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.

Parental Engagement in Consent Processes for Enrollment in Biomedical HIV Prevention Trials: Implications for Minor Adolescents' Willingness to Participate

PURPOSE

Minor adolescents are often excluded from HIV prevention clinical trials due to unresolved ethical issues. Their under-representation in research leads to delayed access to new HIV prevention approaches. We examine the relationship between consent procedures, trial features, demographic and social characteristics, and minor adolescents' willingness to participate (WTP) in biomedical HIV prevention research.

METHODS

We recruited 14-17-year-olds at risk of HIV for this quasi-experimental study. Adolescents were randomly assigned to (1) self-consent, (2) adult permission required, or (3) parental permission required and underwent simulated consent procedures for two types of HIV prevention trials. They rated likelihood of participating in each study if offered the opportunity and completed a survey with demographic, social, and behavioral measures.

RESULTS

One hundred and twenty nine adolescents with diverse identities and socioeconomic status enrolled. Among the 58% of participants who identified as lesbian, gay, bisexual, transgender, or queer (LGBTQ), 76% were out to at least one parent/guardian (outness). Mean WTP was 3.6 (of 5; 5 = definitely would participate) across all participants and both trial types. We found no evidence of an association between WTP and consent condition, LGBTQ identity, or outness. However, medical mistrust, communication with parents, and concern about HIV were associated with WTP.

DISCUSSION

Our results suggest adolescents are willing to participate in HIV prevention trials and parental involvement in the consent process may not be the most important deciding factor. However, variation in WTP within consent groups, and variation in other significant variables, underscores the need for individualized approaches to recruitment and consent for these trials.

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.

POLST recall, concordance, and decision quality outcomes among nursing home residents and surrogate decision-makers

BACKGROUND

POLST orders are actionable in an emergency, so it is important that the decisions be of high quality and concordant with current preferences. The goal of this study is to determine the relationship between concordance and decision quality outcomes, including decision satisfaction and decisional conflict, among nursing facility residents and surrogates who recall POLST.

METHODS

We completed structured interviews in 29 nursing facilities with 275 participants who had previously signed a POLST form. This included residents who were still making their own medical decisions (n = 123) and surrogate decision-makers for residents without decisional capacity (n = 152). POLST recall was defined as remembering talking about and/or completing the POLST form previously signed by the participant. Concordance was determined by comparing preferences elicited during a standardized interview with the POLST form on file. Decisional conflict, decision satisfaction, and conversation quality were assessed with standardized tools.

RESULTS

Half of participants (50%) remembered talking about or completing the POLST form, but recall was not associated with the length of time since POLST completion or concordance with existing preferences. In multivariable analyses, there was no association between POLST recall, concordance, and decision quality outcomes, though satisfaction was associated with conversation quality.

CONCLUSIONS

Half of the residents and surrogates in this study recalled the POLST they previously signed. Neither the age of the form nor the ability to recall the POLST conversation should be considered indicators of whether existing POLST orders match current preferences. Findings confirm a relationship between POLST conversation quality and satisfaction, underscoring the importance of POLST completion as a communication process.

COVID-19 Diagnosis and Risk of Death Among Adults With Cancer in Indiana: Retrospective Cohort Study

BACKGROUND

Prior studies, generally conducted at single centers with small sample sizes, found that individuals with cancer experience more severe outcomes due to COVID-19, caused by SARS-CoV-2 infection. Although early examinations revealed greater risk of severe outcomes for patients with cancer, the magnitude of the increased risk remains unclear. Furthermore, prior studies were not typically performed using population-level data, especially those in the United States. Given robust prevention measures (eg, vaccines) are available for populations, examining the increased risk of patients with cancer due to SARS-CoV-2 infection using robust population-level analyses of electronic medical records is warranted.

OBJECTIVE

The aim of this paper is to evaluate the association between SARS-CoV-2 infection and all-cause mortality among recently diagnosed adults with cancer.

METHODS

We conducted a retrospective cohort study of newly diagnosed adults with cancer between January 1, 2019, and December 31, 2020, using electronic health records linked to a statewide SARS-CoV-2 testing database. The primary outcome was all-cause mortality. We used the Kaplan-Meier estimator to estimate survival during the COVID-19 period (January 15, 2020, to December 31, 2020). We further modeled SARS-CoV-2 infection as a time-dependent exposure (immortal time bias) in a multivariable Cox proportional hazards model adjusting for clinical and demographic variables to estimate the hazard ratios (HRs) among newly diagnosed adults with cancer. Sensitivity analyses were conducted using the above methods among individuals with cancer-staging information.

