Download Reporting Quality of Cancer Care for the National Comprehensive Cancer Network - A SAS Bridge to Excel

Reporting Quality of Cancer Care
for the National Comprehensive Cancer Network – A SAS Bridge to Excel.
Anna ter Veer, Rebecca Ottesen, Ann Vanderplas, Julie Kilburn, Dorothy Romanus,
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 eight NCCN institutions across the United
States. The primary objective of the database is to evaluate patient demographics and clinical characteristics, types
of treatments received, and number of patients concordant with NCCN treatment guidelines and quality measures,
which in turn are reported back to participating institutions and physicians. These treatment guidelines are updated
on a continual basis and developed by multidisciplinary panels from NCCN institutions. This article focuses on a SAS
to Excel application, developed to automate the reporting of the concordance with treatment guidelines and quality
measures. The report includes histograms of treatments received for colorectal cancer, and the number and
proportion of patients that received concordant care, along with the 95% confidence intervals and institution range for
each treatment guideline and quality measure. The sample SAS code and the final Excel report are presented using
aggregate 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 Research Project, consisting of a network-based database, with patient-level data
on demographic and clinical characteristics, treatments administered and reasons for discontinuation, and relapse
and vital status information. Currently, there are over 60,000 patients represented by five cancer sites: breast cancer,
non-Hodgkin’s lymphomas, non-small cell lung, ovarian, and colorectal cancers.
The Colorectal Cancer (CRC) 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 was first presented to the board of NCCN member institutions in 2006. Since then, a number of
abstracts have been presented at the American Society of Clinical Oncology (ASCO) annual conferences from 2007
to 2009. In 2009, three manuscripts were accepted for publication, discussing surgical outcomes and concordance
with treatment guidelines and quality measures1,2,3.
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 histology of adenocarcinoma, including mucinous and signet ring
adenocarcinoma, 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 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. The QA reports are the
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focus of another WUSS 2009 paper by Kilburn , “An Example of Using Differing Methods to Create Excel Output from
SAS”.
The focus of this paper is to discuss the sample SAS code that automates the reporting of concordance with
treatment guidelines and quality measures into an Excel template using DDE. The Excel template consists of various
Worksheets that are customized by the project manager and statistician. The resulting CRC Benchmarking Packet is
a 28-page Excel report that includes histograms of treatments received for colorectal cancer, and the number and
proportion of patients that received concordant care, along with the 95% confidence intervals and institution range for
each treatment guideline and quality measure. The rationale for choosing Dynamic Data Exchange (DDE) for the
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benchmarking packets is further discussed in another WUSS 2009 paper by Ottesen , “Custom Outcomes
Benchmarking Reports Using Dynamic Data Exchange, The Only Way To Go”.
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REPORTING PATTERNS OF CARE FOR COLORECTAL CANCER
One of the main purposes of the NCCN CRC Outcomes Database is to deliver benchmark data to the participating
institutions, practicing physicians, and NCCN Guideline Panels. This annual report of benchmark data consists of
reporting: 1) patterns of care and 2) concordance with treatment guidelines and quality measures. The patterns of
care include patient and clinical characteristics, as well as the types of chemotherapy received for the eight
institutions combined (all patients) and each institution alone (center-specific), along with the ranges of the eight
NCCN centers from low to high percent (center ranges). Because each institution keeps their results confidential, the
center-specific results will not be disclosed in this paper, and only the all patients and center ranges results will be
presented.
As an example from this report, Table 1 shows the types of adjuvant chemotherapy for Stage II colon cancer patients.
The category column lists all the various types of adjuvant chemotherapy. The next column shows the number and
percent of patients receiving each treatment for all eight NCCN institutions combined. Of the 172 Stage II colon
cancer patients receiving adjuvant chemotherapy, 92 (53%) received FOLFOX, 37 (22%) received Capecitabine, and
20 (12%) received 5-FU therapy. The remaining patients either received a combination with Oxaliplatin (7%) or
another type of adjuvant chemotherapy (5%). The center ranges column shows the minimum and maximum percent
for each type of adjuvant chemotherapy across the eight participating institutions. For example, the lowest percent
and the highest percent of patients receiving FOLFOX were 33% and 83% respectively, in comparison to 53% for all
patients. The last column would typically show the number and percent of patients specific to an institution, but due
to confidentiality the column is intentionally left blank.
