Abstract
The vast majority of clinical studies use laboratory data, which should be treated with the same rigorous attention to detail and data quality as any other clinical data. This chapter describes different types of laboratories, different types of laboratory data, and important elements of laboratory data handling. In particular, the chapter discusses the importance of standards and reference ranges for laboratory data, as well as principles and processes to help ensure the accuracy and integrity of all laboratory data.
Introduction
The word “lab” (or “laboratory”), is defined by Merriam-Webster as “A place equipped for experimental study in a science or for testing and analysis.” Within the context of clinical data management (CDM), labs are where biologic samples such as blood or urine are sent for analysis, or diagnostic images or data such as electrocardiograms or Holter monitors are evaluated or interpreted. Because the results of these tests do not originate from a case report form (CRF) at a study site, these types of external data are often transferred as electronic files.
Lab data are used in most preregistration clinical studies and proper handling of these data is crucial to the success of a study. CDM personnel are responsible for data integrity throughout all lab data transfer and cleaning activities. CDM personnel may also be involved with setting up standards and processes for their organization to help ensure the integrity of all data, including those from labs.
Scope
This chapter describes differences between various types of labs and lab data, as well as how CDM practices may vary in different situations. For the purposes of this chapter, the term “lab” generally refers to lab vendors, as opposed to lab tests, which will be referred to as “tests” or “lab tests.” Although local and central labs are not the only lab types discussed, the distinctions between local and central labs can also apply to specialty labs, core labs, and virtual central labs. Specialty labs and core labs may operate as either central or local labs, while virtual central labs operate as central labs. Also, most CDM processes relating to lab data handling primarily vary between local and central labs. As such, the main focus of this chapter will be on local and central lab data handling.
Some of the tasks described in this chapter may be joint responsibilities between different groups, just as there may be many different groups involved in the implementation of various tasks. However, clinical data managers need to be conscious of whether or not these tasks have been performed in a satisfactory manner.
Minimum Standards
Maintain standard operating procedures (SOPs) for all processes relating to lab data collection, transfer, and validation of data loading and data feasibility.
Identify labs involved with a study as early in study setup as possible.
Use standardized names for lab tests and units.
Ensure reference ranges are defined prior to first data receipt when using a central lab.
Where possible, ensure reference ranges are defined prior to first data receipt when using a local lab.
Ensure updates to reference ranges are obtained and implemented in a timely fashion.
Document all data transfer specifications thoroughly when using labs transferring data electronically.
Determine software/hardware required to access data prior to a test transfer and ensure the format of the data medium is compatible.
Best Practices
Use accepted standards such as those from Clinical Data Interchange Standards Consortium (CDISC) when possible.
Define all lab data standards prior to beginning data collection.
Ensure reference ranges are defined for population subgroups (e.g., ethnicity) that differ significantly from other defined groups or subgroups.
Implement a standard process to collect and archive reference range data.
Use a standard method of data review for local lab data and reconciliation of central lab data.
Develop a data transfer agreement for electronic transfers and perform quality control of the test transfer.
Document and confirm all lab variables prior to signing off on data transfer specifications.
Implement a conversion factor table to standardize conversion of conventional units to the International System of Units (SI).
Define edit checks for inclusion/exclusion criteria based on lab data and route to appropriate team members to review.
Use standardized units so that performing edit checks on converted data produces a more consistent review of results.
Send requests for central lab data corrections using a formalized process, for example, on a correction log sent to the lab vendor to update and return after correcting and resubmitting the lab data file.
Implement a system to manage data collected outside protocol parameters.
Distinctions Between Types of Labs
Although data managers most frequently work with central labs or local labs, other kinds of labs include virtual central labs, specialty labs and core labs. These types of labs tend to fall under the categories of local or central in regard to many processes and characteristics. This section details each type of lab and defines which tests and processes they support. Table 1 details advantages and disadvantages of each type of lab. Advantages and disadvantages may vary geographically, due to regional variations in definitions of various types of labs.