RESULTS

During the study period, 41,924 adults were identified with newly diagnosed cancer, of which 2894 (6.9%) tested positive for SARS-CoV-2. The population consisted of White (n=32,867, 78.4%), Black (n=2671, 6.4%), Hispanic (n=832, 2.0%), and other (n=5554, 13.2%) racial backgrounds, with both male (n=21,354, 50.9%) and female (n=20,570, 49.1%) individuals. In the COVID-19 period analysis, after adjusting for age, sex, race or ethnicity, comorbidities, cancer type, and region, the risk of death increased by 91% (adjusted HR 1.91; 95% CI 1.76-2.09) compared to the pre-COVID-19 period (January 1, 2019, to January 14, 2020) after adjusting for other covariates. In the adjusted time-dependent analysis, SARS-CoV-2 infection was associated with an increase in all-cause mortality (adjusted HR 6.91; 95% CI 6.06-7.89). Mortality increased 2.5 times among adults aged 65 years and older (adjusted HR 2.74; 95% CI 2.26-3.31) compared to adults 18-44 years old, among male (adjusted HR 1.23; 95% CI 1.14-1.32) compared to female individuals, and those with ≥2 chronic conditions (adjusted HR 2.12; 95% CI 1.94-2.31) compared to those with no comorbidities. Risk of mortality was 9% higher in the rural population (adjusted HR 1.09; 95% CI 1.01-1.18) compared to adult urban residents.

CONCLUSIONS

The findings highlight increased risk of death is associated with SARS-CoV-2 infection among patients with a recent diagnosis of cancer. Elevated risk underscores the importance of adhering to social distancing, mask adherence, vaccination, and regular testing among the adult cancer population.

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.

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.

Social, economic, and health effects of the COVID-19 pandemic on adolescents retained in or recently disengaged from HIV care in Kenya

INTRODUCTION

Adolescents living with HIV (ALHIV, ages 10-19) experience complex challenges to adhere to antiretroviral therapy (ART) and remain in care, and may be vulnerable to wide-scale disruptions during the COVID-19 pandemic. We assessed for a range of effects of the pandemic on ALHIV in western Kenya, and whether effects were greater for ALHIV with recent histories of being lost to program (LTP).

METHODS

ALHIV were recruited from an ongoing prospective study at 3 sites in western Kenya. The parent study enrolled participants from February 2019-September 2020, into groups of ALHIV either 1) retained in care or 2) LTP and traced in the community. Phone interviews from July 2020-January 2021 assessed effects of the pandemic on financial and food security, healthcare access and behaviors, and mental health. Responses were compared among the parent study groups.

RESULTS

Phone surveys were completed with 334 ALHIV or their caregivers, including 275/308 (89.3%) in the retained group and 59/70 (84.3%) among those LTP at initial enrollment. During the pandemic, a greater proportion of LTP adolescents were no longer engaged in school (45.8% vs. 36.4%, p = 0.017). Over a third (120, 35.9%) of adolescents reported lost income for someone they relied on. In total, 135 (40.4%) did not have enough food either some (121, 36.2%) or most (14, 4.2%) of the time. More LTP adolescents (4/59, 6.8% vs. 2/275, 0.7%, p = 0.010) reported increased difficulties refilling ART. Adolescent PHQ-2 and GAD-2 scores were ≥3 for 5.6% and 5.2%, respectively.

CONCLUSIONS

The COVID-19 pandemic has had devastating socioeconomic effects for Kenyan ALHIV and their households. ALHIV with recent care disengagement may be especially vulnerable. Meanwhile, sustained ART access and adherence potentially signal resilience and strengths of ALHIV and their care programs. Findings from this survey indicate the critical need for support to ALHIV during this crisis.

Factors associated with concordance between POLST orders and current treatment preferences

BACKGROUND

POLST is widely used to document the treatment preferences of nursing facility residents as orders, but it is unknown how well previously completed POLST orders reflect current preferences (concordance) and what factors are associated with concordance.

OBJECTIVES

To describe POLST preference concordance and identify factors associated with concordance.

DESIGN

Chart reviews to document existing POLST orders and interviews to elicit current treatment preferences.

