Robert D. Gibbons

University of Chicago, Chicago, Illinois, United States

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Publications (232)1141.82 Total impact

  • Robert D. Gibbons · David J. Weiss · Ellen Frank · David Kupfer ·

    Annual Review of Clinical Psychology 04/2016; 12(1). DOI:10.1146/annurev-clinpsy-021815-093634 · 12.67 Impact Factor

  • BMJ quality & safety 11/2015; 24(11):726-727. DOI:10.1136/bmjqs-2015-IHIabstracts.10 · 3.99 Impact Factor
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    ABSTRACT: Objective: Computerized adaptive testing (CAT) provides an alternative to fixed-length assessments. The study validated a suite of computerized adaptive tests for mental health (CAT-MH) in a community psychiatric sample. Methods: A total of 145 adults from a community outpatient clinic, including 19 with no history of a mental disorder (control group), were prospectively evaluated with CAT for depression (CAD-MDD and CAT-DI), mania (CAT-MANIA), and anxiety symptoms (CAT-ANX). Ratings were compared with gold-standard psychiatric assessments, including the Structured Clinical Interview for DSM-IV-TR (SCID), Hamilton Rating Scale for Depression (HAM-D-25), Patient Health Questionnaire (PHQ-9), Center for Epidemiologic Studies Depression Scale (CES-D), and Global Assessment of Functioning (GAF). Results: Sensitivity and specificity for CAD-MDD were .96 and .64, respectively (.96 and 1.00 for major depression versus the control group). CAT for depression severity (CAT-DI) correlated well with the HAM-D-25 (r=.79), PHQ-9 (r=.90), and CES-D (r=.90) and had an odds ratio (OR) of 27.88 across its range for current SCID major depressive disorder. CAT-ANX correlated with the HAM-D-25 (r=.73), PHQ-9 (r=.78), and CES-D (r=.81) and had an OR of 11.52 across its range for current SCID generalized anxiety disorder. CAT-MANIA did not correlate well with the HAM-D-25 (r=.31), PHQ-9 (r=.37), and CES-D (r=.39), but it had an OR of 11.56 across its range for a current SCID bipolar diagnosis. Participants found the CAT-MH acceptable and easy to use, averaging 51.7 items and 9.4 minutes to complete the full battery. Conclusions: Compared with gold-standard diagnostic and assessment measures, CAT-MH provided an effective, rapidly administered assessment of psychiatric symptoms.
    Psychiatric Services 06/2015; DOI:10.1176/ · 2.41 Impact Factor
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    ABSTRACT: There are many advantages to individual participant data meta-analysis for combining data from multiple studies. These advantages include greater power to detect effects, increased sample heterogeneity, and the ability to perform more sophisticated analyses than meta-analyses that rely on published results. However, a fundamental challenge is that it is unlikely that variables of interest are measured the same way in all of the studies to be combined. We propose that this situation can be viewed as a missing data problem in which some outcomes are entirely missing within some trials and use multiple imputation to fill in missing measurements. We apply our method to five longitudinal adolescent depression trials where four studies used one depression measure and the fifth study used a different depression measure. None of the five studies contained both depression measures. We describe a multiple imputation approach for filling in missing depression measures that makes use of external calibration studies in which both depression measures were used. We discuss some practical issues in developing the imputation model including taking into account treatment group and study. We present diagnostics for checking the fit of the imputation model and investigate whether external information is appropriately incorporated into the imputed values. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.
    Statistics in Medicine 06/2015; 34(26):n/a-n/a. DOI:10.1002/sim.6562 · 1.83 Impact Factor
  • W. Padula · R.D. Gibbons · R.J. Valuck · M.B. Makic · D. Meltzer ·

