1. Drug Information
Cost-Effective Approach to Managing
Laboratory Reference Ranges for Local
Laboratories in Clinical Research
Vadim Tantsyura, MS, MA, DrPH(c)1
, Imogene Grimes, PhD2,*
,
Jules Mitchel, MBA, PhD3
, Sergiy Sirichenko, MS4
, Jim Crowe, MS5
,
and Deborah Viola, MBA, PhD6
Abstract
The use of a single central laboratory with universal references ranges is not always a viable option in clinical studies; examples are
oncology studies where a rapid turnaround of clinical laboratory results is critical. However, the complexities associated with
multiple sites, multiple laboratories, and multiple age and sex groups can lead to logistical nightmares across clinical trials and make
handling laboratory data one of the most challenging, labor-intensive, and time-consuming tasks for clinical data managers, espe-
cially where different laboratories are used for the same patient. Also, evidence suggests that the reference ranges (RRs) used by
the local laboratories often create a false sense of precision that is not always supported by science. Managing time-specific,
demographic-specific, and site-specific RRs requires significant investment in time and labor. As a result, an alternative approach
to management of local laboratory RRs that uses ‘‘standard’’ (sometimes called ‘‘published’’) ranges has been growing in popularity
over the past several years. This article attempts to compare the pros and cons of this approach relative to the historic ways of
handling local laboratory RRs. Scientific, operational, and economic perspectives are also presented.
Keywords
reference ranges, normal ranges, clinical trials, clinical data management, cost-effectiveness
Background: Importance of
Reference Ranges in Clinical Research
Laboratory data are critical markers of disease, hence they are a
key measures of safety within clinical trials. Laboratory ana-
lytes are among the most common biomarkers used in clinical
research and medical care because laboratory abnormalities
often precede clinical symptoms and serve as a first indicator
of potential adverse events associated with an investigational
medicinal product. Because clinical laboratory results are
essential to clinical trials, there is a need to collect and ensure
the accuracy and integrity of a very large amount of these types
of data. For example, laboratory data account for approxi-
mately 30% of the clinical data in a typical oncology phase 2
clinical trial. According to Karvanen, ‘‘In many studies and
especially in early clinical development phase (such as oncol-
ogy phase 1 studies), laboratory data constitute 50%-80% of the
data to be collected.’’1
Based on the authors’ experience, a typ-
ical small oncology biotechnology company deals with
>30,000–60,000 laboratory records (vs *1000–1500 adverse
events [AEs]) per investigational product. This order of
magnitude is not insignificant. The average new drug application
to the FDA contains about 573,000 laboratory test results—the
sponsors might be evaluating thousands of results each day.2
Laboratory results are of little value without the ability to
analyze the results in comparison with baseline or reference
values. Laboratory results are typically either compared with
other samples taken from the same subject at a different time
point (eg, baseline values) or are compared with an established
1
Independent consultant, Danbury, CT, USA
2
Otsuka Pharmaceutical Company, Rockville, MD, USA
3
Target Health Inc, New York, NY, USA
4
Pinacle21/OpenCDISC, Bedminster, NJ, USA
5
JT Crowe Consulting, Richmond, VA, USA
6
New York Medical College, Valhalla, NY, USA
*
Current affiliation: Transtech Pharma Inc, High Point, NC, USA
Submitted 20-Dec-2011; accepted 20-Mar-2012.
Corresponding Author:
Vadim Tantsyura, MS, MA, DrPH(c), 68 Judith Drive, Danbury, CT 06811, USA
Email: vadim.tantsyura@gmail.com
Drug Information Journal
46(5) 593-599
ª The Author(s) 2012
Reprints and permission:
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DOI: 10.1177/0092861512446333
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2. reference range. Reference ranges (RRs) can also be known as
‘‘normal ranges’’ (although not all populations can be consid-
ered truly ‘‘normal’’ or ‘‘healthy’’).i
RRs are established by analyzing a large number of samples
and statistically determining the appropriate range of values.ii
Because values may differ according to variables such as age,
gender, disease processes, time of day, or regional variations,
multiple ranges are often established for a given laboratory test.
