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## Binary Response (binary + response)
## Selected Abstracts## A COMPARISON OF THE IMPRECISE BETA CLASS, THE RANDOMIZED PLAY-THE-WINNER RULE AND THE TRIANGULAR TEST FOR CLINICAL TRIALS WITH BINARY RESPONSES AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 1 2007Lyle C. Gurrin Summary This paper develops clinical trial designs that compare two treatments with a binary outcome. The imprecise beta class (IBC), a class of beta probability distributions, is used in a robust Bayesian framework to calculate posterior upper and lower expectations for treatment success rates using accumulating data. The posterior expectation for the difference in success rates can be used to decide when there is sufficient evidence for randomized treatment allocation to cease. This design is formally related to the randomized play-the-winner (RPW) design, an adaptive allocation scheme where randomization probabilities are updated sequentially to favour the treatment with the higher observed success rate. A connection is also made between the IBC and the sequential clinical trial design based on the triangular test. Theoretical and simulation results are presented to show that the expected sample sizes on the truly inferior arm are lower using the IBC compared with either the triangular test or the RPW design, and that the IBC performs well against established criteria involving error rates and the expected number of treatment failures. [source] ## Tests for Trend in Binary Response BIOMETRICAL JOURNAL, Issue 3 2003Georgia SalantiAbstract Tests for trend are important in analyzing data where the binary response in ordered categories is of interest. An example is in toxicology where the response in various dose groups is observed. For testing an association between the dose and the response the approach from Cochran and Armitage is widely used. However the result of this test is highly dependent on the scores assigned to the dose groups. Various dose assignments can lead to different outcomes. As an alternative the isotonic regression, a nonparametric method, is proposed. The outcome of this approach is independent of the quantification of the dose. Both methods (Cochran-Armitage test and isotonic regression) are compared within a simulation study to an isotonic version of the Pearson's Chi-squared test and the Wilcoxon rank sum test. [source] ## On Latent-Variable Model Misspecification in Structural Measurement Error Models for Binary Response BIOMETRICS, Issue 3 2009Xianzheng HuangSummary We consider structural measurement error models for a binary response. We show that likelihood-based estimators obtained from fitting structural measurement error models with pooled binary responses can be far more robust to covariate measurement error in the presence of latent-variable model misspecification than the corresponding estimators from individual responses. Furthermore, despite the loss in information, pooling can provide improved parameter estimators in terms of mean-squared error. Based on these and other findings, we create a new diagnostic method to detect latent-variable model misspecification in structural measurement error models with individual binary response. We use simulation and data from the Framingham Heart Study to illustrate our methods. [source] ## Diagnosis of Random-Effect Model Misspecification in Generalized Linear Mixed Models for Binary Response BIOMETRICS, Issue 2 2009Xianzheng HuangSummary Generalized linear mixed models (GLMMs) are widely used in the analysis of clustered data. However, the validity of likelihood-based inference in such analyses can be greatly affected by the assumed model for the random effects. We propose a diagnostic method for random-effect model misspecification in GLMMs for clustered binary response. We provide a theoretical justification of the proposed method and investigate its finite sample performance via simulation. The proposed method is applied to data from a longitudinal respiratory infection study. [source] ## Methods to account for spatial autocorrelation in the analysis of species distributional data: a review ECOGRAPHY, Issue 5 2007Carsten F. DormannSpecies distributional or trait data based on range map (extent-of-occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species' distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing spatial autocorrelation in the errors. However, we found that for presence/absence data the results and conclusions were very variable between the different methods. This is likely due to the low information content of binary maps. Also, in contrast with previous studies, we found that autocovariate methods consistently underestimated the effects of environmental controls of species distributions. Given their widespread use, in particular for the modelling of species presence/absence data (e.g. climate envelope models), we argue that this warrants further study and caution in their use. To aid other ecologists in making use of the methods described, code to implement them in freely available software is provided in an electronic appendix. [source] ## Testing for trends in the violation frequency of an environmental threshold in rivers ENVIRONMETRICS, Issue 1 2009Lieven ClementAbstract Nutrient