COMPARISON OF METHODS FOR SURVIVAL ANALYSIS OF DEPENDENT DATA

Analysis of dependent survival data by conventional partial likelihood methods produces unbiased estimates of the regression coefficients bu t incorrectly estimates their variance. Here we compared the conventio nal partial likelihood methods with two alternative methods for analyz ing dependent survival data. The first alternative method estimated th e regression coefficient by the partial likelihood approach but adjust ed the variance to account for clustering. The second alternative meth od used marginal likelihoods to estimate both the regression coefficie nt and its variance. We evaluated the performance of the three methods using simulated and actual data. Simulated data were used to examine bias, efficiency, type I errors, and power. An Old Order Amish genealo gy was analyzed under these models to illustrate their performance on real data. The simulation study showed that all three methods provided unbiased estimates of the regression coefficient, but the efficiency of the estimated regression coefficient varied according to the simula tion conditions. The standard partial likelihood method showed increas ing type I error as the dependence increased within clusters. Both alt ernative methods had acceptable levels of type I errors at all depende nce levels. In the analysis of genealogic data, the regression coeffic ient was similar in the three methods showing stable estimates of the regression coefficients. The variance estimates from the alternative m ethods were slightly different from the conventional method, suggestin g a flow level of dependence. This study displays the effect of violat ing the independence assumption and provides guidelines for using alte rnative statistical methods. (C) 1996 Wiley-Liss, Inc.