ABSTRACT INTRODUCTION

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1 Survival 101: Reporting Survival Curves Using Sample Data from the Colorectal Cancer Outcomes Database Anna ter Veer, Julie Kilburn, Ann Vanderplas, Rizvan Mamet, Rebecca Ottesen, Joyce Niland, City of Hope, Duarte, CA ABSTRACT The National Comprehensive Cancer Network (NCCN) Colorectal Cancer (CRC) Outcomes Database collects outcomes data on patients presenting with primary colon or rectal cancer at 8 NCCN institutions across the United States. In addition to patient demographics, clinical characteristics, and types of treatments received, the database also includes recurrence and survival data. Data collection began in 2005, and there are 6095 patients in the database as of June 2010 (3,715 colon, 2,380 rectal). This paper focuses on the basic SAS code required to check follow-up status, compute overall survival, and plot overall survival curves with 95% confidence intervals for colorectal cancer (CRC) patients. Also, we will show how to summarize the overall survival results from SAS into a document that can easily be shared across institutions. The SAS code and results are presented using sample data from the NCCN CRC Outcomes Database. INTRODUCTION The NCCN is an alliance of 21 cancer centers across the United States, working together to develop treatment guidelines and provide initiatives for improving the quality and effectiveness of cancer care. One of these initiatives is the NCCN Oncology Outcomes Database Project, consisting of an Internet database, with patient-level data on demographic and clinical characteristics, treatments administered, and relapse and survival information. Currently, there are over 60,000 patients represented across five cancer sites: breast cancer, non-hodgkin s lymphomas, nonsmall cell lung, ovarian, and colorectal cancers (CRC). The Colorectal Cancer Outcomes Database collects outcomes data on patients presenting with primary colon or rectal cancer at the following eight NCCN institutions: City of Hope, Dana-Farber Cancer Center, Fox Chase Cancer Center, Memorial Sloan-Kettering Cancer Center, Arthur G. James Cancer Hospital & Richard J. Solove Research Institute at Ohio State University, Robert H. Lurie Cancer Center at Northwestern University, Roswell Park Cancer Institute, and University of Texas M.D. Anderson Cancer Center. Data collection began in July 2005, and the patterns of care data were first presented to the board members of NCCN institutions in In , four manuscripts were accepted for publication, discussing surgical outcomes and concordance with treatment guidelines and quality measures 1,2,3,4. Given that the data collection began five years ago, none of the publications so far have looked at long term outcomes, such as overall survival and disease-free survival. The patient eligibility requirements for entry into the CRC Outcomes Database are as follows: a minimum age of 18 years, stage I to IV colon or rectal cancer, and receipt of either surgery or chemotherapy at one of the eight NCCN institutions. Patients who presented for a second opinion, or who presented with early stage cancer that was removed with a polypectomy as the only primary cancer treatment, are not eligible. Data are collected longitudinally from the time of diagnosis. To capture treatment and relapse information, the patients medical records are reviewed at baseline, 4, 8, and 12 months after presentation to the NCCN, and annually thereafter. All treatments administered both within and outside of the NCCN center are collected. In addition, reasons for treatment discontinuation, and reasons for lack of treatment (such as patient refusal) are collected, if noted in the chart. The data entry undergoes rigorous data quality assurance (QA) processes, including quarterly review of QA reports by data managers at each institution. Last year, we presented a paper that highlighted the use of Dynamic Data Exchange (DDE) in creating automated reports in Excel for concordance with treatment guidelines and quality measures 5. This year, we will present a paper that provides basic SAS code for checking follow-up status, compute overall survival, and plot overall survival curves with 95% confidence intervals. 1