pollution in rivers is a common problem. It can provoke algae blooms which are related to increased fish mortality. To restore the water status, the regulator recently has promulgated more restrictive regulations. In Flanders for instance, the government has introduced several manure decrees (MDs) to restrict nutrient pollution. Environmental regulations are commonly expressed in terms of threshold levels. This provides a binary response to the decision maker. To handle such data, we propose the use of marginalised generalised linear mixed models. They provide valid inference on trends in the exceedance frequency. The spatio-temporal dependence of the river monitoring network is incorporated by the use of a latent variable. The temporal dependence is assumed to be AR(1) and the spatial dependence is derived from the river topology. The mean model contains a term for the trend and corrects for seasonal variation. The model formulation allows an assessment on the level of individual sampling locations and on a more regional scale. The methodology is applied to a case study on the river Yzer (Flanders). It assesses the impact of the MDs on the violation probability of the nitrate standard. A trend change is detected after the introduction of the second MD. Copyright © 2008 John Wiley & Sons, Ltd. [source] ## A semiparametric model for binary response and continuous outcomes under index heteroscedasticity JOURNAL OF APPLIED ECONOMETRICS, Issue 5 2009Roger KleinThis paper formulates a likelihood-based estimator for a double-index, semiparametric binary response equation. A novel feature of this estimator is that it is based on density estimation under local smoothing. While the proofs differ from those based on alternative density estimators, the finite sample performance of the estimator is significantly improved. As binary responses often appear as endogenous regressors in continuous outcome equations, we also develop an optimal instrumental variables estimator in this context. For this purpose, we specialize the double-index model for binary response to one with heteroscedasticity that depends on an index different from that underlying the ,mean response'. We show that such (multiplicative) heteroscedasticity, whose form is not parametrically specified, effectively induces exclusion restrictions on the outcomes equation. The estimator developed exploits such identifying information. We provide simulation evidence on the favorable performance of the estimators and illustrate their use through an empirical application on the determinants, and affect, of attendance at a government-financed school. Copyright © 2009 John Wiley & Sons, Ltd. [source] ## Application of an adjusted ,2 statistic to site-specific data in observational dental studies JOURNAL OF CLINICAL PERIODONTOLOGY, Issue 1 2002Chul AhnAbstract Background: When a binary response is observed on teeth from each subject belonging to 2 or more exposure groups, application of the usual Pearson ,2 tests is invalid, since such responses within the same subject are not independent. Consequently, special statistical methods are needed to control for the correlation among teeth (sites) within the same subject. A simple adjustment to the Pearson ,2 statistic has been proposed for comparing proportions in site-specific data. However, the required assumptions for this statistic have not yet been thoroughly addressed. These assumptions are guaranteed to hold in experimental comparisons, but may be violated in some observational studies. Method: We investigate the conditions under which the adjusted ,2 statistic is valid and examine the performance of the adjusted ,2 statistic when these conditions are violated. Results: Our simulation study shows that the adjusted ,2 statistic generally produces good empirical type I errors under the assumption of a common intracluster correlation coefficient. Even if the intracluster correlations are different, the adjusted statistic performs well when the groups have equal numbers of clusters (subjects). Conclusion: The discussion is illustrated using an observational study of caries on the roots of teeth. Zusammenfassung Hintergründe: Wenn eine binäre Antwort auf Zähne von jedem Subjekt, das zu 2 oder mehreren experimentellen Gruppen gehört, benutzt wird, ist die Anwendung des gewöhnlich genutzten Pearson ,2 Testes nicht zulässig, da solche Antworten innerhalb des selben Subjektes nicht unabhängig sind. In der Konsequent werden spezielle statistische Methoden gebraucht, um die Korrelation unter den Zähnen (Flächen) innerhalb desgleichen Subjektes zu kontrollieren. Eine simple Adjustierung zu der Pearson ,2 Statistik wurde vorgeschlagen, um die Verhältnisse der flächenspezifischen Daten zu vergleichen. Jedoch wurde sich der notwendigen Anwendung für diese Statistik noch nicht ausführlich gewidmet. Diese Anwendungen sind garantiert bei experimentellen Vergleichen anzuwenden, aber mögen in einigen beobachteten Studien verletzt werden. Methoden: Wir untersuchten die Bedingungen unter welchen der adjustierte ,2 Test richtig ist und prüften die Leistung des adjustierten ,2 Testes, wenn diese Bedingungen verletzt werden. Ergebnisse: Unsere Simulationsstudie zeigt, dass die adjustierte ,2 Statistik im Allgemeinen gute empirische Irrtümer vom Typ I erbringt unter der Anwendung eines gewöhnlichen Intracluster Korrelationskoeffizienten. Auche wenn die Intracluster-Korrelationen unterschiedlich sind, ist die adjustierte Statistik gut zu nutzen, wenn die Gruppen gleiche Nummern von Clustern haben (Subjekte). Schlußfolgerungen: Die Diskussion wird illustriert unter Nutzung einer beobachteten Studie von Wurzelkaries. Résumé Origine: Lorsqu'une réponse binaire est observée sur les dents de chaque sujet appartenant à 2 groupes ou plus, l'application du test habituel ,2 de Pearson n'est pas valable puisque de telles réponses pour le même sujet sont indépendante. En conséquence, des méthodes statistiques spéciales sont nécessaires pour contrôler les corrélation entre les dents (sites) pour le même sujet. Un simple ajustement de la statistique ,2 de Pearson a été proposée pour comparer les proportions pour les données spécifiques de sites. Cependant, les hypothèses requises pour cette statistique n'ont pas encore été consciencieusement émises. Ces hypothèses sont certifiées être valable pour les comparaisons experimentales, mais peuvent ne pas être respectées dans quelques études observationnelles. Méthode: Nous recherchons les conditions pour lesquelles la statistique ,2 ajustée est valable et examinons sa performance lorsque ces conditions ne sont pas respectées. Résultats: Notre étude simulée montre que la statistique ,2 ajustée produit généralement de bonnes érreurs empiriques de type 1 dans l'hypothèse d'un coéfficient de corrélation commun intra-groupe. Même si les corrélations intragroupes sont différentes, la statistique ,2 ajustée s'exécute bien quand les ensembles ont des nombres égaux de groupes (sujets). Conclusion: La discussion est illustrée par une étude observationnelle des caries radiculaires. [source] ## Influence of Health and Environmental Information on Hedonic Evaluation of Organic and Conventional Bread JOURNAL OF FOOD SCIENCE, Issue 4 2008L.E. AnnettABSTRACT:, Grain from paired samples of the hard red spring wheat cultivar "Park" grown on both conventionally and organically managed land was milled and baked into 60% whole wheat bread. Consumers (n= 384) rated their liking of the bread samples on a 9-point hedonic scale before (blind) and after (labeled) receiving information about organic production. Consumers liked organic bread more (P < 0.05) than conventional bread under blind and labeled conditions. Environmental information about organic production did not impact consumer preference changes for organic bread, but health information coupled with sensory evaluation increased liking of organic bread. Ordinary least squares (OLS) and binary response (probit) regression models identified that postsecondary education, income level, frequency of bread consumption, and proenvironmental attitudes played a significant role in preference changes for organic bread. The techniques used in this study demonstrate that a combination of sensory and econometric techniques strengthens the evaluation of consumer food choice. [source] ## Another Aboriginal death in custody: uneasy alliances and tensions in the Mulrunji case LEGAL STUDIES, Issue 4 2008Associate Professor Jennifer CorrinThe death of an Aboriginal man, Mulrunji, in an Australian police cell in 2004 precipitated an extraordinary response from the community. The usual distinctions between the roles of police, coroner, prosecutors and politicians became confused and merged in the media maelstrom that followed the death. Uneasy alliances developed which qualified the binary response of right versus wrong. Could the coroner's findings be reconciled with the decision of the prosecutor not to try the police officer involved? Was the government's response of overriding the decision of the independent prosecutor justified? What does this case tell us about the adversarial and inquisitorial approaches to evidence? This paper examines the tensions at play in the response to the death of Mulrunji and explores the wide reaching implications for law and justice in death in custody cases. [source] ## Theory & Methods: Bias due to Ignoring the Sample Design in Case,Control Studies AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 3 2002John M. NeuhausCase,control studies allow efficient estimation of the associations of covariates with a binary response in settings where the probability of a positive response is small. It is well known that covariate,response associations can be consistently estimated using a logistic model by acting as if the case,control (retrospective) data were prospective, and that this result does not hold for other binary regression models. However, in practice an investigator may be interested in fitting a non,logistic link binary regression model and this paper examines the magnitude of the bias resulting from ignoring the case,control sample design with such models. The paper presents an approximation to the magnitude of this bias in terms of the sampling rates of cases and controls, as well as simulation results that show that the bias can be substantial. [source] ## Tests for Trend in Binary Response BIOMETRICAL JOURNAL, Issue 3 2003Georgia SalantiAbstract Tests for trend are important in analyzing data where the binary response in ordered categories is of interest. An example