    Value in Health 05/2015; 18(3):A275. DOI:10.1016/j.jval.2015.03.1607 · 3.28 Impact Factor
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    ABSTRACT: Classrooms are unique and complex work settings in which teachers and students both participate in and contribute to classroom processes. This article describes the measurement phase of a study that examined the social ecology of urban classrooms. Informed by the dimensions and items of an established measure of organizational climate, we designed the Student Climate Survey (n = 53 items) to assess student psychological climate in third through eighth grades. We administered the survey to 621 students at three time points within one school year in 69 classrooms within eight urban schools. A multidimensional item response theory (IRT) analysis based on a full-information item bifactor model revealed 18 items that loaded on a primary factor and demonstrated good criterion and predictive validity. Opportunities for the Student Climate Survey to advance our contextual understanding of urban classrooms and inform intervention are discussed.
    The Journal of Early Adolescence 02/2015; 35(5). DOI:10.1177/0272431615570056 · 2.30 Impact Factor
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    Robert D Gibbons · Marcelo Coca Perraillon · Jong Bae Kim ·
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    ABSTRACT: The need to harmonize different outcome metrics is a common problem in research synthesis and economic evaluation of health interventions and technology. The purpose of this paper is to describe the use of multidimensional item response theory (IRT) to equate different scales which purport to measure the same construct at the item level. We provide an overview of multidimensional item response theory in general and the bi-factor model which is particularly relevant for applications in this area. We show how both the underlying true scores of two or more scales that are intended to measure the same latent variable can be equated and how the item responses from one scale can be used to predict the item responses for a scale that was not administered but are necessary for the purpose of economic evaluations. As an example, we show that a multidimensional IRT model predicts well both the EQ-5D descriptive system and the EQ-5D preference index from SF-12 data which cannot be directly used to perform an economic evaluation. Results based on multidimensional IRT performed well compared to traditional regression methods in this area. A general framework for harmonization of research instruments based on multidimensional IRT is described.
    Health Services and Outcomes Research Methodology 12/2014; 14(4):213-231. DOI:10.1007/s10742-014-0125-x
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    ABSTRACT: PurposeIn the 2004, FDA placed a black box warning on antidepressants for risk of suicidal thoughts and behavior in children and adolescents. The purpose of this paper is to examine the risk of suicide attempt and self-inflicted injury in depressed children ages 5–17 treated with antidepressants in two large observational datasets taking account time-varying confounding.METHODS We analyzed two large US medical claims databases (MarketScan and LifeLink) containing 221,028 youth (ages 5–17) with new episodes of depression, with and without antidepressant treatment during the period of 2004–2009. Subjects were followed for up to 180 days. Marginal structural models were used to adjust for time-dependent confounding.ResultsFor both datasets, significantly increased risk of suicide attempts and self-inflicted injury were seen during antidepressant treatment episodes in the unadjusted and simple covariate adjusted analyses. Marginal structural models revealed that the majority of the association is produced by dynamic confounding in the treatment selection process; estimated odds ratios were close to 1.0 consistent with the unadjusted and simple covariate adjusted association being a product of chance alone.Conclusions Our analysis suggests antidepressant treatment selection is a product of both static and dynamic patient characteristics. Lack of adjustment for treatment selection based on dynamic patient characteristics can lead to the appearance of an association between antidepressant treatment and suicide attempts and self-inflicted injury among youths in unadjusted and simple covariate adjusted analyses. Marginal structural models can be used to adjust for static and dynamic treatment selection processes such as that likely encountered in observational studies of associations between antidepressant treatment selection, suicide and related behaviors in youth. Copyright © 2014 John Wiley & Sons, Ltd.
    Pharmacoepidemiology and Drug Safety 09/2014; 24(2). DOI:10.1002/pds.3713 · 2.94 Impact Factor
