Tag Archives: research studies

Clinical Trials 101

Clinical trials are research studies that involve people and test new ways to prevent, detect, diagnose, or treat cancer and other diseases. At the conclusion of this webinar, you will be able to demonstrate a basic understanding of the basics of clinical trials, including how they work, protections for participants and factors related to participation.

Source: NCI Events

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Cancer Clinical Trials: What is a Clinical Trial?

Clinical trials are research studies that test how well new medical approaches work in people.

Source: The National Cancer Institute (NCI) supports a vast array of clinical trials designed to test new ways to treat, prevent, detect, or diagnose cancer as well as new methods to improve cancer patients’ quality of life.

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anayansi gamboa

CDISC Clinical Research “A” Terminology

acronym: A word formed from the beginning letters (e.g., ANSI) or
a combination of syllables and letters (e.g., MedDRA) of a name or phrase.
admission criteria:Basis for selecting target population for a clinical trial.
Subjects must be screened to ensure that their characteristics match a list of admission criteria and that none of their characteristics match any single one of the exclusion criteria set up for the study.
algorithm: Step-by-step procedure
for solving a mathematical problem;
also used to describe step-by-step
procedures for making a series of
choices among alternative decisions to
reach a calculated result or decision.
amendment: A written description
of a change(s) to, or formal clarification
of, a protocol.
analysis dataset:An organized collection of data or
information with a common theme arranged in rows and columns and
represented as a single file; comparable to a database table.
analysis variables: Variables used
to test the statistical hypotheses
identified in the protocol and analysis
plan; variables to be analyzed.
approvable letter:An official communication from FDA to an
NDA/BLA sponsor that lists issues to be resolved before an approval can be issued.
[Modified from 21 CFR 314.3;Guidance to Industry and FDA Staff

arm: A planned sequence of elements,
typically equivalent to a treatment
group.

attribute (n): In data modeling,
refers to specific items of data that can
be collected for a class.
audit:A systematic and independent
examination of trial-related activities
and documents to determine whether
the evaluated trial-related activities were
conducted and the data were recorded,
analyzed, and accurately reported
according to the protocol, sponsor’s
standard operating procedures (SOPs),
good clinical practice (GCP), and the
applicable regulatory requirement(s).
[ICH E6 Glossary]
audit report: A written evaluation by
the auditor of the results of the audit.
[Modified from ICH E6 Glossary]
audit trail. A process that captures
details such as additions, deletions,
or alterations of information in an
electronic record without obliterating the original record. An audit trail
facilitates the reconstruction of the
history of such actions relating to the
electronic record.

Source:Applied Clinical Trials

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

Acme Pharma Develops A Drug: Part I

Learn more about how the pharmaceutical industry has traditionally developed and brought drugs to market. Watch part II of this series to learn how Network Fortress can improve the drug development process and save pharma and biotech companies time and money.

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Clinical Trials Terminology for SAS Programmers

Entry Level SAS Programmers

Statistical Programmer:requires him to program using the SAS language to analyze clinical data and produce reports for the FDA

Bioanalyst, Clinical Data Analyst, Statistical Programmer Analyst and SAS Programmer: same as Statistical programmer.

Biotechnology:companies which is a general term used to explain a technique of using living organisms within biological systems to develop micro-organisms for a particular purpose.

protocol:outlined all the procedures and contained detailed plans of the study.

controlled experiment: the clinical trial had patients grouped into different groups such as those in the placebo controlled group which had no active drug. This is how comparisons are made within the controlled clinical trial CFR Part 11:Code of Federal Regulations set by the FDA to regulate food, drug, biologics and device industries. The part 11 specifically deals with the creation and maintenance of electronic records.
Case Report Form or CRF:forms to collect information such as demographic and adverse events. Source Data or the information collected:which include important documents because they contain the core information required to reconstruct the essential capital of the study.
sponsor:company who is responsible for the management, financing and conduct of the entire trial. randomized: subjects that are randomly assigned to groups so that each subject has an equal chance to be assigned to the placebo control
baseline: subjects are assigned to their drug change from baseline:analyses that measure differences between baseline and current visit
placebo or sugar pill:is an inactive substance designed to look like the drug being tested. blinded:they do not know if the drug that they are taking contains the active ingredient.
open-label study:all was out in the open, the drug the subject is assigned to. Pharmacokinetics or PK:analysis of that study showed that with that dosing level, there were high levels of toxicity in the subject.
informed consent: described all the potential benefits and risks involved. TLGs: Tables, Listings and Graphs
trade name:drug name that is collected from the patient and recorded into the source data. For example: Tylenol generic name: refers to its chemical compound. For example: Acetaminophen.
WHO-DRUG: list all the drug names and how they matched to the generic drug names.This dictionary is managed by the World Health Organization MedDRA:This is short for Med (Medical), D (Dictionary), R (Regulatory), and A (Activities).
SAP: Statistical Analysis Plan ANOVA: analysis of variable
confidence interval:gives an estimated range of values being calculated from the sample of patient data that is currently in the study. null hypothesis:lack of difference between the groups in a report
pilot study:perform the same analysis upon an older. DIA: Drug Information Association
CBER: Center for Biologics Evaluation and Research (medical device) CDER: Center for Drug Evaluation and Research (drug)

Source:CDER Acronym List


Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

Adverse Event Monitoring for CRAs

During monitoring visits one of the most important and impacting activities that a CRA performs is the source document verification of Adverse Events. The CRA is the eyes for the research sponsor when it comes to proper collection and documentation of subject safety information. Incorrect and inadequate monitoring of adverse events can lead to inaccurate labeling for clinical trials and impact market application inspectional reviews, as well as post marketing labeling. The safety regulatory and ICH definitions will be reviewed and applied to the monitoring process. This includes Causality, Expectedness/Unanticipated, and other important concepts. Case scenarios will be used to apply the information for better learning.

