Abstract
In clinical research, study outcomes usually consist of various patients’ information corresponding to the treatment. To have a better understanding of the effects of different treatments, one often needs to analyze multiple clinical outcomes simultaneously, while the data are usually mixed with both continuous and discrete variables. We propose the multivariate mixed response model to implement statistical inference based on the conditional grouped continuous model through a pairwise composite-likelihood approach. It can simplify the multivariate model by dealing with three types of bivariate models and incorporating the asymptotical properties of the composite likelihood via the Godambe information. We demonstrate the validity and the statistic power of the multivariate mixed response model through simulation studies and clinical applications. This composite-likelihood method is advantageous for statistical inference on correlated multivariate mixed outcomes.
Highlights
Clinical research, such as toxicity studies and laboratory examinations, can provide relevant information for measuring the effect of various treatments or experiments on the patients
We aim to follow the approach of the conditional grouped continuous model (CGCM) and use the composite-likelihood method to analyze the joint distribution of multivariate mixed-type response variables, where the categorical response variables are modeled by continuous latent variables
The problem of mixed outcomes is widely discussed in health-related studies
Summary
Clinical research, such as toxicity studies and laboratory examinations, can provide relevant information for measuring the effect of various treatments or experiments on the patients. We are studying the efficacy of treatments along with the toxicity and adverse drug reactions simultaneously In this case, the severity level could be measured as discrete or ordinal data, while the clinical examination results such as the blood test measures are continuous. These multiple outcomes are analyzed by different linear models to estimate the effects of the treatments together with the relevant clinical and demographic information This approach ignores the correlation between the outcomes and only provides marginal inferences
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