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    ABSTRACT: Rationale: Most ward risk scores were created using subjective opinion in individual hospitals and only utilize vital signs. Objectives: To develop and validate a risk score using commonly collected electronic health record (EHR) data. Methods: All patients hospitalized on the wards in five hospitals were included in this observational cohort study. Discrete-time survival analysis was used to predict the combined outcome of cardiac arrest (CA), intensive care unit (ICU) transfer, or death on the wards. Laboratory results, vital signs, and demographics were utilized as predictor variables. The model was developed in the first 60% of the data at each hospital and then validated in the remaining 40%. The final model was compared to the Modified Early Warning Score (MEWS) using the area under the receiver operating characteristic curve (AUC). Measurements and Main Results: A total of 269,999 patient admissions were included, with 424 CAs, 13,188 ICU transfers, and 2,840 deaths occurring during the study period. The derived model was more accurate than the MEWS in the validation dataset for all outcomes (AUC 0.83 vs. 0.74 for CA, 0.79 vs. 0.73 for ICU transfer, 0.94 vs. 0.90 for death, and 0.82 vs. 0.76 for the combined outcome; p-value<0.01 for all comparisons). This accuracy improvement was seen across all hospitals. Conclusions: We developed an accurate ward risk stratification tool using commonly collected EHR variables in a large multicenter dataset. Further study is needed to determine whether implementation in real-time would improve patient outcomes.
    American Journal of Respiratory and Critical Care Medicine 08/2014; 190(6). DOI:10.1164/rccm.201406-1022OC · 13.00 Impact Factor
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    ABSTRACT: Abstract Meta-analysis has been used extensively for evaluation of efficacy and safety of medical interventions. Its advantages and utilities are well known. However, recent studies have raised questions about the accuracy of the commonly used moment-based meta-analytic methods in general and for rare binary outcomes in particular. The issue is further complicated for studies with heterogeneous effect sizes. Likelihood-based mixed-effects modeling provides an alternative to moment-based methods such as inverse-variance weighted fixed- and random-effects estimators. In this paper, we compare and contrast different mixed-effect modeling strategies in the context of meta-analysis. Their performance in estimation and testing of overall effect and heterogeneity are evaluated when combining results from studies with a binary outcome. Models that allow heterogeneity in both baseline rate and treatment effect across studies have low type I and type II error rates, and their estimates are the least biased among the models considered.
    Journal of Biopharmaceutical Statistics 06/2014; 25(5). DOI:10.1080/10543406.2014.920348 · 0.59 Impact Factor
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    ABSTRACT: Landfill functional stability provides a target that supports no environmental threat at the relevant point of exposure in the absence of active control systems. With respect to leachate management, this study investigates "gateway" indicators for functional stability in terms of the predictability of leachate characteristics, and thus potential threat to water quality posed by leachate emissions. Historical studies conducted on changes in municipal solid waste (MSW) leachate concentrations over time (longitudinal analysis) have concentrated on indicator compounds, primarily chemical oxygen demand (COD) and biochemical oxygen demand (BOD). However, validation of these studies using an expanded database and larger constituent sets has not been performed. This study evaluated leachate data using a mixed-effects regression model to determine the extent to which leachate constituent degradation can be predicted based on waste age or operational practices. The final dataset analyzed consisted of a total of 1402 samples from 101MSW landfills. Results from the study indicated that all leachate constituents exhibit a decreasing trend with time in the post-closure period, with 16 of the 25 target analytes and aggregate classes exhibiting a statistically significant trend consistent with well-studied indicators such as BOD. Decreasing trends in BOD concentration after landfill closure can thus be considered representative of trends for many leachate constituents of concern.
    Waste Management 06/2014; 34(9). DOI:10.1016/j.wasman.2014.05.016 · 3.22 Impact Factor
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    ABSTRACT: The introduction of shape parameters into statistical distributions provided flexible models that produced better fit to experimental data. The Weibull and gamma families are prime examples wherein shape parameters produce more reliable statistical models than standard exponential models in lifetime studies. In the presence of many independent gamma populations, one may test equality (or homogeneity) of shape parameters. In this paper, we develop two tests for testing shape parameters of gamma distributions using chi-square distributions, stochastic majorization and Schur convexity. The first one tests hypotheses on the shape parameter of a single gamma distribution. We numerically examine the performance of this test and find that it controls Type I error rate for small samples. To compare shape parameters of a set of independent gamma populations, we develop a test that is unbiased in the sense of Schur convexity. These tests are motivated by the need to have simple, easy to use tests and accurate procedures in case of small samples. We illustrate the new tests using three real datasets taken from engineering and environmental science. In addition, we investigate the Bayes Factor in this context and conclude that for small samples, the frequentist approach performs better than the Bayesian approach.
    Communication in Statistics- Simulation and Computation 05/2014; 44(5). DOI:10.1080/03610918.2013.818692 · 0.33 Impact Factor
  • David A Brent · Robert Gibbons ·