SETTING

POLST-using nursing facilities (n = 29) in Indiana.

PARTICIPANTS

Nursing facility residents (n = 123) and surrogates of residents without decisional capacity (n = 152).

MEASUREMENTS

Concordance was determined by comparing existing POLST orders for resuscitation, medical interventions, and artificial nutrition with current treatment preferences. Comfort-focused POLSTs contained orders for do not resuscitate, comfort measures, and no artificial nutrition.

RESULTS

Overall, 55.7% (123/221) of residents and 44.7% (152/340) of surrogates participated (total n = 275). POLST concordance was 44%, but concordance was higher for comfort-focused POLSTs (68%) than for non-comfort-focused POLSTs (27%) (p < 0.001). In the unadjusted analysis, increasing resident age (OR 1.04, 95% CI 1.01-1.07, p < 0.01), better cognitive functioning (OR 1.07, 95% CI 1.02-1.13, p < 0.01), surrogate as the decision-maker (OR 2.87, OR 1.73-4.75, p < 0.001), and comfort-focused POLSTs (OR 6.01, 95% CI 3.29-11.00, p < 0.01) were associated with concordance. In the adjusted multivariable model, only having an existing comfort-focused POLST was associated with higher odds of POLST concordance (OR 5.28, 95% CI 2.59-10.73, p < 0.01).

CONCLUSIONS

Less than half of all POLST forms were concordant with current preferences, but POLST was over five times as likely to be concordant when orders reflected preferences for comfort-focused care. Findings suggest a clear need to improve the quality of POLST use in nursing facilities and focus its use among residents with stable, comfort-focused preferences.

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.

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.

A semiparametric method for the analysis of outcomes during a gap in HIV care under incomplete outcome ascertainment

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.

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.

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 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.

Development and Testing of the Dysmenorrhea Symptom Interference (DSI) Scale

Dysmenorrhea affects most reproductive-age women and increases the risk of future pain. To evaluate dysmenorrhea interventions, validated outcome measures are needed. In this two-phase study, we developed and tested the dysmenorrhea symptom interference scale. During the scale-development phase (n = 30), we created a nine-item scale based on qualitative data from cognitive interviews. During the scale-testing phase (n = 686), we evaluated reliability, validity, and responsiveness to change. The scale measures how dysmenorrhea symptoms interfere with physical, mental, and social activities. Internal consistency was strong with Cronbach's α > 0.9. Test-retest reliability was acceptable (r = 0.8). The scale showed satisfactory content validity, construct validity (supported by confirmatory factor analysis), concurrent validity, and responsiveness to change. The minimally important difference was 0.3 points on a scale with a possible total score ranging from 1 to 5. This new psychometrically sound scale can be used in research and clinical practice to facilitate the measurement and management of dysmenorrhea.

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.

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.

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.

Innovative Approaches to Obtain Minors' Consent for Biomedical HIV Prevention Trials: Multi-Site Quasi-Experimental Study of Adolescent and Parent Perspectives

BACKGROUND

Despite the high burden of new HIV infections in minor adolescents, they are often excluded from biomedical HIV prevention trials, largely owing to the ethical complexities of obtaining consent for enrollment. Researchers and ethics regulators have a duty to protect adolescents-as a special category of human subjects, they must have protection that extends beyond those afforded to all human subjects. Typically, additional protection includes parental consent for enrollment. However, parental consent can present a risk of harm for minor adolescents. Research involving minor adolescents indicate that they are unwilling to join biomedical trials for stigmatized health problems, such as HIV, when parental consent is required. This presents a significant barrier to progress in adolescent HIV prevention by creating delays in research and the translation of new scientific evidence generated in biomedical trials in adult populations.

OBJECTIVE

This protocol aims to examine how parental involvement in the consent process affects the acceptability of hypothetical participation in biomedical HIV prevention trials from the perspectives of minor adolescents and parents of minor adolescents.

