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• 00:00:00,000 --> 00:00:02,910 Hello, my name is Changbin Guo.

• I work for SAS R & D in statistical software

• development, specializing in survival and event history

• analysis.

• In this video I would like to talk to you

• about some new features available in SAS/STAT 15.1 that

• let you analyze the restricted mean survival time.

• The LIFETEST and PHREG procedures in SAS/STAT software

• provide popular survival analysis tools.

• The LIFETEST procedure focuses on nonparametric analysis,

• and it provides the Kaplan-Meier method

• for estimating the survival function and the log-rank test

• for comparing the survival functions

• from different populations.

• The PHREG procedure, on the other hand,

• focuses on regression modeling and supports the popular Cox

• proportional hazards model.

• The proportional hazards, or PH, assumption

• plays a fundamental role in both the log-rank test and the Cox

• regression.

• The proportional hazards assumption

• is the basis of the Cox regression.

• The log-rank test can be derived as a score test of the Cox

• model and has the highest power when the assumption is true.

• Any violation of the PH assumption

• influences the performances of the log-rank test and the Cox

• regression.

• Unfortunately, however, non-proportional hazards

• are often encountered in practice.

• A typical violation of the PH assumption

• occurs when the two survival curves cross.

• This implies that the corresponding hazard

• functions would also cross and therefore violates

• the PH assumption.

• Because the log-rank statistic is essentially

• a weighted sum of the hazard function over time,

• the log-rank test can lose much of its power

• to detect the true difference in survival.

• Results from the Cox PH regression

• have the same problem.

• When the true hazard ratio changes over time,

• the estimated hazard ratio from the fitted model

• ends up being a weighted average of the time-varying hazard

• ratios and can be interpreted as such.

• The problem is that the weights depend on the underlying

• survival and censoring distributions and therefore

• cannot be generalized straightforwardly.

• The restricted mean survival time, or RMST,

• is defined as the expected value of the restricted survival time

• from time zero to tau.

• The RMST is related to the survival mean.

• When tau goes to infinity, the RMST becomes the survival mean.

• The RMST can be more reliably estimated

• than the mean or median.

• For example, if the last observation is censored,

• then the mean becomes inestimable;

• and when the survival does not drop below 0.5,

• the median is undefined.

• You can perform essentially the same types

• of inferences with respect to the RMST

• as you can in the classical setting.

• To compare two groups, you can compare their RMSTs

• given the same tau value.

• For regression modeling, you can model

• the RMST via linear or log-linear models.

• These models enable you to study the effects in the RMST

• directly.

• 00:03:32,650 --> 00:03:36,810 Starting in SAS/STAT 15.1, new, dedicated features

• are available for analyzing the RMST.

• You can use the RMST option in the LIFETEST procedure

• to perform nonparametric analysis with respect

• to the RMST.

• You can also use the new RMSTREG procedure

• to fit linear and log-linear models of the RMST.

• In PROC LIFETEST, you can perform RMST analysis

• by specifying the RMST option in the PROC LIFETEST statement.

• When there are multiple groups, you

• can specify the STRATA statement to compute the RMST

• for each level of the STRATA variable.

• In addition to generating the survival plot,

• you can also request the RMST curve by specifying the PLOTS=

• option.

• You can use the MAXTIME= option to set the upper limit

• of the time axis for these plots.

• You use the DIFF option in the STRATA statement

• to compute the paired differences

• of the RMST among the groups.

• To protect yourself from falsely significant results,

• to the p-values.

• 00:04:50,730 --> 00:04:53,470 Results from the RMST analyses are

• displayed following the existing analyses in PROC LIFETEST.

• By default, PROC LIFETEST uses the smallest value

• among the largest observed times across the groups

• as the tau value for the analysis.

• As you can see from theRMST Estimatestable,

• the Large and Squamous groups appear to have larger RMST

• estimates than the Adeno and Small groups.

• You can interpret the RMST estimates as expected survival

• time for the first 186 days.

• TheRMST Test of Equalitytable displays results from

• the homogeneity test that show whether the RMSTs are the same

• among the groups.

• As the p-value indicates, the RMSTs

• appear to be different among the groups.

• The RMST curve plots the estimated RMST

• against different values of tau.

• Because the RMST can be computed only for tau values smaller

• than the largest observed times, the ranges of the RMST curves

• vary among the groups, with the Adeno group having the shortest

• range.

• You can read the estimated RMST values

• for a specific tau value from the plotted curve

• by drawing a vertical line.

• 00:06:14,700 --> 00:06:17,032 Results from pairwise comparisons

• suggest that you can divide the four risk

• groups into two classes.

• The first class consists of the Small and Adeno groups,

• and there is no significant difference in the RMST between

• them (p = 1.0000).

• The second class consists of the Large and Squamous groups,

• and the paired comparison is not significant (p = 0.9572).

• However, there is a significant difference

• in any paired comparison between the two classes.

• You can use the new RMSTREG procedure

• to perform regression analysis of the RMST.

• The RMSTREG procedure is a new addition

• to the SAS/STAT family for modeling time-to-event data.

• It compares most closely to PROC LIFEREG and PROC PHREG.

• But the three procedures have different focuses and support

• different functionality.

• PROC LIFEREG focuses on the time to event

• and fits accelerated failure time models

• by maximizing the likelihood function.

• PROC PHREG focuses on the hazard function

• and fits the popular Cox proportional hazards models

• by maximizing the partial likelihood function.

• By contrast, PROC RMSTREG focuses on the RMST

• and fits certain generalized linear models.

• Its estimation technique is based on estimating equations.

• PROC RMSTREG can handle right-censored data,

• just as PROC PHREG does.

• It can fit linear and log-linear models to the RMST.

• You can use two estimation techniques,

• the pseudovalue regression technique

• and the inverse probability censoring weighting technique,

• with pseudovalue regression being

• the default model-fitting method.

• After you fit a model, you can perform many types

• of postfitting analyses, just as you can do

• with generalized linear models.

• Consider fitting a model for the RMST at a tau value of 10 years

• with three independent variables--age, bilirubin,

• and Edema.

• As the code shows, you can specify the tau value

• for the analysis by using the TAU= option in PROC RMSTREG.

• You can include categorical variables in the model

• by specifying them in the CLASS statement.

• You can use pseudovalue regression to fit the model

• by specifying METHOD=PV.

• The LINK=LINEAR option specifies the linear model.

• As the equation shows, the model to be analyzed

• contains a total of five regression parameters

• that need to be estimated.

• Outputs from PROC RMSTREG look like outputs

• from a standard modeling procedure.

• The Type 3 test results in theAnalysis of Parameter

• Estimatestable indicate that all three variables are strong

• predictors of the RMST at 10 years.

• Because a linear model of the RMST has been fitted,

• the estimates are in fact on the RMST scale

• and you can interpret them straightforwardly.

• For example, on average the RMST is

• reduced by 0.0686 years with a one-year increase in age.