CALL FOR ABSTRACTS
The China Interchange Program Committee is now accepting abstracts for presentations for the CDISC
2009 China Interchange to be held in Beijing, China, the week of 26 October.
Several sessions are planned with oral presentations lasting between 20 and 30 minutes. CDISC would
like to receive abstracts in areas such as the following, or any area related to standards for medical
research and healthcare, including terminology.
Implementation of CDISC standards
Adoption of CDISC models (Pharma, CRO, Biotech,
Lab, and Medical Devices). Case studies and
lessons learned.
SDTM & ADaM & define.xml
Practical use of SDS/SDTM and ADaM. Migration of
legacy standards data to submission ready SDTM
and ADaM datasets and define.xml. Lessonslearned
and case studies.
SDTM & CDASH used in EDC Data acquisition
Use of and experience with CDASH forms and/or
usage of SDTM domains and controlled terminology
within EDC technology.
Vendor Applications and Tools
Applications and tools for data migration, data
integration, compliance check with CDISC models,
define.xml setup etc.
Demonstration of data integration using CDISC
models (up to end-to-end integration), e.g. EDCforms,
ePRO, central lab, ECG lab, device data.
eSource and eHealth
The integration of clinical research and healthcare
systems, interoperability between CDISC and HL7
standards. Case studies of any integration used for
clinical trials with lessons learned. Integration at
one centre/hospital and / or several centres/
hospitals.
ABSTRACT SUBMISSION
The deadline for submission of abstracts is Friday, 29 May 2009.
Abstracts should be one page in length, formatted in Microsoft Word, and sent via email
to Zibao Zhang, zibao.zhang@excel-cro.com. Abstracts must include the name of the
author/presenter, company or affiliation name and contact details consisting of postal
address, phone number, fax number and email address.
CONFERENCE FORMAT
The main conference will commence at 8:30am on 30 October and run until 5pm on 31.
Additional sessions and workshops will take place on 28-29 October. These sessions will
consist of a combination of workshops given by industry experts along with official CDISC
training courses.
Friday, August 28, 2009
sas in clinical trials
Utilizing Clinical SAS Report Templates
Sunil Kumar Gupta
Gupta Programming, Thousand Oaks, CA
ABSTRACT
SAS programmers often have the responsibility of supporting
the reporting needs of the Clinical Affairs Department of
Pharmaceutical Companies. This involves generating a variety
of reports to fulfill the regulatory requirements of the clinical
investigation of the medical device. By establishing clinical
reporting templates for each of the functional aspects of
Clinical Data Management, the SAS programmer can
dramatically improve the efficiency of developing and
generating reports.
Examples of these functional reports include patient listings,
patient x-ray logs, monitor site visits, monthly status reports, &
reports necessary for pre market approval application. Each
type of report requires different layouts due to the different
objectives of the organization. By establishing a reporting
template for each type, the SAS programmer can make
modifications to an already verified and complete program.
Only minor adjustments may be required for user specific
selections.
Proper tools such as SAS report templates should be in place to
facilitate the rapid development and execution of clinical
reports. Establishing reporting standards will increase
efficiency.
INTRODUCTION
Supporting the reporting needs of the Clinical Affairs
Department can be a challenging task. This is because of the
pressure on the programmer to quickly generate a variety of
reports. Time is often critical as millions of dollars in sales can
be lost due to delays in submission.
By establishing clinical reporting templates for each of the
functional aspects, the SAS programmer becomes organized
and prepared to receive reporting requests. The department
improves their efficiency in the development and generation of
clinical reports.
System standardization can be realized in the following areas:
· Program Header
· File Definitions
· Merged File Definitions
· Field Properties
· Standard & Custom Conditions
· Custom Formats
· Random Sample
· Verification of Dataset Contents
· Output Layout
If possible, it is recommended to use the report specification
similar to one defined in the SAS Service Request Form before
initiating the coding.
This paper will review sample templates for detail & summary
patient listings.
SYSTEM STANDARDIZATION
Program Header
By completing the program header, proper documentation is
maintained for all programs. This is essential for program
support.
