A trait-like propensity to experience internalizing symptoms is associated with problem alcohol involvement across adulthood, but not adolescence

There are stable between-person differences in an internalizing "trait," or the propensity to experience symptoms of internalizing disorders, such as social anxiety, generalized anxiety disorder, and depression. Trait internalizing may serve as a marker of heightened risk for problem alcohol outcomes (such as heavier drinking, binge drinking, or alcohol dependence). However, prior research on the association between internalizing symptoms and alcohol outcomes has been largely mixed in adolescence, with more consistent support for an association during adulthood. It may be that trait internalizing is only associated with problem alcohol outcomes in adulthood, after individuals have gained experience with alcohol. Some evidence suggested that these effects may be stronger for women than men. We used data from a community sample (n = 790) interviewed during adolescence (ages 14-16) and again at ages 21, 24, 27, 30, 33, and 39. Using generalized estimating equations, we tested the association between trait internalizing and alcohol outcomes during both adolescence and adulthood, and tested whether adult trait internalizing mediated the association between adolescent trait internalizing and adult alcohol outcomes. Trait internalizing in adulthood (but not adolescence) was associated with more frequent alcohol use, binge drinking and symptoms of alcohol use disorders, and mediated the effects of adolescent trait internalizing on alcohol outcomes. We observed no moderation by gender or change in these associations over time. Understanding the developmental pathways of trait internalizing may provide further insights into preventing the emergence of problem alcohol use behavior during adulthood. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Excess cumulative incidence estimation for matched cohort survival studies

We suggest a regression approach to estimate the excess cumulative incidence function (CIF) when matched data are available. In a competing risk setting, we define the excess risk as the difference between the CIF in the exposed group and the background CIF observed in the unexposed group. We show that the excess risk can be estimated through an extended binomial regression model that actively uses the matched structure of the data, avoiding further estimation of both the exposed and the unexposed CIFs. The method naturally deals with two time scales, age and time since exposure and simplifies how to deal with the left truncation on the age time-scale. The model makes it easy to predict individual excess risk scenarios and allows for a direct interpretation of the covariate effects on the cumulative incidence scale. After introducing the model and some theory to justify the approach, we show via simulations that our model works well in practice. We conclude by applying the excess risk model to data from the ALiCCS study to investigate the excess risk of late events in childhood cancer survivors.

Glucose control and cognitive and physical function in adults 80+ years of age with diabetes

INTRODUCTION

We modeled associations between glycated hemoglobin (HbA1c) levels (<7%, 7% to 8%, and >8%) and cognitive and physical function among adults 80+ years of age with diabetes and determined whether associations differ by frailty, multimorbidity, and disability.

METHODS

A total of 316, adults with diabetes, 80+ years of age, were from the Adult Changes in Thought Study. The Cognitive Abilities Screening Instrument Item Response Theory (CASI-IRT) measured cognition. Short performance-based physical function (sPPF) and gait speed measured physical function. Glycosylated hemoglobin (HbA1c) levels were from clinical measurements. Analyses estimated associations between average HbA1c levels (<7%, 7% to 8%, and >8%) and functional outcomes using linear regressions estimated with generalized estimating equations.

RESULTS

sPPF scores did not differ significantly by HbA1c levels. Gait speed did, but only for non-frail individuals; those with HbA1c >8% were slower (-0.10 m/s [95% CI, -0.16 to -0.04]) compared to those with HbA1c 7% to 8%. The association between HbA1c and CASI-IRT varied with age (interaction P = 0.04). At age 80, for example, relative to people with HbA1c levels of 7% to 8%, CASI-IRT scores were, on average, 0.18 points lower (95% CI, -0.35 to -0.02) for people with HbA1c <7% and 0.22 points lower (95% CI, -0.40 to -0.05) for people with HbA1c >8%. At older ages, these estimated differences were attenuated. Estimated associations were not modified by multimorbidity or disability.

DISCUSSION

Moderate HbA1c levels of 7% to 8% were associated with better cognition in early but not late octogenarians with diabetes. Furthermore, HbA1c >8% was associated with slower gait speed among those without frailty. These results add to an evidence base for determining glucose targets for very old adults with diabetes.