2 BASIC DATA STRUCTURE FOR SURVIVAL ANALYSES Clarify Cohort Definition and Outcome of Interest Before programming any analyses, it is important to be sure that the cohort eligibility criteria is clearly defined. This process could be as simple as selecting only patients with a clinical Stage I rectal cancer and a transabdominal resection, or as complicated as selecting patients with a Stage III colon cancer with no prior history of colorectal cancer, less than 75 years of age, surgically resected within 90 days of diagnosis, who survived at least 30 days from surgery, and received chemotherapy within 120 days of surgery. Likewise, it is necessary to ensure that the outcome of interest is also clearly defined. In this paper, the outcome of interest is death (an event), specifically time to death due to any cause (overall survival analyses). Depending on the design of the study, the starting point for survival analyses could be diagnosis date, date of treatment randomization, or date of surgery. For the purposes of illustration, this paper looks at a cohort of Stage IV colon cancer patients using data downloaded from the CRC Outcomes Database on July 30, The outcome of interest is overall survival estimated time at 1 year and 2 years from diagnosis. Survival time was defined from date of diagnosis to date of death due to any cause in years, or for patients who are alive at the last assessment period (censored observations), survival time was defined from date of diagnosis to the date of last contact. Understanding Data Collection and Data Structure of Patient Follow-Up Typically, patient follow-up data are collected at varying intervals and stored as multiple records per patient. For the CRC Outcomes Database, follow-up data are collected at 4, 8, and 12 months after presentation to the NCCN, and annually thereafter. The follow-up data are stored as multiple records per patient in a single SAS dataset, as shown in Table 1. Table 1. Data Structure of Patient Follow-Up (Multiple Records per Patient) Center ID Patient ID Assessment ID Data Collection Date NCCN Care Status* NCCN Care Status Determination Date Disease Status** /15/ /25/06 WD /14/ /26/06 WD /28/ /25/06 WD /18/ /29/06 WD /05/ /09/06 WD /28/ /21/06 WD /26/ /13/07 WD /05/ /20/08 WD /30/ /01/08 WD /18/ /14/05 WD /18/ /08/06 NED /18/ /06/06 WD /13/ /28/06 WD /17/ /09/07 WD /05/ /07/08 WD /23/ /23/09 NED *NCCN Care Status: 1=Patient seen at NCCN institution by study-specific provider, 2=Patient transferred out of NCCN institution, 4=Patient diagnosed with new non-colon or non-rectal cancer, 5=Patient not seen at NCCN, 6=Patient seen at NCCN institution by non-nccn provider **Disease Status: WD=With Disease, NED=No Evidence of Disease The Assessment ID corresponds to data at baseline, 4 months, 8 months, and 12 months after presentation to the NCCN assessment period. The data collection date is when the clinical research associate (CRA) abstracted the patient s record in order to determine follow-up status. The NCCN care status records the patient s status in relation to the NCCN institution during that assessment period. The NCCN care status determination date is the date of last contact. The disease status identifies whether the patient was with disease (WD) or had no evidence of disease (NED) during that assessment period. Only the last assessment record is necessary for overall survival analysis. In this example, patient 4 had his/her last assessment at 8 months with a last contact date of 09/25/06, and patient 14 had his/her last assessment at 36 months with a last contact date of 04/01/08. Both patients are with disease (WD) at time of last assessment. As for patient 16, this patient was followed until 48 months with a last contact date of 09/23/09. This patient had no evidence of disease (NED) at time of last assessment. 2

3 As for vital status information, including date of death and cause of death, the data are collected by the CRA using the following hierarchy of data sources: 1) Medical Record/Hospital Systems: Includes institutional-based paper and/or electronic medical records; outpatient visits for tests, procedures, and labs, and billing information. 2) Social Security Death Index (SSDI). 3) NCCN Institution Tumor Registry: Includes documentation from non-nccn provider visits, State Department of Health vital status updates, and patient and/or family contact. 4) National Death Index (NDI): If vital status remains unknown for patients who have not received care or follow-up at the NCCN institution for a period of two years or more, the institution, with guidance from NCCN, is responsible to prepare a file for submission to the National Death Index (NDI) every two years to verify vital status of these patients. The vital status data are stored as one record per patient in a separate SAS dataset, as shown in Table 2. As you will notice, there is no binary data element for vital status (0=alive, 1=dead). This is derived by looking at whether a patient is with or without a date of death, and no date of death means patient is alive as of the last assessment period. In this example, patient 4 and 14 died of progressive disease, and patient 16 is alive as of the last assessment. Table 2. Data Structure of Patient Vital Status (One Record per Patient) Center ID Patient ID Date of Death Source of Date of Death ICD* Cause of Death ICD* Version Descriptive Cause of Death /10/06 SSDI ICD-9 Progressive Disease /01/08 SSDI 153 ICD-9 Progressive Disease *ICD: International Classification of Disease CHECKING FOLLOW-UP STATUS Once the data collection and structure is understood, writing a program to check follow-up status becomes easier for the programmer. Julie Kilburn, who is presenting at Coder s Corner this year, has developed a macro to check whether patients in the database are overdue for follow-up. In other words, a CRA may be behind in collecting follow-up data at one institution, whereas at another institution a CRA has completed all the follow-up that is due at the time the data is downloaded for analyses. Because follow-up may vary by institution, it is important to review the proportion of patients with complete followup by assessment period, as well as by assessment period within each institution. The overdue macro only requires that the input dataset have the center and patient identifier, and that this dataset be sorted by these identifiers. The macro merges this input dataset with the patient follow-up dataset by center and patient identifier, and creates an output dataset (SAS dataset=outfu1) that contains a yes/no flag for whether a patient has complete follow-up (SAS variable=complete). Rather than showing the SAS code that calls in the overdue macro, only the basic SAS code for PROC FREQ is shown below. PROC FREQ is used to run a crosstab of the patient flag for complete follow-up (SAS variable=complete) by assessment period (SAS variable=assessid). It also runs a crosstab of this flag by assessment period for each NCCN institution (SAS variable=cid). The output is stored in an RTF file. 3

