Risk-based SDV Approaches-Pros & Cons, 2010

R E G U L A T O R Y A F F A I R S 745
Vadim Tantsyrra, MS, MA,
DrPH (c)
Director, Data Management,
Infinity Pharmaceuticals
Imogene Grimes, PhD
Vice President. Statistics
and Data Management,
Infinity Pharmaceuticals
Jrlrs Mitchel, PhD, MBA
President. Target Health Inc
Kayo Fendt, MSPH
Director, Regulatory and
Quality. Duke Clinical
Research Institute
Sorgiy Sirichenko, MS
Data Strategist, PAREXEL
International
Joel Waters, MBA
Associate Director, PDG
Technical Support.
PAREXEL International
Jim Crowe, MS
Director, Clinical and
Nonclinical Statistics,
PAREXEL International
Barbara Tardiff, MD, MBA
Corporate VicePresident,
Data Sciences, PAREXEL
lnternational
Key Words
Source data verification;
Data quality; Site
monitoring; Risk-based
approach; Key variables
Carrespondonce Address
Vadim Tantsyura. MS. MA,
DrPH (c), 68 JudithDrive,
Danbury. CT 068I I (email:
vadim.tantsyura@gmail
.corn).
Risk-basedSource Data Verification
Approaches: Pros and Cons
Thehighcostof sourcedata vmjication (SDV),
particulariyin large trials, has made it a target
of scrutiny over the last decade. In addition,
the positive impact (ie, cost-benefit ratio of
SDV)on overall data quality is often ques-
tioned. As a result, regulators and industry
groupshave started looking at alternativeSDV
approaches. This article evaluates the FDA-
supported risk-based approach to SDV and
provides a proposal on how to modifvthe SDV
process without undermining the validity and
integrity of the trial data. It summarizes alter-
native approaches to 100%SDV and evalu-
atesthe advantages and disadvantages of risk-
based SDV (rSDV). The regulatory, data
quality, andcostimplications of each approach
areconsidered. Theeconomicsof rSDVaredis-
cussed and the cost implications of rSDV are
presented based on the results of exploratory
analysesfor four hypothetical trials in cardiol-
ogy and oncology.
INTRODUCTION
Source data verification (SDV)is one of many
quality steps employed by sponsors and CROs
to ensure clinical trial data validity. Other steps
include training of clinical investigators and
study personnel on the protocol and case re-
port forms (CRFs), data validation procedures
using programmed and manual data reviews,
and, finally, audits of clinical sites. SDV,in par-
ticular, allows for the evaluation of the confor-
mity of clinical trial data presented in the CRF
with data collected in the study subject source
record at the clinical trial research site. SDV
also ensures that "the reported trial data are ac-
curate, complete, and verifiable from source
documents" (1). As a component of study qual-
ity management, SDV adds to the scientific and
ethical integrity of the clinical trial. The extent
of the SDV is often debated, as the GCP (ICH
E6, 5.18.3 Extent and Nature of Monitoring)
language leaves much room for interpretation:
The sponsor should ensure that the trials are ad-
equately monitored. The sponsor should deter-
mine the appropriate extent and nature of moni-
toring. The determination of the extent and na-
ture of monitoring should be based on
considerationssuch as the objective,purpose,de-
sign, complexity, blinding, size, and endpoints of
the trial. In general there is a need for on-site
monitoring, before, during, and after the trial;
however in exceptional circumstances the spon-
sor may determine that central monitoring in
conjunction with procedures such as investiga-
tors training and meetings,and extensivewritten
guidance can assure appropriate conduct of the
trial in accordance with GCF! Statistically con-
trolled sampling may be an acceptable method
for selecting the data to be veri$ed. [Authors'
emphasis]
Clinical site monitoring can consume up to
30% of overall trial costs (2). This high cost,
particularly in large trials, has made it a target
of criticism over the last decade. In addition,
the positive impact (ie. cost-benefit ratio of
SDV)on overall data quality is often questioned.
