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Lv D, Grayling MJ, Zhang X, Zhao Q, Zheng H. A Bayesian approach to pilot-pivotal trials for bioequivalence assessment. BMC Med Res Methodol 2023; 23:301. [PMID: 38114931 PMCID: PMC10729540 DOI: 10.1186/s12874-023-02120-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 12/07/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND To demonstrate bioequivalence between two drug formulations, a pilot trial is often conducted prior to a pivotal trial to assess feasibility and gain preliminary information about the treatment effect. Due to the limited sample size, it is not recommended to perform significance tests at the conventional 5% level using pilot data to determine if a pivotal trial should take place. Whilst some authors suggest to relax the significance level, a Bayesian framework provides an alternative for informing the decision-making. Moreover, a Bayesian approach also readily permits possible incorporation of pilot data in priors for the parameters that underpin the pivotal trial. METHODS We consider two-sequence, two-period crossover designs that compare test (T) and reference (R) treatments. We propose a robust Bayesian hierarchical model, embedded with a scaling factor, to elicit a Go/No-Go decision using predictive probabilities. Following a Go decision, the final analysis to formally establish bioequivalence can leverage both the pilot and pivotal trial data jointly. A simulation study is performed to evaluate trial operating characteristics. RESULTS Compared with conventional procedures, our proposed method improves the decision-making to correctly allocate a Go decision in scenarios of bioequivalence. By choosing an appropriate threshold, the probability of correctly (incorrectly) making a No-Go (Go) decision can be ensured at a desired target level. Using both pilot and pivotal trial data in the final analysis can result in a higher chance of declaring bioequivalence. The false positive rate can be maintained in situations when T and R are not bioequivalent. CONCLUSIONS The proposed methodology is novel and effective in different stages of bioequivalence assessment. It can greatly enhance the decision-making process in bioequivalence trials, particularly in situations with a small sample size.
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Affiliation(s)
- Duo Lv
- Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Key Laboratory for Drug Evaluation and Clinical Research, Hangzhou, China
| | - Michael J Grayling
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
| | - Xinyue Zhang
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
| | - Qingwei Zhao
- Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Key Laboratory for Drug Evaluation and Clinical Research, Hangzhou, China
| | - Haiyan Zheng
- Department of Mathematical Sciences, University of Bath, Bath, UK.
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2
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He Y, Deng Y, You C, Zhou XH. Equivalence tests for ratio of means in bioequivalence studies under crossover design. Stat Methods Med Res 2022; 31:1405-1419. [PMID: 35422161 DOI: 10.1177/09622802221093721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
There are several problems concerning the statistical definition of average bioequivalence provided by the U.S. Food and Drug Administration. We proposed a ratio of means based on the original bioavailability measure as the definition for average bioequivalence. Under the log-normal distribution assumption, we proposed a hypothesis testing-based method and a confidence interval-based method to answer the question of whether the ratio of means falls into a predetermined interval. For the hypothesis testing-based method, we decomposed the null two-sided hypothesis of the ratio of means into two one-sided hypotheses. With the intersection-union theorem for asymptotic tests, we constructed two asymptotic size-α tests for the original null hypothesis. The method of variance estimation recovery was adopted to develop the confidence interval-based method. Simulation studies showed that the proposed methods can maintain the empirical type I error rate closely at the nominal level and is as powerful as the two one-sided t-test for testing the ratio of means under different settings. The application of the proposed methods was illustrated through six datasets in real-world examples.