METHODS

In this protocol, we use a quasi-experimental design that involves a simulated consent process for 2 different HIV prevention trials. The first trial is modeled after an open-label study of the use of tenofovir disoproxil fumarate and emtricitabine as preexposure prophylaxis for HIV. The second trial is modeled after a phase IIa trial of an injectable HIV integrase inhibitor. There are 2 groups in the study-minor adolescents aged 14 to 17 years, inclusive, and parents of minor adolescents in the same age range. The adolescent participants are randomized to 1 of 3 consent conditions with varying degrees of parental involvement. After undergoing a simulated consent process, they rate their willingness to participate (WTP) in each of the 2 trials if offered the opportunity. The primary outcome is WTP, given the consent condition. Parents undergo a similar process but are asked to rate the acceptability of each of the 3 consent conditions. The primary outcome is acceptability of the consent method for enrollment. The secondary outcomes include the following: capacity to consent among both participant groups, the prevalence of medical mistrust, and the effects of the study phase (eg, phase IIa vs the open-label study) and drug administration route (eg, oral vs injection) on WTP (adolescents) and acceptability (parents) of the consent method.

RESULTS

Enrollment began in April 2018 and ended mid-September 2019. Data are being analyzed and dissemination is expected in April 2020.

CONCLUSIONS

The study will provide the needed empirical data about minor adolescents' and parents' perspectives on consent methods for minors. The evidence generated can be used to guide investigators and ethics regulators in the design of consent processes for biomedical HIV prevention trials.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/16509.

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.

Confirmatory factor analysis of the Insomnia Severity Index (ISI) and invariance across race: a pooled analysis of MsFLASH data

OBJECTIVE

Women's sleep at menopause is widely reported to be problematic. The Insomnia Severity Index (ISI) is a commonly used tool for quantifying sleep problems in clinical and research settings, but psychometric properties in postmenopausal women have not been reported. Our study aim was to examine the factor structure of the ISI in a large and diverse sample of midlife women with hot flashes.

METHODS

Baseline data were from 899 women enrolled in one of the three clinical trials using similar entry criteria conducted by the Menopause Strategies Finding Lasting Answers to Symptoms and Health research network. We conducted confirmatory factor analyses for the total sample and within strata defined by race/ethnicity (black and white women).

RESULTS

The ISI had two factors in the total sample. The two-factor structure was consistent across black and white women, with the exception of one item "difficulty falling asleep."

CONCLUSIONS

The ISI in midlife women with hot flashes is composed of two factors that capture dimensions of the insomnia severity and daytime impact. The instrument is a psychometrically sound scale appropriate for use in research and clinical practice to capture the severity and daytime impact of insomnia symptoms in diverse samples of midlife women with hot flashes. An abbreviated screening of two items could be considered to determine if further evaluation is needed of sleep complaints.

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.

A Tool to Assess Patient and Surrogate Knowledge About the POLST (Physician Orders for Life-Sustaining Treatment) Program

CONTEXT

It is especially important that patients are well informed when making high-stakes, preference-sensitive decisions like those on the Physician Orders for Life-Sustaining Treatment (POLST) form. However, there is currently no way to easily evaluate whether patients understand key concepts when making these important decisions.

OBJECTIVES

To develop a POLST knowledge survey.

METHODS

Expert (n = 62) ratings of key POLST facts were used to select items for a POLST knowledge survey. The survey was administered to nursing facility residents (n = 97) and surrogate decision-makers (n = 112). A subset (n = 135) were re-administered the survey after a standardized advance care planning discussion to assess the scale's responsiveness to change.

RESULTS

The 19-item survey demonstrated adequate reliability (α = 0.72.). Residents' scores (x = 11.4, standard deviation 3.3) were significantly lower than surrogate scores (x = 14.7, standard deviation 2.5) (P < 0.001). Scores for both groups increased significantly after administration of a standardized advance care planning discussion (P < 0.001). Although being a surrogate, age, race, education, cognitive functioning, and health literacy were significantly associated with higher POLST Knowledge Survey scores in univariate analyses, only being a surrogate (P < 0.001) and being white (P = 0.028) remained significantly associated with higher scores in multivariate analyses.

CONCLUSION

The 19-item POLST Knowledge Survey demonstrated adequate reliability and responsiveness to change. Findings suggest the survey could be used to identify knowledge deficits and provide targeted education to ensure adequate understanding of key clinical decisions when completing POLST.

Semiparametric competing risks regression under interval censoring using the R package intccr

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.

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.

