Use of SAS® for Clinical Trial Management and
Risk-Based Monitoring of Multicenter Clinical
Trial Data from Electronic Data Capture Tools
Bob Hall1, MS, Rebecca V. Fink2 MPH, David Gagnon1, MD,
MPH, PhD
NESUG 2013 Presentation
Objectives of Presentation:
 Introduce Risk-Based Monitoring and Electronic
Data Capture
 Discuss Business Use Cases
 Provide a SAS Approach for Generating Modular
Reports for Site Monitoring Metrics
Multi-Center Clinical Trials:
 Multi-center trials can be complicated:
• Multiple visits for long term trials
• Multiple CRFs with different purposes
Safety (e.g. Adverse Events, Pharmacy Data)
Efficacy (e.g. Outcomes Data for Trial Objectives)
Study Activity (e.g. Protocol Deviations; Disposition)
 Trials require frequent monitoring of data to
insure quality.
Risk-Based Monitoring:
 Trial monitoring has focused on on-site activities
 FDA Guidance Document that describes a riskbased approach to monitoring:
• Recently finalized – August 2013
• Combines on-site monitoring and remote ‘centralized
monitoring’.
• Targeted monitoring of site activity remotely.
Risk-Based Monitoring:
 Improve efficiency and costs of clinical trials
• Reducing the need for frequent on-site monitoring.
• Focus on sites that don’t meet defined study metrics.
• Dictate corrective actions with sites:
 Initiate re-training efforts, improve communication with sites,
increase on-site monitoring activities.
 Probation efforts if needed.
Electronic Data Capture:
 Electronic Data Capture (eDC):
• Real-time data capture and correction.
• Data maintained in relational database architecture.
• Trial management functions (CRFs, DCFs, etc.).
 Operational Tables for Trial Management:
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Participant Status
CRF/Form Status
DCF Status
Master Data Files (i.e. tables for all data transactions)
SAS for Reporting:
 Points to Consider - SAS for Risk-Based Monitoring
• Output Delivery System (ODS) Functionality
 Ability to establish professional level reports in many formats.
• Physical Report Document
 Found sites were more responsive to a physical report.
• Reports in Near Real Time
 Daily reports based on schedule cron jobs.
 Timing was considered acceptable for task.
• SAS Knowledge Base
 Research infrastructure had more SAS experience.
Business Use Cases:
Business Use Cases for Discussion:
1) Case Report Form (CRF) Completion
2) Data Clarification (DCF) Status
Case Report Form (CRF) Completion:
 Business Use Case:
• Important to have data entered during or soon after
participant visits.
• Report for Site Staff:
 Listing of outstanding CRFs
 Days outstanding from expected
 Summary numbers
• Report for Management Teams:
 CRF completion rates by site
 Completion rates by CRF and visit window
CRF Completion:
 Form Status Table
• Captured information on form status markers.
• Record for each expected CRF during course of study.
 Determination of Incomplete CRFs Based on:
• Date of participant enrollment
• Expected date of study visit
• Acceptable grace period for completion
CRF Completion:
CRF Completion:
 Points to Consider:
• Timing of Report Generation
 Report frequency
 Ability to generate ad hoc reports
• Time Limit / Grace Period for CRF Completion
 Protocol defined / May vary by CRF type
• Risk-Based Monitoring
 Overall metrics:
Insure that percentage of CRFs complete w/in time limit
No more than 10% outside 14 days
Graphing completion rates from site launch
Monitoring Data Clarifications:
 Flow of Data Clarification (DCF) :
• DCFs Types:
 Automated – Fire through a system script
 Manual – Entered by coordinating center
• Expected Site Responses:
 Data correction because of true data error.
 Resolving DCF since data value is correct.
• Good Metric for Assessing DCF Correction is DCF Aging.
Monitoring Data Clarifications:
 Business Use Case:
• eDC system - Didn’t Provide Best Metric for DCF Aging:
 Based on date/time DCF opened by staff.
 Aging continued after site response.
