BIoVar Checklist Version 1_3
Reference
Section
Number Section Topic
1 Title
Compliant
SubSection
Number
Checklist Item
Yes
No
Page
Number
Comments
2 Abstract
3 Introduction
3.1
3.2
Context
Previous studies
4.1
4.2
4.3
4.4
4.5
4.6
Analyte
Subjects
Analytical Method
Length of Study
Samples
Analytical conditions
5.1
5.2
5.3
Outlier analysis undertaken
Heterogeneity of variance assessed
Statistical methods described
6.1
6.2
Terminology
Results clearly presented and managed
7.1
Limitations of the Study
4 Study Design
5 Data Analysis
6 Results
7 Discussion
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7.2
7.3
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Generalisability
Interpretation
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1.
Title
The title should indicate that the content relates to a study of biological variation, the subject of the study, and the population studied.
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2.
Abstract
Content will be limited by word count allowed by the journal in which the article is published. As a minimum it should indicate the headline biological
variation data, the major characteristics of the population studied and clearly identify, the analyte and quantities studied, the duration of the study. and the
sample matrix.
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3. Introduction
3.1 Context
3.2 Previous studies
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4.
Study Design
4.1 Analyte
The study should clearly identify the target analyte or measurand. This is required to support the application of the derived data in the correct
setting. Specificity of analytical methods will impact on the utility of the data. Generational changes in methodologies that deliver improved
specificity of methods may exhibit different between and within subject variation. Ideally metrological traceability will exist through to a
primary international standard. It is important that where there is complexity in terminology and loose common usage of names of analytes
that the biological variation data are published are identified in a way that enables future users to assess the applicability of the data set to
their context. A good example of where this might have been of importance in the past is in the context of haemoglobin A1c measurement.
Recent introduction of standards and reference methodologies enables a very clear definition of what is meant by the term HbA1c . Historicaly
published biological variation data have been derived from studies using a variety of methodologies with varying degrees of specificity for HbA1c
in many clinical settings. It is possible to state that in such situations the data can only be validly when a method of the same specificity is
applied to a similar population. In consequence biological variation data must carry sufficient metadata to enable their transportability and
application. The arguments are similar to those used to ensure transportability of reference data
4.1.1
4.2 Subjects
The description of the subjects of any biological variation study is essential to enable data to be critically assessed and applied. Reference data clearly
require the population from which they were derived to be fully characterised. Biological variation data are being derived from a reference population
and should therefore be described accordingly to enable transportability of the data set. Subjects should be objectively assessed against clear inclusion
and exclusion criteria. It is important that those wishing to apply published data to a target population are able to compare their characteristics of the
population from which the data were derived.
4.2.1
Number of subjects
4.2.2
Standard Demographics
4.2.2.1 Age
4.2.2.2 Sex
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4.2.2.3 Ethnicity
State of well being. Define
4.2.3
4.2.3.1 Non-diseased
4.2.3.1.1
Evidence in the form of clinical and diagnostic assessment.
4.2.3.2 Diseased
4.2.3.2.1 Diagnostic criteria used
4.2.3.2.2 Staging of disease
Physical characteristics
4.2.4
4.2.4.1 Height, weight, body mass index
4.2.4.2 Blood pressure, renal function etc
Nutritional status, unusual diets
4.2.5
4.2.5.1 Dietary assessment where relevant
4.2.5.2 Vegan, high protein, high fat
4.2.5.3 Food supplements
Drugs
4.2.6
4.2.6.1 Prescription and non-prescription
4.2.6.2 Contraceptive agents
4.2.6.3 Recreational drugs
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4.2.7
Alcohol intake
4.2.8
Smoking status
4.2.9
Specific inclusion and exclusion criteria
4.3 Analytical Method
A clear description of the methodology used should be from part of the metadata. This may be available via an appropriate reference or may need to
be presented within the publication of the biological variation data. Known issues of specificity should be addressed where known of. Any deviation
from standard operating procedures indicated in the published method or from the manufacturers data sheets in the case of commercially available
assays, should be clearly indicated. These issues are again important in the context of transportability and generalisability of the biological variation
data.
4.3.1
Clear description adequately referenced
End users of published biological variation data must be able to access the fine detail of the analytical method used to assay the analyte of in
question. This will allow a local objective assessment of comparability of methods in use with that used to generate the original data and thus
on the transportability of the data. This will prove to be particularly important where complex molecules are the subject of measurement ,
where there is a lack of traceability to reference methodologies and international standards, where the methodologies are complex with many
variables affecting the value obtained for an assay (e.g. temperature, pH, substrate concentrations, polyclonal versus monoclonal antibodies),
when comparing data derived from new methods with those older generation assay systems
4.3.2
Standardisation and traceability of methods should form part of the data set accompanying the biological variation data
4.3.3
Analytical conditions
4.3.3.1 Deviation from standard operating procedures, use of adaptations of published methods, and deviation from manufacturers
recommended methods in the case of commercially available systems should be documented.