Study protocol testing toolkit versus usual care for implementation of screening, brief intervention, referral to treatment in hospitals: a phased cluster randomized approach

BACKGROUND

Alarming rates of unhealthy alcohol, non-prescription drug, and tobacco use highlight the preventable health risks of substance abuse and the urgent need to activate clinicians to recognize and treat risky use. Screening, brief intervention, and referral to treatment (SBIRT) is an efficacious and effective processes to identify, reduce and prevent risky use of substances. This paper describes a study protocol testing implementation of a toolkit to enhance use of SBIRT in acute care settings to recognize and address patient risky alcohol, drug, and tobacco use.

METHODS

This study uses a phased cluster randomized mixed method design to test nurse-led implementation of an SBIRT toolkit on one medical-surgical unit at 14 acute care hospitals (critical access, community and academic health centers). Medical surgical units will be randomly assigned to implement the SBIRT toolkit (engagement and communication, assessment, planning, training, and evaluation tools) or a wait-list usual care control group that begins implementation 6 months later. Primary endpoints are documentation of SBIRT delivery in randomly selected electronic medical records at baseline, 6 months and 12 months after group 1 implementation (61 records per unit per time period, N = 2562). Two surveys will be administered to unit nurses: smoking cessation activities will be assessed at baseline and SBIRT use will be assessed on randomly-selected days after implementation. In addition, site coordinators will complete a baseline capacity assessment, an implementation fidelity survey post-implementation, and a structured interview at the end of the study. Multilevel mixed-effects effects logistic and linear models will be used to analyze use of SBIRT and cost outcomes.

DISCUSSION

This study will guide subsequent SBIRT implementation, dissemination, and spread across rural, community and urban healthcare systems throughout the state and beyond. The long-term objective is to activate clinicians to recognize, intervene and refer people with risky substance use to improve health and decrease substance use disorders. Trial registration ClinicalTrials.gov NCT03560076.

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.

Use of the Physician Orders for Scope of Treatment Program in Indiana Nursing Homes

OBJECTIVES

To assess the use of the Indiana Physician Orders for Scope of Treatment (POST) form to record nursing home (NH) resident treatment preferences and associated practices.

DESIGN

Survey.

SETTING

Indiana NHs.

PARTICIPANTS

Staff responsible for advance care planning in 535 NHs.

MEASUREMENTS

Survey about use of the Indiana POST, related policies, and educational activities.

METHODS

NHs were contacted by telephone or email. Nonresponders were sent a brief postcard survey.

RESULTS

Ninety-one percent (n=486) of Indiana NHs participated, and 79% had experience with POST. Of the 65% of NHs that complete POST with residents, 46% reported that half or more residents had a POST form. POST was most often completed at the time of admission (68%). Only 52% of participants were aware of an existing facility policy regarding use of POST; 80% reported general staff education on POST. In the 172 NHs not using POST, reasons for not using it included unfamiliarity with the tool (23%) and lack of facility policies (21%).

CONCLUSION

Almost 3 years after a grassroots campaign to introduce the voluntary Indiana POST program, a majority of NHs were using POST to support resident care. Areas for improvement include creating policies on POST for all NHs, training staff on POST conversations, and considering processes that may enhance the POST conversation, such as finding an optimal time to engage in conversations about treatment preferences other than a potentially rushed admission process.

Observational Study of the Effect of Patient Outreach on Return to Care: The Earlier the Better

BACKGROUND

The burden of HIV remains heaviest in resource-limited settings, where problems of losses to care, silent transfers, gaps in care, and incomplete mortality ascertainment have been recognized.

METHODS

Patients in care at Academic Model Providing Access to Healthcare (AMPATH) clinics from 2001-2011 were included in this retrospective observational study. Patients missing an appointment were traced by trained staff; those found alive were counseled to return to care (RTC). Relative hazards of RTC were estimated among those having a true gap: missing a clinic appointment and confirmed as neither dead nor receiving care elsewhere. Sample-based multiple imputation accounted for missing vital status.

RESULTS

Among 34,522 patients lost to clinic, 15,331 (44.4%) had a true gap per outreach, 2754 (8.0%) were deceased, and 837 (2.4%) had documented transfers. Of 15,600 (45.2%) remaining without active ascertainment, 8762 (56.2%) with later RTC were assumed to have a true gap. Adjusted cause-specific hazard ratios (aHRs) showed early outreach (a ≤8-day window, defined by grid-search approach) had twice the hazard for RTC vs. those without (aHR = 2.06; P < 0.001). HRs for RTC were lower the later the outreach effort after disengagement (aHR = 0.86 per unit increase in time; P < 0.001). Older age, female sex (vs. male), antiretroviral therapy use (vs. none), and HIV status disclosure (vs. none) were also associated with greater likelihood of RTC, and higher enrollment CD4 count with lower likelihood of RTC.

