A general model for multivariate analysis by Jeremy D. Finn

By Jeremy D. Finn

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1). 57. S(t) 1 1 - - -...... = ::J ... 00 - { , - - - - . . , - - - - - - . - - - - - - . , . - - - - . - - - - - - - - - . 2. Note that SAS uses a circle (0) as a default symbol for marking censored observations. 4 Comparison of Two Survival Functions: Log-Rank Test To compare the efficacy of two treatments, subjects who enter a clinical trial are randomly placed into two treatment groups. The survival data are then recorded for each group. The question of interest is whether the two treatments are equally effective.

The distribution of x = XtT -xc is N(fJ" 20"2/n), where fJ, is modeled as a random variable. ). A skeptical prior is used with zero mean and a large variance, which for computational convenience is chosen to be 0"2. At group size n = 50, the interim Bayesian test is carried out. 05. Otherwise, the trial continues until the minimum required group size of 97 subjects is accrued. 5. (b) Show that the posterior distribution of fJ, given x is N (x+ 2/n' 2(j2/n) + 2/n 1 1 (c) Find the values of the sample mean x for which the interim test accepts or rejects HI' Describe the stopping rule.

0123 2 . 7647Y4}. The estimated survival function in this model is S(t) = exp { - ), t} , t 2: O. 61 %. 61 % of that for the subjects in NYHA class I. 24 %. 65 %. 07 % . 92 % . 30%. 6. The Kaplan-Meier survival curve suggested that the exponential distribution model would have a better fit to the data. 23) may be computed. The lifereg procedure in SAS produces the test statistic as well as the fitted model parameter estimates. Following is the SAS code for this example. Note that because no regression modeling is involved, the list of covariates is empty.

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