4 SAS Code /* Output proportion of patients with complete follow-up by assessment period */ ods listing close; ods rtf body='c:\files\nccn\wuss\2010\check Complete Followup.rtf'; proc freq data=outfu1; title 'Check Follow-Up Status by Assessment Period'; table assessid*complete /missing nocol nopercent; format complete yncomp.; /* Output proportion of patients with complete follow-up by assessment period for each NCCN institution */ proc sort data=outfu1; by cid; proc freq data=outfu1; title 'Check Follow-Up Status by Assessment Period for Each Center'; by cid; table assessid*complete /missing nocol nopercent; format complete yncomp.; ods rtf close; ods listing; The basic SAS output for the first PROC FREQ is shown below. In this example, there are a total of 1,578 stage IV colon cancer patients at baseline assessment. At 4 months, there are a total of 1576 stage IV patients, and only 1 patient is overdue for follow-up as of July 30, 2010, the date the data was downloaded from the database. As it turns out, the proportion of patients with complete follow-up is 99%, 94%, 88%, and 73% at the 4, 8, 12, and 24 month assessments. At 36 months, 47% of the patients are overdue for follow-up (n=300). At 48 months, 66% of the patients are overdue for follow-up (n=196). Given these high percentages of overdue patients, it makes little sense to report 3-year or 4-year overall survival. Although not displayed below, the proportion of patients with complete followup at 24 months ranges from as low as 61% to as high 88% by NCCN institution. This is something to report back to the CRAs, so that follow-up can be completed for centers that are falling behind in follow-up. SAS Output Table of assessid by complete assessid(assessid) complete Frequency Row Pct Yes No Total Total

5 In addition to checking whether patients are overdue for follow-up, it also is important to check the median follow-up time for the cohort, and then to determine whether further follow-up is needed before proceeding with any survival analysis. For example, if the objective is to report 3-year overall survival, then a median follow-up time of 11 months is well below that of 3 years, and thus it makes sense to wait until more patients are followed for a longer period of time before running an overall survival curve. The basic SAS code below calculates the days of follow-up by subtracting date of diagnosis (SAS variable=dxdt) from the date of last contact (SAS variable=nccncarestatdt). It also creates a patient flag for vital status (SAS variable=censor) based on the death date (SAS variable=deathdt). PROC FREQ and PROC MEANS are used to output descriptive statistics, including the median and range of days of follow-up. The descriptive statistics are reported for all patients, as well as for alive and dead patients by use of the CLASS statement in PROC MEANS. The output is stored in an RTF file. SAS Code /* Point to permanent SAS dataset location */ libname new 'C:\Files\NCCN\WUSS\2010'; proc format; value censorf 1='Dead' 0='Alive'; data cohort; set new.colon4; /* calculate days of follow-up from diagnosis date to last contact */ followup=nccncarestatdt-dxdt; /* create patient flag for vital status (1=dead, 0=alive/censored) */ if deathdt^=. then censor=1; else censor=0; format censor censorf.; label followup='days of Follow-Up' censor='vital Status'; ods listing close; ods rtf body='c:\files\nccn\wuss\2010\check Median Followup.rtf'; /* Output frequency of vital status for all patients*/ proc freq data=cohort; title 'Frequency of Vital Status for All Patients'; table censor /missing nocum; /* Output descriptive stats of days of follow-up for all patients */ proc means data=cohort maxdec=2 n mean std median q1 q3 min max; title 'Descriptive Statistics of Days of Follow-Up for All Patients'; var followup; /* Output descriptive stats of days of follow-up by vital status */ proc means data=cohort maxdec=2 n mean std median q1 q3 min max; title 'Descriptive Statistics of Days of Follow-Up for Alive Patients and Dead Patients'; class censor; var followup; ods rtf close; ods listing; 5