CONCLUSION

Patient outreach efforts have a positive impact on patient RTC, regardless of when undertaken, but particularly soon after the patient misses an appointment.

Symptoms-Based Phenotypes Among Women With Dysmenorrhea: A Latent Class Analysis

Dysmenorrhea is highly prevalent and may increase women's risk for developing other chronic pain conditions. Although it is highly variable, symptom-based dysmenorrhea phenotypes have not been identified. The aims of the study were to identify symptom-based dysmenorrhea phenotypes and examine their relationships with demographic and clinical characteristics. In a cross-sectional study, 762 women with dysmenorrhea rated severity of 14 dysmenorrhea-related symptoms. Using latent class analysis, we identified three distinctive phenotypes. Women in the "mild localized pain" phenotype ( n = 202, 26.51%) had mild abdominal cramps and dull abdominal pain/discomfort. Women in the "severe localized pain" phenotype ( n = 412, 54.07%) had severe abdominal cramps. Women in the "multiple severe symptoms" phenotype ( n = 148, 19.42%) had severe pain at multiple locations and multiple gastrointestinal symptoms. Race, ethnicity, age, and comorbid chronic pain conditions were significantly associated with phenotypes. Identification of these symptom-based phenotypes provides a foundation for research examining genotype-phenotype associations, etiologic mechanisms, and/or variability in treatment responses.

An activated Th17-prone T cell subset involved in chronic graft-versus-host disease sensitive to pharmacological inhibition

Chronic graft-versus-host disease (cGvHD) remains a major complication of allogeneic stem cell transplantation requiring novel therapies. CD146 and CCR5 are expressed by activated T cells and associated with increased T cell migration capacity and Th17 polarization. We performed a multiparametric flow cytometry analysis in a cohort of 40 HSCT patients together with a cGvHD murine model to understand the role of CD146-expressing subsets. We observed an increased frequency of CD146+ CD4 T cells in the 20 patients with active cGvHD with enhanced RORγt expression. This Th17-prone subset was enriched for cells coexpressing CD146 and CCR5 that harbor mixed Th1/Th17 features and were more frequent in cGvHD patients. Utilizing a murine cGvHD model with bronchiolitis obliterans (BO), we observed that donor T cells from CD146-deficient mice versus those from WT mice caused significantly reduced pulmonary cGvHD. Reduced cGvHD was not the result of failed germinal center B cell or T follicular helper cell generation. Instead, CD146-deficient T cells had significantly lower pulmonary macrophage infiltration and T cell CCR5, IL-17, and IFN-γ coexpression, suggesting defective pulmonary end-organ effector mechanisms. We, thus, evaluated the effect of TMP778, a small-molecule RORγt activity inhibitor. TMP778 markedly alleviated cGvHD in murine models similarly to agents targeting the Th17 pathway, such as STAT3 inhibitor or IL-17-blocking antibody. Our data suggest CD146-expressing T cells as a cGvHD biomarker and suggest that targeting the Th17 pathway may represent a promising therapy for cGvHD.

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.

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.

APOE ε4 and Memory Among Patients With Heart Failure

Twenty-three percent to 50% of heart failure (HF) patients have memory loss. Objectives were to (a) characterize major allelic frequency of 2 variants in apolipoprotein ( APOE) gene in HF patients, (b) evaluate differences in memory and serum brain-derived neurotrophic factor (BDNF) levels based on APOE ε4 allele(s), and (c) estimate effect sizes (ESs) and confidence intervals (CIs). In this pilot, 29 HF patients were enrolled and 26 completed. Recall and delayed recall memory were measured at baseline and 12 weeks. Serum was collected at baseline and 8 weeks. Seven (24.1%) patients had APOE ε4 allele. No significant differences were found in recall and delayed recall memory or serum BDNF levels based on APOE ε4 allele. ESs were small to medium; CIs indicated ES precision was small. Future studies are needed to fully understand how genotypic and neuropsychological phenotypic variables influence response to computerized cognitive training.

