Bivariate Binary Outcomes Research Articles

Simultaneity in binary outcome models with an application to employment for couples

Two of Peter Schmidt’s many contributions to econometrics have been to introduce a simultaneous logit model for bivariate binary outcomes and to study estimation of dynamic linear fixed effects panel data models using short panels. In this paper, we study a dynamic panel data version of the bivariate model introduced in Schmidt and Strauss (Econometrica 43:745–755, 1975) that allows for lagged dependent variables and fixed effects as in Ahn and Schmidt (J Econom 68:5–27, 1995). We combine a conditional likelihood approach with a method of moments approach to obtain an estimation strategy for the resulting model. We apply this estimation strategy to a simple model for the intra-household relationship in employment. Our main conclusion is that the within-household dependence in employment differs significantly by the ethnicity composition of the couple even after one allows for unobserved household specific heterogeneity.

Power and sample size for GEE analysis of incomplete paired outcomes in 2 × 2 crossover trials.

The 2 × 2 crossover trial uses subjects as their own control to reduce the intersubject variability in the treatment comparison, and typically requires fewer subjects than a parallel design. The generalized estimating equations (GEE) methodology has been commonly used to analyze incomplete discrete outcomes from crossover trials. We propose a unified approach to the power and sample size determination for the Wald Z-test and t-test from GEE analysis of paired binary, ordinal and count outcomes in crossover trials. The proposed method allows misspecification of the variance and correlation of the outcomes, missing outcomes, and adjustment for the period effect. We demonstrate that misspecification of the working variance and correlation functions leads to no or minimal efficiency loss in GEE analysis of paired outcomes. In general, GEE requires the assumption of missing completely at random. For bivariate binary outcomes, we show by simulation that the GEE estimate is asymptotically unbiased or only minimally biased, and the proposed sample size method is suitable under missing at random (MAR) if the working correlation is correctly specified. The performance of the proposed method is illustrated with several numerical examples. Adaption of the method to other paired outcomes is discussed.

3128 Copula-Based Regression Models for Correlated Bivariate Binary Outcomes: Application to Ophthalmologic Data Structures

OBJECTIVES/SPECIFIC AIMS: To account for association between the pair of binary outcomes, we adopt the Clayton and Frank copulas to indirectly specify their joint distributions. METHODS/STUDY POPULATION: We propose a regression model for the joint modelling of correlated bivariate outcomes using copulas. RESULTS/ANTICIPATED RESULTS: develop full maximum likelihood inference.

Bayesian predictive monitoring with bivariate binary outcomes in phase II clinical trials

Traditionally, phase II single-arm trials are based on a binary response variable that represents the efficacy of the experimental treatment. However, the introduction of an additional binary endpoint to assess whether the new therapy is also sufficiently safe for a further evaluation in larger phase III studies is often suggested. A Bayesian predictive strategy for interim monitoring in phase II trials focused on bivariate binary outcomes is proposed. At any interim analysis, the stopping rules are based on the evaluation of the predictive probability that the trial will show a conclusive result at the planned end of the study, given the observed data. The proposed procedure is applied using hypothetical scenarios that represent different situations which may occur at the interim stage. A real data application is also illustrated with the use of both non-informative and informative prior distributions. Finally, simulation studies to evaluate the operating characteristics of the design have been performed.

Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach.

When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-binomial model for the meta-analysis of studies with binary outcomes. This model is based on the composite likelihood approach and has several attractive features compared with the existing models such as bivariate generalized linear mixed model (Chu and Cole, 2006) and Sarmanov beta-binomial model (Chen et al., 2012). The advantages of the proposed marginal model include modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, because the marginal beta-binomial model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-binomial model with the bivariate generalized linear mixed model and the Sarmanov beta-binomial model by simulation studies. Interestingly, the results show that the marginal beta-binomial model performs better than the Sarmanov beta-binomial model, whether or not the true model is Sarmanov beta-binomial, and the marginal beta-binomial model is more robust than the bivariate generalized linear mixed model under model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration.

