Publication date: 08/13/2020

The Product-Limit Survival Fit red triangle menu contains the following options:

Survival Plot

Shows the overlaid survival plots for each group.

Failure Plot

Shows the overlaid failure plots (proportion failing over time) for each group (in the tradition of the reliability literature.) A failure plot reverses the vertical axis to show the number of failures rather than the number of survivors.

Note: The Failure Plot option replaces the Reverse Y Axis option found in older versions of JMP (which is still available in scripts).

Plot Options

Contains the following options:

Note: The first seven options (Show Points, Show Kaplan Meier, Show Combined, Show Confid Interval, Show Simultaneous CI, Show Shaded Pointwise CI, and Show Shaded Simultaneous CI) and the last two options (Fitted Survival CI, Fitted Failure CI) pertain to the initial survival plot and failure plot. The other five (Midstep Quantile Points, Connect Quantile Points, Fitted Quantile, Fitted Quantile CI Lines, Fitted Quantile CI Shaded) pertain only to the distributional plots.

Show Points

Shows the sample points at each step of the survival plot. Failures appear at the bottom of the steps, and censorings are indicated by points above the steps.

Show Kaplan Meier

Shows the Kaplan-Meier curves. This option is on by default.

Show Combined

Shows the survival curve for the combined groups in the Survival Plot.

Show Confid Interval

Shows the pointwise 95% confidence bands on the survival plot for groups and for the combined plot when it appears with the Show Combined option.

Show Points, Show Combined

When you select Show Points and Show Combined, the survival plot for the total or combined sample appears as a gray line. The points also appear at the plot steps of each group.

Show Simultaneous CI

Shows the simultaneous confidence bands for all groups on the plot. Meeker and Escobar (1998, ch. 3) discuss pointwise and simultaneous confidence intervals and the motivation for simultaneous confidence intervals in survival analysis.

Midstep Quantile Points

Changes the plotting positions to use the modified Kaplan-Meier plotting positions. These plotting positions are equivalent to taking mid-step positions of the Kaplan-Meier curve, rather than the bottom-of-step positions. This option is recommended, so it is on by default.

Connect Quantile Points

Shows the lines in the plot. This option is on by default.

Fitted Quantile

Shows the straight-line fit on the fitted Weibull, lognormal, or exponential plot. This option is on by default.

Fitted Quantile CI Lines

Shows the 95% confidence bands for the fitted Weibull, lognormal, or exponential plot.

Fitted Quantile CI Shaded

Shows the display of the 95% confidence bands for a fit as a shaded area or dashed lines.

Fitted Survival CI

Shows the confidence intervals (on the survival plot) of the fitted distribution.

Fitted Failure CI

Shows the confidence intervals (on the failure plot) of the fitted distribution.

Exponential Plot

Plots the cumulative exponential failure probability by time for each group. Lines that are approximately linear empirically indicate the appropriateness of using an exponential model for further analysis. For example, in Figure 13.5, the lines for Group 1 and Group 2 in the Exponential Plot are curved rather than straight. This indicates that the exponential distribution is not appropriate for this data. See Exponential, Weibull, and Lognormal Plots and Fits.

Exponential Fit

Produces the Exponential Parameters table and the linear fit to the exponential cumulative distribution function in the Exponential Plot (Figure 13.5). The parameter Theta corresponds to the mean failure time. See Exponential, Weibull, and Lognormal Plots and Fits.

Weibull Plot

Plots the cumulative Weibull failure probability by log(time) for each group. A Weibull plot that has approximately parallel and straight lines indicates a Weibull survival distribution model might be appropriate to use for further analysis. See Exponential, Weibull, and Lognormal Plots and Fits.

Weibull Fit

Produces the linear fit to the Weibull cumulative distribution function in the Weibull plot and two popular forms of Weibull estimates. These estimates are shown in the Extreme-value Parameter Estimates table and the Weibull Parameter Estimates tables (Figure 13.5). The Alpha parameter is the 0.632 quantile of the failure-time distribution. The Extreme-value table shows a different parameterization of the same fit, where Lambda = ln(Alpha) and Delta = 1/Beta. See Exponential, Weibull, and Lognormal Plots and Fits.

LogNormal Plot

Plots the cumulative lognormal failure probability by log(time) for each group. A lognormal plot that has approximately parallel and straight lines indicates a lognormal distribution is appropriate to use for further analysis. See Exponential, Weibull, and Lognormal Plots and Fits.

LogNormal Fit

Produces the linear fit to the lognormal cumulative distribution function in the lognormal plot and the LogNormal Parameter Estimates table shown in Figure 13.5. Mu and Sigma correspond to the mean and standard deviation of a normally distributed natural logarithm of the time variable. See Exponential, Weibull, and Lognormal Plots and Fits.

Fitted Distribution Plots

Use in conjunction with the fit options to show three plots corresponding to the fitted distributions: Survival, Density, and Hazard. If you have not performed a fit, no plot appears. See Fitted Distribution Plots.

Competing Causes

Performs an estimation of the Weibull model using the specified causes to indicate a failure event and other causes to indicate censoring. The fitted distribution appears as a dashed line in the Survival Plot. See Competing Causes.

Estimate Survival Probability

Estimates survival probabilities for the time values that you specify.

Estimate Time Quantile

Estimates a time quantile for each survival probability that you specify.

Save Estimates

Creates a data table containing survival and failure estimates, along with confidence intervals, and other distribution statistics.

Want more information? Have questions? Get answers in the JMP User Community (community.jmp.com).

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