Facility-Level Factors Influencing Retention of Patients in HIV Care in East Africa

Losses to follow-up (LTFU) remain an important programmatic challenge. While numerous patient-level factors have been associated with LTFU, less is known about facility-level factors. Data from the East African International epidemiologic Databases to Evaluate AIDS (EA-IeDEA) Consortium was used to identify facility-level factors associated with LTFU in Kenya, Tanzania and Uganda. Patients were defined as LTFU if they had no visit within 12 months of the study endpoint for pre-ART patients or 6 months for patients on ART. Adjusting for patient factors, shared frailty proportional hazard models were used to identify the facility-level factors associated with LTFU for the pre- and post-ART periods. Data from 77,362 patients and 29 facilities were analyzed. Median age at enrolment was 36.0 years (Interquartile Range: 30.1, 43.1), 63.9% were women and 58.3% initiated ART. Rates (95% Confidence Interval) of LTFU were 25.1 (24.7–25.6) and 16.7 (16.3–17.2) per 100 person-years in the pre-ART and post-ART periods, respectively. Facility-level factors associated with increased LTFU included secondary-level care, HIV RNA PCR turnaround time >14 days, and no onsite availability of CD4 testing. Increased LTFU was also observed when no nutritional supplements were provided (pre-ART only), when TB patients were treated within the HIV program (pre-ART only), and when the facility was open ≤4 mornings per week (ART only). Our findings suggest that facility-based strategies such as point of care laboratory testing and separate clinic spaces for TB patients may improve retention.

Patient Perceptions of Provider and Hospital Factors Associated With New Medication Communication

This research examined provider and hospital factors associated with patients’ perceptions of how often explanations of new medications were “always” given to them, using Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores. HCAHPS results were obtained for October 2012 to September 2013, from 3,420 hospitals and combined with a Magnet-designated hospital listing. Multiple regression examined correlates of new medication communication, including health care provider factors (perceptions of nurse and physician communication) and health care system factors (magnet designation, hospital ownership, hospital type, availability of emergency services, and survey numbers). Nurse and physician communication was strongly associated with new medication communication ( r = .819, p < .001; r = .722, p < .001, respectively). Multivariable correlates included nurse communication ( p < .001), physician communication ( p < .001), hospital ownership, availability of emergency services, and survey numbers. There was a significant relationship between patients’ perceptions of nurse and physician communication and the explanations they had received about their new medications during hospitalization.

Reliability and Validity of the Greek Version of the Job Content Questionnaire in Greek Health Care Workers

Background

The Job Content Questionnaire (JCQ), which is based on the Demand–Control–Support model, is designed to measure the psychosocial characteristics of the respondent's work, and has been identified to predict health and psychological outcomes. The purpose of this study was to investigate the psychometric properties of this instrument and the subsequent adaptation of its scales to the population of Greek health workers.

Methods

The Greek version of the JCQ was developed by using forward- and back-translation in accordance with the JCQ policy. The reliability and validity of the measure were investigated in a sample of health workers working in a hospital in Athens, Greece. The internal consistency of the scales was examined based on Cronbach α coefficients, and the validity was evaluated subjecting the items of the three main scales of the JCQ (decision latitude, psychological job demands, and social support) to exploratory and confirmatory factor analysis.

Results

The reliability of the scales was found to be acceptable for all the scales, except for the skill discretion subscale. Confirmatory factor analysis confirmed a slightly modified version of the original construct including several items to more than one factor.

Conclusion

Our findings suggest that the Greek JCQ is reliable and valid for investigating psychosocial job characteristics among Greek health workers.

Impact of and Correction for Outcome Misclassification in Cumulative Incidence Estimation

Cohort studies and clinical trials may involve multiple events. When occurrence of one of these events prevents the observance of another, the situation is called “competing risks”. A useful measure in such studies is the cumulative incidence of an event, which is useful in evaluating interventions or assessing disease prognosis. When outcomes in such studies are subject to misclassification, the resulting cumulative incidence estimates may be biased. In this work, we study the mechanism of bias in cumulative incidence estimation due to outcome misclassification. We show that even moderate levels of misclassification can lead to seriously biased estimates in a frequently unpredictable manner. We propose an easy to use estimator for correcting this bias that is uniformly consistent. Extensive simulations suggest that this method leads to unbiased estimates in practical settings. The proposed method is useful, both in settings where misclassification probabilities are known by historical data or can be estimated by other means, and for performing sensitivity analyses when the misclassification probabilities are not precisely known.