Testing and Sample Size for Polygonal One-Sided Hypotheses on Bivariate Binary Outcomes

In this article, we consider hypothesis testing and computationally feasible sample size determination for bivariate binary outcomes. The hypotheses are formulated as one-sided polygons, which allow flexible trade-offs between the two outcomes. Parameters are estimated by maximizing the likelihood. Hypothesis testing for each linear constraint is performed by the Wald, score, likelihood ratio, and exact tests. The overall hypothesis is then tested using either the union-intersection or intersection-union method. We propose methods to calculate both exact power functions and asymptotic power functions. Finite sample behaviors are evaluated by numerical examples. A data example is used for illustration.

이변량 효능과 안전성 이항변수의 표본수 결정방법

두 군의 처리를 비교하는 임상시험에서 효능(efficacy)과 안전성(safety)이 동일하게 중요한 변수로 취급되는 경우에 이변량(bivariate) 반응변수로서 분석되고 연구계획의 단계에서도 이변량 표본수 결정방법이 사용되어야 한다. Thall과 Cheng (1999)은 효능과 안전성의 반응값이 이변량 이항(bivariate binary) 변수인 경우의 표본수 결정방법을 제시하였으며, 본 연구에서는 목표모수 설정과정은 기존의 연구와 같으나 월콕슨-만-휘트니(Wilcoxon-Mann-Whitney: WMW) 통계량에 근거한 검정법과 표본수 결정방법을 제시한다. Thall과 Cheng (1999)의 검정통계량은 변수 변환시킨 비율의 근사 정규성에 근거하는 반면에, WMW 통계량은 확률에 근거한 비모수적 방법으로 이변량 이항변수 뿐만 아니라 이변량 순위변수로 측정된 반응값에도 적용시킬 수 있다 Thall과 Cheng (1999)에 제시한 항암치료 임상연구의 두 예제에 위의 두 다른 방법으로 계산된 표본수를 비교한 결과, Thall과 Cheng (1999)의 첫째 예제에서는 이변량 WMW 방법에 의한 표본수가 더욱 작았으나 둘째 예제에서는 더욱 큰 것으로 나타났다. We consider sample-size determination problem motivated by comparative clinical trials where patient outcomes are characterized by a bivariate outcome of efficacy and safety. Thall and Cheng (1999) presented a sample size methodology for the case of bivariate binary outcomes. We propose a bivariate Wilcoxon-Mann-Whitney(WMW) statistics for sample-size determination for binary outcomes, and this nonparametric method can be equally used to determine sample sizes of ordinal outcomes. The two methods of sample size determination rely on the same testing strategy for the target parameters but differs in the test statistics, an asymptotic bivariate normal statistic of the transformed proportions in Thall and Cheng (1999) and nonparametric bivariate WMW statistic in the other method. Sample sizes are calculated for the two experimental oncology trials, described in Thall and Cheng (1999), and for the first trial example the sample sizes of a bivariate WMW statistic are smaller than those of Thall and Cheng (1999), while for the second trial example the reverse is true.

Bayesian neural networks for bivariate binary data: an application to prostate cancer study