For instance, significant resources are dedicat-
ed to SDV of data that have no or minimal im-
pact on study conclusions (eg, physical exami-
nation, medical history, or vital signs, especially
when they are not analysis variables). Second,
SDV is a manual review process, and it has been
well documented that the human review pro-
cess is only 85%accurate (3). Third, while SDV
is an effective way to capture certain types of
errors such as protocol violations, transcription
errors, and human errors in reading equipment
or printouts, it is not as effective and efficient
in dealing with other factors, such as transcrip-
tion errors within the source document, infor-
Drug InformationJournnl. Vd.44,pp. 745-756.2010. 0092-8615/2010
Printed in the USA.All rights reserved.Copyright 02010 Drug InformationAssociation. Inc.
Submittedforpublication:March 25,2010
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Tantsyura et al.746 R E G U L A T O R Y A F F A I R S
mation the study subject did not report or mis-
reported, data the subject reported but site
staff considered of no consequence, errors in
the data that the monitor did not review, and
fraud. Furthermore, "it [is] also observed that
current monitoring processes often involve
time- and labor-intensive techniques that are
not actuallyrequired by regulations"(4). There-
fore,it is not unreasonable to assumethat when
a focused SDV approach is used, there will be
an increase in data quality since the site moni-
tors will be able to focustheir attention on criti-
cal data elements that are vital for the analysis
of the study data rather than being box check-
ers. As a result, regulatorsand the industry alike
have started looking at alternative SDV ap-
proaches. This article summarizescurrent alter-
native approaches to SDV and:
1.Critically evaluates the relative value of verifica-
tion against source documents and records by
data category,and evaluatesthe risksof not source
verifying.
2. Evaluates the FDA-supported risk-based approach
3.Identifies alternative approaches to 100%SDV
and evaluates the advantages, disadvantages, and
risks associated with each approach.
4. Providesa proposal on how to modify the SDVpro-
cess without undermining the validityand integri-
ty of the clinical trial data.
to SDV.
LITERATURE REVIEW
A recent literature review found only 21 print
sources in which monitoring objectivesfor clin-
ical trials were mentioned (4). None of these
publications provided a standard for reduced
SDV.However, reduction in SDV is encouraged
where it is solidly grounded on a risk-basedap-
proach and other scientificprinciples.
EXTENTOFMONITORING
Khosla et al. (5)cite "Britishstandard specifica-
tions for SDV,"which are based on a random
sampleof 65 data points from 100CRFs for 100
patients. If fewer than three errors are identi-
fied,the data are accepted. If three or more er-
rors are identified, then the batch is rejected
and the data are verified in greater detail.
FDA (6)providesthe followingguidance:
From a scientific standpoint ... it is recognized
that the extent of documentation necessary de-
pends on the particular study, the types of data
involved, and the other evidence available to sup-
port the claim.Therefore,the Agency is able to ac-
cept different levels of documentation of data
quality, as long as the adequacy of the scientific
evidence can be assured.. ..
Industry-sponsored studies typically use exten-
sive on-site and central monitoring and auditing
procedures to assure data quality. Studies sup-
ported by other sponsors may employ less strin-
gent procedures and may use no on-site monitor-
ing at all. An International Conference on
Harmonization guideline on good clinical prac-
tices, recently accepted internationally, empha-
sizesthat the extent of monitoring in a trial should
be based on trial-specific factors (eg, design,
complexity,size, and type of study outcome mea-
sures) and that different degrees of on-site moni-
toring can be appropriate. In recent years, many
credible and valuable studies conducted by gov-
ernment or independent study groups, often with
important mortality outcomes, had very little on-
site monitoring. These studies have addressed
quality control in other ways, such as by close
control and review of documentation and exten-
sive guidance and planning efforts with investiga-
tors.
The FDA concept paper "Quality in FDA-Regu-
lated Clinical Research"(7)suggestslookingfor
alternative models for on-site monitoring:
For commercial studies done to develop pharma-
ceutical products, on site monitoring of perfor-
mance has become the rule, with sponsors visiting
essentially all sites every 4 to 8 weeks to assure
performance. This is costly,of course, and while it
contributesto aspects of study quality,it is not re-
ally practicable for large outcome trials, and has
failed to detect real fraud.As interest in largetrials
grows, it is becoming apparent that the industry
model is not feasible, without modification, for
those trials.