is in toxicology where the response in various dose groups is observed. For testing an association between the dose and the response the approach from Cochran and Armitage is widely used. However the result of this test is highly dependent on the scores assigned to the dose groups. Various dose assignments can lead to different outcomes. As an alternative the isotonic regression, a nonparametric method, is proposed. The outcome of this approach is independent of the quantification of the dose. Both methods (Cochran-Armitage test and isotonic regression) are compared within a simulation study to an isotonic version of the Pearson's Chi-squared test and the Wilcoxon rank sum test. [source] ## Evaluating Normal Approximation Confidence Intervals for Measures of 2 × 2 Association with Applications to Twin Data BIOMETRICAL JOURNAL, Issue 1 2003M.M. ShoukriAbstract Twin data are of interest to genetic epidemiologists for exploring the underlying genetic basis of disease development. When the outcome is binary, several indices of 2 × 2 association can be used to measure the degree of within twin similarity. All such measures share a common feature, in that they can be expressed as a monotonic increasing function of the within twin correlation. The sampling distributions of their estimates are influenced by the sample size, the correlation and the marginal distribution of the binary response. In this paper we use Monte-Carlo simulations to estimate the empirical coverage probabilities and evaluate the adequacy of the classical normal confidence intervals on the population values of these measures. [source] ## Longitudinal Studies of Binary Response Data Following Case,Control and Stratified Case,Control Sampling: Design and Analysis BIOMETRICS, Issue 2 2010Jonathan S. SchildcroutSummary We discuss design and analysis of longitudinal studies after case,control sampling, wherein interest is in the relationship between a longitudinal binary response that is related to the sampling (case,control) variable, and a set of covariates. We propose a semiparametric modeling framework based on a marginal longitudinal binary response model and an ancillary model for subjects' case,control status. In this approach, the analyst must posit the population prevalence of being a case, which is then used to compute an offset term in the ancillary model. Parameter estimates from this model are used to compute offsets for the longitudinal response model. Examining the impact of population prevalence and ancillary model misspecification, we show that time-invariant covariate parameter estimates, other than the intercept, are reasonably robust, but intercept and time-varying covariate parameter estimates can be sensitive to such misspecification. We study design and analysis issues impacting study efficiency, namely: choice of sampling variable and the strength of its relationship to the response, sample stratification, choice of working covariance weighting, and degree of flexibility of the ancillary model. The research is motivated by a longitudinal study following case,control sampling of the time course of attention deficit hyperactivity disorder (ADHD) symptoms. [source] ## On Latent-Variable Model Misspecification in Structural Measurement Error Models for Binary Response BIOMETRICS, Issue 3 2009Xianzheng HuangSummary We consider structural measurement error models for a binary response. We show that likelihood-based estimators obtained from fitting structural measurement error models with pooled binary responses can be far more robust to covariate measurement error in the presence of latent-variable model misspecification than the corresponding estimators from individual responses. Furthermore, despite the loss in information, pooling can provide improved parameter estimators in terms of mean-squared error. Based on these and other findings, we create a new diagnostic method to detect latent-variable model misspecification in structural measurement error models with individual binary response. We use simulation and data from the Framingham Heart Study to illustrate our methods. [source] ## Diagnosis of Random-Effect Model Misspecification in Generalized Linear Mixed Models for Binary Response BIOMETRICS, Issue 2 2009Xianzheng HuangSummary Generalized linear mixed models (GLMMs) are widely used in the analysis of clustered data. However, the validity of likelihood-based inference in such analyses can be greatly affected by the assumed model for the random effects. We propose a diagnostic method for random-effect model misspecification in GLMMs for clustered binary response. We provide a theoretical justification of the proposed method and investigate its finite sample performance via simulation. The proposed method is applied to data from a longitudinal respiratory infection study. [source] ## A semiparametric model for binary response and continuous outcomes under index heteroscedasticity JOURNAL OF APPLIED ECONOMETRICS, Issue 5 2009Roger KleinThis paper formulates a likelihood-based estimator for a double-index, semiparametric binary response equation. A novel feature of this estimator is that it is based on density estimation under local smoothing. While the proofs differ from those based on alternative density estimators, the finite sample