class gender; Biometrics. proc sgplot data = dfbeta; 1469-82. We focus on basic model tting rather than the great variety of options. proc univariate data = whas500(where=(fstat=1)); We could test for different age effects with an interaction term between gender and age. Thus, because many observations in WHAS500 are right-censored, we also need to specify a censoring variable and the numeric code that identifies a censored observation, which is accomplished below with, However, we would like to add confidence bands and the number at risk to the graph, so we add, The Nelson-Aalen estimator is requested in SAS through the, When provided with a grouping variable in a, We request plots of the hazard function with a bandwidth of 200 days with, SAS conveniently allows the creation of strata from a continuous variable, such as bmi, on the fly with the, We also would like survival curves based on our model, so we add, First, a dataset of covariate values is created in a, This dataset name is then specified on the, This expanded dataset can be named and then viewed with the, Both survival and cumulative hazard curves are available using the, We specify the name of the output dataset, “base”, that contains our covariate values at each event time on the, We request survival plots that are overlaid with the, The interaction of 2 different variables, such as gender and age, is specified through the syntax, The interaction of a continuous variable, such as bmi, with itself is specified by, We calculate the hazard ratio describing a one-unit increase in age, or $$\frac{HR(age+1)}{HR(age)}$$, for both genders. Provides detailed reference material for using SAS/STAT software to perform statistical analyses, including analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, nonparametric analysis, mixed-models analysis, and survey data analysis, with numerous examples in addition to syntax and usage information. Biomedical and social science researchers who want to analyze survival data with SAS will find just what they need with Paul Allison's easy-to-read and comprehensive guide. ), Department of Statistics Consulting Center, Department of Biomathematics Consulting Clinic. fstat: the censoring variable, loss to followup=0, death=1, Without further specification, SAS will assume all times reported are uncensored, true failures. Checking the Cox model with cumulative sums of martingale-based residuals. This relationship would imply that moving from 1 to 2 on the covariate would cause the same percent change in the hazard rate as moving from 50 to 100. Notice that the interval during which the first 25% of the population is expected to fail, [0,297) is much shorter than the interval during which the second 25% of the population is expected to fail, [297,1671). where $$n_i$$ is the number of subjects at risk and $$d_i$$ is the number of subjects who fail, both at time $$t_i$$. $F(t) = 1 – exp(-H(t))$ 1 Notes on survival analysis using SAS These notes describe how some of the methods described in the course can be implemented in SAS. Data sets in SAS format and SAS code for reproducing some of the exercises are available on Biometrika. Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D. Allison, is an accessible, data-based introduction to methods of survival analysis. A simple transformation of the cumulative distribution function produces the survival function, $$S(t)$$: The survivor function, $$S(t)$$, describes the probability of surviving past time $$t$$, or $$Pr(Time > t)$$. For example, we found that the gender effect seems to disappear after accounting for age, but we may suspect that the effect of age is different for each gender. ; Using the equations, $$h(t)=\frac{f(t)}{S(t)}$$ and $$f(t)=-\frac{dS}{dt}$$, we can derive the following relationships between the cumulative hazard function and the other survival functions: $S(t) = exp(-H(t))$ model lenfol*fstat(0) = gender|age bmi|bmi hr; model lenfol*fstat(0) = gender age;; Using the assess statement to check functional form is very simple: First let’s look at the model with just a linear effect for bmi. In the code below, we model the effects of hospitalization on the hazard rate. Therneau, TM, Grambsch PM, Fleming TR (1990). A popular method for evaluating the proportional hazards assumption is to examine the Schoenfeld residuals. This study examined several factors, such as age, gender and BMI, that may influence survival time after heart attack. run; proc phreg data = whas500; As an example, imagine subject 1 in the table above, who died at 2,178 days, was in a treatment group of interest for the first 100 days after hospital admission. The primary focus of survival analysis is typically to model the hazard rate, which has the following relationship with the $$f(t)$$ and $$S(t)$$: The hazard function, then, describes the relative likelihood of the event occurring at time $$t$$ ($$f(t)$$), conditional on the subject’s survival up to that time $$t$$ ($$S(t)$$). In large datasets, very small departures from proportional hazards can be detected. Use PROC SUMMARY to calculate the number of events and person-time at risk in each exposure group and save this to a SAS data set (I've used a format to de ne the grouping); Notice