Chandrasekaran V, Gopal G, Thomas A
Tuberculosis Research Centre, Chennai 600 031, India.
Stat Med. 2005 Apr 30;24(8):1139-51. doi: 10.1002/sim.1994.
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.
大多数临床试验研究都基于纵向设计，这种设计涉及对感兴趣变量的重复测量。此类设计在统计和科学方面都非常强大。统计理论和软件开发的最新进展纳入了协方差结构，如非结构化、复合对称、自回归和随机效应等，用于分析纵向数据。海瑟薇等人提出了一种使用线性增长曲线模型总结纵向数据的技术，并确定无论研究时长如何，总结统计量的数量固定为四个。在本文中，我们按照海瑟薇等人的思路，通过分段线性增长曲线模型开发了一种分析纵向数据的程序。在不同的协方差结构下，对麻风病数据拟合线性模型并计算残差平方和。还考虑了各种模型的拟合优度。为了证明所提出的方法具有稳健性且在拟合优度方面优于其他方法，我们进行了模拟研究并展示了结果。