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Pharmacokinetic/pharmacodynamic—Pharmacokinetics studies drug absorption, distribution, metabolism, interaction and excretion. Drugs exist in a dynamic state within the body, and different drug events often occur simultaneously. To describe a complex biologic system, simplifying assumptions are often made concerning the movement of drugs. A pharmacokinetic model is conceived using mathematical terms, which are a concise means of expressing quantitative relationships. The intensity of the pharmacologic or toxic effect of a drug is often related to the concentration of the drug. For example, monitoring the concentration of drugs in the blood or plasma confirms that the calculated dose actually delivers the plasma level required for therapeutic effect. Pharmacokinetic models allow more accurate interpretation of the relationship between plasma drug levels and pharmacologic response.2
Biomarkers—Biomarkers are substances that are objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes or pharmacologic responses to a therapeutic intervention. According to some experts, to be defined as a viable biomarker, the biomarker should meet the following conditions:
Highly sensitive and specific in detecting a desired characteristic
Validated in postmortem confirmed cases
Standardized with sound bioinformatics
Specific for the desired characteristic compared with related disorders or biologic states
Reliable in many testing environments and labs
Minimally invasive
Simple to perform
Inexpensive
Standards
The more standardized lab data are, the easier they will be to collect, process, combine, analyze and submit. Although standardization during study setup is optimal, standardization may also be performed during lab data collection or analysis of final results. A number of data standards have been published or are in development by Clinical Data Interchange Standards Consortium (CDISC), including a standard specific to lab data (LAB). For more information on CDISC standards, visit http://www.cdisc.org.
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One way to make unit conversion easier is to develop an internal conversion factor table using publicly available references. A table can be created for all tests, listing the most common conventional units as well as the conversion factor to transform to SI units. This conversion table will take significant effort up front; however once completed and verified it will save an enormous amount of time by being applied to subsequent studies.
Unexpected/Unscheduled Lab Data
During the course of a clinical study, lab tests are performed according to the schedule of the protocol. Sometimes an investigator decides to order a lab test outside protocol parameters, usually when a subject is experiencing adverse events or exhibiting symptoms of another disorder. When these lab tests are performed, they are considered unexpected or outside the protocol.
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Unscheduled lab data, on the other hand, refers to tests that are within the scope of the protocol but are not performed according to the time and events schedule. This may occur for a number of reasons, including follow-up tests due to previous abnormal values, a subject’s unavailability for sample collection at a specified time, or damaged samples (which may be classified as repeat lab tests by some organizations). These tests are captured in the same manner as scheduled sample collections, but must be identified as unscheduled data. For unscheduled results from a central lab, the lab should have a way to differentiate unscheduled sample collections from those that are scheduled. One convention is to have the visit number left blank and the visit name labeled as “Uns” or “U” for unscheduled, although some organizations may design a numbering convention in advance for these circumstances. The sample collection date will then be used to sequence the sample collection among others for that subject. For local labs, the CRF should capture the lab name, sample collection date and unscheduled status.
Lab Reference Ranges
Lab results are of little value without the ability to analyze the results in comparison to other values. Lab results are typically either compared with other samples taken from the same subject at a different time point (e.g., baseline values), or are compared with a reference range. Reference ranges can also be known as “normal ranges,” although not all populations can be considered truly “normal.” Reference ranges are established by analyzing a large number of samples and statistically determining the appropriate reference range. Because values may differ according to variables such as age, gender, disease processes, or regional variations, multiple ranges are often established for a given test. Labs may either establish their own set of reference ranges or obtain ranges from published sources. 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. These values need to be collected only once per study unless there are changes to the specimen collection, instrumentation or methodology. Lab relicensure may also trigger the need to update documentation of reference ranges.
Use by Clinicians During a Study
In clinical studies physicians use lab results to determine if a subject meets study enrollment criteria and to monitor the subject’s safety profile or efficacy effects, which may be attributable to the treatment received or from existing or new conditions. Physicians may use other tests to confirm a diagnosis or eliminate error due to false-positive results. They are aware that the reference range provided by a lab has confidence limits and that some normal individuals will have a value outside the reference range. Therefore, most physicians will consider a result normal if it is within the reference range, suspicious if it is slightly outside the range, and abnormal if it is considerably outside the range. Ultimately, the clinical assessment will determine if a particular analyte has clinical significance.