• Build a Better Report:
 Use DCF status table
 Re-calculate aging based on time DCF fired
 Define calculation solely on site action
 Develop reports for sites and management teams
SAS Tools for Reporting:
 SAS Tools for Reporting:
• Output Delivery System (ODS) Functionality
 Professional level reports in many formats (HTML, PDF, RTF)
• Access to SQL Relational Databases
 ODBC Connections
• Macros to Facilitate Reporting
 Macro Looping to Generate by Site Reports
 Customized Reporting of Study Benchmarks
Customized DCF Report:
 Business Case
• Aggregate Report of Site Level Activity
 Example - DCF Aging
• Repeatability of Reports
• Professional Looking Report – ODS Approach
• Modular Reporting Process
 Flexibility in Defining/Re-Defining Metrics
 Flexibility in Ordering of Metrics in Report
DCF Report - Elements
The SAS System
Site Type
VA
exposurecat
DCF Age
DCF Status
exposure
Non-VA
Site 1
Site Number
Site 2
Site 3
Site 4
Site Number
Site 5
Site 6
N(%)
N(%)
N(%)
N(%)
N(%)
N(%)
DCF Age Mean
43.66 (53.22) 33.02 (50.85) 40.69 (58.54) 19.91 (41.1) 24.7 (37.96) 25.62 (50.37)
DCF Age Median
32 (2,59)
9 (0,48)
15 (0,58)
7 (1,21)
7 (0,30)
7 (0,28)
DCF Age 0-15
346 (43.7)
536 (58.7)
802 (50.3)
845 (71)
670 (58.1)
298 (60.7)
DCF Age 16-30
49 (6.2)
75 (8.2)
115 (7.2)
133 (11.2)
196 (17)
93 (18.9)
DCF Age 30-45
84 (10.6)
64 (7)
153 (9.6)
88 (7.4)
83 (7.2)
27 (5.5)
DCF Age 45-60
117 (14.8)
68 (7.4)
145 (9.1)
41 (3.4)
35 (3)
16 (3.3)
DCF Age >60
195 (24.7)
170 (18.6)
378 (23.7)
83 (7)
170 (14.7)
57 (11.6)
DCF Status Open
49 (6.2)
31 (3.4)
26 (1.6)
24 (2)
11 (1)
10 (2)
DCF Status Answered 0 (0)
5 (0.5)
4 (0.3)
6 (0.5)
12 (1)
4 (0.8)
DCF Status Closed
877 (96.1)
1563 (98.1)
1160 (97.5) 1131 (98)
742 (93.8)
477 (97.1)
DCF Report – Format Table
VARCAT: Row Variable Category
The SAS System
Site Type
VA
exposurecat
DCF Age
DCF Status
exposure
Non-VA
Site 1
Site Number
Site 2
Site 3
Site 4
Site Number
Site 5
Site 6
N(%)
N(%)
N(%)
N(%)
N(%)
N(%)
DCF Age Mean
43.66 (53.22) 33.02 (50.85) 40.69 (58.54) 19.91 (41.1) 24.7 (37.96) 25.62 (50.37)
DCF Age Median
32 (2,59)
9 (0,48)
15 (0,58)
7 (1,21)
7 (0,30)
7 (0,28)
DCF Age 0-15
346 (43.7)
536 (58.7)
802 (50.3)
845 (71)
670 (58.1)
298 (60.7)
DCF Age 16-30
49 (6.2)
75 (8.2)
115 (7.2)
133 (11.2)
196 (17)
93 (18.9)
DCF Age 30-45
84 (10.6)
64 (7)
153 (9.6)
88 (7.4)
83 (7.2)
27 (5.5)
DCF Age 45-60
117 (14.8)
68 (7.4)
145 (9.1)
41 (3.4)
35 (3)
16 (3.3)
DCF Age >60
195 (24.7)
170 (18.6)
378 (23.7)
83 (7)
170 (14.7)
57 (11.6)
DCF Status Open
49 (6.2)
31 (3.4)
26 (1.6)
24 (2)
11 (1)
10 (2)
DCF Status Answered 0 (0)
5 (0.5)
4 (0.3)
6 (0.5)
12 (1)
4 (0.8)
DCF Status Closed
877 (96.1)
1563 (98.1)
1160 (97.5) 1131 (98)
742 (93.8)
VARNAME: Variable Name
TYPE: Continuous / Categorical
FFORMAT / VARLABEL / ORDER
477 (97.1)
DCF Report – Macro Variables
BYVAR: Column Groups
- VA vs Non-VA
COLVAR: Column Variable
- Site