Figure 1 is a graphical display of the distribution of the types of adjuvant chemotherapy for all patients, and matches
what is reported in Table 1 under the All Patients column. Both the table and figure reside in an Excel template that
can be easily customized by the project manager and statistician. The numbers are output directly from SAS to the
Excel template using DDE, and the SAS code for the patterns of care section of the benchmarking packet is
discussed in detail in WUSS 2009 paper by Ottesen5, and therefore not presented in this paper.
Table 1. Adjuvant Chemotherapy in Stage II Colon Cancer Patients
All Patients
N (%)
Category
5FU
5FU/Oxaliplatin
Capecitabine
Capecitabine/Oxaliplatin
FOLFIRI
FOLFOX
FOLFOX/Bevacizumab
Other
20 ( 12 )
5(3 )
37 ( 22 )
7(4 )
1 ( <1 )
92 ( 53 )
2(1 )
8(5 )
Center Ranges
(low - high %)
(0
(0
(7
(0
(0
( 33
(0
(0
Center Specific
N (%)
- 32 )
-8 )
- 41 )
-7 )
-8 )
- 83 )
- 13 )
- 27 )
Figure 1. Adjuvant Chemotherapy in Stage II Colon Cancer Patients
100
90
80
70
60
50
40
30
20
10
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LF
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REPORTING CONCORDANCE WITH TREATMENT GUIDELINES
In 1995, the NCCN set out to develop a comprehensive set of diagnostic, treatment, and supportive care guidelines
for oncology practice (http://www.nccn.org/clinical.asp). The NCCN Clinical Practice Guidelines in Oncology are
updated on a continual basis and developed by multidisciplinary panels from NCCN institutions. Each NCCN
guideline consists of an algorithm or decision pathway outlining care management.
Fox example, Figure 2 show the NCCN guideline for patients with rectal cancer and clinical stage T3, NO or Tany,
N1-2. The decision pathway shows that the primary treatment is transabdominal resection with or without
neoadjuvant therapy. Under the guidance of the project manager, the statistician first programs the cohort definition.
For this guideline, the cohort is defined as rectal cancer patients with clinical stage IIA or III. This can be easily
programmed by using the disease classification and primary site data elements, and the use of a staging algorithm to
pull clinical stage IIA or III. The statistician then programs whether a patient is concordant or non-concordant with the
treatment guideline. A patient is concordant if at least one of three conditions is met: 1) patient had a transabdominal
resection, or 2) patient had a transabdominal resection with neoadjuvant therapy, or 3) patient was enrolled on a
clinical trial for surgery. If none of these conditions are met, then the patient is non-concordant and categorized into
one of two non-concordant categories: 1) patient received neoadjuvant therapy alone or 2) patient received neither
transabdominal resection nor neoadjuvant therapy.
Figure 2. Rectal Cancer Treatment Guideline
The CRC benchmarking packet evaluates six NCCN guidelines, including the guideline for rectal cancer patients with
clinical stage IIA or III. Table 2 displays the concordance results for this guideline. The first column lists the
recommended treatment guideline in abbreviated format, and further below it lists the various treatments that the
patients received, and whether these treatments were concordant or non-concordant with the recommended
treatment. The first row reports the overall number and percent of patients concordant with the guideline, followed by
the center range. The subsequent rows report the number and percent of patients concordant within each
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concordant treatment category, as well as each non-concordant treatment category. Of the 584 rectal cancer
patients, 569 (97%) received the recommended treatment, and concordance among the eight participating NCCN
institution ranged from 92% to 100%. The majority of concordant patients received transabdominal resection with
neoadjuvant therapy (74%), followed by transabdominal resection (20%) and enrollment on a clinical trial for surgery
(3%). Only 3% of the patients were non-concordant. The majority of the non-concordant patients (80%) received
neoadjuvant therapy only without a primary cancer resection, and the remaining patients (20%) received neither
therapy nor surgery. The overall concordance results for the six guidelines, along with the center-specific
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concordance results, are discussed in detail in the JNCCN manuscript .