Prostate cancer is one of the most common cancers in American men. The cancer could either be locally confined, or it could spread outside the organ. When locally confined, there are several options for treating and curing this disease. Otherwise, surgery is the only option, and in extreme cases of outside spread, it could very easily recur within a short time even after surgery and subsequent radiation therapy. Hence, it is important to know, based on pre-surgery biopsy results how likely the cancer is organ-confined or not. The paper considers a hierarchical Bayesian neural network approach for posterior prediction probabilities of certain features indicative of non-organ confined prostate cancer. In particular, we find such probabilities for margin positivity (MP) and seminal vesicle (SV) positivity jointly. The available training set consists of bivariate binary outcomes indicating the presence or absence of the two. In addition, we have certain covariates such as prostate specific antigen (PSA), gleason score and the indicator for the cancer to be unilateral or bilateral (i.e. spread on one or both sides) in one data set and gene expression microarrays in another data set. We take a hierarchical Bayesian neural network approach to find the posterior prediction probabilities for a test and validation set, and compare these with the actual outcomes for the first data set. In case of the microarray data we use leave one out cross-validation to access the accuracy of our method. We also demonstrate the superiority of our method to the other competing methods through a simulation study. The Bayesian procedure is implemented by an application of the Markov chain Monte Carlo numerical integration technique. For the problem at hand, our Bayesian bivariate neural network procedure is shown to be superior to the classical neural network, Radford Neal's Bayesian neural network as well as bivariate logistic models to predict jointly the MP and SV in a patient in both the data sets as well as in the simulation study.

Dose-finding based on efficacy-toxicity trade-offs.

We present an adaptive Bayesian method for dose-finding in phase I/II clinical trials based on trade-offs between the probabilities of treatment efficacy and toxicity. The method accommodates either trinary or bivariate binary outcomes, as well as efficacy probabilities that possibly are nonmonotone in dose. Doses are selected for successive patient cohorts based on a set of efficacy-toxicity trade-off contours that partition the two-dimensional outcome probability domain. Priors are established by solving for hyperparameters that optimize the fit of the model to elicited mean outcome probabilities. For trinary outcomes, the new algorithm is compared to the method of Thall and Russell (1998, Biometrics 54, 251-264) by application to a trial of rapid treatment for ischemic stroke. The bivariate binary outcome case is illustrated by a trial of graft-versus-host disease treatment in allogeneic bone marrow transplantation. Computer simulations show that, under a wide rage of dose-outcome scenarios, the new method has high probabilities of making correct decisions and treats most patients at doses with desirable efficacy-toxicity trade-offs.

Maximum Likelihood Estimation of Bivariate Logistic Models for Incomplete Responses with Indicators of Ignorable and Non-Ignorable Missingness

Summary Missing observations are a common problem that complicate the analysis of clustered data. In the Connecticut child surveys of childhood psychopathology, it was possible to identify reasons why outcomes were not observed. Of note, some of these causes of missingness may be assumed to be ignorable, whereas others may be non-ignorable. We consider logistic regression models for incomplete bivariate binary outcomes and propose mixture models that permit estimation assuming that there are two distinct types of missingness mechanisms: one that is ignorable; the other non-ignorable. A feature of the mixture modelling approach is that additional analyses to assess the sensitivity to assumptions about the missingness are relatively straightforward to incorporate. The methods were developed for analysing data from the Connecticut child surveys, where there are missing informant reports of child psychopathology and different reasons for missingness can be distinguished.

A Likelihood Approach to Analysing Longitudinal Bivariate Binary Data

AbstractTo study the effect of methadone treatment in reducing multiple drug use, say heroin and benzodiazepines while controlling for their possible interaction, we analyse the results of urine drug screens from patients in treatment at a Sydney clinic in 1986. Weekly tests are either positive or negative for each type of drug and a bivariate binary model was developed to analyse such repeated bivariate binary outcomes. It models simultaneously the logit of each type of drug use and their log odds ratio linearly in some covariates. The serial correlation within subject is accounted for by including the ‘previous outcome’ of both drugs and their interaction as covariates. Our main conclusion is that drug use is reduced over time and the interaction between dose and time effects is not significant. It also suggests that while methadone maintenance is effective in reducing heroin use (CHAN et al., 1995), it does not suppress non‐opioid drug use. Concerning the association between the two drugs, it is found that the present strength of their association depends on the previous outcomes only through a measure of concordance. The proposed model has a tractable likelihood function and so a full likelihood analysis is possible. It can be easily extended to incorporate mixture effects. The EM algorithm is used for the estimation of parameters in the mixture model and model selection can be based on the Akaike Information Criterion.