LONGITUDINAL AND INCOMPLETE CLINICAL STUDIES

Analyzing incomplete longitudinal clinical trial data.

Using standard missing data taxonomy, due to Rubin and co-workers, and simple algebraic derivations, it is argued that some simple but commonly used methods to handle incomplete longitudinal clinical trial data, such as complete case analyses and methods based on last observation carried forward, require restrictive assumptions and stand on a weaker theoretical foundation than likelihood-based ...

Analyzing Incomplete Discrete Longitudinal Clinical Trial Data

Commonly used methods to analyze incomplete longitudinal clinical trial data include complete case analysis (CC) and last observation carried forward (LOCF). However, such methods rest on strong assumptions, including missing completely at random (MCAR) for CC and unchanging profile after dropout for LOCF. Such assumptions are too strong to generally hold. Over the last decades, a number of ful...

Α Markov model for longitudinal studies with incomplete dichotomous outcomes

Missing outcome data constitute a serious threat to the validity and precision of inferences from randomized controlled trials. In this paper, we propose the use of a multistate Markov model for the analysis of incomplete individual patient data for a dichotomous outcome reported over a period of time. The model accounts for patients dropping out of the study and also for patients relapsing. Th...

Modeling Incomplete Longitudinal Data

analysis of incomplete longitudinal binary responses with bayesian method

longitudinal  study  plays  an  important  role  in  the  epidemiological,  clinical,  and  social  science studies. in these kinds of studies, every individual is observed frequently during a period of time. the statistical analysis of longitudinal presents special opportunities  and challenges. the repeated outcomes for one individual tend to be correlated among themselves also one of the pro...