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Risk-basedSource DataVerification R E G U L A T O R Y A F F A I R S 747
ACCEPTABILITYOFSOME ERRORS AND
"Quality in FDA-Regulated Clinical Research"
(7)recommends that sponsors "consider imple-
menting risk-based approach to data collection,
analysis, and monitoring." "It is commonly un-
derstood that risk is defined as the combination
of the probability of occurrence of harm and
the severity of that harm. However,achieving a
shared understanding of the application of risk
management among diverse stakeholders is dif-
ficult because each stakeholder might perceive
different potential harms, place a different
probability on each harm occurring, and attri-
bute different severities to each harm" (8)."In-
cluded in the risk assessment should be a dis-
cussion of how the study design impacts the de-
gree of monitoring needed and endpoints war-
ranting active monitoring and review. For
example,large simple trials with mortality end-
points may require less monitoring and audit-
ing than a randomized clinical trial with surro-
gate endpoints or subjective endpoints"
(section, Possible Steps Towards Better Quality
in the Clinical Research Enterprise). Further-
more, a risk-management approach assumes
"certain characteristics of a clinical trial may
trigger additional scrutiny e.g., no preceding
experience with a country or sponsor, a spon-
sor-investigator(individual investigator) clinical
trial, or a clinical trial with a vulnerable subject
population."
In an unpublished summary document (4)
from a meeting of the Clinical Trials Transfor-
mation Initiative (CTTI; https://www.trialstrans
formation.org/) held in Washington, DC,on No-
vember 4, 2009, the question was posed as to
whether some levelof error is acceptable within
a clinical trial data set and, if so,whether inten-
sive monitoring efforts-such as repeated site
visits, source document verification, and CRF
review-might be reduced or avoided with the
understanding that these errors would not af-
fect study results in any meaningful way. It was
noted that, in trials attempting to demonstrate
superiority of one treatment over another, ran-
dom (ie, undirected) error in the effectiveness
RISK-BASED APPROACH
assessments or, possibly, in meeting entry crite-
ria will bias the results toward the null (ie, to-
ward a finding of no difference), making a false
positive finding unlikely but increasing the
chance of a false negative. There is clearly a ma-
jor interest in minimizing such errors. On the
other hand, random error in a noninferiority
trial will bias the results toward the alternative
hypothesis (that there is no difference between
the treatments), a major concern of regulators.
The safety aspects of studies have properties
similar to noninferiority studies, so that errors
in critical safety assessments are very impor-
tant. It was noted that important safety and ef-
fectiveness conclusions can turn on a small
number of cases, again suggesting major inter-
est in ensuring accuracy of the important effec-
tiveness and safety end points. It was proposed
that data, such as covariate data describing pa-
tient subsets, are almost always less critical to
main study results and that a modest error rate
could be acceptable in these data sets; for ex-
ample, in data concerning date of birth or age,
other illnesses, concomitant drugs unrelated to
the disease being studied, non-endpoint evalu-
ations, and less critical secondary endpoints. In
light of these remarks, meeting participants
came to a tentative consensus that some degree
of error in noncritical data may be acceptable
as long as important effectiveness and safety
endpoints have been verified. In addition, risk-
based monitoring, focusing on defects that
would make a critical difference to data quality
and patient safety, might be considered as a
means of efficiently ensuring the data quality
and subject protection without requiring inten-
sive effort and incurring unnecessary costs.
FOCUSON CRITICAL VARIABLES
ICH E9 (9)suggests that "the form and content
of the information collected.. .should focus on
the data necessary to implement the planned
analysis." The GCDMP (3) suggests focusing
the quality checks on the variables that are
"critical to the analysis,"and the Assuring Data
Quality section of GCDMP also suggests focus-
ing resources on "only data that are essential
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for the interpretation of trial results." The
GCDMPadds,"It is not practical to design a qual-
ity check for every possible error. Quality checks
performed as part of data processing, such as
data validation checks or edit checks should tar-
get fields critical to the analysis,where errors are
expected to be frequent, and it is reasonable to
expect a high percent of error resolution."
Khosla et al. (5)recommend identifying some
different categories of data, for example,critical
and noncritical data, and to focus the SDV pro-
cess on critical variables. Critical variables are
the data that are focal to the aims and objec-
tives of the study, and which must be correct.