Ultra high-dimensional semiparametric longitudinal data analysis

As ultra high-dimensional longitudinal data are becoming ever more apparent in fields such as public health and bioinformatics, developing flexible methods with a sparse model is of high interest. In this setting, the dimension of the covariates can potentially grow exponentially as exp ( n 1 / 2 ) with respect to the number of clusters n. We consider a flexible semiparametric approach, namely, partially linear single-index models, for ultra high-dimensional longitudinal data. Most importantly, we allow not only the partially linear covariates but also the single-index covariates within the unknown flexible function estimated nonparametrically to be ultra high dimensional. Using penalized generalized estimating equations, this approach can capture correlation within subjects, can perform simultaneous variable selection and estimation with a smoothly clipped absolute deviation penalty, and can capture nonlinearity and potentially some interactions among predictors. We establish asymptotic theory for the estimators including the oracle property in ultra high dimension for both the partially linear and nonparametric components, and we present an efficient algorithm to handle the computational challenges. We show the effectiveness of our method and algorithm via a simulation study and a yeast cell cycle gene expression data.

Tutorial on Biostatistics: Longitudinal Analysis of Correlated Continuous Eye Data

PURPOSE

To describe and demonstrate methods for analyzing longitudinal correlated eye data with a continuous outcome measure.

METHODS

We described fixed effects, mixed effects and generalized estimating equations (GEE) models, applied them to data from the Complications of Age-Related Macular Degeneration Prevention Trial (CAPT) and the Age-Related Eye Disease Study (AREDS). In CAPT (N = 1052), we assessed the effect of eye-specific laser treatment on change in visual acuity (VA). In the AREDS study, we evaluated effects of systemic supplement treatment among 1463 participants with AMD category 3.

RESULTS

In CAPT, the inter-eye correlations (0.33 to 0.53) and longitudinal correlations (0.31 to 0.88) varied. There was a small treatment effect on VA change (approximately one letter) at 24 months for all three models (p = .009 to 0.02). Model fit was better with the mixed effects model than the fixed effects model (p < .001). In AREDS, there was no significant treatment effect in all models (p > .55). Current smokers had a significantly greater VA decline than non-current smokers in the fixed effects model (p = .04) and the mixed effects model with random intercept (p = .0003), but marginally significant in the mixed effects model with random intercept and slope (p = .08), and GEE models (p = .054 to 0.07). The model fit was better with the fixed effects model than the mixed effects model (p < .0001).

CONCLUSION

Longitudinal models using the eye as the unit of analysis can be implemented using available statistical software to account for both inter-eye and longitudinal correlations. Goodness-of-fit statistics may guide the selection of the most appropriate model.

The life-course association of birth-weight genes with self-rated health

This study examines the impact of genes associated with normal-range birth weight (2500-4500 grams) on self-rated health in mid-to-late life course. Fifty-eight previously identified genetic variants that explain the variation in the normal-range birth weight were used to construct a genetic measure of birth weight for the non-Hispanic white sample from the Health and Retirement Study. Our results show that the genetic tendency toward higher birth weight predicts better self-rated health in mid-to-late life course net of various demographic, socioeconomic, and health behavioral factors. We also examine the heterogeneous effects of birth-weight genes across birth cohorts and age groups. Moreover, to clarify the paradox that higher birth weight can predict both better self-rated health and higher BMI, we show the positive association between birth weight genes and BMI can only hold within the normal-range BMI (18 ≤ BMI < 30). Overall, these findings suggest the genetic factors underlying the normal-range birth weight can have life-courseimpacts on health.

Maintaining the validity of inference in small-sample stepped wedge cluster randomized trials with binary outcomes when using generalized estimating equations

Stepped wedge cluster trials are an increasingly popular alternative to traditional parallel cluster randomized trials. Such trials often utilize a small number of clusters and numerous time intervals, and these components must be considered when choosing an analysis method. A generalized linear mixed model containing a random intercept and fixed time and intervention covariates is the most common analysis approach. However, the sole use of a random intercept applies a constant intraclass correlation coefficient structure, which is an assumption that is likely to be violated given stepped wedge trials (SWTs) have multiple time intervals. Alternatively, generalized estimating equations (GEE) are robust to the misspecification of the working correlation structure, although it has been shown that small-sample adjustments to standard error estimates and the use of appropriate degrees of freedom are required to maintain the validity of inference when the number of clusters is small. In this article, we show, using an extensive simulation study based on a motivating example and a more general design, the use of GEE can maintain the validity of inference in small-sample SWTs with binary outcomes. Furthermore, we show which combinations of bias corrections to standard error estimates and degrees of freedom work best in terms of attaining nominal type I error rates.