File Definitions
All files containing the clinical information are defined with
any access & view descriptors. This is a central source of file
documentation.
Merged File Definitions
All links are defined and established to create new files for
data consolidation & reporting.
Field Properties
For each file, fields are defined with label, format, & informat
statements. These properties are utilized in all data analysis
and presentation.
Standard & Custom Conditions on the file
Any required conditions can be applied to assure data validity.
Any user specific conditions can also be applied.
Custom Formats
Standards in study formats can be recognized.
Random Sample
With a small random sample, a sufficient amount of data is
available for testing. This allows for rapid testing without
utilizing all the data in the dataset.
Verification of Dataset Contents
This is important to assure proper data extraction and to verify
programming assumptions.
Output Layout
The end user knows what to expect on the report. The header
contains the title, date of execution, and selection conditions.
The following procedures are presented with the macro
language: DATA Step, PROC SORT, FORMAT & REPORT.
SAMPLE DATASETS
The two datasets represent patient demographics and follow-up
visit information.
* Program Header;
* Name - List1;
* Function - Detail & Summary listing of Clinical
Data;
* Programmer - Sunil K. Gupta;
* Date - 7/1/95;
* Verification Date - 7/2/95 By - ;
* Revision Log;
* Date Programmer Change;
LIBNAME CLINRPT 'C:\SAS\CLINICAL\MONTHLY';
* Patient Demographics File;
DATA DEMO;
INPUT INV 1-2 PATNO 4-5 PTNAME $5.
STATUS 12 SEX 14 AGE 16-18 DX 20;
* Custom Conditions;
* WHERE INV IN (1,2)
AND STATUS IN (1)
AND SEX IN (1)
AND AGE < 45
AND DX IN (1);
* Random Sample;
* IF RANUNI(-1) LE 0.1;
* Field Properties;
LABELS
INV = 'Investigator'
PATNO = 'Patient*#'
PTNAME = 'Patient*Name'
STATUS = 'Status'
SEX = 'Sex'
AGE = 'Age'
DX = 'Diagnosis';
FORMAT
INV 2.
PATNO 2.
PTNAME $5.
STATUS 1.
SEX 1.
AGE 3.
DX 1.;
CARDS;
1 1 JOHN 1 1 31 2
1 2 JAN 2 1 41 1
2 1 TIM 1 2 100 1
3 1 JOE 1 2 32 3
4 1 SUE 2 2 42 3
5 1 MARY 1 2 102 3
;
RUN;
* Patient Followup Visits File;
DATA PVISIT;
INPUT INV 1-2 PATNO 4-5 VISIT 7-8 EFF1 10-11
EFF2 13-14 SAF1 16-17;
* Custom Conditions;
* WHERE INV IN (1,2)
AND VISIT IN (1,2,3);
* Random Sample;
* IF RANUNI(-1) LE 0.1;
* Field Properties;
LABELS
INV = 'Investigator'
PATNO = 'Patient*#'
VISIT = 'Visit'
EFF1 = 'Efficacy*Variable*1'
EFF2 = 'Efficacy*Variable*2'
SAF1 = 'Safety*Variable*1';
FORMAT INV 2.
PATNO 2.
VISIT 1.
EFF1 2.
EFF2 2.
