Retrieved-Dropout-Based multiple imputation for time-to-event data in cardiovascular outcome trials.

Recently, retrieved-dropout-based multiple imputation has been used in some therapeutic areas to address the treatment policy estimand, mostly for continuous endpoints. In this approach, data from subjects who discontinued study treatment but remained in study were used to construct a model for multiple imputation for the missing data of subjects in the same treatment arm who discontinued study. We extend this approach to time-to-event endpoints and provide a practical guide for its implementation. We use a cardiovascular outcome trial dataset to illustrate the method and compare the results with those from Cox proportional hazard and reference-based multiple imputation methods.

Semiparametric analysis of a generalized linear model with multiple covariates subject to detection limits.

Studies on the health effects of environmental mixtures face the challenge of limit of detection (LOD) in multiple correlated exposure measurements. Conventional approaches to deal with covariates subject to LOD, including complete-case analysis, substitution methods, and parametric modeling of covariate distribution, are feasible but may result in efficiency loss or bias. With a single covariate subject to LOD, a flexible semiparametric accelerated failure time (AFT) model to accommodate censored measurements has been proposed. We generalize this approach by considering a multivariate AFT model for the multiple correlated covariates subject to LOD and a generalized linear model for the outcome. A two-stage procedure based on semiparametric pseudo-likelihood is proposed for estimating the effects of these covariates on health outcome. Consistency and asymptotic normality of the estimators are derived for an arbitrary fixed dimension of covariates. Simulations studies demonstrate good large sample performance of the proposed methods vs conventional methods in realistic scenarios. We illustrate the practical utility of the proposed method with the LIFECODES birth cohort data, where we compare our approach to existing approaches in an analysis of multiple urinary trace metals in association with oxidative stress in pregnant women.

Cumulative incidence regression for dynamic treatment regimens.

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

Quantile association regression on bivariate survival data.

The association between two event times is of scientific importance in various fields. Due to population heterogeneity, it is desirable to examine the degree to which local association depends on different characteristics of the population. Here we adopt a novel quantile-based local association measure and propose a conditional quantile association regression model to allow covariate effects on local association of two survival times. Estimating equations for the quantile association coefficients are constructed based on the relationship between this quantile association measure and the conditional copula. Asymptotic properties for the resulting estimators are rigorously derived, and induced smoothing is used to obtain the covariance matrix. Through simulations we demonstrate the good practical performance of the proposed inference procedures. An application to age-related macular degeneration (AMD) data reals interesting varying effects of the baseline AMD severity score on the local association between two AMD progression times.

L'association entre les temps jusqu'à deux événements revêt une importance scientifique dans plusieurs domaines. Il est intéressant de pouvoir observer à quel point leur degré d'association local dépend de différentes caractéristiques d'une population lorsque celle-ci exhibe de l'hétérogénéité. Les auteures adoptent une nouvelle mesure d'association locale basée sur les quantiles et proposent un modèle conditionnel de régression quantile permettant aux covariables d'avoir un effet sur l'association locale de deux temps de survie. Elles construisent les équations d'estimation pour les coefficients du modèle à partir de la relation entre cette mesure d'association quantile et la copule conditionnelle. Elles dérivent rigoureusement les propriétés asymptotiques des estimateurs résultants et utilisent un lissage induit afin d'obtenir la matrice de covariance. À l'aide de simulations, les auteures démontrent les bonne performances pratiques des procédures d'inférence proposées. Elles présentent une application à des données de dégénérescence maculaire liées à l'âge (DMA) qui montrent des effets variables du score de sévérité de base de la DMA sur l'association locale entre deux temps de progression de la DMA.

Clinical Course among Cases of Acute Liver Failure of Indeterminate Diagnosis.

OBJECTIVE: To investigate the heterogeneity in clinical course among those with pediatric acute liver failure (PALF) of indeterminate disease etiology. STUDY DESIGN: We studied participants enrolled in the PALF registry study with indeterminate final diagnosis. Growth mixture modeling was used to analyze participants' international normalized ratio, total bilirubin, and hepatic encephalopathy trajectories in the first 7 days following enrollment. Participants with at least 3 values for 1 or more of the measurements were included. We examined the association between the resulting latent subgroup classification with participants' characteristics and disease outcomes. Data from participants with PALF of specified etiologies were used to investigate the potential diagnostic value of the latent subgroups. RESULTS: In this sample of 380 participants with indeterminate final diagnosis, 115 (30%) experienced mild and quickly improving disease trajectories and another 48 (13%) started with severe disease but improved by day 7. The majority of participants (216, 57%) had disease trajectories that worsened over time. The identified patterns of disease trajectories are predictive of outcome (P < .001). The trajectory patterns are associated with the underlying disease etiology (P < .001) for the 488 participants with PALF of specified etiologies. CONCLUSIONS: The clinical courses of participants with PALF of indeterminate disease etiology exhibit distinct trajectory patterns, which have important prognostic and potentially diagnostic value.