6 The basic SAS output for PROC FREQ is shown below. Of the 1,578 stage IV colon cancer patients, 923 patients are alive (58%) and 655 patients died (42%). The basic SAS output for the last PROC MEANS is also shown below. The median follow-up time is 394 days for patients that are alive (range, 18 to 1590 days). Nearly half of the patients had the event (death), 50% of the patients had more than 1 year of follow-up (median), and 25% of the patients had 2 years of follow-up (upper quartile). Given these descriptive statistics, it makes sense to at least report the 1-year overall survival. SAS Output Vital Status censor Frequency Percent Alive Dead Vital Status Analysis Variable : followup Days of Follow-Up N Obs N Mean Std Dev Median Lower Quartile Upper Quartile Minimum Maximum Alive Dead COMPUTING OVERALL SURVIVAL AND REPORTING SUMMARY STATISTICS The overall survival time definition was mentioned in the beginning of the paper. It was defined from date of diagnosis to date of death due to any cause in years. For patients who are alive at the last assessment period (censored observations), survival time was defined from date of diagnosis to the date of last contact. PROC LIFETEST estimates survivor functions using either the Kaplan-Meier or the life-table/actuarial method. In addition to these methods, PROC LIFETEST can compare two or more strata by using the STRATA statement. If all patients were followed for the same length of time (2 years) and no patients dropped out of the study (lost to followup), an estimate of 2-year survival probability would be the number of patients alive at 2 years divided by the total number of patients followed. However, because follow-up time varies by patient and patients drop out at each assessment, the Kaplan-Meier method is used to estimate both 1-year and 2-year survival for stage IV colon cancer. The basic SAS code below calculates the survival time in days, as well as in years. In addition to calculating the survival time, a patient flag for censored observations needs to be created for PROC LIFETEST to run. The value that equals censoring is placed within parentheses in the TIME statement. In this example, 0 represents censoring. The PLOTS=s option displays the survival curve and the OUTSURV=a option outputs the 95% confidence intervals for the survival probabilities to a temporary SAS dataset, which can then be printed using PROC PRINT. The output is stored in an RTF file. SAS Code /* Point to permanent SAS dataset location */ libname new 'C:\Files\NCCN\WUSS\2010'; proc format; value censorf 1='Dead' 0='Alive'; data cohort; set new.colon4; /* create patient flag for vital status (1=dead, 0=alive/censored) */ if deathdt^=. then censor=1; else censor=0; 6

7 /* calculate overall survival time (days) */ if censor=1 then osdays=deathdt-dxdt; else if censor=0 then osdays=nccncarestatdt-dxdt; /* calculate overall survival time (years) */ osyears=osdays/365.25; format censor censorf.; label censor='vital Status' osdays='overall Survival (Days)' osyears='overall Survival (Years)'; /* Output kaplan meier overall survival into RTF file */ ods listing close; ods rtf body='c:\files\nccn\wuss\2010\overall Survival.rtf'; proc lifetest data=cohort plots=s outsurv=a; title 'Overall Survival'; time osyears*censor(0); proc print data=a; ods rtf close; ods listing; SAS Output Only partial output of PROC LIFETEST is shown below. The first page of SAS output starts out with 1,578 stage IV colon cancer patients ( Left column). The first death ( Failed column) occurred at years. The first censored observation (patient is alive) occurred at years and is denoted by an asterisk. Overall survival probabilities are only calculated for deaths, thus explaining why the row is empty for the first censored observation. osyears Survival Failure Product-Limit Survival Estimates Survival Standard Error Failed Left * (continued in table below) The 1-year overall survival probability is displayed in the SAS output below in the first row under the header. The number of deaths ( Failed column) is 268 at 1 year, and the 1-year overall survival probability is 82% (Survival column). osyears Survival Failure Product-Limit Survival Estimates Survival Standard Error Failed Left * * (continued in table below) 7

8 The 2-year overall survival probability is displayed in the SAS output below in the first row under the header. The number of deaths ( Failed column) is 512 at 2 years, and the 2-year overall survival probability is 55% (Survival column). osyears Survival Failure Product-Limit Survival Estimates Survival Standard Error Failed Left * * * The summary statistics from PROC LIFETEST are displayed in the SAS output below. The median overall survival time is 2.24 years (Point Estimate column). The 95% confidence interval of the median overall survival time is 2.09 to 2.44 years (Lower and Upper columns). There were a total of 655 patients (42%) that died (Failed column). There were a total of 923 patients (58%) that were alive at the last assessment period (Censored column). Note that the mean survival time and standard error are also reported, but the preferred statistics to report are the median and 95% confidence interval if any censoring is present. Percent Quartile Estimates 95% Confidence Interval Point Estimate Transform [Lower Upper) LOGLOG LOGLOG LOGLOG Mean Standard Error Summary of the of Censored and Uncensored Values Total Failed Censored Percent Censored Following the summary statistics, the PLOTS=s option in PROC LIFETEST displays the overall survival curve (not shown), and the OUTSURV=a option stores the 95% confidence intervals for the survival probabilities to a temporary SAS dataset. Only partial output of PROC PRINT is shown below. The 1-year and 2-year overall survival probabilities are repeated, and the 95% confidence interval for 1 year is 79% to 83%, and the 95% confidence interval for 2 years is 52% to 58% (SDF_LCL and SDF_UCL columns). Obs osyears _CENSOR_ SURVIVAL SDF_LCL SDF_UCL (continued in table below) 8