Table 2. Concordance with Rectal Cancer Treatment Guideline
All Centers
Recommended Treatment
Transabdominal Resection + / Neoadjuvant Therapy
Concordant
Patients
Total
Patients
Concordant
Patients
Center Range
n
N
%
(low % - high %)
569
584
(97%)
( 92%-100%)
Concordant
Neoadjuvant Therapy +Transabdominal
Resection
432 / 584
(74%)
Transabdominal Resection
118 / 584
(20%)
Clinical Trial
19 / 584
(3%)
Neoadjuvant Therapy Alone
12 / 584
(2%)
No Neoadjuvant Therapy, No
Transabdominal Resection
3 / 584
(1%)
Non-concordant
Sample Data Structure for Table 2
The sample data structure for Table 2 consists of only four SAS variables. The derivation of these variables depends
on the guideline programming itself and is rather complicated. For example, the cohort first needs to be defined
based on clinical characteristics of the guideline branch. Then concordance is defined and can be complicated if a
patient has multiple therapy and surgery records. Complex algorithms have been developed in order to pick a first
line therapy and primary cancer resection. The four SAS variables are center identifier (cid), patient identifier (pid),
the reason a patient is concordant/non-concordant (reason), and the concordance result (conc), coded as either Yes
or No.
cid pid reason conc
1 51 Clinical Trial Yes
1 52 Neoadjuvant Therapy + Transabdominal Resection Yes
2 44 Neoadjuvant Therapy Only No
Sample SAS Code for Table 2
The sample SAS code for Table 2 consists of using PROC MEANS and PROC FREQ to output summary statistics
into a temporary dataset, and then the summary statistics are concatenated into a character variable with a DATA
STEP. Prior to invoking DDE with the FILENAME statement , the output variables are merged together in one
dataset.
/* Point to permanent SAS datasets location */
libname old 'C:\Files\NCCN\WUSS\2009';
/* Open the customized Excel template */
options noxsync noxwait;
X 'C:\Files\NCCN\WUSS\2009\Example2.xls';
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/* Calculate the total number of patients under a guideline */
proc means data=old.example2 noprint;
var pid;
output out=AggTot (keep=NTot) N=NTot;
run;
/* Calculate the frequency and percent of concordant patients (conc) for all
centers */
proc freq data=old.example2 noprint;
table conc / missing missprint out=AggFreq;
run;
data AggFreq2;
set AggFreq;
rename count=AggTot;
/* Format percent for Excel */
AggPct='('||trim(left(put(round(percent),3.)))||'%)';
run;
/* Calculate the frequency and percent of concordant and non-concordant patients
by reason (reason) for all centers */
proc freq data=old.example2 noprint;
table reason / missing missprint out=ReasonFreq;
run;
data ReasonFreq2;
set ReasonFreq;
rename count=ReasonTot;
/* Format percent for Excel */
ReasonPct='('||trim(left(put(round(percent),3.)))||'%)';
run;
/* Calculate the frequency and percent of concordant patients (conc) for each
center */
proc sort data=old.example2;
by cid;
run;
proc freq data=old.example2 noprint;
table conc / missing missprint out=CenterFreq;
by cid;
run;
data CenterFreq2;
set CenterFreq;
rename count=CenterTot;
/* Format percent for Excel */
CenterPct='('||trim(left(put(round(percent),3.)))||'%)';
run;
/* Calculate the center ranges of percent concordant patients using dataset from
previous data step */
proc means data=CenterFreq2 noprint;