performance of the estimator is significantly improved. As binary responses often appear as endogenous regressors in continuous outcome equations, we also develop an optimal instrumental variables estimator in this context. For this purpose, we specialize the double-index model for binary response to one with heteroscedasticity that depends on an index different from that underlying the ,mean response'. We show that such (multiplicative) heteroscedasticity, whose form is not parametrically specified, effectively induces exclusion restrictions on the outcomes equation. The estimator developed exploits such identifying information. We provide simulation evidence on the favorable performance of the estimators and illustrate their use through an empirical application on the determinants, and affect, of attendance at a government-financed school. Copyright © 2009 John Wiley & Sons, Ltd. [source] ## Sample Size Determination for Categorical Responses JOURNAL OF FORENSIC SCIENCES, Issue 1 2009Dimitris Mavridis Ph.D.Abstract:, Procedures are reviewed and recommendations made for the choice of the size of a sample to estimate the characteristics (sometimes known as parameters) of a population consisting of discrete items which may belong to one and only one of a number of categories with examples drawn from forensic science. Four sampling procedures are described for binary responses, where the number of possible categories is only two, e.g., licit or illicit pills. One is based on priors informed from historical data. The other three are sequential. The first of these is a sequential probability ratio test with a stopping rule derived by controlling the probabilities of type 1 and type 2 errors. The second is a sequential variation of a procedure based on the predictive distribution of the data yet to be inspected and the distribution of the data that have been inspected, with a stopping rule determined by a prespecified threshold on the probability of a wrong decision. The third is a two-sided sequential criterion which stops sampling when one of two competitive hypotheses has a probability of being accepted which is larger than another prespecified threshold. The fifth procedure extends the ideas developed for binary responses to multinomial responses where the number of possible categories (e.g., types of drug or types of glass) may be more than two. The procedure is sequential and recommends stopping when the joint probability interval or ellipsoid for the estimates of the proportions is less than a given threshold in size. For trinomial data this last procedure is illustrated with a ternary diagram with an ellipse formed around the sample proportions. There is a straightforward generalization of this approach to multinomial populations with more than three categories. A conclusion provides recommendations for sampling procedures in various contexts. [source] ## Multivariate tests comparing binomial probabilities, with application to safety studies for drugs JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 4 2005Alan AgrestiSummary., In magazine advertisements for new drugs, it is common to see summary tables that compare the relative frequency of several side-effects for the drug and for a placebo, based on results from placebo-controlled clinical trials. The paper summarizes ways to conduct a global test of equality of the population proportions for the drug and the vector of population proportions for the placebo. For multivariate normal responses, the Hotelling T2 -test is a well-known method for testing equality of a vector of means for two independent samples. The tests in the paper are analogues of this test for vectors of binary responses. The likelihood ratio tests can be computationally intensive or have poor asymptotic performance. Simple quadratic forms comparing the two vectors provide alternative tests. Much better performance results from using a score-type version with a null-estimated covariance matrix than from the sample covariance matrix that applies with an ordinary Wald test. For either type of statistic, asymptotic inference is often inadequate, so we also present alternative, exact permutation tests. Follow-up inferences are also discussed, and our methods are applied to safety data from a phase II clinical trial. [source] ## Correcting for Survey Misreports Using Auxiliary Information with an Application to Estimating Turnout AMERICAN JOURNAL OF POLITICAL SCIENCE, Issue 3 2010Jonathan N. KatzMisreporting is a problem that plagues researchers who use survey data. In this article, we develop a parametric model that corrects for misclassified binary responses using information on the misreporting patterns obtained from auxiliary data sources. The model is implemented within the Bayesian framework via Markov Chain Monte Carlo (MCMC) methods and can be easily extended to address other problems exhibited by survey data, such as missing response and/or covariate values. While the model is fully general, we illustrate its application in the context of estimating models of turnout using data from the American National Elections Studies. [source] ## A class of sequential tests for two-sample composite hypotheses THE CANADIAN JOURNAL OF STATISTICS, Issue 2 2006Edit GombayAbstract The authors propose