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Affiliation(s)
- Yingdong He
- Department of Biostatistics, School of Public Health, 12465Peking University, China
| | - Yuhao Deng
- School of Mathematical Sciences, 12465Peking University, China
| | - Chong You
- Beijing International Center for Mathematical Research, 12465Peking University, China
| | - Xiao-Hua Zhou
- Department of Biostatistics, School of Public Health, 12465Peking University, China.,Beijing International Center for Mathematical Research, 12465Peking University, China.,Pazhou Lab, China
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3
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An H, Shin D. Multivariate Assessment for Bioequivalence Based on the Correlation of Random Effect. Drug Des Devel Ther 2021; 15:3675-3683. [PMID: 34465979 PMCID: PMC8396372 DOI: 10.2147/dddt.s318576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 08/18/2021] [Indexed: 11/23/2022] Open
Abstract
Background and Objective Bioequivalence tests are fundamental step in assessing the equivalence in bioavailability between a test and reference product. In practice, two separate linear mixed models (LMMs) with random subject effects, which have an area under the concentration-time curve (AUC) and the peak concentration (Cmax) as the responses, have become the gold standard for evaluating bioequivalence. Recently, Lee et al developed a multivariate hierarchical generalized linear model (HGLM) for several responses that modeled correlations among multivariate responses via correlated random effects. The objective of this study was to apply this multivariate analysis to the bioequivalence test in practice and to compare the performance of multivariate HGLM and separate LMMs. Methods Three pharmacokinetic datasets, fixed-dose combination (naproxen and esomeprazole), tramadol and fimasartan data were analyzed. We compared the 90% confidence interval (CI) for the geometric mean ratio (GMR) of a test product to a reference product using the multivariate HGLM and two conventional separate LMMs. Results We found that the 90% CIs for the GMRs of both AUC and Cmax from the multivariate HGLM were narrower than those from the separate LMMs: (0.843, 1.152) vs (0.825, 1.177) for Cmax of esomeprazole in fixed-dose combination data; (0.805, 0.931) vs (0.797, 0.941) for Cmax in tramadol data; (0.801, 1.501) vs (0.762, 1.578) for Cmax and (1.163, 1.332) vs (1.009, 1.341) for AUC in fimasartan data, consistent with the random subject effects from two separate LMMs being highly correlated in the three datasets (correlation coefficient r = 0.883; r = 0.966; r = 0.832). Conclusion This multivariate HGLM had good performance in the bioequivalence test with multiple endpoints. This method would provide a more reasonable option to reduce the 90% CI by adding correlation parameters and thus an advantage especially in evaluating the bioequivalence of highly variable drugs with broad 90% CIs.
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Affiliation(s)
- Hyungmi An
- Institute of Convergence Medicine, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | - Dongseong Shin
- Department of Pharmacology, Gachon University College of Medicine, Incheon, Korea.,Clinical Trials Center, Gachon University Gil Medical Center, Incheon, Korea
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4
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Peck CC, Campbell G. Bayesian Approach to Establish Bioequivalence: Why and How? Clin Pharmacol Ther 2019; 105:301-303. [DOI: 10.1002/cpt.1288] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 10/11/2018] [Indexed: 11/10/2022]
Affiliation(s)
- Carl C. Peck
- Department of Bioengineering and Therapeutic SciencesUniversity of California San Francisco California USA
- NDA Partners LLC San Luis Obispo California USA
| | - Gregory Campbell
- GCStat Consulting, LLC Silver Spring Maryland USA
- NDA Partners LLC Silver Spring Maryland USA
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5
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Pallmann P, Jaki T. Simultaneous confidence regions for multivariate bioequivalence. Stat Med 2017; 36:4585-4603. [PMID: 28857229 DOI: 10.1002/sim.7446] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 07/28/2017] [Accepted: 08/02/2017] [Indexed: 11/06/2022]
Abstract
Demonstrating bioequivalence of several pharmacokinetic (PK) parameters, such as AUC and Cmax , that are calculated from the same biological sample measurements is in fact a multivariate problem, even though this is neglected by most practitioners and regulatory bodies, who typically settle for separate univariate analyses. We believe, however, that a truly multivariate evaluation of all PK measures simultaneously is clearly more adequate. In this paper, we review methods to construct joint confidence regions around multivariate normal means and investigate their usefulness in simultaneous bioequivalence problems via simulation. Some of them work well for idealised scenarios but break down when faced with real-data challenges such as unknown variance and correlation among the PK parameters. We study the shapes of the confidence regions resulting from different methods, discuss how marginal simultaneous confidence intervals for the individual PK measures can be derived, and illustrate the application to data from a trial on ticlopidine hydrochloride. An R package is available.