4.3.3.2 Optional assay conditions adopted should be clearly identified
4.3.4
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Assay Specificity
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4.3.4.1 The fact that specificity issues have been considered should be identified. Any known specificity issues should be identified or references
given to them. These may have an impact on the applicability of the data as more specific methods become available.
4.3.4.2 Where physiological or pathological conditions (e.g. chronic kidney disease impacting on clearance of cross reactants (e.g. aldosterone)
or uraemia leading to production of interfering moieties (e.g. carbamylated haemoglobins)) are likely to be common and influence
variability these should be described or referenced.
4.4 Length of Study
Length of study may be of importance for many targets measurements. This should be clearly recorded and any heterogeneity in the sampling
schedules that may confound the interpretation or application of the data by end users should be clearly recorded. There may be several factors that
may need to be considered and accounted for ranging from the in vivo half life of the substance to be measured, to biological rhythms in measured
indices ranging from hours, to days, to months, to seasonal.
4.4.1
Biological rhythms operate across time periods ranging from minutes to months. The level of variability will consequently be a function of the
sample timing and frequency relative to the periodicity of the target analyte in vivo. This will impact on the utility of the data for setting quality
standards for assays used clinically across differing time periods in different contexts.
4.4.2
Turnover and in vivo half life of target analytes will be of relevance to the design of the study protocol.
4.5 Samples
The number of samples required per individual will depend upon the periodicity of the target analyte, the proposed application of the assay, and
practicability of the study. If for example you are monitoring a disease state by sampling once every two months, then you need to understand the
magnitude of the variability over that period and whether it constant in those cases that do not progress or improve. Smaller degrees of variability of
the same analyte within a day may be measurable but less relevant in the context of the clinical application of the measurement.
4.5.1
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Numbers of samples should be appropriate and based on a rationale that is explained
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Timing of samples should be clearly described and form part of the metadata that accompanies the biological variation data set
4.5.2
4.5.2.1 Time of day
4.5.2.2 Dates and location (northern/southern hemispheres have disparate seasons, latitude will effect hours of daylight at different times of
year)
4.5.2.3 Duration of the study should be considered and discussed in the context of known biological rhythms and relevance in the context of the
clinical applications of the target measurement.
Collection conditions should be adequately described
4.5.3
4.5.3.1 The study design should enable samples to be taken under conditions that are well characterised and these should be described in detail
in the publication of the data. The following should be taken as a non exhaustive list of factors to be controlled:
4.5.3.1.1
Stress (e.g. venepunture with a needle versus in dwelling cannula in situ).
4.5.3.1.2
Dietary restrictions (specific foodstuffs, calories, special diets versus fasting)
4.5.3.1.3
Posture: supine, erect, sitting (report should include time that the subject chosen posture prior to sampling. It should be
remembered that it takes a time equivalent 7 half lives for a peak concentration to fall to <1.0% f there is no additional input
into a system).
4.5.3.1.4
Duration of an collection (e.g. urine (timed, random, mid flow, early morning), other fluid or faeces)
4.5.3.1.5
Blood sampling should be characterised in terms of:  Method of collection (e.g. syringe and needle, evacuated tubes, indwelling cannula/catheter, lancet(finger prick)
 Volume of sample taken
 Clear description of sites from which samples are taken (venous versus arterial, capillary) and anatomical site. The latter is
important in that venous concentrations of an analyte in the ante-cubital vein may not reflect accurately
concentrations at all other sites measured at that point in time.
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 Number of phlebotomist used
4.5.3.2 Anticoagulants, stabilisers and sample preservatives used should be described in detail or a suitable reference given to the technical
specification for the system used.
4.5.3.2.1
Evidence should be presented to identify stability of the analyte under conditions collected
4.5.3.3 Stability of samples should be considered, as this should influence the analytical approach used to generate the required numerical data.
4.5.3.3.1
Pre-analytical processing conditions adequately should be described and assessed to be appropriate for the stability of the
sample.
4.5.3.3.2
Storage conditions should guarantee stability of the analyte. Degradation during storage under certain conditions may affect
older specimens more.
4.5.3.3.3
Evidence should be presented demonstrating stability under storage conditions used.
4.6 Conditions for analysis of samples
A description of the conditions under which the samples were analysed is critical.
4.6.1
Model for analysis should appropriate to the stability of the analyte
4.6.2
Analysis should enable a robust measure of analytical imprecision and minimises the impact of analytical variability on the data set. (Optimal
Conditions Precision preferred).