The noncritical data should also be correct, but
if an item of such data is in error, it is not criti-
cal to the outcome of the study. The authors
recommend:
100% of the critical data should be verified
against the source documents for every patient in
the trial. Examples of critical data that should be
checked for each subject include the following:
1. Informed consent to participate in the study;
2. Physician ...notes, preferably on the hospi-
taVclinical stationery;
3. Primary efficacyend points;
4. Secondary efficacyend points;
5. Recording and reporting of serious adverse
events;
6. A confirmation in medication history or else-
where that the study drug was prescribed and
in the specified dosage;
7. Conformance to the patient inclusion / exclu-
sion criteria;
8. Visit dates as per the window period specified
in the protocol of the study.
Non-critical data can be checked for a proportion
of CRFs. The percentage varies from trial to trial
and company to company. Generally it is 15 to
25%.(5)
EARLY ERROR DETECTION
"Careful monitoring can ensure that difficulties
are noticed early and their occurrence or recur-
rence minimised" (9). This suggests that the
SDV (and monitoring activities in general)
should not be evenly distributed over the
course ofa study, but skewed toward the earlier
visits (eg, screening and baseline). CTTI echoes
ICH in stating that "monitoring is most useful
when it identifies errors early so that corrective
training can be carried out" (4).
ALTERNATIVE SDV APPROACHES
The following discussion covers five SDV ap-
proaches for consideration. The first approach
is a classic full-scale SDV that involves manual
verification of 100% of the collected data
against the source documents and records. The
other four methods allow some reduction in
SDV using different algorithms.
STANDARD(100%)SDV APPROACH
The standard 100%SDV approach assumes that
all the study data are verified against source
documents and records. The major advantage is
that there is the perception, at least, that this
approach guarantees the highest data quality.
However, because this approach lacks a formal
and systematic risk assessment, this perception
is not necessarily supported by the evidence or
documented in the scientific literature.The ma-
jor disadvantage of this approach is that its im-
plementation is both resource and time inten-
sive.
RANDOM SDVAPPROACH
The random SDV approach has two steps. This
approach assumes starting (step 1)SDV at a low
level (eg, only 10-20%of subjects, sites,or data
points are randomly selected for SDV).The first
step may include very few visits-screening and
baseline, for instance. Then the quality of step 1
data is evaluated using either error rates or oth-
erwise predetermined criteria, and the decision
is made about adjusting the SDV level up (to
SO-loo%), if necessary. Figure 1 outlines the
process.
The major advantage of this approach is the
possibility of significantly reducing the number
of monitoring visits and their associated costs.
However,the somewhat limited amount of data
that the SDV adjustment decision is based upon
may make it difficult to define the adequate lev-
el of SDV.Also, random monitoring, without fo-

Risk-basedSource Data Verification R E G U L A T O R Y A F F A I R S 749
Ifdata qwlii
isruesoreidentified,
thenSDV kdustedto
a hiikbvel
OtherwiseSDV ot
(10-20%1
theoriginallevel
t
cus on key variables,will most likely lead to low
return on investment of monitoring time. In ad-
dition, there are some anecdotal reports that
Europeanregulatoryauthorities have refused to
accept a study for a major sponsor company
that was using a random-like approach where
there was no requirement to check screening or
eligibilityvisits unless they appeared in the sta-
tistical sampleto be monitored.In this study,28
out of 40 randomized subjects were found to be
ineligible for the study.
DECLININGSDVAPPROACH
The declining SDV approach is a reverse ver-
sion of the random SDV approach. The initial
(screening and baseline) visits are verified at a
100%level and,if no significantissues are iden-
tified (to be defined), then the adjustment to
perform less SDV is made. Figure 2 outlines the
process.
This approach may be viewed as an improved
version of the random SDV approach, since the
decliningSDV approach improvesthe detection
of early errors or issues relative to the random
SDV approach. However, the return on invest-
ment is still suboptimal due to inefficient re-
source utilization; that is, no distinction is
made between high-value variables (ie, key safe-
ty and efficacy)and low-valuevariables. In addi-
tion, reducing SDV from 100%to 10-20% is
not a trivial decision and could add complexity
to the monitoring process,and may also require
extensivenegotiations with a regulatory agency
such as the FDA.Finally, the resource utilization
using this approach is highly variable and, thus,
unpredictable. This fact alone makes the de-
clining SDV approach extremelychallenging in
terms of planning and execution as well as re-
source allocation and is ultimately least practi-
cal from the monitoring perspective (especially
if the monitoring is outsourced).
