To study the effect of methadone treatment in reducing multiple drug uses while controlling for their joint dependency and non-random dropout, we propose a bivariatebinary model with a separate informative dropout (I...
详细信息
To study the effect of methadone treatment in reducing multiple drug uses while controlling for their joint dependency and non-random dropout, we propose a bivariatebinary model with a separate informative dropout (ID) model. In the model, the logit of the probabilities of each type of drug-use and dropout indicator as well as the log of the odds ratio of both drug-uses are linear in some covariates and outcomes. The model allows the evaluation of the joint probabilities of bivariate outcomes. To account for the heterogeneity of drug use across patients, the model is further extended to incorporate mixture and random effects. Parameter estimation is conducted using a Bayesian approach and is demonstrated using a methadone treatment data. A simulation experiment is conducted to evaluate the effect of including an ID modeling to parameters in the outcome models.
To 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 ...
详细信息
To 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 bivariatebinary model was developed to analyse such repeated bivariatebinary outcomes. It models simultaneously the legit of each type of drug use and their log adds 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.
暂无评论