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Affiliation(s)
- Philip Pallmann
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Thomas Jaki
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
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Tian S, Chang HH, Orange D, Gu J, Suárez-Fariñas M. A Bioequivalence Test by the Direct Comparison of Concentration-versus-Time Curves Using Local Polynomial Smoothers. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:4680642. [PMID: 28050196 PMCID: PMC5165228 DOI: 10.1155/2016/4680642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Revised: 08/29/2016] [Accepted: 09/07/2016] [Indexed: 11/17/2022]
Abstract
In order to test if two chemically or pharmaceutically equivalent products have the same efficacy and/or toxicity, a bioequivalence (BE) study is conducted. The 80%/125% rule is the most commonly used criteria for BE and states that BE cannot be claimed unless the 90% CIs for the ratio of selected pharmacokinetics (PK) parameters of the tested to the reference drug are within 0.8 to 1.25. Considering that estimates of these PK parameters are derived from the concentration-versus-time curves, a direct comparison between these curves motivates an alternative and more flexible approach to test BE. Here, we propose to frame the BE test in terms of an equivalence of concentration-versus-time curves which are constructed using local polynomial smoother (LPS). A metric is presented to quantify the distance between the curves and its 90% CIs are calculated via bootstrapping. Then, we applied the proposed procedures to data from an animal study and found that BE between a generic drug and its brand name cannot be concluded, which was consistent with the results by applying the 80%/125% rule. However, the proposed procedure has the advantage of testing only on a single metric, instead of all PK parameters.
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Affiliation(s)
- Suyan Tian
- Division of Clinical Research, First Hospital of Jilin University, 71 Xinmin Street, Changchun, Jilin 130021, China
- Center for Clinical and Translational Science, The Rockefeller University, 1230 York Avenue, New York, NY 10065, USA
- School of Mathematics, Jilin University, 2699 Qianjin Street, Changchun, Jilin 130012, China
| | - Howard H. Chang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA
| | - Dana Orange
- Laboratory of Molecular Neurooncology, The Rockefeller University, 1230 York Avenue, New York, NY 10065, USA
- Division of Rheumatology, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Jingkai Gu
- Clinical Pharmacology Center, Research Institute of Translational Medicine, First Hospital of Jilin University, Dongminzhu Street, Changchun 130021, China
- College of Life Science, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Mayte Suárez-Fariñas
- Center for Clinical and Translational Science, The Rockefeller University, 1230 York Avenue, New York, NY 10065, USA
- Center for Biostatistics, Department of Population, Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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7
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de Souza RM, Achcar JA, Martinez EZ, Mazucheli J. The use of asymmetric distributions in average bioequivalence. Stat Med 2016; 35:2525-42. [PMID: 26840012 DOI: 10.1002/sim.6885] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2014] [Revised: 12/31/2015] [Accepted: 01/05/2016] [Indexed: 11/06/2022]
Abstract
Generic drugs have been commercialized in numerous countries. Most of these countries approve the commercialization of a generic drug when there is evidence of bioequivalence between the generic drug and the reference drug. Generally, the pharmaceutical industry is responsible for the bioequivalence test under the supervision of a regulatory agency. This procedure is concluded after a statistical data analysis. Several agencies adopt a standard statistical analysis based on procedures that were previously established. In practice, we face situations in which this standard model does not fit to some sets of bioequivalence data. In this study, we propose an evaluation of bioequivalence using univariate and bivariate models based on an extended generalized gamma distribution and a skew-t distribution, under a Bayesian perspective. We introduce a study of the empirical power of hypothesis tests for univariate models, showing advantages in the use of an extended generalized gamma distribution. Three sets of bioequivalence data were analyzed under these new procedures and compared with the standard model proposed by the majority of regulatory agencies. In order to verify that the asymmetrical distributions are usually better fitted for the data, when compared with the standard model, model discrimination methods were used, such as the Deviance Information Criterion (DIC) and quantile-quantile plots. The research concluded that, in general, the use of the extended generalized gamma distribution may be more appropriate to model bioequivalence data in the original scale. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Roberto Molina de Souza
- Department of Social Medicine, University of São Paulo, São Paulo, Brazil.,Department of Mathematics, Federal Technological University of Paraná, Cornélio Procópio, Brazil
| | | | | | - Josmar Mazucheli
- Department of Statistics, State University of Maringá, Maringá, Brazil
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8
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Brown S, Ghosh P, Su L, Taylor K. Modelling household finances: A Bayesian approach to a multivariate two-part model. JOURNAL OF EMPIRICAL FINANCE 2015; 33:190-207. [PMID: 27212801 PMCID: PMC4871239 DOI: 10.1016/j.jempfin.2015.03.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
We contribute to the empirical literature on household finances by introducing a Bayesian multivariate two-part model, which has been developed to further our understanding of household finances. Our flexible approach allows for the potential interdependence between the holding of assets and liabilities at the household level and also encompasses a two-part process to allow for differences in the influences on asset or liability holding and on the respective amounts held. Furthermore, the framework is dynamic in order to allow for persistence in household finances over time. Our findings endorse the joint modelling approach and provide evidence supporting the importance of dynamics. In addition, we find that certain independent variables exert different influences on the binary and continuous parts of the model thereby highlighting the flexibility of our framework and revealing a detailed picture of the nature of household finances.