Laboratories may either establish their own set of RRs or obtain
ranges from published sources. RRs are usually collected at the
beginning of a study, unless there are changes to the specimen
collection, instrumentation, or methodology.iii
Three categories of use of RRs in oncology clinical trials are
typically considered:3
Inclusion/exclusion(I/E)criteriaareoftendefinedasmultiples
of upper limit of normal (ULN), rather than in absolute values
Protocol defined serious adverse event (SAE) criteria (eg,
laboratory results of aspartate aminotransferase [AST] and/
or alanine aminotransferase [ALT] 3Â ULN with total
bilirubin 2Â ULN will be considered an ‘‘important medi-
cal event’’ and will be reported to the sponsor as an SAE)
Standard common terminology criteria (CTC) toxicity
grades (in about half of the laboratory analytes collected
in oncology clinical trials, the toxicity is graded based on
the result as a multiple of the ULN)
Table 1 provides an example of the common terminology cri-
teria for adverse events (CTCAE) by the National Cancer Insti-
tute (version 3) using AST as an example.
The costs of maintaining multiple normal laboratory ranges
from multiple laboratories are very high. Briefly, for each labora-
tory, (1) clinical operations determine RRs, (2) sites or data man-
agement enter RRs into electronic data capture (EDC) systems,
(3) during the monitoring process, clinical operations check that
the laboratory units and ranges displayed in EDC system reflect
what is on the patient laboratory report, and (4) often laboratories
update RRsduringthe course ofa study requiringadditionalentry
andsubsequentdatamanagement.Asa result,multitudes ofhours
are invested each year to maintain and verify RRs.
Why is there a need to maintain site-specific laboratory nor-
mal ranges? An informal survey conducted at the 2011 annual
DIA meeting revealed that many participants believed that no
alternative approach was published and no alternatives were
explicitly supported by the FDA. As a result, this article attempts
to fill this gap by proposing a scientifically solid and cost-
effective way of handling laboratory RRs in clinical trials.
Inherent Variability of Laboratory RRs and
Problems With Interpretation of RRs
The inherent variability of lab RRs is a well-documented
fact.4,5
Figures 1 and 2 illustrate a typical distribution of lower
and ULN RRs for potassium across participating laboratories.4
In this report, the lower limit of normal varied from 3.0 to 3.9
mmol/L and the upper of limit of normal varied from 4.5 to 6.2
Table 1. Common terminology criteria for adverse events (CTCAE)
version 3.0, aspartate aminotransferase grading example.a
CTCAE v. 3.0 Grade
Normalized Result
(ULN)
0 ULN
1 1 to 2.5Â ULN
2 2.5 to 55Â ULN
3 5.0 to 205Â ULN
4 205Â ULN
ULN, upper limit of normal.
a
Rules vary for each analyte.
Figure 1. Potassium—lower limit of normal frequency. Source: Image
adapted from Haag et al.4
Figure 2. Potassium—upper limit of normal frequency. Source: Image
adapted from Haag et al.4
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3. mmol/L. The variability of such magnitude is typical across
many analytes.
From a statistical perspective, major sources of variability
can include the following:
Patient population (eg, contrast a patient population from a
hospital where the proportions of heavy smokers and heavy
drinkers are higher than average and a hospital associated
with a health-conscious university where there are smaller
proportions of smokers and heavy drinkers)
Site and laboratory environment itself (including equipment,
standard operating procedures, operational environment)
Attributes of key laboratory and support staff, such as their
judgment relative to laboratory mishaps, training, and
experience (technical performance affects the ranges)
Ruvuna et al6
list the following 3 major causes of RR varia-
bility (and ultimately imprecision): (1) variation in assay specs
used by different laboratories, (2) variation in clinical decision
(in allowable false-positive vs false-negative rates) between
laboratories, (3) variation in mathematical decisions (95% CI
vs 90% CI). (Unusual values from the sample may be censored
just because they are several standard deviations from the mean
and make the curve messy.2
) In addition, Thompson et al2
argue that in some cases the environmental and biological
sources of variability (diet, age, and national origin) are not
compensated for in calculation of RRs.
This inherent variability of RRs makes interpretation of
results more difficult and makes it a target of criticism from
many perspectives. Thus, Solberg and Stamm7
raised the issue
of ‘‘obtaining a sufficient number of adequate specimens for
the production of the reference values.’’ Henny et al8
presented
a ‘‘need for revisiting the concept of reference values,’’ point-
ing to the ‘‘need for more practical recommendations regarding
systematic errors and transferability, regarding the reference
population, regarding statistical methods used, regarding refer-
ence and decision limits and the question about which percen-
tiles to be used.’’ Hyltoft-Petersen9
points to ‘‘the flaws of
population-based reference intervals due to the biological indi-
viduality presented by all, as the dispersion of values for any
individual may span only a small part of the traditional refer-
ence interval for many quantities.’’ He also points to ‘‘a high
percentage of false positive results when the traditional
description of reference values as 95% reference intervals is
used.’’ Thus, it is reasonable to conclude that the RR used by
the local laboratories often create a false sense of precision that
is not always supported by science.