xtgeebcv: A command for bias-corrected sandwich variance estimation for GEE analyses of cluster randomized trials

Cluster randomized trials, where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on individuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must consider that outcomes for members of the same cluster tend to be more similar than outcomes for members of other clusters. A popular individual-level analysis technique is generalized estimating equations (GEE). However, it is common to randomize a small number of clusters (for example, 30 or fewer), and in this case, the GEE standard errors obtained from the sandwich variance estimator will be biased, leading to inflated type I errors. Some bias-corrected standard errors have been proposed and studied to account for this finite-sample bias, but none has yet been implemented in Stata. In this article, we describe several popular bias corrections to the robust sandwich variance. We then introduce our newly created command, xtgeebcv, which will allow Stata users to easily apply finite-sample corrections to standard errors obtained from GEE models. We then provide examples to demonstrate the use of xtgeebcv. Finally, we discuss suggestions about which finite-sample corrections to use in which situations and consider areas of future research that may improve xtgeebcv.

Bivariate traits association analysis using generalized estimating equations in family data

Genome wide association study (GWAS) is becoming fundamental in the arduous task of deciphering the etiology of complex diseases. The majority of the statistical models used to address the genes-disease association consider a single response variable. However, it is common for certain diseases to have correlated phenotypes such as in cardiovascular diseases. Usually, GWAS typically sample unrelated individuals from a population and the shared familial risk factors are not investigated. In this paper, we propose to apply a bivariate model using family data that associates two phenotypes with a genetic region. Using generalized estimation equations (GEE), we model two phenotypes, either discrete, continuous or a mixture of them, as a function of genetic variables and other important covariates. We incorporate the kinship relationships into the working matrix extended to a bivariate analysis. The estimation method and the joint gene-set effect in both phenotypes are developed in this work. We also evaluate the proposed methodology with a simulation study and an application to real data.

Longitudinal Analysis of Ventilation Perfusion Mismatch in Congenital Diaphragmatic Hernia Survivors

OBJECTIVE

To determine the natural history of pulmonary function for survivors of congenital diaphragmatic hernia (CDH).

STUDY DESIGN

This was a retrospective cohort study of survivors of CDH born during 1991-2016 and followed at our institution. A generalized linear model was fitted to assess the longitudinal trends of ventilation (V), perfusion (Q), and V/Q mismatch. The association between V/Q ratio and body mass index percentile as well as functional status was also assessed with a generalized linear model.

RESULTS

During the study period, 212 patients had at least one V/Q study. The average ipsilateral V/Q of the cohort increased over time (P < .01), an effect driven by progressive reduction in relative perfusion (P = .012). A higher V/Q ratio was correlated with lower body mass index percentile (P < .001) and higher probability of poor functional status (New York Heart Association class III or IV) (P = .045).

CONCLUSIONS

In this cohort of survivors of CDH with more severe disease characteristics, V/Q mismatch worsens over time, primarily because of progressive perfusion deficit of the ipsilateral side. V/Q scans may be useful in identifying patients with CDH who are at risk for poor growth and functional status.

Marginal analysis of multiple outcomes with informative cluster size

In surveillance studies of periodontal disease, the relationship between disease and other health and socioeconomic conditions is of key interest. To determine whether a patient has periodontal disease, multiple clinical measurements (eg, clinical attachment loss, alveolar bone loss, and tooth mobility) are taken at the tooth-level. Researchers often create a composite outcome from these measurements or analyze each outcome separately. Moreover, patients have varying number of teeth, with those who are more prone to the disease having fewer teeth compared to those with good oral health. Such dependence between the outcome of interest and cluster size (number of teeth) is called informative cluster size and results obtained from fitting conventional marginal models can be biased. We propose a novel method to jointly analyze multiple correlated binary outcomes for clustered data with informative cluster size using the class of generalized estimating equations (GEE) with cluster-specific weights. We compare our proposed multivariate outcome cluster-weighted GEE results to those from the convectional GEE using the baseline data from Veterans Affairs Dental Longitudinal Study. In an extensive simulation study, we show that our proposed method yields estimates with minimal relative biases and excellent coverage probabilities.