THREE-TIEREDSDVAPPROACH
The underlying premise of the three-tiered SDV
approach is that the level of SDV is commensu-
rate with the value of the particular data points
within the study. Figure 3 outlines the process.
The three-tiered SDV approach,at least theo-
retically,leads to the most cost-effectiveutiliza-
tion of site-monitoring time. Data quality is
likely to be improved and, because of the more
focusedapproach, site monitorscan direct their
F I G U R E 1
Random SDVapproach.
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750 R E G U L A T O R Y A F F A I R S T a n t s p et al.
F I G U R E 2
Declining SDVapproach.
F I G U R E 3~ ~
Three-tiered approach.
100%SDV I , DecisionPoint ' 4 10-20% SDV,-
Sponsarsign-Off
> ,
0 4
I , W:100%SDV
(Ex:AEs, Dates, Dosing,
I , KeyVoriables of Analysis)I ,
0
rsr_2:10-20%SDV
I ,
, c
of variablesto be includedin (Ex: VtolSigns, ConMed, Lobs)
0 ,
I M:0%SDV
,
(Ex: PhysicalExam, Med. Hx)
I ,
I ,
0 ,
I '
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Sponsor Sign-Off
attention and efforts to the CRFs and data
points that matter the most, and do not need to
monitor the less important data. However, the
complexity associated with the implementation
of this approach may slow the acceptance of
such a method.
MIXEDAPPROACH
The mixed approach attempts to capitalize on
the strengths of the declining SDV and three-
tiered SDV approaches, while at the same time
reducing the complexity of the three-tiered
SDV approach, and may be more feasible. The
underlying premise of the three-tiered SDV ap-
proach that the level of SDV should be com-
mensurate with the value of the particular data
points for the study is further extended by
stressing the critical importance of the baseline
and study endpoint values. In the mixed ap-
proach, screening, baseline and study endpoint

Risk-basedSourceDataVerikation R E G U L A T O R Y A F F A I R S 751
I
I l00XsDV I
- 1 I
visits require 100%SDV. while all other CRFs
require less extensive SDV. Other than the key
value variables such as efficacy outcomes (ad-
verse events, dosing information, visit dates,
etc), most other variables could be monitored
remotely, making it more appealing when elec-
tronic data capture (EDC) is used. Figure 4 out-
lines the process.
This approach should be easier for the site
monitors to implement than the three-tiered
SDV approach. The reason is twofold: first, the
number of tiers is reduced from three to two,
and second, for screening, baseline, and end-
point visits, site monitors will review all data
points, and thus do not need to remember
which forms and data points are required for
SDV and which are not.
The remote (or centralized) monitoring com-
ponent, if employed, should further reduce
monitoring costs without having a negative im-
pact on data quality. Some data, such as hema-
tology, chemistry, and urinalysis, are more con-
duciveto remote monitoring than others. That is
why the study team, in the monitoring plan,
should make study-specificassessmentswhether
to employvirtual monitoring and, if so. for what
type of data. Based on this risk-based SDV(rSDV)
approach, the baseline and study endpoints are
deemed to be the most important, and these vis-
its require on-site monitoring. All other visits
may (or may not) be considered by study teams
as candidates for virtual monitoring.
The major objection to this methodology
comes from the inability of the currently uti-
lized EDC systems to support this rSDV ap-
proach. If future EDC systemswill support more
flexible (data-point) SDV, that will likely accel-
erate the acceptance of this particular rSDV ap-
proach.
ECONOMICSOF RSDV
For all of the approaches that result in reduced
SDV,the followingconclusions can be made:
Initial (fixed, per study) costs are increased be-
cause of an additionalinvestment of time and re-
sources for risk assessment and SDV plan develop-
ment.
Variable (per study subject) costs, which are pro-
portionate to the number of data points to be
SDVed,are decreaseddue to the reduction in SDV
from 100%tox%.
