Abstract
One of the most important issues that confront statisticians in longitudinal studies is dropouts. A variety of reasons may lead to withdrawal from a study and produce two different missingness mechanisms, namely, missing at random and non-ignorable dropouts. Nevertheless, none of these mechanisms is tenable in most studies. In addition, it may be that not all of dropouts are nonignorable. Many dropout handling methods have been employed by assuming only one of these dropout mechanisms. In this study, the dropout indicator is improved to take into account both dropout mechanisms. In this two-stage approach, a selection model is combined with an imputation method for dropout process in a longitudinal study with two time points. Simulation studies in a variety of situations are conducted to evaluate this approach in estimating the mean of the response variable at the second time point. This parameter is estimated by using maximum likelihood method. The results of the simulation studies indicate the superiority of the proposed method to the existing ones in estimating the mean of the variable with dropouts. In addition, this method is performed on a methadone dataset of 161 patients admitted to an Iranian clinic to estimate the final methadone dose.
Highlights
Dropouts still occur because of different sample and can be analyzed by using common statistical reasons
We introduce a dropout mechanism indicator instead of a dropout indicator to account for both dropout at random and dropout not at random mechanisms in a dataset when doubts exist with regard to the real dropout mechanism in a longitudinal study with two time points
Four measures are obtained from these studies: The mean estimate of the second variable Y2, absolute bias, Relative Bias (RB) and mean square error
Summary
Dropouts still occur because of different sample and can be analyzed by using common statistical reasons. The ID is a situation wherein dropouts are related to the dropout mechanism has a principal role in data outcome. Both the observed responses and dropout analysis because parameters that are related to the mechanism are modeled in ID. If the dropout mechanism probability of dropout may affect the parameter is RD or CRD, the mechanism is called ignorable and if estimation of the response’s distribution. It is ID the dropout mechanism is called nongnorable. Different models have been recommended for handling non-ignorable dropouts, wherein a joint model of dropout indicator and the outcome variable is assumed. A dropout indicator is applied as: 1, R=
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