Furthermore, when the normal range changes, tracking the
significance of the result can be challenging or even compro-
mised. A case was observed for which ALT values for a patient
were 51 and 48, respectively, for baseline and postbaseline.
When the upper limit of the normal range decreased from 52
to 45, the patient was classified as shifting from normal to
above normal, when the patient’s values actually declined.10
These statistical artifacts can occur when normal ranges vary
during a study for any reason.
The difficulty with interpretation of laboratory results is not
the only problem, which is caused by the variability of RRs. As
is shown in the following section, such variability is also a
source of operational challenges for clinical and data manage-
ment professionals.
Inherent Labor Intensiveness of the
Current Standard Laboratory RR
Management Process
The process of collecting and maintaining local laboratory RRs
usually includes the following 6 steps, which require intensive
use of resources:
1. Laboratory RRs received by the site coordinator or con-
tract research organization (CRO)/sponsor
2. RRs transcribed into the electronic case report form
(eCRF)
3. RRs document validated (quality control) in the eCRF with
appropriate revisions of the RR within the EDC system
4. Documentation filed in the data management project files
5. Project team notified of entry of new (or updated) RR
6. EDC laboratory units managed and converted (of original
laboratory units of measurement) to standard (ie, syste`me
international [SI]) units
7. There are many data and data quality issues that clinical
and data management professionals deal with during the
life cycle of laboratory RR management processes. Com-
mon examples include the following:
Multiple sets of normal ranges per site are used, which
leads to tracking issues
The ability to collect the RR from the study sites is not
as smooth as in the case of a central laboratory because
of site-specific resource constraints
Missing information for dates, units, ranges, age, and
gender as well as typographical errors
Managing units utilizes resources when different local
laboratories use different units for the same analytes
(up to 7, typically 3 or 4)6
; Thus, unit conversions take
significant time, and cleaning local laboratory data
becomes much more difficult because of multiple
ranges used by the different sites
The large number of laboratory-range related issues
leads to lengthy interactions between the sponsor and
the sites, which can lead to frustration; consequently,
clinical and data management personnel need to deal
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4. with the ‘‘upset sites’’ when, for example, supporting
documentation for revised RRs is not provided
To cope with the multitude of laboratory RR issues, sponsor
companies employ data visualization tools and (built around
them) expensive processes. Unfortunately, the software allevi-
ates only a portion of the task, leaving data managers with heav-
ily manual, tedious, and labor-intensive work. In addition to
being extremely resource-consuming, the process is difficult to
control for quality, and thus it is inherently error prone, regard-
less of the assistance of software visualization tools. It can be
argued that the current process artificially imposes a significant
burden on data managers, and it can be estimated that hundreds
of millions are spent in the United States annually on the main-
tenance of the existing system (see Table 2 for more details).
One may argue that the lion’s share of these resources can be
saved. Economic reality presents an incentive to pharmaceutical
professionals regarding the reasonableness of spending signifi-
cant resources on every tiny detail of questionable importance,
especially knowing that 66%–71% of trials in phases 2 and 3
in oncology, for example, are negative.11
Additional scientific
considerations include statistical anomalies and artifacts that can
arise as the result of changes in metainformation about a patient
that is unrelated to the patient’s disease, which can result in diag-
nostic changes or conflicting conclusions. Thus, the soundness
of the process from the scientific and resource-investment per-
spectives can benefit from a reevaluation. As a result, we present
alternative strategies to managing RRs that could be both scien-
tifically and economically more plausible.
Alternative Strategies to Managing RRs
In addition to the classic approach of using individual local
RRs (discussed above and presented as alternative 1 below),
a literature review helped to identify 2 previously published
approaches (‘‘RR from highest enrollment site’’14
and ‘‘the
phantom laboratory GLN’’6
). These options are listed as alter-
natives 2 and 3 below. (Also, Chuang-Stein15
suggested ‘‘con-
structing disease-specific or protocol-specific RRs.’’
