric proportional cause-specific hazards model. Using modern empirical process theory we derive the asymptotic properties of the proposed estimators for the regression coefficients and the covariate-specific cumulative incidence functions, and provide methodology for constructing simultaneous confidence bands for the latter. Simulation studies show that our estimators perform well even in the presence of a large fraction of missing cause of failures, and that the regression coefficient estimator can be substantially more efficient compared to the previously proposed augmented inverse probability weighting estimator. The method is applied using data from an HIV cohort study and a bladder cancer clinical trial.

Nonparametric tests for transition probabilities in nonhomogeneous Markov processes.

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 L 2-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 L 2-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.

Nonparametric estimation of the cumulative incidence function under outcome misclassification using external validation data.

Misclassification of outcomes or event types is common in health sciences research and can lead to serious bias when estimating the cumulative incidence functions in settings with competing risks. Recent work has shown how to estimate nonparametric cumulative incidence functions in the presence of nondifferential outcome misclassification when the misclassification probabilities are known. Here, we extend this approach to account for misclassification that is differential with respect to important predictors of the outcome using misclassification probabilities estimated from external validation data. Moreover, we propose a bootstrap approach in which the observations from both the main study data and the external validation study are resampled to allow the uncertainty in the misclassification probabilities to propagate through the analysis into the final confidence intervals, ensuring appropriate confidence interval coverage probabilities. The proposed estimator is shown to be uniformly consistent and simulation studies indicate that both the estimator and the standard error estimation approach perform well in finite samples. The methodology is applied to estimate the cumulative incidence of death and disengagement from HIV care in a large cohort of HIV infected individuals in sub-Saharan Africa, where a significant death underreporting issue leads to outcome misclassification. This analysis uses external validation data from a separate study conducted in the same country.

NONPARAMETRIC INFERENCE FOR MARKOV PROCESSES WITH MISSING ABSORBING STATE.

This paper deals with the issue of nonparametric estimation of the transition probability matrix of a non-homogeneous Markov process with finite state space and partially observed absorbing state. We impose a missing at random assumption and propose a computationally efficient nonparametric maximum pseudolikelihood estimator (NPMPLE). The estimator depends on a parametric model that is used to estimate the probability of each absorbing state for the missing observations based, potentially, on auxiliary data. For the latter model we propose a formal goodness-of-fit test based on a residual process. Using modern empirical process theory we show that the estimator is uniformly consistent and converges weakly to a tight mean-zero Gaussian random field. We also provide methodology for simultaneous confidence band construction. Simulation studies show that the NPMPLE works well with small sample sizes and that it is robust against some degree of misspecification of the parametric model for the missing absorbing states. The method is illustrated using HIV data from sub-Saharan Africa to estimate the transition probabilities of death and disengagement from HIV care.

A Multisite Health System Survey to Assess Organizational Context to Support Evidence-Based Practice.

BACKGROUND: Implementation and sustainability of a culture of evidence-based practice (EBP) require a systematic approach. A baseline assessment of the organizational context can inform implementation efforts. AIMS: To examine organizational hospital context and provider characteristics associated with EBP readiness and to describe EBP context across hospitals. METHODS: A nonexperimental descriptive correlational design was used to conduct a web-based survey of direct-care registered nurses (N = 701) and nurse managers (N = 94) across a large Midwestern multisite healthcare system using the Alberta Context Tool (ACT). RESULTS: Many significant relationships existed among nurse characteristics and ACT domains, including age (lower age had higher Leadership, Evaluation, and Formal Interactions), education (graduate education had lower Social Capital than a bachelor's or associate degree), role (direct-care nurses had lower Culture than managers and lower Social Capital), and work status (full-time employees had lower Evaluation and Social Capital). EBP context across type of hospitals is similar, with marginal differences in Social Capital and Organizational Slack (higher in critical access hospitals). LINKING EVIDENCE TO ACTION: Assessing organizational context to support EBP is the first step in developing and enhancing a sustainable culture of inquiry. The ACT has been tested across countries, settings, and healthcare disciplines to measure perception of readiness of the practice environment toward EBP. Optimal organizational context is essential to support EBP and sustain the use of evidence in professional nursing practice. Nursing leaders can use baseline assessment information to identify strengths and opportunities to enhance EBP implementation. Enhancing organizational context across nurse characteristics (e.g., age, role, and work status) to acknowledge nurses' contributions, balance nurses' personal and work life, enhance connectedness, and support work culture is beneficial. Fostering development of Social Capital in nurses is needed to influence EBP readiness. A systematic and standardized approach to foster EBP across health systems is key to successful implementation.