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Introduction to Clinical Trials

Video introducing cancer clinical trials and their use in clinical practice guidelines

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Source: Cancer Guidelines – Canada

Data Management: Queries in Clinical Trials

When an item or variable has an error or a query raised against it, it is said to have a “discrepancy” or “query”.

All EDC systems have a discrepancy management tool or also refer to “edit check” or “validation check” that is programmed using any known programming language (i.e. PL/SQL, C# sharp, SQL, Python, etc).

So what is a ‘query’? A query is an error generated when a validation check detects a problem with the data. Validation checks are run automatically whenever a page is saved “submitted” and can identify problems with a single variable, between two or more variables on the same eCRF page, or between variables on different pages. A variable can have multiple validation checks associated with it.

Errors can be resolved in several ways:

  • by correcting the error – entering a new value for example or when the datapoint is updated
  • by marking the variable as correct – some EDC systems required additional response or you can raise a further query if you are not satisfied with the response

Dealing with queries
Queries can be issued and/or answered by a number of people involved in the trial. Some of the common setups are: CDM, CRA or monitors, Site or coordinators.

Types of Queries

  • Auto-Queries or Systems checks
  • Manual Queries
  • Coding Queries
  • SDV related Queries generated during a Monitor visit
  • External Queries – for external loaded data in SAS format

EDC Systems and Discrepancy Output Examples

InForm

Note: All queries are associated to a single data item relevant to that query.

RAVE

Note: Users are only able to see / perform an action on a query based on their
role and the permissions via Core Config.

Timaeus

Note: Queries are highlighted by a red outline and a Warning icon.

OpenClinica

Note: Extensive interfaces for data query.

Query Metrics – It is important to measure the performance of your clinical trials.
Metrics are the same for all clinical studies but not all EDC systems are the same. Standardized metrics encourage performance improvement, effectiveness, and efficiency. Some common metrics are:

  • Outstanding Query
  • Query Answer Time
  • Average Time to Query Resolution
  • Number of closed discrepancies on all ongoing studies

Data management’s experience with data queries in clinical trials

FAIR USE
“Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.”

Trademarks: InForm is a trademark or registered trademark of Oracle Corporation. Rave is a trademark or registered trademark of Medidata. Timaeus is a trademark or registered trademark of Cmed Clinical Research.


Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

One Bad Day in a CRA’s Life

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A New Way to Collect Data – CDASH

There is a general consensus that the old paper-based data management tools and processes were inefficient and should be optimized. Electronic Data Capture has transformed the process of clinical trials data collection from a paper-based Case Report Form (CRF) process (paper-based) to an electronic-based CRF process (edc process).

In an attempt to optimize the process of collecting and cleaning clinical data, the Clinical Data Interchange Standards Consortium (CDISC), has developed standards that span the research spectrum from preclinical through postmarketing studies, including regulatory submission. These standards primarily focus on definitions of electronic data, the mechanisms for transmitting them, and, to a limited degree, related documents, such as the protocol.

Clinical Data Acquisition Standards Harmonization (CDASH)

The newest CDISC standard, and the one that will have the most visible impact on investigative sites and data managers, is Clinical Data Acquisition Standards Harmonization (CDASH).

As its name suggests, CDASH defines the data in paper and electronic CRFs.

Although it is compatible with CDISC’s standard for regulatory submission (SDTM), CDASH is optimized for data captured from subject visits, so some mapping between the standards is required. In addition to standardizing questions, CDASH also references CDISC’s Controlled Terminology standard, a compilation of code lists that allows answers to be standardized as well.

Example: Demographics (DM)

Description/definition variable name Format
Date of Birth* BRTHDTC dd MMM yyyy
Sex** SEX $2
Race RACE 2
Country COUNTRY $3

*CDASH recommends collecting the complete date of birth, but recognizes that in some cases only BIRTHYR and BIRTHMO are feasible.

* *This document lists four options for the collection of Sex: Male, Female, Unknown and Undifferentiated (M|F|U|UN). CDASH allows for a subset of these codelists to be used, and it is typical to only add the options for Male or Female.

The common variables: STUDYID, SITEID or SITENO, SUBJID, USUBJID, and INVID that are all SDTM variables with the exception of SITEID which can be used to collect a Site ID for a particular study, then mapped to SITEID for SDTM.

Common timing variables are VISIT, VISITNUM, VISDAT and VISTIM where VISDAT and VISTIM are mapped to the SDTM –DTM variable.

Note: Certain variables are populated using the Controlled Terminology approach. The COUNTRY codes are populated using ISO3166 standards codes from country code list. This is typically not collected but populated using controlled terminology.

Each variable is defined as:

  • Highly Recommended: A data collection field that should be on the CRF (e.g., a regulatory requirement).
  • Recommended/Conditional: A data collection field that should be collected on the CRF for specific cases or to address TA requirements (may be recorded elsewhere in the CRF or from other data collection sources).
  • Optional: A data collection field that is available for use if needed

The CDASH and CDICS specifications are available on the CDICS website free of charge. There are several tool available to help you during the mapping process from CDASH to SDTM. For example, you could use Base SAS, SDTM-ETL or CDISC Express to easily map clinical data to SDTM.

In general you need to know CDISC standards and have a good knowledge of data collection, processing and analysis.

With the shift in focus of data entry, getting everyone comfortable with using a particular EDC system is a critical task for study sponsors looking to help improve the inefficiencies of the clinical trial data collection process. Certainly the tools are available that can be used to help clinical trial personnel adapt to new processes and enjoy better productivity.


Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.