/* For concordant patients with a minimum of 10 patients in a center*/
where conc='Yes' and CenterTot>=10;
var percent;
output out=Ranges min=min max=max;
run;
data Ranges2;
set Ranges;
/* Format range for Excel, by combining min and max */
Range='('||put(min,3.)||'%-'||right(put(max,3.))||'%)';
run;
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/* Output variables (AggTot, NTot, AggPct, Range) to row 8, columns 5 to 8 of the
Excel Table */
filename out1 DDE "Excel|C:\Files\NCCN\WUSS\2009\[Example2.xls]Rec St IIA III Sx
Guideline!R8C5:R8C8";
data _null_;
merge AggFreq2 (where=(conc='Yes'))
AggTot
Ranges2;
file out1 notab;
put AggTot '09'x NTot '09'x AggPct '09'x Range '09'x;
run;
/* Output variables (ReasonTot, NTot, ReasonPct) to rows 11 to 17,
of the Excel Template */
filename out2 DDE "Excel|C:\Files\NCCN\WUSS\2009\[Example2.xls]Rec
Guideline!R11C5:R11C6";
filename out3 DDE "Excel|C:\Files\NCCN\WUSS\2009\[Example2.xls]Rec
Guideline!R12C5:R12C6";
filename out4 DDE "Excel|C:\Files\NCCN\WUSS\2009\[Example2.xls]Rec
Guideline!R13C5:R13C6";
filename out5 DDE "Excel|C:\Files\NCCN\WUSS\2009\[Example2.xls]Rec
Guideline!R16C5:R16C6";
filename out6 DDE "Excel|C:\Files\NCCN\WUSS\2009\[Example2.xls]Rec
Guideline!R17C5:R17C6";
columns 5 to 6
St IIA III Sx
St IIA III Sx
St IIA III Sx
St IIA III Sx
St IIA III Sx
/* Simple macro to run same code for each concordant/non-concordant reason */
%macro reason(name,outfile);
data _null_;
merge ReasonFreq2 (where=(reason=&name))
AggTot;
file &outfile notab;
put ReasonTot ' / ' Ntot '09'x ReasonPct '09'x;
run;
%mend;
%reason('Neoadjuvant Therapy + Transabdominal Resection',out2)
%reason('Transabdominal Resection',out3)
%reason('Clinical Trial',out4)
%reason('Neoadjuvant Therapy Only',out5)
%reason('No Neoadjuvant Therapy, No Transabdominal Resection',out6)
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REPORTING CONCORDANCE WITH ASCO/NCCN QUALITY MEASURES
In 2007, the American Society of Clinical Oncology (ASCO) collaborated with the NCCN to develop a set of evidencebased quality measures for breast, colon, and rectal cancers (http://www.asco.org/qualitymeasures). The
ASCO/NCCN quality measures take into account both the NCCN Clinical Practice Guidelines in Oncology and the
ASCO’s National Initiative on Cancer Care Quality. The quality measures are updated regularly in consultation with
the American College of Surgeon’s Commission on Cancer.
The CRC benchmarking packet also evaluates four ASCO/NCCN quality measures, and the concordance results for
these quality measures are reported in Table 3. For example, the first quality measure is for stage I-III colon cancer
patients that had a curative colon surgery without preoperative chemotherapy or radiation therapy. The treatment
recommendation for this subgroup of patients is a minimum of 12 lymph nodes removed and examined. The first and
second row of Table 3 show the overall concordance, the 95% confidence interval, and the institution range for the 12
lymph nodes for patients treated at outside centers, and for patients treated at NCCN centers. Patients treated at
NCCN institutions have a higher overall concordance rate than patients treated at outsides centers (92% vs. 69%).
The overall concordance results, along with the center-specific concordance results, are discussed in detail in the
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JNCCN manuscript .