However, no sufficient operational details were provided, which
is why this particular alternative is not discussed further.) Alter-
native 4 below is a strategy of using the ‘‘standard’’ or ‘‘pub-
lished’’ laboratory RRs that has not been considered or
discussed in the peer-reviewed literature. These are summarized
in Table 3.
Alternative 1 is the commonly used method wherein local
RRs are collected and used for analysis as well as dose escalation
purposes. This approach is considered by many to be the most
clinically meaningful. However, this approach can be debated
on scientific grounds. Furthermore, this approach is expensive.
Alternative 2 is a potential alternative wherein the labora-
tory with the highest enrollment is used as the standard.6
Ruvuna et al state that ‘‘[t]his strategy works, but the draw-
backs as defined by Chuang-Stein (1998) include possible
chemical, analytical, and mathematical biases associated with
estimated normal ranges from a single laboratory data that may
render them inappropriate for generalizing across multiple
laboratories.’’
Alternative 3 provides a strategy of pooling and summariz-
ing laboratory values from multiple laboratories by implement-
ing a 2-step process. ‘‘Step one involves converting all
laboratory units of measurements into the same units of mea-
surements by transforming all units to syste`me international
d’unite´s (SI units). . . . Step two involves creating a ‘phantom’
laboratory of estimated GLN [generalized lab norms] using the
percentile method.’’6
This method allows pulling the RR from
multiple local sites and successfully creating an unbiased
‘‘default’’ normal RR (labeled as a ‘‘phantom laboratory’’) that
can be used for computing statistical summaries or toxicity
grading for a trial or a program. However, the ‘‘phantom
laboratory’’ method does not reduce the resource consumption
associated with the need for local RR collection and mainte-
nance. Quite to the contrary, although statistically sound, this
method leads to a further complication of the process and thus
is considered suboptimal.
Alternative 4 is the strategy of using ‘‘standard’’ RRs, which
is supported by this publication. In many cases, using standard
RRs (ie, those published in the New England Journal of
Medicine16
or other sources) for laboratory analysis is a more
scientifically sound and cost-effective alternative to
laboratory-specific ranges. More specifically, this process may
include the following details:
Site laboratory ranges are collected for the trial master file
(per Good Clinical Practice guidelines according to the Inter-
national Conference on Harmonisation, these are collected
Table 2. A quick summary of calculations.
Total spending on clinical trials $US49.8 billion/year in 2006.
Congressional Budget Office12
cites PhRMA’s estimates of $US39
billion in 2006; linear extrapolation using 5% inflation-adjusted
growth rate from 2006 to 2011 allows to estimate the annual
spending at US$49.8 billion/year. According to Kaitin13
the US
pharmaceutical spending reached US$50 billion/year in 2008. The
authors selected more conservative estimate of US$49.8 billion/
year in 2011.
Oncology (primary use of local laboratories) cost ¼ 18%–20% of
total ¼ US$9.0–$10 billion
Data Management costs are typically 10%–20% of total budgets for
a oncology clinical trial ¼ US$900–2000 million/year
Laboratory reference range management is estimated at 20%–30%
of total data management costs in oncology ¼ US$180–$600
million/year.
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5. but might not be easily accessible); they are not further pro-
cessed electronically nor used for any standard statistical
analysis.
I/E criteria in protocols based on ULN are changed to abso-
lute values (ie, instead of saying a patient is eligible if base-
line ‘‘AST 2Â ULN’’ we change this to ‘‘AST 72 U/L’’).
Sites are instructed to perform dose modifications and
grade AEs based on the local laboratory ranges. As a result,
when the sponsor company or CRO reviews the results,
they might think a dose should have been modified because
of the laboratory grade when it was not, or vice versa.
Summary tables may appear to have minor discrepancies.
For example, a laboratory table might report a grade 3 AST
rate of 10% versus the AE table report of 11%. Also, all
laboratory summary tables showing grades will have a foot-
note on how they were calculated.
For documentation, ICH E617
requires documentation at
the site,iv
and this requirement should be followed. It is
anticipated that updates to protocols and statistical analysis
plans will be required (if in doubt, seek advice from the
regulators).