Sample size estimation for stratified individual and cluster randomized trials with binary outcomes

Individual randomized trials (IRTs) and cluster randomized trials (CRTs) with binary outcomes arise in a variety of settings and are often analyzed by logistic regression (fitted using generalized estimating equations for CRTs). The effect of stratification on the required sample size is less well understood for trials with binary outcomes than for continuous outcomes. We propose easy-to-use methods for sample size estimation for stratified IRTs and CRTs and demonstrate the use of these methods for a tuberculosis prevention CRT currently being planned. For both IRTs and CRTs, we also identify the ratio of the sample size for a stratified trial vs a comparably powered unstratified trial, allowing investigators to evaluate how stratification will affect the required sample size when planning a trial. For CRTs, these can be used when the investigator has estimates of the within-stratum intracluster correlation coefficients (ICCs) or by assuming a common within-stratum ICC. Using these methods, we describe scenarios where stratification may have a practically important impact on the required sample size. We find that in the two-stratum case, for both IRTs and for CRTs with very small cluster sizes, there are unlikely to be plausible scenarios in which an important sample size reduction is achieved when the overall probability of a subject experiencing the event of interest is low. When the probability of events is not small, or when cluster sizes are large, however, there are scenarios where practically important reductions in sample size result from stratification.

Variable selection via penalized generalized estimating equations for a marginal survival model

Clustered and multivariate survival times, such as times to recurrent events, commonly arise in biomedical and health research, and marginal survival models are often used to model such data. When a large number of predictors are available, variable selection is always an important issue when modeling such data with a survival model. We consider a Cox's proportional hazards model for a marginal survival model. Under the sparsity assumption, we propose a penalized generalized estimating equation approach to select important variables and to estimate regression coefficients simultaneously in the marginal model. The proposed method explicitly models the correlation structure within clusters or correlated variables by using a prespecified working correlation matrix. The asymptotic properties of the estimators from the penalized generalized estimating equations are established and the number of candidate covariates is allowed to increase in the same order as the number of clusters does. We evaluate the performance of the proposed method through a simulation study and analyze two real datasets for the application.

The role of alimentary and biliopancreatic limb length in outcomes of Roux-en-Y gastric bypass

Introduction

Roux-en-Y gastric bypass (RYGB) is one of the safe and easily reproducible bariatric procedures.

Aim

To evaluate the effect of biliopancreatic limb (BPL) and alimentary limb (AL) length on weight loss outcomes after RYGB.

Material and methods

This retrospective cohort study included 313 morbidly obese patients who underwent primary laparoscopic RYGB 2009–2015. Patients’ BPL and AL lengths were categorized into three groups: group 1 (BPL: 50 cm and AL: 150 cm), group 2 (BPL: 150 cm and AL: 50 cm), and group 3 (BPL: 100 cm and AL: 100 cm). Data were provided from the Iranian National Obesity Surgery Database. The generalized estimating equations method was used to assess the effect of limbs length on %excess weight loss (%EWL).

Results

Mean ± standard deviation age and body mass index (BMI) of 252 patients were 38.55 ±10.24 years and 45.8 ±4.77 kg/m², respectively. Totally, 172 (68.3%, BMI of 46 ±5 kg/m²), 48 (19%, BMI of 45.12 ±4.26 kg/m²), and 32 (12.7%, BMI of 45.43 ±4.23 kg/m²) were in group 1, 2, and 3, respectively (p = 0.44). The results showed that the choice of different limb lengths had no significant effect on %EWL over 12 months follow-up (p = 0.625) adjusted for baseline BMI (p = 0.25). Mean %EWL in the patients with longer BPL and shorter AL was 5.43% (1.91, 8.95) higher in comparison to the patients with shorter BPL and longer AL during 36 months postoperatively adjusted for baseline BMI (p = 0.002).

Conclusions

During 12 months after RYGB, %EWL was not associated with BPL or AL length. However, during 36 months postoperatively, the patients with longer BPL had a significantly higher %EWL in comparison to the patients with shorter BPL.