Current Smokers' Preferences for Receiving Cessation Information in a Lung Cancer Screening Setting.

The purpose of this study was to identify current smokers' communication format preferences for receiving smoking cessation information in a lung cancer screening setting. A cross-sectional correlational design using survey methodology with 159 screening-eligible current smokers was the method used. Data was dichotomized (digital versus traditional preference) and analyzed using Pearson's chi-squared test, Mann-Whitney U test, and logistic regression. Race was a statistically significant predictor with White participants having four times greater odds of reporting preference for a digital format for receiving smoking cessation information such as social media and/or supportive text messages (OR: 4.06; p = 0.004). Lung cancer screening is a new venue where current long-term smokers can be offered information about smoking cessation while they are engaging in a health-promoting behavior and potentially more likely to contemplate quitting. It is important to consider the communication format preference of current smokers to support cessation uptake. This study is the first to examine communication format preference of current smokers in the context of the lung cancer screening venue. Key differences noted by race support the need for further research examining multiple formats of communication with efforts to maximize options in the cancer screening setting.

Plasma-derived proteomic biomarkers in human leukocyte antigen-haploidentical or human leukocyte antigen-matched bone marrow transplantation using post-transplantation cyclophosphamide.

Recent studies have suggested that plasma-derived proteins may be potential biomarkers relevant for graft-versus-host disease and/or non-relapse mortality occurring after allogeneic blood or marrow transplantation. However, none of these putative biomarkers have been assessed in patients treated either with human leukocyte antigen-haploidentical blood or marrow transplantation or with post-transplantation cyclophosphamide, which has been repeatedly associated with low rates of severe acute graft-versus-host disease, chronic graft-versus-host disease, and non-relapse mortality. We explored whether seven of these plasma-derived proteins, as measured by enzyme-linked immunosorbent assays, were predictive of clinical outcomes in post-transplantation cyclophosphamide-treated patients using plasma samples collected at serial predetermined timepoints from patients treated on prospective clinical studies of human leukocyte antigen-haploidentical (n=58; clinicaltrials.gov Identifier: 00796562) or human leukocyte antigen-matched-related or -unrelated (n=100; clinicaltrials.gov Identifiers: 00134017 and 00809276) T-cell-replete bone marrow transplantation. Day 30 levels of interleukin-2 receptor α, tumor necrosis factor receptor 1, serum STimulation-2 (IL1RL1 gene product), and regenerating islet-derived 3-α all had high areas under the curve of 0.74-0.97 for predicting non-relapse mortality occurrence by 3 months post-transplant in both the human leukocyte antigen-matched and human leukocyte antigen-haploidentical cohorts. In both cohorts, all four of these proteins were also predictive of subsequent non-relapse mortality occurring by 6, 9, or 12 months post-transplant and were significantly associated with non-relapse mortality in univariable analyses. Furthermore, day 30 elevations of interleukin-2 receptor α were associated with grade II-IV and III-IV acute graft-versus-host disease occurring after day 30 in both cohorts. These data confirm that plasma-derived proteins previously assessed in other transplantation platforms appear to retain prognostic and predictive utility in patients treated with post-transplantation cyclophosphamide.

Semiparametric regression on cumulative incidence function with interval-censored competing risks data.

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. Copyright © 2017 John Wiley & Sons, Ltd.