The alternative 4 methodology allows for significant opera-
tional flexibility and adaptability. It also gives sponsor compa-
nies the ability to assess and mitigate risks on a case-by-case
basis. For instance, if an investigational new drug bears risk
of drug-induced liver injury, close attention might be given
to those biomarkers that are indicative of liver injuries (such
as bilirubin, alkaline phosphatase [ALP], ALT, AST—see FDA
drug-induced liver injury guidance18
for more details). In addi-
tion, those analytes with higher variability of local RRs might
be scrutinized more carefully (ie, using the cutoff point of coef-
ficient of variation [CV] 10% suggested by Ruvuna et al6
)
when pooling results across laboratories relative to the analytes
of lower variability or of those that bear lower risk for the par-
ticular patient population.
Furthermore, compared to the inherent labor-intensiveness
of the current practice (alternative 1), it is estimated that
approximately 80%–90% of RR management costs can be cut.
Therefore, savings of the equivalent of 0.25%–1.1% of the total
typical oncology trial budget can be expected (product of all
percentage points in Table 2). Thus, aggregate industry cost
savings associated with alternative 4 across all oncology clini-
cal trials in the United States are estimated at US$144–$540
million per year.
Potential Risks and Criticism
of the Proposed Method
Currently, the acceptance of alternative 4, utilizing published
RRs, is constrained by perceived potential risks associated with
this approach. What are these potential risks, and how much
risk is acceptable? According to the FDA,19
‘‘[R]isk manage-
ment is an iterative process of (1) assessing a . . . benefit-risk
balance, (2) developing and implementing tools to minimize its
risks while preserving its benefits, (3) evaluating tool effective-
ness and reassessing the benefit-risk balance, and (4) making
adjustments, as appropriate, to the risk minimization tools to
further improve the benefit-risk balance’’ Also, according to
the FDA,20
‘‘There is a growing consensus that risk-based
approaches to monitoring, such as focusing on the most critical
data elements, are more likely to ensure subject protection and
overall study quality.’’ Furthermore, ‘‘Sponsors should perform
a risk assessment that generally considers the types of data to
be collected in a clinical trial, the specific activities required
to collect these data, and the range of potential safety and other
human subject protection concerns that are inherent to the clin-
ical investigation. Sponsors should consider the findings of the
risk assessment when developing a monitoring plan. There is
increasing recognition that some types of errors in a clinical
trial are more important than others.’’20
One should take a close
look at the following 3 sources of potential ‘‘risks’’ associated
with the proposed methodology of handling laboratory RRs.
Table 3. Alternative approaches to laboratory reference range handling: advantages and limitations.
Alternative Advantages Limitations
1. Local RRs Perceived by many as clinically
meaningful
The most expensive option/lowest ROI
2. RRs from highest patient enrollment
site14
Single range is easy to use Not clinically meaningful; resulting summaries may not be
comparable among laboratories
3. The ‘‘phantom’’ laboratory generalized lab
norms6
Not subject of violation of
normal distribution
Cross-sectional
representation of all sites
Labor intensive; suboptimal ROI
4. Standard RRs (eg, New England Journal of
Medicine16
or other)
Scientifically solid
Marginal efforts/highest ROI
Nonconventional (ie, will required time to adopt)
RR, reference range; ROI, return on investment.
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6. Clearly, risk to patient safety is the primary concern of the
clinicians and regulators in clinical trials. Thus, one of the risks
frequently mentioned by the opponents of the proposed process
is that patients’ safety might be jeopardized in case of use of
standard/published RRs because of potential misclassification
of AEs. However, in this approach, use of standard/published
RRs is limited to the collection/analysis of data by the sponsor
companies. It is possible and recommended that the clinical
investigators participating in clinical trials continue using local
RRs. In essence, the clinical decision criteria at the patient level
do not change. Thus, there is no additional risk to the patients
from using alternative 4.
The second set of concerns is the risks associated with anal-
ysis and reporting of laboratory data, more specifically poten-
tial underreporting or overreporting of AEs (because of the
small differences in CTC grade calculations using local and
standard RRs). For example, one may argue that there is a pos-
sibility of ‘‘incorrect’’ calculations of maximum tolerated dose
in dose-escalation studies. In our opinion, these risks are over-
estimated, for worrying about ‘‘incorrect’’ dosing presumes
that a set of normal ranges is correct and another set is incor-
rect, and we do think they are merely inconsistent. One could
propose that the local laboratory ranges be used for patient care
if there is a difference. Many trials use central laboratories as
well as local laboratories, and the results will vary. If there are
concerns about incorrect dosing presuming that the local
laboratory range is correct, then use of a central laboratory
would carry the same concern, and most major drug trials use
central laboratories.
