Robert D Gibbons

University of Chicago, Chicago, Illinois, United States

Are you Robert D Gibbons?

Claim your profile

Publications (149)626.26 Total impact

  • [Show abstract] [Hide abstract]
    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.
  • [Show abstract] [Hide abstract]
    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; · 2.90 Impact Factor
  • [Show abstract] [Hide abstract]
    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; · 0.73 Impact Factor
  • [Show abstract] [Hide abstract]
    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; · 0.30 Impact Factor
  • [Show abstract] [Hide abstract]
    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; · 3.44 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    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. · 4.87 Impact Factor
  • The Journal of Clinical Psychiatry 01/2014; 75(1):85-6. · 5.81 Impact Factor
  • [Show abstract] [Hide abstract]
    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 01/2014; 124:166–178. · 1.06 Impact Factor
  • JAMA Psychiatry 11/2013; · 12.01 Impact Factor
  • 60th Meeting of American Academy of Child and Adolescent Psychiatry; 10/2013
  • Robert D Gibbons, J John Mann
    [Show abstract] [Hide abstract]
    ABSTRACT: OBJECTIVE In 2009, the U.S. Food and Drug Administration issued a black box warning for varenicline regarding neuropsychiatric events. The authors used data from randomized controlled trials and from a large Department of Defense (DOD) observational study to assess the efficacy and safety of varenicline. METHOD The authors reanalyzed data from the 17 placebo-controlled randomized controlled trials (N=8,027) of varenicline conducted by Pfizer, using complete intent-to-treat person-level longitudinal data to assess smoking abstinence and reports of suicidal thoughts and behavior, depression, aggression/agitation, and nausea and to compare effects in patients with (N=1,004) and without (N=7,023) psychiatric disorders. The authors also analyzed a large DOD data set to compare acute (30-day and 60-day) rates of neuropsychiatric adverse events in patients receiving varenicline or nicotine replacement therapy (N=35,800) and to assess reports of anxiety, mood, and psychotic symptoms and disorders, other mental disorders, and suicide attempt. RESULTS In the randomized controlled trials, varenicline increased the risk of nausea (odds ratio=3.69, 95% CI=3.03-4.48) but not rates of suicidal events, depression, or aggression/agitation. It significantly increased the abstinence rate, by 124% compared with placebo and 22% compared with bupropion. Having a current or past psychiatric illness increased the risk of neuropsychiatric events equally in treated and placebo patients. In the DOD study, after propensity score matching, the overall rate of neuropsychiatric disorders was significantly lower for varenicline than for nicotine replacement therapy (2.28% compared with 3.16%). CONCLUSIONS This analysis revealed no evidence that varenicline is associated with adverse neuropsychiatric events. The evidence supports the superior efficacy of varenicline relative to both placebo and bupropion, indicating considerable benefit without evidence of risk of serious neuropsychiatric adverse events, in individuals with and without a recent history of a psychiatric disorder.
    American Journal of Psychiatry 09/2013; · 14.72 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Objective: This article reports outcomes from the Child STEPs randomized effectiveness trial conducted over a 2-year period to gauge the longer term impact of protocol design on the effectiveness of evidence-based treatment procedures. Method: An ethnoracially diverse sample of 174 youths ages 7- 13 (N = 121 boys) whose primary clinical concerns involved diagnoses or clinical elevations related to anxiety, depression, or disruptive behavior were treated by community therapists randomly assigned to 1 of 3 conditions: (a) standard, which involved the use of 1 or more of 3 manualized evidence-based treatments, (b) modular, which involved a single modular protocol (Modular Approach to Treatment of Children With Anxiety, Depression, or Conduct Problems; MATCH) having clinical procedures similar to the standard condition but flexibly selected and sequenced using a guiding clinical algorithm, and (c) usual care. Results: As measured with combined Child Behavior Checklist and Youth Self-Report Total Problems, Internalizing, and Externalizing scales, the rate of improvement for youths in the modular condition was significantly better than for those in usual care. On a measure of functional impairment (Brief Impairment Scale), no significant differences were found among the 3 conditions. Analysis of service utilization also showed no significant differences among conditions, with almost half of youths receiving some additional services in the 1st year after beginning treatment, and roughly one third of youths in the 2nd year. Conclusions: Overall, these results extend prior findings, supporting incremental benefits of MATCH over usual care over a 2-year period. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
    Journal of Consulting and Clinical Psychology 08/2013; · 4.85 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: OBJECTIVE The authors developed a computerized adaptive test for anxiety that decreases patient and clinician burden and increases measurement precision. METHOD A total of 1,614 individuals with and without generalized anxiety disorder from a psychiatric clinic and community mental health center were recruited. The focus of the present study was the development of the Computerized Adaptive Testing-Anxiety Inventory (CAT-ANX). The Structured Clinical Interview for DSM-IV was used to obtain diagnostic classifications of generalized anxiety disorder and major depressive disorder. RESULTS An average of 12 items per subject was required to achieve a 0.3 standard error in the anxiety severity estimate and maintain a correlation of 0.94 with the total 431-item test score. CAT-ANX scores were strongly related to the probability of a generalized anxiety disorder diagnosis. Using both the Computerized Adaptive Testing-Depression Inventory and the CAT-ANX, comorbid major depressive disorder and generalized anxiety disorder can be accurately predicted. CONCLUSIONS Traditional measurement fixes the number of items but allows measurement uncertainty to vary. Computerized adaptive testing fixes measurement uncertainty and allows the number and content of items to vary, leading to a dramatic decrease in the number of items required for a fixed level of measurement uncertainty. Potential applications for inexpensive, efficient, and accurate screening of anxiety in primary care settings, clinical trials, psychiatric epidemiology, molecular genetics, children, and other cultures are discussed.
    American Journal of Psychiatry 08/2013; · 14.72 Impact Factor
  • J John Mann, Robert D Gibbons
    American Journal of Psychiatry 07/2013; · 14.72 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: To develop a computerized adaptive diagnostic screening tool for depression that decreases patient and clinician burden and increases sensitivity and specificity for clinician-based DSM-IV diagnosis of major depressive disorder (MDD). 656 individuals with and without minor and major depression were recruited from a psychiatric clinic and a community mental health center and through public announcements (controls without depression). The focus of the study was the development of the Computerized Adaptive Diagnostic Test for Major Depressive Disorder (CAD-MDD) diagnostic screening tool based on a decision-theoretical approach (random forests and decision trees). The item bank consisted of 88 depression scale items drawn from 73 depression measures. Sensitivity and specificity for predicting clinician-based Structured Clinical Interview for DSM-IV Axis I Disorders diagnoses of MDD were the primary outcomes. Diagnostic screening accuracy was then compared to that of the Patient Health Questionnaire-9 (PHQ-9). An average of 4 items per participant was required (maximum of 6 items). Overall sensitivity and specificity were 0.95 and 0.87, respectively. For the PHQ-9, sensitivity was 0.70 and specificity was 0.91. High sensitivity and reasonable specificity for a clinician-based DSM-IV diagnosis of depression can be obtained using an average of 4 adaptively administered self-report items in less than 1 minute. Relative to the currently used PHQ-9, the CAD-MDD dramatically increased sensitivity while maintaining similar specificity. As such, the CAD-MDD will identify more true positives (lower false-negative rate) than the PHQ-9 using half the number of items. Inexpensive (relative to clinical assessment), efficient, and accurate screening of depression in the settings of primary care, psychiatric epidemiology, molecular genetics, and global health are all direct applications of the current system.
    The Journal of Clinical Psychiatry 07/2013; 74(7):669-74. · 5.81 Impact Factor
  • JAMA Psychiatry 07/2013; 70(7):1-2. · 12.01 Impact Factor
  • The Journal of Clinical Psychiatry 06/2013; 74(6):630-631. · 5.81 Impact Factor
  • Anup Amatya, Dulal Bhaumik, Robert D Gibbons
    [Show abstract] [Hide abstract]
    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 04/2013; · 2.04 Impact Factor
  • Source
    Robert D Gibbons
    Shanghai archives of psychiatry. 04/2013; 25(2):124-30.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: To determine long-term effects on substance use and substance use disorder (SUD), up to 8 years after childhood enrollment, of the randomly assigned 14-month treatments in the multisite Multimodal Treatment Study of Children with Attention-Deficit/Hyperactivity Disorder (MTA; n = 436); to test whether medication at follow-up, cumulative psychostimulant treatment over time, or both relate to substance use/SUD; and to compare substance use/SUD in the ADHD sample to the non-ADHD childhood classmate comparison group (n = 261). Mixed-effects regression models with planned contrasts were used for all tests except the important cumulative stimulant treatment question, for which propensity score matching analysis was used. The originally randomized treatment groups did not differ significantly on substance use/SUD by the 8-year follow-up or earlier (mean age = 17 years). Neither medication at follow-up (mostly stimulants) nor cumulative stimulant treatment was associated with adolescent substance use/SUD. Substance use at all time points, including use of two or more substances and SUD, were each greater in the ADHD than in the non-ADHD samples, regardless of sex. Medication for ADHD did not protect from, or contribute to, visible risk of substance use or SUD by adolescence, whether analyzed as randomized treatment assignment in childhood, as medication at follow-up, or as cumulative stimulant treatment over an 8-year follow-up from childhood. These results suggest the need to identify alternative or adjunctive adolescent-focused approaches to substance abuse prevention and treatment for boys and girls with ADHD, especially given their increased risk for use and abuse of multiple substances that is not improved with stimulant medication. Clinical trial registration information-Multimodal Treatment Study of Children With Attention Deficit and Hyperactivity Disorder (MTA); http://clinical trials.gov/; NCT00000388.
    Journal of the American Academy of Child and Adolescent Psychiatry 03/2013; 52(3):250-63. · 6.97 Impact Factor