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Many variables complicate establishing reference ranges, including sex, age, ethnicity, weight, geography, or time of specimen collection. Reference ranges should be defined for each subgroup that differs significantly from another subgroup. When ranges are not divided into subgroups, there may be a broadening of the reference range and loss of discriminatory power. Variations in reference ranges are most commonly seen between different sex and age groups.
Lab Processes in Studies
Local Labs
When using local labs, more responsibility is placed on the site to record information. The process begins with obtaining and identifying a sample, then sending it to the local lab for analysis. Once the sample is tested and the report is received at the site, it is the responsibility of the primary investigator or subinvestigator to assess the lab report and determine if out-of-range values are deemed clinically significant (CS) or not clinically significant (NCS). If out-of-range values are deemed clinically significant, the site investigator(s) must then determine if these values are due to an underlying disease state or constitute an adverse event (potentially even a serious adverse event).
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When using a central lab (or any lab that transfers data electronically), the lab and sponsor will complete a data transfer agreement (DTA) during study setup. The DTA defines the format of files, frequency of data transfer, file naming conventions, encryption levels, method of transfer, type of transfer (complete versus partial), recipient, test names, formats, high and low value flags or alerts, and any additional information concerning the lab data. A very important part of the DTA is the definition of data that need to remain blinded. If the result of a certain test could potentially identify which treatment a subject is randomized to or if the subject is responding to treatment, these results need to be blinded. Typically, blinded results remain blank in the file until the clinical database is locked and an unblinding memo is provided. Once this unblinding memo is supplied, the lab releases the information and analysis can occur. The DTA should also include range or data checks being performed by the lab, as well as reconciliation processes.
Cleaning Lab Data
Typical Types of Errors
The most common types of errors from central lab data are demographic errors. When a sample is sent to the lab, a requisition form is completed to identify the subject number, site, sample collection date and time, birth date and gender of the subject (optional) and visit number. If an error is made on the requisition form, this information may differ from the clinical database and prompt a query to be sent to the site or lab.
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Invalid specimen dates or times
Blank data, including lab names
If collecting clinical significance, flagged or out-of-range lab data should be appropriately identified and an associated adverse event should be recorded, when applicable.
Instances when one test value requires another test value to be provided. For example, if the total bilirubin is greater than 1.0 mg/dL, a direct bilirubin value should be provided.
Inclusion/exclusion criteria involving lab data can be programmed into edit checks, where appropriate, for flagging when values exceed protocol- defined criteria.
Listings should be used to compare abnormal results to medical history, adverse events, or other appropriate data.
Lab Accreditation/Certification
According to the International Organization for Standardization (ISO), accreditation is determined as “a procedure by which an authoritative body gives formal recognition that an organization or a person is competent to carry out specific tasks,” whereas certification is defined as “a procedure by which a third party gives written assurance that a product, process, or service conforms to specific requirements.”
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The development of quality systems in medical labs of the European Union is based on adherence to the requirements of ISO standards (primarily ISO 15189:2007). The process of accreditation in most European countries is carried out by cooperation among national accreditation bodies, medical experts appointed by scientific associations and health departments. This collaboration has proven successful in the UK, Germany, Hungary, France and Croatia.
Regulatory Agencies
Although it is not a legally binding document, ICH Guidelines for Good Clinical Practice provides a solid framework for determining what lab-related documentation should be retained for a study. The regulatory requirements of individual countries will in most cases be very similar to these guidelines, and in some cases the regulatory agencies may be less stringent. Although the ICH guidelines are a great resource, CDM personnel should always consult the regulations of the country in which the study is being conducted. Information regarding regulations from various countries can be found at http://www.hhs.gov/ohrp/international/HSPCompilation.pdf.
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Reference values or ranges for all medical/lab/technical procedures or tests
Changes or updates to reference values or ranges for all medical/lab/technical procedures or tests
Documentation of certification, accreditation, established quality control, or other validation (where required) of all medical/lab/technical procedures or tests
Documentation of changes or updates relating to certification, accreditation, established quality control, or other validation (where required) of all medical/lab/technical procedures or tests10
Recommended Standard Operating Procedures
- Data Cleaning
- Laboratory Data Entry
- Laboratory Data Transfers