On the analysis of two-phase designs in cluster-correlated data settings

In public health research, information that is readily available may be insufficient to address the primary question(s) of interest. One cost-efficient way forward, especially in resource-limited settings, is to conduct a two-phase study in which the population is initially stratified, at phase I, by the outcome and/or some categorical risk factor(s). At phase II detailed covariate data is ascertained on a subsample within each phase I strata. While analysis methods for two-phase designs are well established, they have focused exclusively on settings in which participants are assumed to be independent. As such, when participants are naturally clustered (eg, patients within clinics) these methods may yield invalid inference. To address this, we develop a novel analysis approach based on inverse-probability weighting that permits researchers to specify some working covariance structure and appropriately accounts for the sampling design and ensures valid inference via a robust sandwich estimator for which a closed-form expression is provided. To enhance statistical efficiency, we propose a calibrated inverse-probability weighting estimator that makes use of information available at phase I but not used in the design. In addition to describing the technique, practical guidance is provided for the cluster-correlated data settings that we consider. A comprehensive simulation study is conducted to evaluate small-sample operating characteristics, including the impact of using naïve methods that ignore correlation due to clustering, as well as to investigate design considerations. Finally, the methods are illustrated using data from a one-time survey of the national antiretroviral treatment program in Malawi.

Sequential adaptive variables and subject selection for GEE methods

Modeling correlated or highly stratified multiple-response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time-consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process-such as the active-learning methods found in the machine-learning literature-will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method.

The Influence of Inpatient Physician Continuity on Hospital Discharge

BACKGROUND

Inpatient attending physicians may change during a patient's hospital stay. This study measured the association of attending physician continuity and discharge probability.

METHODS

All patients admitted to general medicine service at a tertiary care teaching hospital in 2015 were included. Attending inpatient physician continuity was measured as the consecutive number of days each patient was treated by the same staff-person. Generalized estimating equation methods were used to model the adjusted association of attending inpatient physician continuity with daily discharge probability.

RESULTS

6301 admissions involving 41 internists, 5134 patients, and 38,242 patient-days were studied. The final model had moderate discrimination (c-statistic = 0.70) but excellent calibration (Hosmer-Lemeshow statistic 11.5, 18 df, p value 0.89). Daily discharge probability decreased significantly with greater severity of illness, higher patient death risk, and longer length of stay, on admission day, for elective admissions, and on the weekend. Discharge likelihood increased significantly with attending inpatient physician continuity; daily discharge probability increased for the average patient from 15.3 to 20.9% when the consecutive number of days the patient was treated by the same attending inpatient physician increased from 1 to 7 days.

CONCLUSIONS

Inpatient attending physician continuity is significantly associated with the likelihood of patient discharge. This finding could be considered if resource utilization is a factor when scheduling attending inpatient physician coverage.

A GEE approach to estimating accuracy and its confidence intervals for correlated data

In this paper, we provide a method for constructing confidence interval for accuracy in correlated observations, where one sample of patients is being rated by two or more diagnostic tests. Confidence intervals for other measures of diagnostic tests, such as sensitivity, specificity, positive predictive value, and negative predictive value, have already been developed for clustered or correlated observations using the generalized estimating equations (GEE) method. Here, we use the GEE and delta-method to construct confidence intervals for accuracy, the proportion of patients who are correctly classified. Simulation results verify that the estimated confidence intervals exhibit consistent/appropriate coverage rates.

Domain-specific and total sedentary behaviors associated with psychological distress in older adults

Purpose: Time spent in sedentary behaviors has become a major public health problem, affecting both physical and mental conditions, which is regularly evident in older adults. The aim of this study was to explore the association between each domain-specific sedentary behavior (screen-time, leisure-time sedentary behavior and transport) and total sedentary behavior (sum of all indicators) with "high" psychological distress among older individuals. Patients and methods: In this cross-sectional study, we recruited 810 participants aged ≥85 (16% men) from 6 neighborhoods in the city of Zagreb. We used Measure of Older Adults' Sedentary Time sedentary behavior questionnaire to assess the time spent in a specific domain of sedentary behavior and Kessler K6 scale to assess the level of psychological distress. Participants who had a score ≥13 points were treated as those with "high" psychological distress. Generalized estimating equations with Poisson regression models and risk ratios were used to calculate the association. Results: After adjusting for sex, body mass index, sleep quality, self-rated health, material status, physical activity, diet and chronic diseases, participants categorized in the second, third and fourth quartile of screen-time, in the fourth quartile of leisure-time sedentary behavior and in the third and fourth quartile of total sedentary behavior were less likely to have "high" psychological distress. However, participants categorized in the fourth quartile of transport were more likely to have "high" psychological distress. Conclusion: Our study shows that more time spent in front of screens, leisure and in total sedentary behavior is associated with lower levels, while more time spent in transport is associated with higher levels of psychological distress, pointing out that the aforementioned associations remained even after adjusting for variables describing "general" physical health. Thus, strategies aiming to reduce the time spent in passive transport and enhance active transport in a sample of older adults are warranted.