a class of statistics based on Rao's score for the sequential testing of composite hypotheses comparing two treatments (populations). Asymptotic approximations of the statistics lead them to propose sequential tests and to derive their monitoring boundaries. As special cases, they construct sequential versions of the two-sample t-test for normal populations and two-sample z-score tests for binomial populations. The proposed algorithms are simple and easy to compute, as no numerical integration is required. Furthermore, the user can analyze the data at any time regardless of how many inspections have been made. Monte Carlo simulations allow the authors to compare the power and the average stopping time (also known as average sample number) of the proposed tests to those of nonsequential and group sequential tests. A two-armed comparative clinical trial in patients with adult leukemia allows them to illustrate the efficiency of their methods in the case of binary responses. Une classe de tests séquentiels pour des hypothèses composites sur deux échantillons Les auteurs proposent une classe de statistiques, basées sur le score de Rao, pouvant servir à tester séquentiellement des hypothèses composites comparant deux traitements (ou populations). Des approximations asymptotiques les conduisent à proposer des tests séquentiels dont ils déterminent les zones de rejet. Comme cas particulier, ils construisent des versions séquentielles du test de Student pour deux échantillons normaux et du test du score z pour deux populations binomiales. Les algorithmes proposés sont simples et faciles à mettre en ,uvre, puisqu'ils ne nécessitent aucune intégration numérique. De plus, l'utilisateur peut analyser ses données à n'importe quel moment, sans égard au nombre d'inspections déjà effectuées. Des simulations de Monte-Carlo permettent aux auteurs de comparer la puissance et le temps d'arr,t moyen (aussi appelé l'effectif moyen) des tests proposés à ceux de tests non séquentiels ou séquentiels groupés. Une étude clinique comparative à deux bras effectuée sur des patients atteints de leucémie adulte leur permet d'illustrer l'efficacité de leurs méthodes pour des réponses binaires. [source] ## Analysis of Misclassified Correlated Binary Data Using a Multivariate Probit Model when Covariates are Subject to Measurement Error BIOMETRICAL JOURNAL, Issue 3 2009Surupa RoyAbstract A multivariate probit model for correlated binary responses given the predictors of interest has been considered. Some of the responses are subject to classification errors and hence are not directly observable. Also measurements on some of the predictors are not available; instead the measurements on its surrogate are available. However, the conditional distribution of the unobservable predictors given the surrogate is completely specified. Models are proposed taking into account either or both of these sources of errors. Likelihood-based methodologies are proposed to fit these models. To ascertain the effect of ignoring classification errors and /or measurement error on the estimates of the regression and correlation parameters, a sensitivity study is carried out through simulation. Finally, the proposed methodology is illustrated through an example. [source] ## Optimal Response-Adaptive Designs for Normal Responses BIOMETRICAL JOURNAL, Issue 1 2009Atanu BiswasAbstract Most of the available response-adaptive designs in phase III clinical trial set up are not from any optimal consideration. An optimal design for binary responses is given by Rosenberger et al. (2001) and an optimal design for continuous responses is provided by Biswas and Mandal (2004). Recently, Zhang and Rosenberger (2006) [ZR] provided another design for normal responses. Biswas, Bhattacharya and Zhang (2007) pointed out that the design of ZR is not suitable for normally distributed responses, or any distribution having the possibility of negative mean, in general. But they only indicated the problem and bypassed the original problem and set up. In the present paper, we first start with the drawback of ZR. We then provide the appropriate optimal response-adaptive design for normal or continuous distributions which provides the necessary correction for the ZR problem. The proposed methods are illustrated using some real data (© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] ## On Latent-Variable Model Misspecification in Structural Measurement Error Models for Binary Response BIOMETRICS, Issue 3 2009Xianzheng HuangSummary We consider structural measurement error models for a binary response. We show that likelihood-based estimators obtained from fitting structural measurement error models with pooled binary responses can be far more robust to covariate measurement error in the presence of latent-variable model misspecification than the corresponding estimators from individual responses. Furthermore, despite the loss in information, pooling can provide improved parameter estimators in terms of mean-squared error. Based on these and other findings, we create a new diagnostic method to detect latent-variable model misspecification in structural measurement error models with individual binary response. We use simulation and data from the Framingham Heart Study to illustrate our methods. [source] |