Based on personal experience of the authors
and statements by the FDA at scientific meet-
ings that typically only 30% of submitted data
are the most essential for drug approvals, it is
estimated that it is reasonable to expect a 50-
70% reduction [ie, x = 1- (50+ 70%)= 30 +
50%)in the number of data points required for
SDV. However, to verify this assessment, an ad-
F I G U R E 4
Mixed approach.
~~

752 R E G U L A T O R Y A F F A I R S
F I G U R E 5
Break-evenanalysis.
Tantsy~uaet al.
1,200
3 1
r E 800
B E
n B
zcs=P
> > 400
852
200
>
-A- Total Cost (100%SDVI
-x- Total Cost (reducedSDVI
600
- =
gg
-e
s
Unitsof quantity
(eg, # of subiectsper study x 10)
ditional analysis is needed. For example, a sam-
ple of studies can be analyzed to determine the
potential time and resource savings using the
approaches described above.
Figure 5 represents a hypothetical scenario
when the fixed costs (associated with the pre-
planning of the site monitoring) are doubled
and the variable costs are 30%(ie. reduction by
x = 70%)of the original level as a result of the
reduction of the amount of SDV. The triangles
represent the costs associated with classic
(100%)SDV and the stars represent the costs
associated with the reduced (to 30%)SDV for
the same clinical trial (ie, the slope of the high-
er line is 30%of the slope of the lower line). The
distance between the two lines represents the
cost savings associated with risk-based SDV.
Figure 5 shows that cost efficiencies associat-
ed with reduced SDV are unlikely in small
(phase 1/2a) studies due to the additional
(fixed) costs that offset the reduction in the
variable costs. There may be value in the plan-
ning and setup for some small studies if the
planning and setup can be applied to a whole
class of studies that has the same design and
collects the same kinds of data (eg,bioavailabil-
ity or bioequivalence studies). The most signifi-
cant savings can be gained in large phase 2b/3
studies when the additional (fixed) cost in-
crease is insignificant relative to the reduction
ofthe variable component of the costs.
A number of exploratory analyses were con-
duced to estimate the potential cost savings of-
fered by risk-based SDV approaches based on
estimating time and costs associated with dif-
ferent clinical study activities (see the appendix
for more details). The hypothetical examples
below summarize the cost savings based on the
assumption of 50%SDV and associated reduc-
tion in monitoring frequency from 6- to 10-
week periods. The examples provided were de-
veloped based on average project parameters
gathered from a sample of studies (eg, number
of patients, sites, serious adverse event [SAE]
rate, enrollment period, etc) in the cardiology
and oncology therapeutic areas. Baselinevalues
utilizing standard SDV were developed to allow
for comparison for the phase 2 and phase 3
studies. A second set of data was prepared based
on the proposed method where a reduced SDV
method would result in fewer monitoring visits
being required (eg, reduction from 6-week
monitoring frequency to 10-week).This method
allows for reductions in clinical research associ-
ate (CRA)hours related to the reduced SDV and
travel time as well as reduced travel expenses. In
addition, the followingassumptions were used
EDCstudy.
USsites only.
100%SDV assumed approximately 6 minutes per
page review time (for cardiology) and 11 minutes
per page for oncology.

Risk-basedSource DataVerification R E G U L A T O R Y A F F A I R S 753
study PhaM 2 3 2 3
Studysubiects(completed) 238 1,282 100 460
Studycost reduction 11.6% 14.3% 16.7% 23.5%
50% SDV assumed 1.5 minutes per page review
time for pages that are not included in the SDV
plan.
As indicated in Table 1,the expected savingsare
11.6% for a typical cardiology phase 2 study
(238 subjects) and 16.7%for a typical oncology
phase 2 study (100subjects). The potential sav-
ings in a typical cardiology phase 3 trial (1,282
subjects) are expected to be 14.3%, and 23.5%
in a typical oncology phase 3 trial (460 sub-
jects). The impact of the reduced SDV on the
costs comes from the reduction of per-page re-
view time and resulting reduction of on-site
time, assuming that on-site time is highly cor-
related with per-page review time.
Exploratory analyses for phase 1 studies indi-
cate that it is not realistic to expect a noticeable
reduction in monitoring frequency for very ear-
ly phase studies. As a result, the potential sav-
ings from the reduced amount of SDV will be
much smaller in phase 1 than the 11.6-16.7%
in phase 2 and 14.3-23.5% in phase 3. Given
the relatively modest monitoring budgets for
phase 1studies, the investment in the new pro-
cess may not be justified. Thus, the standard
100%SDV approach may still be appropriate
for phase 1studies.
