Summarizing data through a piecewise linear growth curve model.
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.
Chandrasekaran
V
V
Tuberculosis Research Centre, Chennai 600 031, India.
Gopal
G
G
Thomas
A
A
D001699:Biometry; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D006801:Humans; D007917:Leprostatic Agents / Q000627:therapeutic use; D007918:Leprosy / Q000188:drug therapy / Q000382:microbiology; D016014:Linear Models; D008137:Longitudinal Studies; D009166:Mycobacterium leprae / Q000187:drug effects / Q000302:isolation & purification; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012293:Rifampin / Q000627:therapeutic use
D007917:Leprostatic Agents; D012293:Rifampin