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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,
you use the ADJ= option to make multiple-comparison adjustments
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 the “RMST Estimates” table,
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.
The “RMST Test of Equality” table 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 the “Analysis of Parameter
Estimates” table 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.
As you have seen from this brief presentation,
the restricted mean survival time
is a good alternative to the survival outcome.
Compared to the survival mean and median,
it can be more robustly estimated and is valid
when the proportional hazards assumption is violated.
Starting in SAS/STAT 15.1, you can use the new RMSTREG
procedure to perform regression analysis and the new RMST
option in PROC LIFETEST to perform nonparametric analyses.
You can consider the RMST option in PROC LIFETEST
to be a bonus option because it performs
RMST analysis in addition to the existing functionality in PROC
LIFETEST.
For more information about restricted mean analysis
and other resources, visit our website
at support.sas.com/statistics.
You can find overview papers about new developments
and past SAS Global Forum papers.
You can also sign up for newsletters
and browse through examples.
Thank you for watching!
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Performing Restricted Mean Survival Time Analysis (RMST) Using SAS/STAT

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qianh7 2019 年 10 月 7 日 に公開

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