Quality and Reliability Methods - SAS

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354 Reliability and Survival Analysis Chapter 19 Univariate Survival Analysis Figure 19.5 Weibull Output for Fan Data Since the fit is reasonable and the Beta estimate is near 1, you can conclude that this looks like an exponential distribution, which has a constant hazard rate. The Fitted Distribution Plots option produces three views of each distributional fit. Plots of the Weibull fit are shown in Figure 19.6. Figure 19.6 Fitted Distribution Plots Example: Rats Data An experiment was undertaken to characterize the survival time of rats exposed to a carcinogen in two treatment groups. The data are in the Rats.jmp table found in the sample data folder. The days variable is the survival time in days. Some observations are censored. The event in this example is death. The objective is to see whether rats in one treatment group live longer (more days) than rats in the other treatment group.
Chapter 19 Reliability and Survival Analysis 355 Univariate Survival Analysis Figure 19.7 Launch Window for Rats Data Use the Survival launch dialog to assign columns to the roles as shown in the example dialog above. Overview of the Univariate Survival Platform The Survival platform computes product-limit (Kaplan-Meier) survival estimates for one or more groups. It can be used as a complete analysis or is useful as an exploratory analysis to gain information for more complex model fitting. The Kaplan-Meier Survival platform does the following: • Shows a plot of the estimated survival function for each group and, optionally, for the whole sample. • Calculates and lists survival function estimates for each group and for the combined sample. • Shows exponential, Weibull, and lognormal diagnostic failure plots to graphically check the appropriateness of using these distributions for further regression modeling. Parameter estimates are available on request. • Computes the Log Rank and generalized Wilcoxon Chi-square statistics to test homogeneity of the estimated survival function across groups. • Analyzes competing causes, prompting for a cause of failure variable, and estimating a Weibull failure time distribution for censoring patterns corresponding to each cause. Initially, the Survival platform displays overlay step plots of estimated survival functions for each group as shown in Figure 19.8. A legend identifies groups by color and line type. Tables beneath the plot give summary statistics and quantiles for survival times. Estimated survival time for each observation are computed within groups. Survival times are computed from the combined sample. When there is more than one group, statistical tests compare the survival curves.
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Chapter 11 Pareto Plots 187 Launch
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References Abernethy, Robert B. (19
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References 407 Carroll, R.J., Ruppe
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References 409 Haaland, P.D. (1989)
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References 411 Lawless, J.F. (1982)
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References 413 Nelson, W.B. (2003),
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Index JMP Quality and Reliability M
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Index 419 Fit SEV 242 Fit Weibull 2
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Index 421 Q Quantiles table 356 R R
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Index 423 Weibull Plot 360 Westgard
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Quality and Reliability Methods - SAS

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