SAF1 2.;
CARDS;
1 1 1 50 52 62
1 1 2 60 41 73
1 1 3 70 12 25
1 1 4 80 41 75
1 1 5 90 25 72
1 2 1 40 13 36
1 2 2 50 15 26
1 2 3 60 18 42
2 1 1 80 17 53
2 1 2 90 26 47
;
RUN;
* Standard Study Format;
PROC FORMAT;
VALUE INV
1 = 'Investigator 1'
2 = 'Investigator 2'
3 = 'Investigator 3'
4 = 'Investigator 4'
5 = 'Investigator 5';
VALUE STATUS
1 = 'Active'
2 = 'Dead';
VALUE SEX
1 = 'Male'
2 = 'Female';
VALUE DX
1 = 'OA'
2 = 'RA'
3 = 'PT';
VALUE VISIT
1 = 'Preop'
2 = 'Operative'
3 = 'Week 1'
4 = 'Week 2'
5 = 'Week 3';
QUIT;
* Merged File - Demographics & Followup File;
PROC SORT DATA=DEMO;
BY INV PATNO;
RUN;
PROC SORT DATA=PVIST;
BY INV PATNO;
RUN;
DATA PDVISIT;
MERGE DEMO (IN=A) PVISIT (IN=B);
BY INV PATNO;
* IF RANUNI(-1) LE 0.1;
IF A;
RUN;
* Verification of Dataset Contents;
PROC FREQ DATA=PDVISIT;
TABLES INV PATNO STATUS SEX DX VISIT;
RUN;
MACRO VARIABLES
%LET DATAV = PDVISIT;
%LET CLASSV = INV PATNO VISIT;
%LET VARV = EFF1;
%LET STATV1 = MEAN;
Level 1 Controls
Macro variables enable the programmer to specify the data set
name, factor & analysis variable, & statistics. The factor
variable may be more than one variable. The analysis variable
is usually one of the efficacy or safety variables. The statistics
option includes the following: mean, sum, min, max, & std.
%LET INVV = (1, 2);
%LET PATNOV = (1, 2, 3, 4, 5);
%LET VISITV = (1, 2, 3);
Level 2 Controls
This enables the programmer to futher subset the data for
additional analysis. For example, the programmer can specify
the investigator number, patient number, or visit period. This
section can be uncommeted if needed.
SIMPLE PATIENT LISTING
For a simple patient listing, the report procedure can be
utilized.
Once the file is sorted, then PROC REPORT can be applied.
This generates a sorted listing of the investigator name, patient
number, visit period, and score value. See output 1 for the
report generated.
PROC SORT DATA=PDVIST;
BY INV PATNO;
RUN;
TITLE1 "CLINICAL REPORT : Simple Patient Listing -
&SYSDATE, &SYSTIME";
PROC REPORT
DATA = &DATAV
OUTREPT = CLINRPT.LIST1
CENTER HEADLINE HEADSKIP MISSING LIST SPLIT='*';
* Level 2 controls;
* WHERE INV IN &INVV
AND PATNO IN &PATNOV
AND VISIT IN &VISITV;
* Apply custom formats;
FORMAT INV INV. VISIT VISIT.;
* List column variables;
COLUMN &CLASSV &VARV;
* Define each column field;
DEFINE INV / GROUP CENTER
'Investigator*Name';
DEFINE PATNO / GROUP CENTER 'Patient*#';
DEFINE VISIT / GROUP CENTER 'Visit';
DEFINE &VARV / &STATV1 CENTER
'Efficacy*Variable*1'
FORMAT=COMMA6.;
* Generate a line after each Investigator;
COMPUTE AFTER INV;
LINE 45*'-';
LINE 'Efficacy Variable 1 Scores for ' INV 2.
+2 ' Investigator ';
LINE ' ';
ENDCOMP;
RUN;
OUTPUT 1
CLINICAL REPORT : Simple Patient Listing - Monday,
3JULY95 15:03
EFFICACY
INVESTIGATOR PATNO VARIABLE
NAME # VISIT 1 .
Investigator 1 1 Operative 60
Preop 50
Week 1 70
Week 2 80
Week 3 90
2 Operative 50
Preop 40
Week 1 60
Efficacy Variable 1 Scores for 1 Investigator
Investigator 2 1 Operative 90
Preop 80
Efficacy Variable 1 Scores for 2 Investigator
SUMMARY PATIENT LISTING
For a summary listing across follow-up visits, the REPORT
procedure is applied again. This procedure generates a
summary listing by investigator name and patient number. The
visit scores reported include: mean, minimum, maximum, and
standard deviation. See output 2 for the report generated.