The SAS System
Site Type
VA
exposurecat
DCF Age
DCF Status
exposure
Non-VA
Site 1
Site Number
Site 2
Site 3
Site 4
Site Number
Site 5
Site 6
N(%)
N(%)
N(%)
N(%)
N(%)
N(%)
DCF Age Mean
43.66 (53.22) 33.02 (50.85) 40.69 (58.54) 19.91 (41.1) 24.7 (37.96) 25.62 (50.37)
DCF Age Median
32 (2,59)
9 (0,48)
15 (0,58)
7 (1,21)
7 (0,30)
7 (0,28)
DCF Age 0-15
346 (43.7)
536 (58.7)
802 (50.3)
845 (71)
670 (58.1)
298 (60.7)
DCF Age 16-30
49 (6.2)
75 (8.2)
115 (7.2)
133 (11.2)
196 (17)
93 (18.9)
DCF Age 30-45
84 (10.6)
64 (7)
153 (9.6)
88 (7.4)
83 (7.2)
27 (5.5)
DCF Age 45-60
117 (14.8)
68 (7.4)
145 (9.1)
41 (3.4)
35 (3)
16 (3.3)
DCF Age >60
195 (24.7)
170 (18.6)
378 (23.7)
83 (7)
170 (14.7)
57 (11.6)
DCF Status Open
49 (6.2)
31 (3.4)
26 (1.6)
24 (2)
11 (1)
10 (2)
DCF Status Answered 0 (0)
5 (0.5)
4 (0.3)
6 (0.5)
12 (1)
4 (0.8)
DCF Status Closed
877 (96.1)
1563 (98.1)
1160 (97.5) 1131 (98)
DSN: Report Data
RDSN: Format Table
LIBOUT / FILEOUT
742 (93.8)
477 (97.1)
Customized Reporting:
Table to Define Report Structure
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data tabformat;
input varname $14. order @18 varlabel $14. @34 type $2. +2 fformat $8.;
if fformat eq ' ' then fformat = '4.';
if _n_ gt 3 then varcat = 'DCF Status’; else varcat ='DCF Age';
call symput('maxcount',_n_);
datalines;
aging_s_x 1 DCF Age Mean n1
- Elements from DCF Table
aging_s_x 1 DCF Age Median n2
- n1, n2, c1, c2 represent
dcfagecat 2 DCF Age
c2 dcfagef.
different types of metrics
dcfstatus 3 DCF Status c2 dcfstaf.
;
run;
Customized Reporting:
Macro: %tab1mac_doc - Do-Loop w/ Call Symput
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DSN: Report Data
RDSN: Format Table
COLVAR: Column Var.
BYVAR: By Var.
LIBOUT / FILEOUT
%macro tab1mac_doc (rdsn, dsn, colvar, byvar, libout, fileout);
………
%do count = 1 %to &maxcount;
data tmpk1; set &rdsn;
if _n_ eq &count;
call symput('vcat',varcat);
call symput('rf',varname);
VARNAME: Variable Name
call symput('vartype',type);
TYPE: Continuous / Categorical
call symput('varlab',varlabel);
call symput('vorder',order);
/ VARLABEL / ORDER
call symput('fformat',fformat);
run;
%end;
Customized Reporting:
Macro: %tab1mac_doc / Ex. Continuous Variable
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**** for Continuous variables (mean and sd)****;
%if &vartype eq n1 %then %do;
proc means data=&dsn noprint;
by &byvar &colvar;
var &rf;
output out= contout1 mean=mean std=std;
run;
data contout1; set contout1;
length exposure $42;
sorder = &vorder; exposurecat = "&vcat";
exposure = "&varlab";
run;
data results_m; set results_m contout1;
run;
%end;
Data Table: TabFormat /
N1 == Calculate Means
and Standard Deviation
Formatting Statements
Data Table: results_m
(holds Mean, SD values)
Customized Reporting:
Macro: %tab1mac_doc / Proc Report Statements
Generate a dataset tabres – combination of all results (continuous, categorical).