Marginal analysis of ordinal clustered longitudinal data with informative cluster size

The issue of informative cluster size (ICS) often arises in the analysis of dental data. ICS describes a situation where the outcome of interest is related to cluster size. Much of the work on modeling marginal inference in longitudinal studies with potential ICS has focused on continuous outcomes. However, periodontal disease outcomes, including clinical attachment loss, are often assessed using ordinal scoring systems. In addition, participants may lose teeth over the course of the study due to advancing disease status. Here we develop longitudinal cluster-weighted generalized estimating equations (CWGEE) to model the association of ordinal clustered longitudinal outcomes with participant-level health-related covariates, including metabolic syndrome and smoking status, and potentially decreasing cluster size due to tooth-loss, by fitting a proportional odds logistic regression model. The within-teeth correlation coefficient over time is estimated using the two-stage quasi-least squares method. The motivation for our work stems from the Department of Veterans Affairs Dental Longitudinal Study in which participants regularly received general and oral health examinations. In an extensive simulation study, we compare results obtained from CWGEE with various working correlation structures to those obtained from conventional GEE which does not account for ICS. Our proposed method yields results with very low bias and excellent coverage probability in contrast to a conventional generalized estimating equations approach.

Impact of academic detailing on benzodiazepine use among veterans with posttraumatic stress disorder

Background: Benzodiazepine use in the US Veterans Administration (VA) has been decreasing; however, a small number of veterans with posttraumatic stress disorder (PTSD) continue to receive benzodiazepine. Academic detailing, a targeted-educational outreach intervention, was implemented at VA to help reduce the disparity between existing and evidence-based practices, including the reduction in benzodiazepine use in veterans with PTSD. Since evidence to support the national implementation of academic detailing in this clinical scenario was scarce, we performed a quality improvement evaluation on academic detailing's impact on benzodiazepine use in veterans with PTSD. Methods: A retrospective cohort design was used to evaluate the impact of academic detailing on benzodiazepine prescribing in veterans with PTSD from January 1, 2016, to December 31, 2016. Providers exposed to academic detailing (AD-exposed) were compared with providers unexposed to academic detailing (AD-unexposed) using generalized estimating equations (GEEs) controlling for baseline covariates. Secondary aims evaluated academic detailing's impact on average lorazepam equivalent daily dose (LEDD), total LEDD, and benzodiazepine day supply. Results: Overall, there was a decrease in the prevalence in benzodiazepine use in veterans with PTSD from 115.5 to 103.3 per 1000 population (P < .001). However, the decrease was greater in AD-exposed providers (18.37%; P < .001) compared with AD-unexposed providers (8.74%; P < .001). In the GEE models, AD-exposed providers had greater reduction in the monthly prevalence of veterans with PTSD and a benzodiazepine prescription compared with AD-unexposed providers, by -1.30 veterans per 1000 population (95% confidence interval [CI]: -2.14, -0.46). Similar findings were reported for the benzodiazepine day supply; however, no significant differences were reported for total and average LEDD. Conclusions: Although benzodiazepine use has been decreasing in veterans with PTSD, opportunities to improve prescribing continue to exist at the VA. In this quality improvement evaluation, AD-exposed providers were associated with a greater reduction in the prevalence of veterans with PTSD and a benzodiazepine prescription compared with AD-unexposed providers.