%LET CLASS=INV PATNO;
TITLE1 "CLINICAL REPORT : Summary Patient Listing -
&SYSDATE, &SYSTIME";
PROC REPORT
DATA = &DATAV
OUTREPT = CLINRPT.LIST2
NOWD HEADLINE HEADSKIP MISSING LIST SPLIT='*';
* Level 2 controls;
* WHERE INV IN &INVV
AND PATNO IN &PATNOV
AND VISIT IN &VISITV;
* List column fields;
COLUMN &CLASSV &VARV
&VARV=SMIN &VARV=SMAX
&VARV=SSTD;
* Apply custom formats;
FORMAT INV INV.;
* Define each column fields;
DEFINE INV / GROUP CENTER
'Investigator*Name';
DEFINE PATNO / GROUP CENTER 'Patient*#';
DEFINE &VARV / &STATV1 CENTER
'Efficacy*Variable*1'
FORMAT=COMMA6.;
DEFINE SMIN / MIN CENTER
'MIN*SCORE' FORMAT=COMMA6.;
DEFINE SMAX / MAX CENTER
'MAX*SCORE' FORMAT=COMMA6.;
DEFINE SSTD / STD CENTER
'STD*SCORE' FORMAT=COMMA6.;
COMPUTE AFTER INV;
LINE 45*'-';
LINE 'Efficacy Variable 1 Scores for ' INV 2.
+2 ' Investigator ';
LINE ' ';
ENDCOMP;
RUN;
OUTPUT 2
CLINICAL REPORT : Summary Patient Listing - Monday,
3JULY95 15:03
INVESTIGATOR MEAN MIN MAX STD
NAME PATNO SCORE SCORE SCORE SCORE
Invest. 1 1 70 50 90 16
2 50 40 60 10
Efficacy Variable 1 Scores for 1 Investigator
Invest. 2 1 85 80 90 7
Efficacy Variable 1 Scores for 2 Investigator
Use the REPORT = option for using a predefined clinical
template for reporting of a data subset.
* Subset Dataset for Male patients;
DATA PDVISITM;
SET PDVISIT;
WHERE SEX = 1;
RUN;
%LET DATAV = PDVISITM;
PROC REPORT
DATA = &DATAV
REPORT = CLINRPT.LIST2;
RUN;
SUMMARY
A similar template can be developed for each of the other
functional reports.
The two PROC REPORT OPTIONS - OUTREPT & REPORT
allow for additional reporting power.
Use the OUTREPT = option to save the report template. This
template can be utilized for other datasets of the same dataset
structure. A subset population of the original dataset is an
application example.
Use the REPORT= option to load the template containing the
report definition to format the current SAS dataset.
PROC FORMS can be used to generate reports that resemble
the case report form. This can be useful in assisting in the
verification of the clinical data.
With the clinical report templates, efficiency is realized
because the programmer starts working with code from a
similar program. Often, there are similar studies being
conducted and the method of clinical review is similar across
the efficacy variables. In addition, a great deal of time is saved
because the modified code does not need to go through a
complete and comprehensive test as the original code has.
By effectively utilizing SAS's flexibility and power for report
generation, it is possible to expedite the process of report
development. Clinical SAS report templates serve as a set of
tools to facilitate the rapid development and execution of
clinical reports. The standards established also improve the
communication and training of new SAS programmers.
Sunil Kumar Gupta
Gupta Programming, Thousand Oaks, CA
ABSTRACT
SAS programmers often have the responsibility of supporting
the reporting needs of the Clinical Affairs Department of
Pharmaceutical Companies. This involves generating a variety
of reports to fulfill the regulatory requirements of the clinical
investigation of the medical device. By establishing clinical
reporting templates for each of the functional aspects of
Clinical Data Management, the SAS programmer can
dramatically improve the efficiency of developing and
generating reports.
Examples of these functional reports include patient listings,
patient x-ray logs, monitor site visits, monthly status reports, &
reports necessary for pre market approval application. Each
type of report requires different layouts due to the different
objectives of the organization. By establishing a reporting
template for each type, the SAS programmer can make
modifications to an already verified and complete program.
Only minor adjustments may be required for user specific
selections.
Proper tools such as SAS report templates should be in place to
facilitate the rapid development and execution of clinical
reports. Establishing reporting standards will increase
efficiency.