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Affiliation(s)
- Sarah Brown
- Department of Economics, University of Sheffield, 9 Mappin Street, Sheffield S1 4DT, UK
| | - Pulak Ghosh
- Department of Quantitative Methods and Information Systems, Indian Institute of Management, Bangalore, India
| | - Li Su
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Robinson Way, Cambridge, CB2 0SR, UK
| | - Karl Taylor
- Department of Economics, University of Sheffield, 9 Mappin Street, Sheffield S1 4DT, UK
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9
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Du L, Choi L. Likelihood approach for evaluating bioequivalence of highly variable drugs. Pharm Stat 2015; 14:82-94. [PMID: 25408492 PMCID: PMC4482106 DOI: 10.1002/pst.1661] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2014] [Revised: 09/22/2014] [Accepted: 10/23/2014] [Indexed: 01/31/2023]
Abstract
Bioequivalence (BE) is required for approving a generic drug. The two one-sided tests procedure (TOST, or the 90% confidence interval approach) has been used as the mainstream methodology to test average BE (ABE) on pharmacokinetic parameters such as the area under the blood concentration-time curve and the peak concentration. However, for highly variable drugs (%CV > 30%), it is difficult to demonstrate ABE in a standard cross-over study with the typical number of subjects using the TOST because of lack of power. Recently, the US Food and Drug Administration and the European Medicines Agency recommended similar but not identical reference-scaled average BE (RSABE) approaches to address this issue. Although the power is improved, the new approaches may not guarantee a high level of confidence for the true difference between two drugs at the ABE boundaries. It is also difficult for these approaches to address the issues of population BE (PBE) and individual BE (IBE). We advocate the use of a likelihood approach for representing and interpreting BE data as evidence. Using example data from a full replicate 2 × 4 cross-over study, we demonstrate how to present evidence using the profile likelihoods for the mean difference and standard deviation ratios of the two drugs for the pharmacokinetic parameters. With this approach, we present evidence for PBE and IBE as well as ABE within a unified framework. Our simulations show that the operating characteristics of the proposed likelihood approach are comparable with the RSABE approaches when the same criteria are applied.
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Affiliation(s)
- Liping Du
- Vanderbilt Center for Quantitative Sciences, Vanderbilt University, 2525 West End, Suite 11000, Nashville TN 37203
| | - Leena Choi
- Department of Biostatistics, School of Medicine, Vanderbilt University, 2525 West End, Suite 11000, Nashville TN 37203
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10
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Castro LM, Lachos VH, Ferreira GP, Arellano-Valle RB. Partially linear censored regression models using heavy-tailed distributions: A Bayesian approach. ACTA ACUST UNITED AC 2014. [DOI: 10.1016/j.stamet.2013.10.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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11
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Ghosh P, Nathoo F, Gönen M, Tiwari RC. Assessing noninferiority in a three-arm trial using the Bayesian approach. Stat Med 2011; 30:1795-808. [DOI: 10.1002/sim.4244] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2010] [Accepted: 01/31/2011] [Indexed: 11/09/2022]
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12
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Modeling neighborhood effects: the futility of comparing mixed and marginal approaches. Epidemiology 2010; 21:475-8; discussion 479-81. [PMID: 20539108 DOI: 10.1097/ede.0b013e3181d74a71] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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13
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Molina de Souza R, Achcar JA, Martinez EZ. Use of Bayesian methods for multivariate bioequivalence measures. J Biopharm Stat 2009; 19:42-66. [PMID: 19127466 DOI: 10.1080/10543400802513676] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
In this paper, we introduce a Bayesian analysis for bioequivalence data assuming multivariate pharmacokinetic measures. With the introduction of correlation parameters between the pharmacokinetic measures or between the random effects in the bioequivalence models, we observe a good improvement in the bioequivalence results. These results are of great practical interest since they can yield higher accuracy and reliability for the bioequivalence tests, usually assumed by regulatory offices. An example is introduced to illustrate the proposed methodology by comparing the usual univariate bioequivalence methods with multivariate bioequivalence. We also consider some usual existing discrimination Bayesian methods to choose the best model to be used in bioequivalence studies.
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Affiliation(s)
- Roberto Molina de Souza
- Departamento de Medicina Social, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, S.P. Brazil
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