PReLIMINARYQUALITYANALYSIS OF
SDV REDUCTION
The impact of SDV reduction on quality is con-
troversial. It is often assumed that a higher de-
gree of SDV correlates with higher data quality:
however, no empirical or statistical evidence
supports this hypothesis. In the final analysis,
the focused approach to SDV grounded in a
carefully executed risk assessment should gen-
erate at least the same level of data quality. Ide-
ally, data quality benchmarks should be devel-
oped and data collected over time for the
ongoing assessment of the selected approach.
Different data quality benchmarks may be
needed in the different phases of drug develop-
ment.
IMPLEMENTATlO N APPROACH ES
Proper risk assessment and management is a
critical component of any rSDV approach.
Study teams need to consider themselves in the
position of regulatory medical and statistical
reviewers and ask what data are most critical to
evaluate for subject safety and evaluation from
the efficacy perspective. The answers to these
questions should guide the study team in devel-
opingtheir study monitoring or SDV plan.
To implement the rSDV approach, it is neces-
sary to address the following:
1. The rationale behind the classification and divi-
sion of data elements into tiers.
2. The decision whether CRF-level or data-point-lev-
el rSDV is appropriate. rSDV can be executed in
twodistinct flavors: (a) at the CRF level-when ei-
ther all or none of the data points on a CRF are
source verified, or (b) at the data-point level-
when some data points on a CRF are verified and
the other data points on the same CRF are not
verified. From the efficiency perspective, the
smaller studies are better suited for the CRF-level
rSDV, and the larger studies are better suited for
the data-point-level SDV.
3.Training,tools, and other strategies needed to en-
sure that the study monitors know exactly what
data are to be verified and what they need to focus
on in verifying data (eg, subject eligibility during
verification of screening and baseline data).
4. The impact of potential data errors (eg. random-

714 R E G U L A T O R Y A F F A I R S Tantsyura et al.
ization and primary endpoint data) and prioritiza-
tion and redundant approaches (eg, source verifi-
cation plus data validation checks plus statistical
consistency checks) on the final data-based deci-
sions.
5. The larger set of strategies to ensure data quality
and fraud detection. This should be emphasized in
both communication and execution.
6. The activities associated with preparing for source
data review (traveling to site, gathering docu-
ments) in addition to that associated with verifica-
tion of each data element.
7. The impact of e-source data and the electronic
health record when data cannot be source verified
in the classical sense. Under this scenario, these
data need to be specified prospectively in the pro-
tocol.
An appropriate organizational governance and
operating model,with both steering and opera-
tions components and allowing for participa-
tion of relevant functions including medical,
clinical, data management, statistics, project
management, quality assurance, and regulatory
affairs,is required to support planning and exe-
cution.
Key artifacts should be prepared as documen-
tation of planned approach and to guide execu-
tion:
1.The SDV approach (100%SDV vs rSDV) that has
been determined to be appropriate and feasible
(eg, 100%SDV may be selected for phase 1/2a
studies and rSDV for phase 2b/3)
2. A critical data point specification providing the
3. An overall risk-based monitoring plan (that in-
necessary details at the data point level
cludes the rSDV specification document)
The following changes are foreseen in the
clinical and data management practices that
will accompany a wider spread of rSDV ap-
proaches:
1.A high-level risk-based monitoring section of the
protocols.
2. Training of clinical personnel in the risk-based
monitoring plan.
3. Dissemination of the critical data point specifica-
tion through data management will occur as part
of the study team to facilitate the development of
the data validating specification (DVS),data man-
agement plan (DMP),quality plan, and other rele-
vant documents.
CONCLUSION
1. Clear understanding of risks and benefits of vari-
ous SDV and monitoring practices is necessary for
identification of the most efficient and effective
technique for a specific study.
2. The standard 100%SDV (option 1)is feasibleand
may be optimal for early phase studies because the
relative value of each data point in early phase
studies is much higher (given the small sample
size).
3. Although the random SDV approach (option 2)
appears to be the simplest to implement. it is the
riskiest from the scientific and regulatory perspec-
tives, and the implementation of this approach is
not recommended.