INTRODUCTION
Supporting the reporting needs of the Clinical Affairs
Department can be a challenging task. This is because of the
pressure on the programmer to quickly generate a variety of
reports. Time is often critical as millions of dollars in sales can
be lost due to delays in submission.
By establishing clinical reporting templates for each of the
functional aspects, the SAS programmer becomes organized
and prepared to receive reporting requests. The department
improves their efficiency in the development and generation of
clinical reports.
System standardization can be realized in the following areas:
· Program Header
· File Definitions
· Merged File Definitions
· Field Properties
· Standard & Custom Conditions
· Custom Formats
· Random Sample
· Verification of Dataset Contents
· Output Layout
If possible, it is recommended to use the report specification
similar to one defined in the SAS Service Request Form before
initiating the coding.
This paper will review sample templates for detail & summary
patient listings.
SYSTEM STANDARDIZATION
Program Header
By completing the program header, proper documentation is
maintained for all programs. This is essential for program
support.
File Definitions
All files containing the clinical information are defined with
any access & view descriptors. This is a central source of file
documentation.
Merged File Definitions
All links are defined and established to create new files for
data consolidation & reporting.
Field Properties
For each file, fields are defined with label, format, & informat
statements. These properties are utilized in all data analysis
and presentation.
Standard & Custom Conditions on the file
Any required conditions can be applied to assure data validity.
Any user specific conditions can also be applied.
Custom Formats
Standards in study formats can be recognized.
Random Sample
With a small random sample, a sufficient amount of data is
available for testing. This allows for rapid testing without
utilizing all the data in the dataset.
Verification of Dataset Contents
This is important to assure proper data extraction and to verify
programming assumptions.
Output Layout
The end user knows what to expect on the report. The header
contains the title, date of execution, and selection conditions.
The following procedures are presented with the macro
language: DATA Step, PROC SORT, FORMAT & REPORT.
SAMPLE DATASETS
The two datasets represent patient demographics and follow-up
visit information.
* Program Header;
* Name - List1;
* Function - Detail & Summary listing of Clinical
Data;
* Programmer - Sunil K. Gupta;
* Date - 7/1/95;
* Verification Date - 7/2/95 By - ;
* Revision Log;
* Date Programmer Change;
LIBNAME CLINRPT 'C:\SAS\CLINICAL\MONTHLY';
* Patient Demographics File;
DATA DEMO;
INPUT INV 1-2 PATNO 4-5 PTNAME $5.
STATUS 12 SEX 14 AGE 16-18 DX 20;
* Custom Conditions;
* WHERE INV IN (1,2)
AND STATUS IN (1)
AND SEX IN (1)
AND AGE < 45
AND DX IN (1);
* Random Sample;
* IF RANUNI(-1) LE 0.1;
* Field Properties;
LABELS
INV = 'Investigator'
PATNO = 'Patient*#'
PTNAME = 'Patient*Name'
STATUS = 'Status'
SEX = 'Sex'
AGE = 'Age'
DX = 'Diagnosis';
FORMAT
INV 2.
PATNO 2.
PTNAME $5.
STATUS 1.
SEX 1.
AGE 3.
DX 1.;
CARDS;
1 1 JOHN 1 1 31 2
1 2 JAN 2 1 41 1
2 1 TIM 1 2 100 1
3 1 JOE 1 2 32 3
4 1 SUE 2 2 42 3
5 1 MARY 1 2 102 3
;
RUN;
* Patient Followup Visits File;
DATA PVISIT;
INPUT INV 1-2 PATNO 4-5 VISIT 7-8 EFF1 10-11
EFF2 13-14 SAF1 16-17;
* Custom Conditions;
* WHERE INV IN (1,2)
AND VISIT IN (1,2,3);
* Random Sample;
* IF RANUNI(-1) LE 0.1;
* Field Properties;
LABELS
INV = 'Investigator'
PATNO = 'Patient*#'
VISIT = 'Visit'
EFF1 = 'Efficacy*Variable*1'
EFF2 = 'Efficacy*Variable*2'
SAF1 = 'Safety*Variable*1';
FORMAT INV 2.