    JAMA Internal Medicine 04/2014; 174(6). DOI:10.1001/jamainternmed.2013.14016 · 13.12 Impact Factor
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    ABSTRACT: Limited translation of research into practice has prompted study of diffusion and implementation, and development of effective methods of encouraging adoption, dissemination and implementation. Mixed methods techniques offer approaches for assessing and addressing processes affecting implementation of evidence-based interventions. We describe common mixed methods approaches used in dissemination and implementation research, discuss strengths and limitations of mixed methods approaches to data collection, and suggest promising methods not yet widely used in implementation research. We review qualitative, quantitative, and hybrid approaches to mixed methods dissemination and implementation studies, and describe methods for integrating multiple methods to increase depth of understanding while improving reliability and validity of findings.
    Administration and Policy in Mental Health and Mental Health Services Research 04/2014; 42(5). DOI:10.1007/s10488-014-0552-6 · 3.44 Impact Factor
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    ABSTRACT: Many factors affect the risks for neurodevelopmental maladies such as autism spectrum disorders (ASD) and intellectual disability (ID). To compare environmental, phenotypic, socioeconomic and state-policy factors in a unified geospatial framework, we analyzed the spatial incidence patterns of ASD and ID using an insurance claims dataset covering nearly one third of the US population. Following epidemiologic evidence, we used the rate of congenital malformations of the reproductive system as a surrogate for environmental exposure of parents to unmeasured developmental risk factors, including toxins. Adjusted for gender, ethnic, socioeconomic, and geopolitical factors, the ASD incidence rates were strongly linked to population-normalized rates of congenital malformations of the reproductive system in males (an increase in ASD incidence by 283% for every percent increase in incidence of malformations, 95% CI: [91%, 576%], p<6×10-5). Such congenital malformations were barely significant for ID (94% increase, 95% CI: [1%, 250%], p = 0.0384). Other congenital malformations in males (excluding those affecting the reproductive system) appeared to significantly affect both phenotypes: 31.8% ASD rate increase (CI: [12%, 52%], p<6×10-5), and 43% ID rate increase (CI: [23%, 67%], p<6×10-5). Furthermore, the state-mandated rigor of diagnosis of ASD by a pediatrician or clinician for consideration in the special education system was predictive of a considerable decrease in ASD and ID incidence rates (98.6%, CI: [28%, 99.99%], p = 0.02475 and 99% CI: [68%, 99.99%], p = 0.00637 respectively). Thus, the observed spatial variability of both ID and ASD rates is associated with environmental and state-level regulatory factors; the magnitude of influence of compound environmental predictors was approximately three times greater than that of state-level incentives. The estimated county-level random effects exhibited marked spatial clustering, strongly indicating existence of as yet unidentified localized factors driving apparent disease incidence. Finally, we found that the rates of ASD and ID at the county level were weakly but significantly correlated (Pearson product-moment correlation 0.0589, p = 0.00101), while for females the correlation was much stronger (0.197, p<2.26×10-16).
    PLoS Computational Biology 03/2014; 10(3):e1003518. DOI:10.1371/journal.pcbi.1003518 · 4.62 Impact Factor
  • Subhash Aryal · Dulal K. Bhaumik · Thomas Mathew · Robert D. Gibbons ·
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    ABSTRACT: In this article we derive an optimal test for testing the significance of covariance matrices of random-effects of two multivariate mixed-effects linear models. We compute the power of this newly derived test via simulation for various alternative hypotheses in a bivariate set up for unbalanced designs and observe that power responds sharply when sample size and alternative hypotheses are changed. For some balanced designs we compare power of the optimal test to that of the likelihood ratio test via simulation, and find that the proposed test has greater power than the likelihood ratio test. The results are illustrated using real data on human growth. Other relevant applications of the model are highlighted.
    Journal of Multivariate Analysis 02/2014; 124:166–178. DOI:10.1016/j.jmva.2013.10.014 · 0.93 Impact Factor