4. The rSDV approaches (options 3-5: declining
SDV, three-tiered, or mixed approaches) should
lead to improved data quality and potentially re-
duced time to database lock. There should be sig-
nificant cost efficiencies in large (phase 2b/3)
studies, and these approaches should be seriously
considered for these clinical trials.
5. The mixed approach (option 5) has potential to
become widely used. This approach appears to be
most practical from a resource perspective due to
minimal variability of resource demand over the
course of a study relative to the other approaches
(2-4).
APPENDIX: COST CALCULATIONS-THREE HYPOTHETICAL SCENARIOS
This section provides the details of the cost comparison for three hypothetical scenarios. Scenario
1reflects a standard monitoring process with 100%SDV and the monitoring visits occurring every
6 weeks. Scenario 2 reflects the reduced (by 50%)SDV,but no reduction in the frequencyof moni-
toring visits (once per 6 weeks). Scenario 3 reflects the reduced (by 50%)SDV and the reduced

Risk-basedSource Data Vcrification R E G U L A T O R Y A F F A I R S 755
frequency of monitoring visits (once per 10 weeks). Each scenario considers four hypothetical US
studies (two in cardiology and two in oncology).
The examples or scenarios provided are developed based on average project parameters gathered
from a sample of proposals (eg, number of subjects, sites, SAE rate, enrollment period, etc) in the
cardiology and oncology therapeutic areas. These parameters (carried through all three scenarios)
are displayed in the following table.
wArea b r d i d o g y c a r d k l o s r o - b Y ~
StudyPLan 2 3 2 3
S u b(enrdled) 276 1,389 120 543
subiects~completed) 238 1,282 100 460
Shtdysites(US) 28 106 28 75
subieashmened) 373 1,593 147 654
Enrdlment(weeks) 40 52 53 70
Treatment (weeks) 24 30 46 82
SAErate(%) 0.41 0.53 0.83 0.83
UniqueCRFs 22 19 30 30
CRFsperSUM 72 73 128 139
Minutesperpoge 8 8 15 14
The first scenario assumes a standard monitoring visit frequency of one visit every 6 weeks.
TlmaPrrtkh ( a d l d o g y - o - b Y -
StudypLaw 2 3 2 3
Monitoringfrequency 6.0 6.0 6.0 6.0
Timeon-site(hours) 8.0 8.2 7.2 7.9
The following table summarizes the changes in the parameters and assumptions for scenario 2
relative to standard (100%)SDV, scenario 1. As demonstrated below, if the monitoring frequency
remains unchanged, the reduced SDV does not lead to a significant cost reduction.
I StudvPLaw 2 3 2 3
Monioringfreqwncy 6.0 6.0 6.0 6.0
ITimeon-site(hours) 5.5 5.7 4.7 5.1I
I Direct(SDV) cost reduction 3.1% 1.9% 4.8% 6.9%I
I Travelexpensereduction 0.0% 0.0% 0.4% 0.1%I
I overall(totalstudycost)reduction 2.7% 1.7% 4.3% 6.1%I
All the expense reductions in scenario 2 are relative to scenario 1.
Drug Momation Journal by guest on June 4, 2015dij.sagepub.comDownloaded from

The final table was prepared based on the method where a reduced SDV approach would result in
fewer monitoring visits being required (eg, reduction from 6-week to 10-week monitoring frequen-
cy). This method allows for reductions in CRA hours related to the reduced SDV and travel time as
well as reduced travel expenses.
‘ V k O-baYO-baY
2 3 2 3
~
S * h
l - n a h 10.6 10.0 10.0 10.5
Timeon-site(hours) 7.0 8.0 7.0 7.8
Direct(SDV)costreduaion 10.4% 12.7% 15.2% 22.1%
ldexpenser#ludion 21.6% 25.8% 27.5% 33.5%
ovemu(tot01study cost)red- 11.6% 14.3% 16.7% 23.5%
All expense reductions in scenario 3 are relative to scenario 1.As demonstrated in the table above,
a reduction in SDV coupled with a reduction in monitoring frequency leads to significant (11.6-
23.5%)cost savings.
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The authors report no relevant relationships to disclose.

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