PATNO 2.
VISIT 1.
EFF1 2.
EFF2 2.
SAF1 2.;
CARDS;
1 1 1 50 52 62
1 1 2 60 41 73
1 1 3 70 12 25
1 1 4 80 41 75
1 1 5 90 25 72
1 2 1 40 13 36
1 2 2 50 15 26
1 2 3 60 18 42
2 1 1 80 17 53
2 1 2 90 26 47
;
RUN;
* Standard Study Format;
PROC FORMAT;
VALUE INV
1 = 'Investigator 1'
2 = 'Investigator 2'
3 = 'Investigator 3'
4 = 'Investigator 4'
5 = 'Investigator 5';
VALUE STATUS
1 = 'Active'
2 = 'Dead';
VALUE SEX
1 = 'Male'
2 = 'Female';
VALUE DX
1 = 'OA'
2 = 'RA'
3 = 'PT';
VALUE VISIT
1 = 'Preop'
2 = 'Operative'
3 = 'Week 1'
4 = 'Week 2'
5 = 'Week 3';
QUIT;
* Merged File - Demographics & Followup File;
PROC SORT DATA=DEMO;
BY INV PATNO;
RUN;
PROC SORT DATA=PVIST;
BY INV PATNO;
RUN;
DATA PDVISIT;
MERGE DEMO (IN=A) PVISIT (IN=B);
BY INV PATNO;
* IF RANUNI(-1) LE 0.1;
IF A;
RUN;
* Verification of Dataset Contents;
PROC FREQ DATA=PDVISIT;
TABLES INV PATNO STATUS SEX DX VISIT;
RUN;
MACRO VARIABLES
%LET DATAV = PDVISIT;
%LET CLASSV = INV PATNO VISIT;
%LET VARV = EFF1;
%LET STATV1 = MEAN;
Level 1 Controls
Macro variables enable the programmer to specify the data set
name, factor & analysis variable, & statistics. The factor
variable may be more than one variable. The analysis variable
is usually one of the efficacy or safety variables. The statistics
option includes the following: mean, sum, min, max, & std.
%LET INVV = (1, 2);
%LET PATNOV = (1, 2, 3, 4, 5);
%LET VISITV = (1, 2, 3);
Level 2 Controls
This enables the programmer to futher subset the data for
additional analysis. For example, the programmer can specify
the investigator number, patient number, or visit period. This
section can be uncommeted if needed.
SIMPLE PATIENT LISTING
For a simple patient listing, the report procedure can be
utilized.
Once the file is sorted, then PROC REPORT can be applied.
This generates a sorted listing of the investigator name, patient
number, visit period, and score value. See output 1 for the
report generated.
PROC SORT DATA=PDVIST;
BY INV PATNO;
RUN;
TITLE1 "CLINICAL REPORT : Simple Patient Listing -
&SYSDATE, &SYSTIME";
PROC REPORT
DATA = &DATAV
OUTREPT = CLINRPT.LIST1
CENTER HEADLINE HEADSKIP MISSING LIST SPLIT='*';
* Level 2 controls;
* WHERE INV IN &INVV
AND PATNO IN &PATNOV
AND VISIT IN &VISITV;
* Apply custom formats;
FORMAT INV INV. VISIT VISIT.;
* List column variables;
COLUMN &CLASSV &VARV;
* Define each column field;
DEFINE INV / GROUP CENTER
'Investigator*Name';
DEFINE PATNO / GROUP CENTER 'Patient*#';
DEFINE VISIT / GROUP CENTER 'Visit';
DEFINE &VARV / &STATV1 CENTER
'Efficacy*Variable*1'
FORMAT=COMMA6.;
* Generate a line after each Investigator;
COMPUTE AFTER INV;
LINE 45*'-';
LINE 'Efficacy Variable 1 Scores for ' INV 2.
+2 ' Investigator ';
LINE ' ';
ENDCOMP;
RUN;
OUTPUT 1
CLINICAL REPORT : Simple Patient Listing - Monday,
3JULY95 15:03
EFFICACY
INVESTIGATOR PATNO VARIABLE
NAME # VISIT 1 .
