PubMed · 14820073
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1951. TUBERCULOSIS problem.. https://pubmed.ncbi.nlm.nih.gov/14820073/
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In this paper, we propose a novel approach using the concept of generalized variable (GV) for the confidence interval estimation of the difference of two intraclass correlation coefficients under unequal family sizes. This approach can also easily provide P-values for hypothesis testing. Simulation results show that the GV approach can provide confidence intervals with good coverage properties and perform hypothesis testing with satisfactory type-I error control. Furthermore, the confidence intervals and P-values by GV approach can be easily obtained by simulation. Therefore the GV approach is a suitable candidate for making inference concerning two intraclass correlation coefficients.
We present a simple semiparametric model for fitting subject-specific curves for longitudinal data. Individual curves are modelled as penalized splines with random coefficients. This model has a mixed model representation, and it is easily implemented in standard statistical software. We conduct an analysis of the long-term effect of radiation therapy on the height of children suffering from acute lymphoblastic leukaemia using penalized splines in the framework of semiparametric mixed effects models. The analysis revealed significant differences between therapies and showed that the growth rate of girls in the study cannot be fully explained by the group-average curve and that individual curves are necessary to reflect the individual response to treatment. We also show how to implement these models in S-PLUS and R in the appendix.
Most of the research in clinical trials is based on longitudinal designs, which involve repeated measurements of a variable of interest. Such designs are very powerful, both statistically and scientifically. Recent advances in statistical theory and software development incorporate the covariance structures such as unstructured, compound symmetry, auto-regressive and random effects, etc., for analysing longitudinal data. Hathaway et al. propose a technique for summarizing longitudinal data using linear growth curve model and establish that the number of summary statistics is fixed as four irrespective of the length of study. In this paper, we develop a procedure for analysing the longitudinal data through a piecewise linear growth curve model on the lines of Hathaway et al. Under different covariance structures, the linear model is fitted for Leprosy data and the residual sum of squares computed. Goodness of fit has also been considered for various models. In order to prove that the proposed method is robust and better than the others in terms of goodness of fit, simulation studies are carried out and the results presented.