A simulation study of the performance of statistical models for count outcomes with excessive zeros.

Background: Outcome measures that are count variables with excessive zeros are common in health behaviors research. Examples include the number of standard drinks consumed or alcohol-related problems experienced over time. There is a lack of empirical data about the relative performance of prevailing statistical models for assessing the efficacy of interventions when outcomes are zero-inflated, particularly compared with recently developed marginalized count regression approaches for such data.

A bias correction method in meta-analysis of randomized clinical trials with no adjustments for zero-inflated outcomes.

Many clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive zeros. However, the zero-inflated nature of such outcomes is sometimes ignored in analyses of clinical trials. This leads to biased estimates of study-level intervention effect and, consequently, a biased estimate of the overall intervention effect in a meta-analysis.

Multivariate survival analysis in big data: A divide-and-combine approach.

Multivariate failure time data are frequently analyzed using the marginal proportional hazards models and the frailty models. When the sample size is extraordinarily large, using either approach could face computational challenges. In this paper, we focus on the marginal model approach and propose a divide-and-combine method to analyze large-scale multivariate failure time data.

A CD-based mapping method for combining multiple related parameters from heterogeneous intervention trials.

Effect size can differ as a function of the elapsed time since treatment or as a function of other key covariates, such as sex or age. In evidence synthesis, a better understanding of the precise conditions under which treatment does work or does not work well has been highly valued. With increasingly accessible individual patient or participant data (IPD), more precise and informative inference can be within our reach.

Trajectories and predictors of response in youth anxiety CBT: Integrative data analysis.

Objective: Integrative data analysis was used to combine existing data from nine trials of cognitive-behavioral therapy (CBT) for anxious youth ( = 832) and identify trajectories of symptom change and predictors of trajectories.

Method: Youth- and parent-reported anxiety symptoms were combined using item-response theory models. Growth mixture modeling assessed for trajectories of treatment response across pre-, mid-, and posttreatment and 1-year follow-up.

Exact inference on the random-effects model for meta-analyses with few studies.

We describe an exact, unconditional, non-randomized procedure for producing confidence intervals for the grand mean in a normal-normal random effects meta-analysis. The procedure targets meta-analyses based on too few primary studies, , say, to allow for the conventional asymptotic estimators, e.g.

Meta-analysis framework for exact inferences with application to the analysis of rare events.

The usefulness of meta-analysis has been recognized in the evaluation of drug safety, as a single trial usually yields few adverse events and offers limited information. For rare events, conventional meta-analysis methods may yield an invalid inference, as they often rely on large sample theories and require empirical corrections for zero events. These problems motivate research in developing exact methods, including Tian et al.

Multivariate Meta-Analysis of Heterogeneous Studies Using Only Summary Statistics: Efficiency and Robustness.

Meta-analysis has been widely used to synthesize evidence from multiple studies for common hypotheses or parameters of interest. However, it has not yet been fully developed for incorporating heterogeneous studies, which arise often in applications due to different study designs, populations or outcomes. For heterogeneous studies, the parameter of interest may not be estimable for certain studies, and in such a case, these studies are typically excluded from conventional meta-analysis.

Mediators of exposure therapy for youth obsessive-compulsive disorder: specificity and temporal sequence of client and treatment factors.

Objective: Behavioral engagement and cognitive coping have been hypothesized to mediate effectiveness of exposure-based therapies. Identifying which specific child factors mediate successful therapy and which therapist factors facilitate change can help make our evidence-based treatments more efficient and robust. The current study examines the specificity and temporal sequence of relations among hypothesized client and therapist mediators in exposure therapy for pediatric Obsessive Compulsive Disorder (OCD).

Temporal relationship between vitamin D status and parathyroid hormone in the United States.

Background: Interpretation of parathyroid hormone (iPTH) requires knowledge of vitamin D status that is influenced by season.

Objective: Characterize the temporal relationship between 25-hydroxyvitamin D3 levels [25(OH)D3] and intact iPTH for several seasons, by gender and latitude in the U.S.

Exact meta-analysis approach for discrete data and its application to 2 × 2 tables with rare events.

This paper proposes a general exact meta-analysis approach for synthesizing inferences from multiple studies of discrete data. The approach combines the (also known as ) associated with the exact tests from individual studies. It encompasses a broad class of exact meta-analysis methods, as it permits broad choices for the combining elements, such as tests used in individual studies, and any parameter of interest.

Efficient network meta-analysis: a confidence distribution approach.

Network meta-analysis synthesizes several studies of multiple treatment comparisons to simultaneously provide inference for all treatments in the network. It can often strengthen inference on pairwise comparisons by borrowing evidence from other comparisons in the network. Current network meta-analysis approaches are derived from either conventional pairwise meta-analysis or hierarchical Bayesian methods.

Nurses' practice environments, error interception practices, and inpatient medication errors.