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5.
Data Analysis
The usefulness of the reported indices of biological variation is dependent upon the fact that data set is truly representative of the population described,
and that there is homogeneity the data, that is to say that the observations are representative of a single “collective”. This means that outlying data not
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representative of the population must be excluded. This further endorses the need for clear characterisation of the population studied to accompany the
published data and to be accessible from any data-based collection of biological variation data. Examination of the data and exclusion of outliers is a
critical step to be undertaken prior to the final analysis of variance. If this is not undertaken the reported indices of biological variation must be considered
to be critically flawed. Heterogeneity of the variances compromises the usefulness and generalisability of biological variation data. It is highly
recommended that the paper of Fraser and Harris1 is considered as a seminal paper in this context.
5.1 Outlier analysis
There a number of statistical techniques that can be used to exclude outliers These should be applied to the analytical data within the following
contexts with outliers identified excluded from the final data analysis: 5.1.1
Outlying observations.
If the assays are done in duplicate and one result is excluded the remaining value should also be excluded.
If there is an outlying value observed within a subjects data set, that may be excluded and the remaining values used in the subsequent analysis of
variance.
5.1.2
Outlying within subject variances (see 5.2)
5.1.3
Outliers among mean values of subjects:
A subject within the group may have an outlying mean suggesting that the belong to another population. Again this can be tested using standard
statistical methods. Graphical plots of each subjects mean value with the range of surrounding values is a godd initial way of identifying the possibility
of outliers in this context.
5.2 Heterogeneity of variance assessed
The question arise as to whether the within subject variances calculated fro each subject is an estimate of a common true variance. This examination
must be undertaken to enable delivery of data. This can be tested using the approach of Harris2 which enables an index of heterogeneity to be
calculated. See also Fraser and Harris1.
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5.3 Statistical methods described and appropriate
Statistical methods used should be appropriately identified and referenced. Methods for outlier identification should be robust and clearly identified as
having been applied.
5.3.1
Methods described
Outlier identification.
Statistical packages used.
5.3.2
Data appropriately transformed
5.3.3
Groups appropriately stratified
Data should be assessed to identify whether there is a need to stratify into subsets, based on characteristics such as sex, age etc.
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6.
Results
6.1 Terminology
Unified terminology may aide understanding and transportation of the data. Reference to “Group” and “Subject” using subscript “G” and “I” to refer to
within group and within subject (used to be identified as between individual and intra-individual) indices.
Therefore standard deviation group would be represented as SDG with coefficient of variation being identified as CVG. Within subject these would be SDI
and CVI respectively. Analytical indices will carry the subscript “A”. This is consistent with the terminology described by Fraser and adopted by Ricos et
al.
6.2 Results clearly presented and managed
Format of results will need to meet the requirements of the publishing journal. Tabulation of appropriately stratified biological variation data with
derived indices would be advantageous. The matrix studied and the duration of the study should be clearly identified.
Some work may need to be undertaken to define a data archetype model for biological variation data. This will include the relevant metadata and
descriptors to accompany the biological variation data to enable wider dissemination and appropriate usage. This may for instance include as a
minimum description of the population from which they were derived, state of well being, specimen types, length of the study, the analytical method,
used possibly coded in the form of a LOINC code (Logical Observation Identifiers Names and Codes (LOINC®)), and a reference to the published data.
The results section should clearly identify the results of outlier analysis undertaken and confirm homogeneity of the data sets
If data are stratified the parameters used to enable this should be clearly characterised.
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7.
Discussion
The discussion of the data should clearly include a focus on the limitations; generalisability of the data should always be included in addition to any other
focus
7.1 Limitations of Study
7.1.1
These may range from technical issues relating to the analytical system to issues relating to the data set identified during the statistical analysis.
7.2 Generalisability
7.2.1 Discussion of Factors that may impact on extrapolation of the data to similar or dissimilar populations at other locations
7.2.2 Potential difficulties with the analytical methods used that may affect extrapolation of the data to populations served by alternative
techniques
7.3 Interpretation
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References
1. Fraser CG, Harris EK. Generation and application of data on biological variation in Clinical Chemistry. Crit Rev in Clin Lab Sci 1989:27(5);409437.
2. Harris, E.K. Distinguishing physiologic from analytic variation. J. Chronic Dis. 1970;23:469–480.
3. Fraser CG. Biological Variation from Principles to Practice. Washington DC: AACC Press 2001
4. Ricos C, Iglesias N, Garcia-Lario J, Simon M, Cava F et al. Ann Clin Biochem. 2007;44:343-352
5. Logical Observation Identifiers Names and Codes, LOINC. http://loinc.org/
6.
7.
8.
9.
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