Statistical power considerations in the use of win ratio in cardiovascular outcome trials.

BACKGROUND: In cardiovascular outcome trials, the win ratio (WR) method models the composite endpoint under a hierarchical structure to account for clinical priorities. It also can be applied to both survival and nonsurvival outcomes. METHODS: In this article, we assess the performance of the WR method via extensive simulation studies and real data analyses and discuss power considerations of the method with respect to hierarchical order, variable type, magnitude of treatment effect, and event rates when applied to clinical studies. RESULTS AND CONCLUSION: In the hierarchy of the WR method, the first-ordered component (e.g., death) plays a dominant role in statistical power, especially when that component has a large treatment effect and a high event rate. This is in contrast with the score test of the Cox proportional hazards model in which the power is more likely affected by the nonfatal events that are usually observed earlier. Furthermore, when adding an additional component to the composite endpoint, the performance of the WR method varies depending on the treatment effect, event rate, and hierarchical position of the component. If the additional component has a relatively smaller or no treatment effect, the statistical power will decrease; if the additional component has a relatively larger treatment effect and higher event rate, the statistical power will increase. When adding a nonsurvival continuous outcome (e.g., 6-min walk distance) with even a tiny treatment effect, the statistical power could dramatically increase.

Making Residents Part of the Safety Culture: Improving Error Reporting and Reducing Harms.

OBJECTIVES: Reporting medical errors is a focus of the patient safety movement. As frontline physicians, residents are optimally positioned to recognize errors and flaws in systems of care. Previous work highlights the difficulty of engaging residents in identification and/or reduction of medical errors and in integrating these trainees into their institutions' cultures of safety. METHODS: The authors describe the implementation of a longitudinal, discipline-based, multifaceted curriculum to enhance the reporting of errors by pediatric residents at Children's Hospital of Pittsburgh of University of Pittsburgh Medical Center. The key elements of this curriculum included providing the necessary education to identify medical errors with an emphasis on systems-based causes, modeling of error reporting by faculty, and integrating error reporting and discussion into the residents' daily activities. The authors tracked monthly error reporting rates by residents and other health care professionals, in addition to serious harm event rates at the institution. RESULTS: The interventions resulted in significant increases in error reports filed by residents, from 3.6 to 37.8 per month over 4 years (P < 0.0001). This increase in resident error reporting correlated with a decline in serious harm events, from 15.0 to 8.1 per month over 4 years (P = 0.01). CONCLUSIONS: Integrating patient safety into the everyday resident responsibilities encourages frequent reporting and discussion of medical errors and leads to improvements in patient care. Multiple simultaneous interventions are essential to making residents part of the safety culture of their training hospitals.

How Successful Are Residents and Fellows at Quality Improvement?

BACKGROUND: Nationally, there is an expectation that residents and fellows participate in quality improvement (QI), preferably interprofessionally. Hospitals and educators invest time and resources in projects, but little is known about success rates or what fosters success. PURPOSE: To understand what proportion of trainee QI projects were successful and whether there were predictors of success. METHODS: We examined resident and fellow QI projects in an integrated healthcare system that supports diverse training programs in multiple hospitals over 2 years. All projects were reviewed to determine whether they represented actual QI. Projects determined as QI were considered completed or successful based on QI project sponsor self-report. Multiple characteristics were compared between successful and unsuccessful projects. RESULTS: Trainees submitted 258 proposals, of which 106 (41.1%) represented actual QI. Non-QI projects predominantly represented needs assessments or retrospective data analyses. Seventy-six percent (81/106) of study sponsors completed surveys about their projects. Less than 25% of projects (59/258) represented actual QI and were successful. Project category was predictive of success, specifically those aimed at preventive care or education. CONCLUSION: Less than a quarter of trainee QI projects represent successful QI. IMPLICATIONS: Hospitals and training programs should identify interventions to improve trainee QI experience.