the. Below we plot survivor curves across several ages for each gender through the follwing steps: As we surmised earlier, the effect of age appears to be more severe in males than in females, reflected by the greater separation between curves in the top graaph. run; lenfol: length of followup, terminated either by death or censoring. This seminar introduces procedures and outlines the coding needed in SAS to model survival data through both of these methods, as well as many techniques to evaluate and possibly improve the model. We can estimate the cumulative hazard function using proc lifetest, the results of which we send to proc sgplot for plotting. run; proc phreg data = whas500; We can plot separate graphs for each combination of values of the covariates comprising the interactions. class gender; Nevertheless, the bmi graph at the top right above does not look particularly random, as again we have large positive residuals at low bmi values and smaller negative residuals at higher bmi values. Our goal is to transform the data from its original state: to an expanded state that can accommodate time-varying covariates, like this (notice the new variable in_hosp): Notice the creation of start and stop variables, which denote the beginning and end intervals defined by hospitalization and death (or censoring). The cumulative distribution function (cdf), $$F(t)$$, describes the probability of observing $$Time$$ less than or equal to some time $$t$$, or $$Pr(Time ≤ t)$$. proc sgplot data = dfbeta; Thus, to pull out all 6 $$df\beta_j$$, we must supply 6 variable names for these $$df\beta_j$$. We cannot tell whether this age effect for females is significantly different from 0 just yet (see below), but we do know that it is significantly different from the age effect for males. Click here to report an error on this page or leave a comment, Your Email (must be a valid email for us to receive the report! SAS omits them to remind you that the hazard ratios corresponding to these effects depend on other variables in the model. Finally, we see that the hazard ratio describing a 5-unit increase in bmi, $$\frac{HR(bmi+5)}{HR(bmi)}$$, increases with bmi. Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D. Allison, is an accessible, data-based introduction to methods of survival analysis. We can examine residual plots for each smooth (with loess smooth themselves) by specifying the, List all covariates whose functional forms are to be checked within parentheses after, Scaled Schoenfeld residuals are obtained in the output dataset, so we will need to supply the name of an output dataset using the, SAS provides Schoenfeld residuals for each covariate, and they are output in the same order as the coefficients are listed in the “Analysis of Maximum Likelihood Estimates” table. Here are the typical set of steps to obtain survival plots by group: Let’s get survival curves (cumulative hazard curves are also available) for males and female at the mean age of 69.845947 in the manner we just described. proc sgplot data = dfbeta; Thus, it might be easier to think of $$df\beta_j$$ as the effect of including observation $$j$$ on the the coefficient. Springer: New York. First, each of the effects, including both interactions, are significant. This indicates that omitting bmi from the model causes those with low bmi values to modeled with too low a hazard rate (as the number of observed events is in excess of the expected number of events). Notice there is one row per subject, with one variable coding the time to event, lenfol: A second way to structure the data that only proc phreg accepts is the “counting process” style of input that allows multiple rows of data per subject. Any serious endeavor into data analysis should begin with data exploration, in which the researcher becomes familiar with the distributions and typical values of each variable individually, as well as relationships between pairs or sets of variables. Because of the positive skew often seen with followup-times, medians are often a better indicator of an “average” survival time. Now let’s look at the model with just both linear and quadratic effects for bmi. Stratify the model by the nonproportional covariate. run; proc print data = whas500(where=(id=112 or id=89)); In the code below, we show how to obtain a table and graph of the Kaplan-Meier estimator of the survival function from proc lifetest: Above we see the table of Kaplan-Meier estimates of the survival function produced by proc lifetest. As time progresses, the Survival function proceeds towards it minimum, while the cumulative hazard function proceeds to its maximum. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. of contact. In such cases, the correct form may be inferred from the plot of the observed pattern. Expressing the above relationship as $$\frac{d}{dt}H(t) = h(t)$$, we see that the hazard function describes the rate at which hazards are accumulated over time. The other covariates, including the additional graph for the quadratic effect for bmi all look reasonable. None of the solid blue lines looks particularly aberrant, and all of the supremum tests are non-significant, so we conclude that proportional hazards holds for all of our covariates. This analysis proceeds in much the same was as dfbeta analysis, in that we will: We see the same 2 outliers we identifed before, id=89 and id=112, as having the largest influence on the model overall, probably primarily through their effects on the bmi coefficient. The covariate effect of $$x$$, then is the ratio between these two hazard rates, or a hazard ratio(HR): $HR = \frac{h(t|x_2)}{h(t|x_1)} = \frac{h_0(t)exp(x_2\beta_x)}{h_0(t)exp(x_1\beta_x)}$. PDF WITH TEXT download. where $$d_i$$ is the number who failed out of $$n_i$$ at risk in interval $$t_i$$. The hazard function for a particular time interval gives the probability that the subject will fail in that interval, given that the subject has not failed up to that point in time. ; Survival Handbook Addeddate 2017-02-22 03:58:17 Identifier ... PDF download. In this interval, we can see that we had 500 people at risk and that no one died, as “Observed Events” equals 0 and the estimate of the “Survival” function is 1.0000. Each row of the table corresponds to an interval of time, beginning at the time in the “LENFOL” column for that row, and ending just before the time in the “LENFOL” column in the first subsequent row that has a different “LENFOL” value. Language: english. tells SAS to create the Kaplan estimate survival plots Specify data Tells SAS which method to use for life test procedure tells SAS which values are censored tells SAS the Survival time (event time) variable Censor Symbol One can request that SAS estimate the survival function by exponentiating the negative of the Nelson-Aalen estimator, also known as the Breslow estimator, rather than by the Kaplan-Meier estimator through the method=breslow option on the proc lifetest statement. Survival Analysis Approaches and New Developments using SAS, continued . download 1 file . It appears that for males the log hazard rate increases with each year of age by 0.07086, and this AGE effect is significant, AGE*GENDER term is negative, which means for females, the change in the log hazard rate per year of age is 0.07086-0.02925=0.04161. Thus, by 200 days, a patient has accumulated quite a bit of risk, which accumulates more slowly after this point. The PHREG procedure is a semi-parametric regression analysis using partial likelihood estimation. Before we dive into survival analysis, we will create and apply a format to the gender variable that will be used later in the seminar. With such data, each subject can be represented by one row of data, as each covariate only requires only value. format gender gender. However, in many settings, we are much less interested in modeling the hazard rate’s relationship with time and are more interested in its dependence on other variables, such as experimental treatment or age. Proportional hazards tests and diagnostics based on weighted residuals. Second, all three fit statistics, -2 LOG L, AIC and SBC, are each 20-30 points lower in the larger model, suggesting the including the extra parameters improve the fit of the model substantially. However, nonparametric methods do not model the hazard rate directly nor do they estimate the magnitude of the effects of covariates. Below we demonstrate use of the assess statement to the functional form of the covariates. In regression models for survival analysis, we attempt to estimate parameters which describe the relationship between our predictors and the hazard rate. The regression analyses of relative survival can be conducted easily using mainstream statistical software packages (SAS and STATA), thereby removing the reliance on special-purpose software. Researchers who want to analyze survival data with SAS will find just what they need with this fully updated new edition that incorporates the many enhancements in SAS procedures for survival analysis in SAS 9. In intervals where event times are more probable (here the beginning intervals), the cdf will increase faster. The estimator is calculated, then, by summing the proportion of those at risk who failed in each interval up to time $$t$$. Because this likelihood ignores any assumptions made about the baseline hazard function, it is actually a partial likelihood, not a full likelihood, but the resulting $$\beta$$ have the same distributional properties as those derived from the full likelihood. The Survival node performs survival analysis on mining customer databases when there are time-dependent outcomes. The estimate of survival beyond 3 days based off this Nelson-Aalen estimate of the cumulative hazard would then be $$\hat S(3) = exp(-0.0385) = 0.9623$$. run; proc phreg data = whas500; Wiley: Hoboken. Let us further suppose, for illustrative purposes, that the hazard rate stays constant at $$\frac{x}{t}$$ ($$x$$ number of failures per unit time $$t$$) over the interval $$[0,t]$$. The significant AGE*GENDER interaction term suggests that the effect of age is different by gender. Note: The terms event and failure are used interchangeably in this seminar, as are time to event and failure time. The SAS Enterprise Miner Survival node is located on the Applications tab of the SAS Enterprise Miner tool bar. 515-526. The mean time to event (or loss to followup) is 882.4 days, not a particularly useful quantity.