Table 3. ASCO/NCCN Quality Measures Using NCCN Colorectal Cancer Outcomes Data
From September 1, 2005 to December 10, 2008
Overall
Subgroup
95% CI
Treatment
Recommendation
Institution
Range
(low-high %)
Stage I-III, colon cancer, has not
received preoperative chemotherapy
or radiation therapy, and undergoes
curative colon surgery**
(outside centers)
A minimum of 12 lymph
nodes should be
removed and examined
156 / 225
(69%)
[ 62% - 76%]
( 48%- 88%)
Stage I-III, colon cancer, has not
received preoperative chemotherapy
or radiation therapy, and undergoes
curative colon surgery**
(NCCN centers)
A minimum of 12 lymph
nodes should be
removed and examined
661 / 722
(92%)
[ 90% - 94%]
( 80%- 94%)
Stage II-III, rectal cancer, less than
80 years of age, and undergoes
curative rectal surgery
Postoperative adjuvant
chemotherapy is
administered within 9
months of diagnosis
354 / 439
(81%)
Clinical or pathologic AJCC T4N0M0
or Stage III, rectal cancer, less than
80 years of age, and undergoes
curative rectal surgery
Radiation therapy is
administered within 6
months of diagnosis
339 / 363
(93%)
[ 90% - 96%]
( 87%-100%)
Stage III, colon cancer, less than 80
years of age
Adjuvant chemotherapy
is administered within 4
months of diagnosis
380 / 420
(90%)
[ 87% - 93%]
( 77%- 98%)
[ 77% - 85%]
( 58%- 88%)
**curative surgery includes all definitive cancer procedures except transanal excisions, appendectomies, extended
right/left hemicolectomies, subtotal colectomies, total proctocolectomies, Hartmann's excisions, total pelvic
exenterations, proctectomies, NOS, colectomies, NOS
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Sample Data Structure for Table 3
The sample data structure for Table 3 also consists of only four SAS variables and they are center identifier (cid),
patient identifier (pid), the label of the quality measure (label), and the concordance result (conc), coded as either Yes
or No.
cid pid label conc
1 33 Colon\I-III\12+LN\NCCN Yes
1 34 Colon\I-III\12+LN\Outside Yes
2 61 Colon\III\adj\chemo No
Sample SAS Code for Table 3
The sample SAS code for Table 3 consists of using PROC MEANS and PROC FREQ to output summary statistics
into a temporary dataset, and then the summary statistics are concatenated into a character variable with a DATA
STEP. Prior to invoking DDE with the FILENAME statement , the output variables are merged together in one
dataset. To avoid repeating the code for each quality measure, a simple macro is created to call up each quality
measure separately and output the variables to a separate row.
/* Point to permanent SAS datasets location*/
libname old 'C:\Files\NCCN\WUSS\2009';
/* Open the customized Excel template */
options noxsync noxwait;
X 'C:\Files\NCCN\WUSS\2009\Example3.xls';
/* Simple macro to run same code for each quality measure */
%macro qualitymeasure(name,outfile);
/* Calculate the total number of patients under a quality measure */
proc means data=old.example3 noprint;
where label=&name;
var pid;
output out=AggTot (keep=NTot) N=NTot;
run;
/* Calculate the frequency and percent of concordant patients (conc) for all
centers */
proc freq data=old.example3 noprint;
where label=&name;
table conc / missing missprint out=AggFreq;
run;
data AggFreq2;
set AggFreq;
rename count=AggTot;
/* Format percent for Excel */
AggPct='('||trim(left(put(round(percent),3.)))||'%)';
run;
/* Call in the macro that calculates the 95% confidence interval of concordant
patients (conc) for all centers using dataset from previous data step */
%include 'C:\Files\NCCN\WUSS\2009\computeCI.sas';
/* Calculate the frequency and percent of concordant patients (conc) for each
center */
proc sort data=old.example3;
by label cid;
run;
proc freq data=old.example3 noprint;
where label=&name;
table conc / missing missprint out=CenterFreq;
by cid;
run;
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data CenterFreq2;
set CenterFreq;
rename count=CenterTot;
/* Format percent for Excel */
CenterPct='('||trim(left(put(round(percent),3.)))||'%)';
run;
/* Calculate the center ranges of percent concordant patients using dataset from
previous data step */
proc means data=CenterFreq2 noprint;
/* For concordant patients with a minimum of 10 patients in a center*/