The third type of risk might come from the potential bias
when the authors of the study protocol are free to select across
multiple published RRs for a particular marker. This risk can be
minimized either by setting the laboratory RR standards at the
industry level or by additional requirements imposed on the
sponsor companies being held to the question of why one pub-
lished RR was chosen over another.
Finally, it can be argued that the existing (alternative 1, local
RRs) method bears some risks to the patient, for it is noted that
when urgent (nonscheduled) laboratory results are needed and
alternative laboratories are used, errors in interpretation may arise.
Conclusion
The evidence above suggests that the RRs used by the local
laboratories often create a false sense of precision that is not
always supported by science. Local RRs are based on the local
populations evaluated by the hospital. It is possible but not very
likely that this local population of the particular hospital is
more representative of the patients in the study than the popu-
lation used to calculate the standard/published RRs, unless the
hospital is a specialty hospital that treats only a narrow set of
diseases. Even oncology centers are usually located in a general
hospital whose laboratories serve a broad range of patients of
different ages, races, and genders, many of whom may be
healthy persons who are getting annual physicals. Thus, the
fears associated with perceived ‘‘risks’’ of using standard RRs
are unfounded. The concern over ‘‘incorrect’’ dosing (or
‘‘incorrect’’ analysis) because of the use of standard/textbook
RRs presumes that a set of normal ranges is correct and another
set is incorrect, while they may merely be inconsistent. In addi-
tion, based on the authors’ experience, the differences in grad-
ing laboratory AEs using standard RRs versus local RRs are
relatively clinically insignificant (5%-15%).
Based on the evidence presented, it is recommended that the
existing process be altered to the use of standard RR methods
(ie, those set by the New England Journal of Medicine)16
by
pharmaceutical companies for data analysis purposes. At the
same time, personnel at investigational sites can be instructed
to perform dose modifications and grade AEs based on site-
specific ranges, if the sponsor company prefers a less contro-
versial approach.
There is no single best uniform approach to every possible
situation in managing local RRs in clinical trials, and study-
specific approaches should be selected based on study details
such as indication, study population, critical analytes, sample
size, and so on. In addition, research might be needed to deter-
mine the precise sensitivities of each parameter. In approaching
any new study, it is important to discern between the analytes
of the higher risk/importance and the analytes or lower risk/
importance. The high importance analytes need to be scruti-
nized much more thoroughly than the others. In doing so, the
clinical trial sponsors will follow the FDA recommendation
that ‘‘each sponsor [should] design a monitoring plan that is tai-
lored to the specific human subject protection and data integrity
risks of the trial.’’20
There is anecdotal evidence that the standard RR methodol-
ogy has been successfully used in the United States by a num-
ber of reputable pharmaceutical companies. If the standard RR
method is adapted, industry-wide cost savings in the United
States could approach US$144–$540 million per year. Open
support of the standard RRs by the FDA and EMA (European
Medicines Agency) could assist with changing the status quo
and accelerating the wider acceptance of the method in the
pharmaceutical industry.
Acknowledgments
The authors thank John Walker (Novartis) and Michael Gusmano,
PhD (New York Medical College) for their thoughtful comments.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
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7. Funding
The author(s) received no financial support for the research, author-
ship, and/or publication of this article.
Notes
i. ‘‘How is health defined? Often these are observations of a few
young, Caucasian, non-obese males who do not smoke, drink
ethanol, or abuse drugs.’’2
ii. Reference ranges typically consist of a high value, a low value, the
unit of measurement, and an effective date. Reference ranges can
also be age- and gender-specific, necessitating identification of
these parameters.
iii. Laboratory relicensure may also trigger the need to update docu-
mentation of reference ranges.
iv. The ICH Guidelines for Good Clinical Practice17
in sections 8.2.
11 and 8.3.7 recommend the following information be kept in the
files of the investigator/institution and Sponsor:
Reference values or ranges for all medical/laboratory/technical
procedures or tests.
Changes or updates to reference values or ranges for all medi-
cal/laboratory/technical procedures or tests.
Documentation of certification, accreditation, established qual-
ity control, or other validation (where required) of all medical/
laboratory/technical procedures or tests.
Documentation of changes or updates relating to certification,
accreditation, established quality control, or other validation
(where required) of all medical/laboratory/technical procedures
or tests.
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