Simplified Estimation and Testing in Unbalanced Repeated Measures Designs

In this paper we propose a simple estimator for unbalanced repeated measures design models where each unit is observed at least once in each cell of the experimental design. The estimator does not require a model of the error covariance structure. Thus, circularity of the error covariance matrix and estimation of correlation parameters and variances are not necessary. Together with a weak assumption about the reason for the varying number of observations, the proposed estimator and its variance estimator are unbiased. As an alternative to confidence intervals based on the normality assumption, a bias-corrected and accelerated bootstrap technique is considered. We also propose the naive percentile bootstrap for Wald-type tests where the standard Wald test may break down when the number of observations is small relative to the number of parameters to be estimated. In a simulation study we illustrate the properties of the estimator and the bootstrap techniques to calculate confidence intervals and conduct hypothesis tests in small and large samples under normality and non-normality of the errors. The results imply that the simple estimator is only slightly less efficient than an estimator that correctly assumes a block structure of the error correlation matrix, a special case of which is an equi-correlation matrix. Application of the estimator and the bootstrap technique is illustrated using data from a task switch experiment based on an experimental within design with 32 cells and 33 participants.

Repeated Measures Regression in Laboratory, Clinical and Environmental Research: Common Misconceptions in the Matter of Different Within- and between-Subject Slopes

When using repeated measures linear regression models to make causal inference in laboratory, clinical and environmental research, it is typically assumed that the within-subject association of differences (or changes) in predictor variable values across replicates is the same as the between-subject association of differences in those predictor variable values. However, this is often false. For example, with body weight as the predictor variable and blood cholesterol (which increases with higher body fat) as the outcome: (i) a 10-lb weight increase in the same adult affects more greatly an increase in cholesterol in that adult than does (ii) one adult weighing 10 lbs more than a second indicate higher cholesterol in the heavier adult. A 10-lb weight gain in the first adult more likely reflects a build-up of body fat in that person, while a second person being 10 lbs heavier than the first could be influenced by other factors, such as the second person being taller. Hence, to make causal inferences, different within- and between-subject slopes should be separately modeled. A related misconception commonly made using generalized estimation equations (GEE) and mixed models on repeated measures (i.e., for fitting cross-sectional regression) is that the working correlation structure only influences variance of the parameter estimates. However, only independence working correlation guarantees that the modeled parameters have interpretability. We illustrate this with an example where changing working correlation from independence to equicorrelation qualitatively biases parameters of GEE models and show that this happens because within- and between-subject slopes for the outcomes regressed on the predictor variables differ. We then systematically describe several common mechanisms that cause within- and between-subject slopes to differ: change effects, lag/reverse-lag and spillover causality, shared within-subject measurement bias or confounding, and predictor variable measurement error. The misconceptions we describe should be better publicized. Repeated measures analyses should compare within- and between-subject slopes of predictors and when they do differ, investigate the causal reasons for this.

A Monte Carlo method for comparing generalized estimating equations to conventional statistical techniques for discounting data

Discounting is the process by which outcomes lose value. Much of discounting research has focused on differences in the degree of discounting across various groups. This research has relied heavily on conventional null hypothesis significance tests that are familiar to psychologists, such as t-tests and ANOVAs. As discounting research questions have become more complex by simultaneously focusing on within-subject and between-group differences, conventional statistical testing is often not appropriate for the obtained data. Generalized estimating equations (GEE) are one type of mixed-effects model that are designed to handle autocorrelated data, such as within-subject repeated-measures data, and are therefore more appropriate for discounting data. To determine if GEE provides similar results as conventional statistical tests, we compared the techniques across 2,000 simulated data sets. The data sets were created using a Monte Carlo method based on an existing data set. Across the simulated data sets, the GEE and the conventional statistical tests generally provided similar patterns of results. As the GEE and more conventional statistical tests provide the same pattern of result, we suggest researchers use the GEE because it was designed to handle data that has the structure that is typical of discounting data.

Methods for comparing durability of immune responses between vaccine regimens in early-phase trials

The ability to produce a long-lasting, or durable, immune response is a crucial characteristic of many highly effective vaccines. A goal of early-phase vaccine trials is often to compare the immune response durability of multiple tested vaccine regimens. One parameter for measuring immune response durability is the area under the mean post-peak log immune response profile. In this paper, we compare immune response durability across vaccine regimens within and between two phase I trials of DNA-primed HIV vaccine regimens, HVTN 094 and HVTN 096. We compare four estimators of this durability parameter and the resulting statistical inferences for comparing vaccine regimens. Two of these estimators use the trapezoid rule as an empirical approximation of the area under the marginal log response curve, and the other two estimators are based on linear and nonlinear models for the marginal mean log response. We conduct a simulation study to compare the four estimators, provide guidance on estimator selection, and use the nonlinear marginal mean model to analyze immunogenicity data from the two HIV vaccine trials.