Purpose: Medication errors remain a threat to patient safety. Therefore, the purpose of this study was to determine the relationships among characteristics of the nursing practice environment, nurse staffing levels, nurses' error interception practices, and rates of nonintercepted medication errors in acute care hospitals.

Design: This study, using a nonexperimental design, was conducted in a sample of 82 medical-surgical units recruited from 14 U.

A note on dichotomization of continuous response variable in the presence of contamination and model misspecification.

The purpose of this note is to raise awareness of the complexity of the practice involving dichotomization. It is well known that the regular regression models are effective tools for analyzing Gaussian-type response variables, and researchers are often told that it is a 'bad idea' to practice dichotomization if continuous measurements are available. We demonstrate through special cases, however, that there is another side of the story if the response variable is contaminated.

A latent model to detect multiple clusters of varying sizes.

This article develops a latent model and likelihood-based inference to detect temporal clustering of events. The model mimics typical processes generating the observed data. We apply model selection techniques to determine the number of clusters, and develop likelihood inference and a Monte Carlo expectation-maximization algorithm to estimate model parameters, detect clusters, and identify cluster locations.

Patient complexity in quality comparisons for glycemic control: an observational study.

Background: Patient complexity is not incorporated into quality of care comparisons for glycemic control. We developed a method to adjust hemoglobin A1c levels for patient characteristics that reflect complexity, and examined the effect of using adjusted A1c values on quality comparisons.

Methods: This cross-sectional observational study used 1999 national VA (US Department of Veterans Affairs) pharmacy, inpatient and outpatient utilization, and laboratory data on diabetic veterans.

Semiparametric Analysis of Heterogeneous Data Using Varying-Scale Generalized Linear Models.

This article describes a class of heteroscedastic generalized linear regression models in which a subset of the regression parameters are rescaled nonparametrically, and develops efficient semiparametric inferences for the parametric components of the models. Such models provide a means to adapt for heterogeneity in the data due to varying exposures, varying levels of aggregation, and so on. The class of models considered includes generalized partially linear models and nonparametrically scaled link function models as special cases.

Air-water exchange of polychlorinated biphenyls in the Delaware River.

The air-water exchange of polychlorinated biphenyls (PCBs) often results in net volatilization, which is thought to be the most important loss process for PCBs in many systems. Previous investigations of the air-water exchange of PCBs have been hampered by difficulties in treatment of the uncertainty in the calculation of air/water fugacity ratios. This work presents a new framework for the treatment of uncertainty, where uncertainty in physical constants is handled differently from random measurement uncertainty associated with random samples, and it further investigates the sorption of PCBs to colloids (dissolved organic carbon).

Assessing quality of diabetes care by measuring longitudinal changes in hemoglobin A1c in the Veterans Health Administration.

Context: A1c levels are widely used to assess quality of diabetes care provided by health care systems. Currently, cross-sectional measures are commonly used for such assessments.

Objective: To study within-patient longitudinal changes in A1c levels at Veterans Health Administration (VHA) facilities as an alternative to cross-sectional measures of quality of diabetes care.

Diabetes healthcare quality report cards: how accurate are the grades?

Objective: To evaluate the accuracy and precision of random sampling in identifying healthcare system outliers in diabetes performance measures.

Study Design: Cross-sectional analysis of 79 Veterans Health Administration facilities serving 250 317 patients with diabetes mellitus between October 1, 1999, and September 30, 2000.

Methods: Primary outcome measures were poor glycosylated hemoglobin (A1C) control and good low-density lipoprotein cholesterol (LDL-C) and blood pressure (BP) control.

Seasonal patterns in monthly hemoglobin A1c values.

The purpose of this study was to investigate seasonal variations in population monthly hemoglobin A(1c) (A1c) values over 2 years (from October 1998 to September 2000) among US diabetic veterans. The study cohort included 285,705 veterans with 856,181 A1c tests. The authors calculated the monthly average A1c values for the overall population and for subpopulations defined by age, sex, race, insulin use, and climate regions.

Use of administrative data to risk adjust amputation rates in a national cohort of medicare-enrolled veterans with diabetes.

Background: A reduction in diabetes-related lower extremity amputations is a national health care priority.

Objective: To develop a risk adjustment model for total amputation rates, using claims data.

Research Design: A retrospective longitudinal cohort analysis of veteran clinical users of the Veterans Health Administration (VHA)--Veterans with diabetes who were Medicare nonhealth maintenance organization enrolled in 1997 or 1998.

Transformations of covariates for longitudinal data.

This paper develops a general approach for dealing with parametric transformations of covariates for longitudinal data, where the responses are modeled marginally and generalized estimating equations (GEEs) are used for estimation of regression parameters. We propose an iterative algorithm for obtaining regression and transformation parameters from estimating equations, utilizing existing software for GEE problems. The algorithmic technique is closely related to that used in the Box-Tidwell transformation in classical linear regression, but we develop it under the GEE setting and for more general transformation functions.