Formatting of table columns for final report
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%if (&byvar ne ) and (&vcat ne ) %then %do;
ods listing; ods document name=&libout..&fileout (write);
proc report data=tabres nowd headline ps=130;
column exposurecat exposure &byvar, &colvar, freqpercent ;
define exposurecat/ group order=data width=55;
define exposure /group order=data width=45;
define &byvar /across order=internal format=byf.;
define &colvar /across order=internal format=colf.;
define freqpercent /group width=20; run;
ods document close;
run; quit;
%end;
Customized Reporting:
ODS Output Destination – This case MS Excel
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ods listing;
ods tagsets.ExcelXP style=listing
options (Sheet_Interval='proc' embedded_titles='Yes' Index='Yes'
Absolute_Column_Width='20,20,20'
Row_Heights='15,15,15,15,15,15,15')
file="O:\SAS\Users\VA101010\Reports\Tables.xls";
ods tagsets.ExcelXP options(sheet_name="Table 1a");
proc document name=tabout.Table1a;
replay ;
run;
ods tagsets.ExcelXP close;
run;
Proc Document allows
replay of stored reports.
DCF Report
The SAS System
Site Type
VA
exposurecat
DCF Age
DCF Status
exposure
Non-VA
Site 1
Site Number
Site 2
Site 3
Site 4
Site Number
Site 5
Site 6
N(%)
N(%)
N(%)
N(%)
N(%)
N(%)
DCF Age Mean
43.66 (53.22) 33.02 (50.85) 40.69 (58.54) 19.91 (41.1) 24.7 (37.96) 25.62 (50.37)
DCF Age Median
32 (2,59)
9 (0,48)
15 (0,58)
7 (1,21)
7 (0,30)
7 (0,28)
DCF Age 0-15
346 (43.7)
536 (58.7)
802 (50.3)
845 (71)
670 (58.1)
298 (60.7)
DCF Age 16-30
49 (6.2)
75 (8.2)
115 (7.2)
133 (11.2)
196 (17)
93 (18.9)
DCF Age 30-45
84 (10.6)
64 (7)
153 (9.6)
88 (7.4)
83 (7.2)
27 (5.5)
DCF Age 45-60
117 (14.8)
68 (7.4)
145 (9.1)
41 (3.4)
35 (3)
16 (3.3)
DCF Age >60
195 (24.7)
170 (18.6)
378 (23.7)
83 (7)
170 (14.7)
57 (11.6)
DCF Status Open
49 (6.2)
31 (3.4)
26 (1.6)
24 (2)
11 (1)
10 (2)
DCF Status Answered 0 (0)
5 (0.5)
4 (0.3)
6 (0.5)
12 (1)
4 (0.8)
DCF Status Closed
877 (96.1)
1563 (98.1)
1160 (97.5) 1131 (98)
742 (93.8)
477 (97.1)
Concluding Remarks:
 Centralized monitoring activity can improve
efficiency of multi-center trials.
• Part of a Risk-Based approach to monitoring.
 eDC applications contains operational tables that
can assist in these activities.
• Beyond defined reports in eDC application.
 SAS / ODS Reporting Features can be used to
generate professional reports.
• Modular reporting macro can assist in providing center
metrics.
Concluding Remarks:
 Full Macro Information
• Available at (http://people.bu.edu/gagnon)
 Acknowledgements
• SAS® Acknowledgements
 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.
• Co-authors:
 Rebecca Fink / David Gagnon
• Staff at VA CSP Boston Coordinating Center / MAVERIC
 Erika Holmberg / Allan Lewis
Audience Questions