Publication Stats

4k Citations
626.26 Total Impact Points

Institutions

  • 2011–2014
    • University of Chicago
      • Department of Medicine
      Chicago, Illinois, United States
    • National Institutes of Health
      Maryland, United States
    • Harvard University
      • Department of Psychology
      Cambridge, MA, United States
  • 2013
    • New Mexico State University
      • Department of Health Science
      Las Cruces, NM, United States
  • 2012
    • Columbia University
      • Department of Psychiatry
      New York City, NY, United States
  • 1986–2011
    • University of Illinois at Chicago
      • • Center for Pharmacoeconomic Research
      • • Center for Cognitive Medicine
      • • Department of Psychiatry (Chicago)
      Chicago, IL, United States
  • 2010
    • University of Cincinnati
      Cincinnati, Ohio, United States
  • 2009
    • Harvard Medical School
      • Department of Health Care Policy
      Boston, Massachusetts, United States
  • 2008–2009
    • Loyola University Chicago
      Chicago, Illinois, United States
    • University of South Florida
      • Department of Epidemiology and Biostatistics
      Tampa, FL, United States
  • 2007
    • New York State Psychiatric Institute
      • Anxiety Disorders Clinic
      New York City, New York, United States
  • 2005
    • Stanford University
      Palo Alto, California, United States
  • 2001
    • Western Psychiatric Institute and Clinic
      Pittsburgh, Pennsylvania, United States
    • University of Pennsylvania
      • Department of Biostatistics and Epidemiology
      Philadelphia, PA, United States