Kaplan Meier Survival Plot

Abstract: A typical random effects meta-analysis of odds-ratios assumes binomially distributed numbers of events in a treatment and control group and requires the proportion of deaths to be extracted from published papers. This data is often not available in the publications due to loss to follow-up. When the Kaplan Meier survival plot is available, it is common practice to manually measure the needed information from the plot and infer the probability of survival and then to infer a best-guess of the number of deaths. Uncertainty introduced from theses guesses is not accounted for in current models. This naive approach leads to over-certain results and potentially inaccurate conclusions. We propose the Uncertain Reading-Estimated Events model to construct each study's contribution to the meta-analysis separately using the data available for extraction in the publications. We use real and simulated data to illustrate our methods. Meta-analysis based on the observed number of deaths lead to biased estimates while our proposed model does not. Our results show increases in the standard deviation of the log-odds as compared to a naive meta-analysis that assumes ideal extracted data, equivalent to a reduction of the overall sample size of 43% in our example.

Abstract: If you are a medical, pharmaceutical, or life sciences researcher, you have probably analyzed time-to-event data (survival data). One of several survival analysis procedures that SAS/STAT® provides, the LIFETEST procedure computes Kaplan-Meier estimates of the survivor functions and compares survival curves between groups of patients. You can use the Kaplan-Meier plot to display the number of subjects at risk, confidence limits, equal-precision bands, Hall-Wellner bands, and homogeneity test p-value. You can control the contents of the survival plot by specifying procedure options with PROC LIFETEST. When the procedure options are insufficient, you can modify the graph templates with SAS macros. This paper provides examples of survival plot modifications using procedure options, graph template modifications using macros, and style template modifications. INTRODUCTION Data that measure lifetime or the length of time until the occurrence of an event are called survival data. Survival data are often medical data; examples include the survival time for heart transplant or cancer patients. Survival time is a measure of the duration of time until a specified event (such as relapse or death) occurs. Survival data consist of survival time and possibly a set of independent variables thought to be associated with the survival time variable. The system that gives rise to the event of interest can be biological (as for most medical data) or physical (as for engineering data). Survival analysis estimates the underlying distribution of the survival time variable and assesses the dependence of the survival time variable on the independent variables. Standard data analysis methods are not appropriate for survival data. Survival times are generally positively skewed, and it is not reasonable to assume that data of this type have a normal distribution. Furthermore, survival times are often censored. The survival time of an individual is right censored when the event of interest has not been observed for that individual. For example, a patient who is recruited for a clinical trial drops out of the trial or the event is not observed when the period of data collection ends. In either case, the observed time is less than the true survival time. Analysis of survival data must take censoring into account and correctly use both the censored observations and the uncensored observations. The LIFETEST procedure in SAS/STAT is a nonparametric procedure for analyzing survival data. You can use PROC LIFETEST to compute the Kaplan-Meier (1958) curve, which is a nonparametric maximum likelihood estimate of the survivor function. You can display the Kaplan-Meier plot that contains step functions representing the Kaplan-Meier curves of different samples. You can also use PROC LIFETEST to compare the survivor functions of different samples by the log-rank test. The data that are used in this paper come from 137 bone marrow transplant patients in a study by Klein and Moeschberger (1997) and are available in the BMT data set in the Sashelp library. At the time of transplant, each patient is classified in one of three risk categories: ALL (acute lymphoblastic leukemia), AML (acute myelocytic leukemia)—Low-Risk, and AML—High-Risk. The endpoint of interest is the disease-free survival time, which is the time in days until death, relapse, or the end of the study. The variable Group represents the patient’s risk category, the variable T represents the disease-free survival time, and the variable Status is the censoring indicator. A status of 1 indicates an event time, and a status of 0 indicates a censored time. All examples use the 12.1 release of SAS software from 2012. Three types of examples are provided: specifying procedure options, modifying graph templates, and modifying style templates. CONTROLLING THE SURVIVAL PLOT BY SPECIFYING PROCEDURE OPTIONS This section provides a series of examples that use ODS Graphics and the PLOTS= option in the PROC LIFETEST statement to control the appearance of the survival plot. You can use the following statements to enable ODS Graphics and run PROC LIFETEST: ods graphics on; ods select survivalplot(persist) failureplot(persist); proc lifetest data=sashelp.BMT; time T * Status(0); strata Group; run; The results are displayed in Figure 1. ODS Graphics is enabled for this step and all subsequent steps until ODS Graphics is disabled. ODS Graphics remains enabled throughout the examples in this paper. The ODS SELECT statement persistently selects just the survival and failure time plot for this and subsequent steps. Each analysis produces only one of these two plots. You specify in the TIME statement that the disease-free survival time is recorded in the variable T. You can further specify that the variable Status indicates censoring and 0 indicates a censored time. Separate survivor functions are compared for each of the groups in the Group variable, which you specify in the STRATA statement. The graph in Figure 1 consists of three step functions, one for each of the three groups of patients. The graph shows that patients in the AML—Low-Risk group have longer disease-free survival than patients in the ALL and AML—High-Risk groups.

Abstract: If you are a medical, pharmaceutical, or life sciences researcher, you have probably analyzed time-to-event data (survival data). One of several survival analysis procedures that SAS/STAT ® provides, the LIFETEST procedure computes Kaplan-Meier estimates of the survivor functions and compares survival curves between groups of patients. You can use the Kaplan-Meier plot to display the number of subjects at risk, confidence limits, equal-precision bands, Hall-Wellner bands, and homogeneity test p-value. You can control the contents of the survival plot by specifying procedure options with PROC LIFETEST. When the procedure options are insufficient, you can modify the graph templates with SAS macros. This paper provides examples of survival plot modifications using procedure options, graph template modifications using macros, and style template modifications.