where conc='Yes' and CenterTot>=10;
var percent;
output out=Ranges min=min max=max;
run;
data Ranges2;
set Ranges;
/* Format range for Excel, by combining min and max */
Range='('||put(min,3.)||'%-'||right(put(max,3.))||'%)';
run;
/* Output variables (AggTot, NTot, AggPct, CI, Range) to row 5 to 9, columns 3 to 5
of the Excel Table */
filename out1 DDE
"Excel|C:\Files\NCCN\WUSS\2009\[Example3.xls]ASCO_NCCN_Summary!R5C3:R5C5";
filename out2 DDE
"Excel|C:\Files\NCCN\WUSS\2009\[Example3.xls]ASCO_NCCN_Summary!R6C3:R6C5";
filename out3 DDE
"Excel|C:\Files\NCCN\WUSS\2009\[Example3.xls]ASCO_NCCN_Summary!R7C3:R7C5";
filename out4 DDE
"Excel|C:\Files\NCCN\WUSS\2009\[Example3.xls]ASCO_NCCN_Summary!R8C3:R8C5";
filename out5 DDE
"Excel|C:\Files\NCCN\WUSS\2009\[Example3.xls]ASCO_NCCN_Summary!R9C3:R9C5";
data _null_;
merge AggFreq2 (where=(conc='Yes'))
CI (where=(conc='Yes'))
AggTot
Ranges2;
file &outfile notab;
put AggTot ' / ' NTot AggPct '09'x CI '09'x Range '09'x;
run;
%mend;
%qualitymeasure("Colon\I-III\12+LN\Outside",out1);
%qualitymeasure("Colon\I-III\12+LN\NCCN",out2);
%qualitymeasure("Rectal\II-III\adj\chemo",out3);
%qualitymeasure("Rectal\IIB-III\XRT",out4);
%qualitymeasure("Colon\III\adj\chemo",out5);
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CONCLUSION
The CRC benchmarking packet is an annual report of patterns of care and concordance with treatment guidelines
and quality measures. This highly customizable Excel report is essential in providing feedback to the NCCN
Guideline Panel involved in the development process of the guideline, and in providing feedback to the NCCN
member institutions and practicing physicians who provide quality of cancer care to the patients.
Every year, the benchmarking packets are updated, because guidelines are revised, and also because new
guidelines are added. The CRC benchmarking packet will be updated this year to include the plots of the guideline
concordance by diagnosis year, and the detailed reasons for non-concordance, such as patient refusal to treatment.
The plots are useful in showing adoption of guidelines over time, and the detailed reasons for non-concordance are
useful in describing if the reason is patient-driven or physician-driven, such as patient refusal or treatment
contraindicated by medical oncologist due to comorbidity and/or age.
The advantage of creating an Excel report over a SAS based output is that the project manager does not need to
have expertise in SAS programming. The advantage of using DDE to output patterns of care and concordance
results into the Excel is that it allows flexibility in how the data are output and formatted, and the statistician can
develop a macro that derives the numeric and character variables for the Excel report. In turn, this macro gets called
into another program that specifies the exact location of the variables with DDE, by specifying the cell rows and
columns of the Excel report. The macro can be run for each institution, by simply calling in the center identifier, thus
eliminating any repetitive SAS code. For this specific case the macro is run a total of eight times, as CRC has eight
NCCN member institutions. Additionally we share the macro code across NCCN disease sites, including breast
cancer, which has 27 NCCN member institutions. Clearly, the use of SAS macros and DDE saves time, and
minimizes repetitive code when generating multiple benchmarking packets.
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, in press.
2) 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 (SPS) for Rectal Cancer at Specialty Centers in the United States, Annals
of Surgery, in press.
3) 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 of National Comprehensive Cancer Network, in press.
4) Kilburn J, An Example of Using Differing Methods to Create Excel Output from SAS, Proceedings of the WUSS
2009 Conference, HOR-Kilburn.
5) Ottesen O, Custom Outcomes Benchmarking Reports Using Dynamic Data Exchange, The Only Way To Go”,
Proceedings of the WUSS 2009 Conference, HOR-Ottesen.
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 91010
(818)848-6197
[email protected]
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.
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