9 Obs osyears _CENSOR_ SURVIVAL SDF_LCL SDF_UCL DISPLAYING OVERALL SURVIVAL WITH 95% CONFIDENCE INTERVALS Finally, ODS graphics is used to output a cleaner looking overall survival figure that can be saved as a picture file and imported into MS Word. This overall survival figure is displayed below, and is truncated at 4 years by using the WHERE statement within PROC LIFETEST. The tick marks represent censored observations (alive patients). Each step down represents an event (death). SAS Code /* Use ODS graphics to output overall survival curve */ ods graphics on; proc lifetest data=cohort plots=s outsurv=a; title 'Overall Survival'; where osyears<=4; /*truncate curve at 4 years*/ time osyears*censor(0); ods graphics off; SAS Figure 9

10 To add 95% confidence intervals to the overall survival figure, add the CL option. SAS Code /* Use ODS graphics and output 95% confidence interval band */ ods graphics on; proc lifetest data=cohort plots=s(cl) outsurv=a; title 'Overall Survival'; where osyears<=4; /*truncate curve at 4 years*/ time osyears*censor(0); ods graphics off; SAS Figure And finally, to add summary statistics to the figure, just cut and paste the summary statistics from PROC LIFETEST into the figure itself within MS Word. 10

11 CONCLUSION Checking patient follow-up status is a necessary step before proceeding with any survival analysis, in order to determine the proportion of patients that are overdue for follow-up. Also, checking the median follow-up time ensures that the majority of the patients have been followed for a specific time. The FREQ and MEANS procedures are simple ways of reporting descriptive statistics for follow-up. The LIFETEST procedure generates the overall survival statistics and figures. Finally, ODS graphics is used to output a cleaner looking overall survival figures that can be saved as picture files and imported into MS Word. For further discussion of LIFETEST, and also other procedures used in survival analysis, please refer to Paul D. Allison s book. 6 REFERENCES 1) Earle CC, Weiser MR, Ter Veer A, Skibber JM, Wilson J, Rajput A, Wong Y-N, Benson A, Shibata S, Romanus D, Niland J, Schrag D, Effect of Lymph Node Retrieval Rates on the Uilization of Adjuvant Chemotherapy in Stage II Colon Cancer, Journal of Surgical Oncology Dec 1; 100(7): ) Temple L, Romanus D, Weiser MR, Skibber JM, Wilson J, Rajput A, Benson A, Wong Y-N, Niland J, Ter Veer A, Schrag D, Sphincter-Preserving Surgery for Rectal Cancer at Specialty Centers in the United States, Annals of Surgery Aug; 250 (2): ) Schrag D, Weiser MR, Skibber JM, Ter Veer, A, Niland J, Wilson J, Rajput A, Wong Y-N, Benson A, Shibata S, Concordance with NCCN Colorectal Cancer Guidelines and ASCO/NCCN Quality Measures: An NCCN Institutional Analysis, Journal National Comprehensive Cancer Network Sep; 7(8) ) Rajput A, Romanus D, Weiser MR, Ter Veer A, Niland J, Skibber JM, Wong Y-N, Benson A, Schrag D, Meeting the 12 Lymph Node Benchmark in Colon Cancer, Journal of Surgical Oncology July 1; 102 (1): ) Ter Veer A, Reporting Quality of Cancer Care for the National Comprehensive Cancer Network A SAS Bridge to Excel, Proceedings of WUSS 2009 Conference, HOR-terVeer. 6) Allison, PD, Survival Analysis Using the SAS System: A Practical Guide, SAS Books by Users, CONTACT INFORMATION Your comments and questions are valued and encouraged. Please contact the author at: Anna ter Veer City of Hope 1500 E. Duarte Road Duarte, CA (818) aterveer@coh.org SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies. 11