Investigator 1 1 Operative 60
Preop 50
Week 1 70
Week 2 80
Week 3 90
2 Operative 50
Preop 40
Week 1 60
Efficacy Variable 1 Scores for 1 Investigator
Investigator 2 1 Operative 90
Preop 80
Efficacy Variable 1 Scores for 2 Investigator
SUMMARY PATIENT LISTING
For a summary listing across follow-up visits, the REPORT
procedure is applied again. This procedure generates a
summary listing by investigator name and patient number. The
visit scores reported include: mean, minimum, maximum, and
standard deviation. See output 2 for the report generated.
%LET CLASS=INV PATNO;
TITLE1 "CLINICAL REPORT : Summary Patient Listing -
&SYSDATE, &SYSTIME";
PROC REPORT
DATA = &DATAV
OUTREPT = CLINRPT.LIST2
NOWD HEADLINE HEADSKIP MISSING LIST SPLIT='*';
* Level 2 controls;
* WHERE INV IN &INVV
AND PATNO IN &PATNOV
AND VISIT IN &VISITV;
* List column fields;
COLUMN &CLASSV &VARV
&VARV=SMIN &VARV=SMAX
&VARV=SSTD;
* Apply custom formats;
FORMAT INV INV.;
* Define each column fields;
DEFINE INV / GROUP CENTER
'Investigator*Name';
DEFINE PATNO / GROUP CENTER 'Patient*#';
DEFINE &VARV / &STATV1 CENTER
'Efficacy*Variable*1'
FORMAT=COMMA6.;
DEFINE SMIN / MIN CENTER
'MIN*SCORE' FORMAT=COMMA6.;
DEFINE SMAX / MAX CENTER
'MAX*SCORE' FORMAT=COMMA6.;
DEFINE SSTD / STD CENTER
'STD*SCORE' FORMAT=COMMA6.;
COMPUTE AFTER INV;
LINE 45*'-';
LINE 'Efficacy Variable 1 Scores for ' INV 2.
+2 ' Investigator ';
LINE ' ';
ENDCOMP;
RUN;
OUTPUT 2
CLINICAL REPORT : Summary Patient Listing - Monday,
3JULY95 15:03
INVESTIGATOR MEAN MIN MAX STD
NAME PATNO SCORE SCORE SCORE SCORE
Invest. 1 1 70 50 90 16
2 50 40 60 10
Efficacy Variable 1 Scores for 1 Investigator
Invest. 2 1 85 80 90 7
Efficacy Variable 1 Scores for 2 Investigator
Use the REPORT = option for using a predefined clinical
template for reporting of a data subset.
* Subset Dataset for Male patients;
DATA PDVISITM;
SET PDVISIT;
WHERE SEX = 1;
RUN;
%LET DATAV = PDVISITM;
PROC REPORT
DATA = &DATAV
REPORT = CLINRPT.LIST2;
RUN;
SUMMARY
A similar template can be developed for each of the other
functional reports.
The two PROC REPORT OPTIONS - OUTREPT & REPORT
allow for additional reporting power.
Use the OUTREPT = option to save the report template. This
template can be utilized for other datasets of the same dataset
structure. A subset population of the original dataset is an
application example.
Use the REPORT= option to load the template containing the
report definition to format the current SAS dataset.
PROC FORMS can be used to generate reports that resemble
the case report form. This can be useful in assisting in the
verification of the clinical data.
With the clinical report templates, efficiency is realized
because the programmer starts working with code from a
similar program. Often, there are similar studies being
conducted and the method of clinical review is similar across
the efficacy variables. In addition, a great deal of time is saved
because the modified code does not need to go through a
complete and comprehensive test as the original code has.
By effectively utilizing SAS's flexibility and power for report
generation, it is possible to expedite the process of report
development. Clinical SAS report templates serve as a set of
tools to facilitate the rapid development and execution of
clinical reports. The standards established also improve the
communication and training of new SAS programmers.
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