  • The Journal of Clinical Psychiatry 01/2014; 75(1):85-6. DOI:10.4088/JCP.13lr08758a · 5.50 Impact Factor
  • Robert D Gibbons · David J Weiss · Paul A Pilkonis · Ellen Frank · David J Kupfer ·

    JAMA Psychiatry 11/2013; 71(1). DOI:10.1001/jamapsychiatry.2013.3888 · 12.01 Impact Factor
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    ABSTRACT: Over 200,000 in-hospital cardiac arrests occur in the United States each year and many of these events may be preventable. Current vital sign-based risk scores for ward patients have demonstrated limited accuracy, which leads to missed opportunities to identify those patients most likely to suffer cardiac arrest and inefficient resource utilization. We derived and validated a prediction model for cardiac arrest while treating ICU transfer as a competing risk using electronic health record data. A retrospective cohort study. An academic medical center in the United States with approximately 500 inpatient beds. Adult patients hospitalized from November 2008 until August 2011 who had documented ward vital signs. None. Vital sign, demographic, location, and laboratory data were extracted from the electronic health record and investigated as potential predictor variables. A person-time multinomial logistic regression model was used to simultaneously predict cardiac arrest and ICU transfer. The prediction model was compared to the VitalPAC Early Warning Score using the area under the receiver operating characteristic curve and was validated using three-fold cross-validation. A total of 56,649 controls, 109 cardiac arrest patients, and 2,543 ICU transfers were included. The derived model more accurately detected cardiac arrest (area under the receiver operating characteristic curve, 0.88 vs 0.78; p < 0.001) and ICU transfer (area under the receiver operating characteristic curve, 0.77 vs 0.73; p < 0.001) than the VitalPAC Early Warning Score, and accuracy was similar with cross-validation. At a specificity of 93%, our model had a higher sensitivity than the VitalPAC Early Warning Score for cardiac arrest patients (65% vs 41%). We developed and validated a prediction tool for ward patients that can simultaneously predict the risk of cardiac arrest and ICU transfer. Our model was more accurate than the VitalPAC Early Warning Score and could be implemented in the electronic health record to alert caregivers with real-time information regarding patient deterioration.
    Critical care medicine 11/2013; 42(4). DOI:10.1097/CCM.0000000000000038 · 6.31 Impact Factor
  • Anup Amatya · Dulal Bhaumik · Robert D Gibbons ·
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    ABSTRACT: We consider the problem of sample size determination for count data. Such data arise naturally in the context of multicenter (or cluster) randomized clinical trials, where patients are nested within research centers. We consider cluster-specific and population-averaged estimators (maximum likelihood based on generalized mixed-effect regression and generalized estimating equations, respectively) for subject-level and cluster-level randomized designs, respectively. We provide simple expressions for calculating the number of clusters when comparing event rates of two groups in cross-sectional studies. The expressions we derive have closed-form solutions and are based on either between-cluster variation or intercluster correlation for cross-sectional studies. We provide both theoretical and numerical comparisons of our methods with other existing methods. We specifically show that the performance of the proposed method is better for subject-level randomized designs, whereas the comparative performance depends on the rate ratio for the cluster-level randomized designs. We also provide a versatile method for longitudinal studies. Three real data examples illustrate the results. Copyright © 2013 John Wiley & Sons, Ltd.
    Statistics in Medicine 10/2013; 32(24). DOI:10.1002/sim.5819 · 1.83 Impact Factor

Publication Stats

11k Citations
1,141.82 Total Impact Points


  • 1983-2014
    • University of Chicago
      • • Department of Health Studies
      • • Department of Medicine
      • • School of Social Service Administration
      Chicago, Illinois, United States
  • 1984-2011
    • Harvard University
      • Department of Psychology
      Cambridge, MA, United States
  • 1982-2011
    • University of Illinois at Chicago
      • • Division of Epidemiology and Biostatistics
      • • Department of Psychiatry (Chicago)
      • • School of Public Health
      Chicago, Illinois, United States
  • 2009
    • Harvard Medical School
      • Department of Health Care Policy
      Boston, Massachusetts, United States
  • 2006-2007
    • Columbia University
      • Department of Psychiatry
      New York, New York, United States
  • 2001
    • Western Psychiatric Institute and Clinic
      Pittsburgh, Pennsylvania, United States
  • 1999
    • Children's Memorial Hospital
      Chicago, Illinois, United States
  • 1990
    • IIT Research Institute (IITRI)
      Chicago, Illinois, United States
    • Rush Medical College
      Chicago, Illinois, United States
  • 1989
    • Universidad de Extremadura
      Ara Pacis Augustalis, Extremadura, Spain
  • 1987
    • Loyola University Chicago
      • Stritch School of Medicine
      Chicago, Illinois, United States
  • 1986
    • Illinois State University
      State College, Pennsylvania, United States