Resident Case Volume Correlates with Clinical Performance: Finding the Sweet Spot.

RATIONALE AND OBJECTIVES: To determine whether the total number of studies interpreted during radiology residency correlates with clinical performance as measured by objective criteria. MATERIALS AND METHODS: We performed a retrospective cohort study of three graduating classes of radiology residents from a single residency program between the years 2015-2017. The total number of studies interpreted by each resident during residency was tracked. Clinical performance was determined by tracking an individual resident's major discordance rate. A major discordance was recorded when there was a difference between the preliminary resident interpretation and final attending interpretation that could immediately impact patient care. Accreditation council for graduate medical education milestones at the completion of residency, Diagnostic radiology in-training scores in the third year, and score from the American board of radiology core exam were also tabulated. Pearson correlation coefficients and polynomial regression analysis were used to identify correlations between the total number of interpreted films and clinical, test, and milestone performance. RESULTS: Thirty-seven residents interpreted a mean of 12,709 studies (range 8898-19,818; standard deviation [SD] 2351.9) in residency with a mean major discordance rate of 1.1% (range 0.34%-2.54%; stand dev 0.49%). There was a nonlinear correlation between total number of interpreted films and performance. As the number of interpreted films increased to approximately 16,000, clinical performance (p = 0.004) and test performance (p = 0.01) improved, but volumes over 16,000 correlated with worse performance. CONCLUSION: The total number of studies interpreted during radiology training correlates with performance. Residencies should endeavor to find the "sweet spot": the amount of work that maximizes clinical exposure and knowledge without overburdening trainees.

Do Residency Selection Factors Predict Radiology Resident Performance?

RATIONALE AND OBJECTIVES: The purpose of our study is to determine what information in medical student residency applications predicts radiology residency success as defined by objective clinical performance data. MATERIALS AND METHODS: We performed a retrospective cohort study of residents who entered our institution's residency program through the National Resident Matching Program as postgraduate year 2 residents and completed the program over the past 2 years. Medical school grades, selection to Alpha Omega Alpha (AOA) Honor Society, United States Medical Licensing Examination (USMLE) scores, publication in peer-reviewed journals, and whether the applicant was from a peer institution were the variables examined. Clinical performance was determined by calculating each resident's cumulative major discordance rate for on-call cases the resident read and gave a preliminary interpretation. A major discordance was defined as a difference between the preliminary resident and the final attending interpretations that could immediately impact the care of the patient. A multivariate logistic regression was performed to determine significant variables. RESULTS: Twenty-seven residents provided preliminary reports on call for 67,145 studies. The mean major discordance rate was 1.08% (range 0.34%-2.54%). Higher USMLE Step 1 scores, publication before residency, and election to AOA Honor Society were all statistically significant predictors of lower major discordance rates (P values 0.01, 0.01, and <0.001, respectively). CONCLUSIONS: Overall resident performance was excellent. There are predictors that help select the better performing residents, namely higher USMLE Step 1 scores, one to two publications during medical school, and election to AOA in the junior year of medical school.

Condition Help: A Patient- and Family-Initiated Rapid Response System.

BACKGROUND: Rapid response teams (RRTs) help in delivering safe, timely care. Typically they are activated by clinicians using specific parameters. Allowing patients and families to activate RRTs is a novel intervention. The University of Pittsburgh Medical Center developed and implemented a patient- and family-initiated rapid response system called Condition Help (CH). METHODS: When the CH system is activated, a patient care liaison or an on-duty administrator meets bedside with the unit charge nurse to address the patient's concerns. In this study, we collected demographic data, call reasons, call designations (safety or nonsafety), and outcome information for all CH calls made during the period January 2012 through June 2015. RESULTS: Two hundred forty patients/family members made 367 CH calls during the study period. Most calls were made by patients (76.8%) rather than family members (21.8%). Of the 240 patients, 43 (18%) made multiple calls; their calls accounted for 46.3% of all calls (170/367). Inadequate pain control was the reason for the call in most cases (48.2%), followed by dissatisfaction with staff (12.5%). The majority of calls involved nonsafety issues (83.4%) rather than safety issues (11.4%). In 41.4% of cases, a change in care was made. CONCLUSIONS: Patient- and family-initiated RRTs are designed to engage patients and families in providing safer care. In the CH system, safety issues are identified, but the majority of calls involve nonsafety issues. Journal of Hospital Medicine 2017;12:157-161.