Tag Archives: data cleaning

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Central Designer – Troubleshooting Tips

If an edit check or function fails to behave as expected, it is time to use your ‘troubleshooting’ skills. The following tips may help you when you are troubleshooting rules in InForm:

Rules:

  • check if rules are running
  • check the rule logic
  • check Rule Dependencies: a rule on a form has access to items on that form, but not other forms or other visits
  • check InForm machine’s Application Event Log

Though some vendors will correct major problems with their products by releasing entirely new versions, other vendors may fix minor bugs by issuing patches, small software updates that address problems detected by users or developers.

Check the release notes for Central Designer for known problems. The release notes provide descriptions and workaround solutions for known problems.
Remember that there is a report available you can run “Data Entry Rule Actions Report”. This report outputs all data entry rules in CSV format and can be formatted into an edit check specification documentation for QA testing.
A rule can be written in more than one way, which makes it difficult to impose any restrictions:
Scenario: Route item has 3 choices. OP, SC and IV. Query should fire if the user does not choose either OP or SC. This rule could be written in many ways:

–Value = route.Value

If (value == 3)

–Value = route.Value == 3

If (value == true)

–Value = !(route.Value == 3)

If (value == false)

–Value = (route.Value == 1 || route.Value == 2)

If (value == false)

–Value = route.Value !=1 && route.Value != 2

If (value == true)

Keep it consistent across the trial. Do not overuse the conditional statements when a simple range check should be program.

Note: Be aware that if you want to reuse a rule that uses data from a logical schema in another study, the other study must also contain the logical schema.

If you have explored most of the obvious possibilities and still
cannot get your rule / edit check to work, ask someone in your team to peer review the build.

 

  • unit test your code
  • context available for defining test cases
  • Site name, date/time, locale; Form associations; Empty values; Unknown dates; Repeating objects
  • test case results: Pass or Fail based on expected results
  • perform formal QA / QT

Remember to check the Event log via Control Panel -> Administrative Tools -> Event Viewer

Reference Document : Central Designer – Rule Troubleshooting.pdf

Your comments and questions are valued and encouraged.
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.

iReview in Clinical Data Management

JReview® is the web-enabled version of Integrated Review™ (iReview). It allows users to view, create, print, and interact with their Integrated Review™ objects locally on an Intranet or securely over the Internet. JReview® can be run in two different modes of operation (authoring and non-authoring) in addition to two modes of communication (clear-text and SSL).

iReview Common Development Practice:

  • iReview allows you to saved the library of objects to be deployed at “Global” level in the production environment.
  • Create separate categories (folders for DEV/QC/UAT) before approval (deployment into production)
    – “Development”
    – “QC”
  • Create study specific folders under those categories (e.g. DEV/QC/UAT)
  • Configure UserGroups to manage privileges appropriately at the category level– – “Developers can access – Development”
    – “QR/QT can access QC”

QC/UAT PROCESS

  • You can query iReview metadata
  • Business rule verification by checking

– “Panel names, item names”
– “Object location e.g. Public, private or usergroup”

  • Use of SQL to query iReview objects metadata
  • The information in CONTENTBLOCK is parsed to get
    additional metadata information for a particular iReview
    object
  • Define a detailed QC checklist for each object in the Global Library
  • Maintain a lessons learned document (knowledge base) to improve the development process

  • Continuously improve processes by collecting Metrics

    – Development time

    – QC time

    – Rework time

Advance Functionality

  • Deploy reports with dynamic Filter values
  • Filter values are not static and change during trial conduct
  • Deployment for non-technical end-users
  • Provide easy access to report
  • Create Lookup table(s) in the backend
  • Populate Lookup table(s) with study specific Filter values

  • Using “Filter Output” in IR, add appropriate nested queries to the WHERE clause

  • The use of ImportSQL, more complex dynamic filtering so no need to hardcode values in the front end

  • Saves development time by avoiding the creation of study specific filters and increases re-usability

  • Flexibility to activate/inactivate filter values via backend

Import SQL

  • Modify an Import SQL panel by adding more items will not impact existing reports already using this Import SQL

  • Import SQL has a limitation with max of 2000 characters (will result in the error below)

A workaround would be to create a stored procedure or a view

Patient Selection Criteria

  • Modifying a PSC has no impact on already saved existing reports using this PSC

Object Specifications window

  • Removing Objects (missing folders)
    – When all the objects are removed from a folder in the Object
    Specifications window, the folder with no objects will be hidden but
    not removed
    e.g. Drug Safety ..> All AEs ..> SAE Reports ..>SAE reconciliation
  • Removing all objects under “SAE Reports” folder will result in the “SAE Reports” folder being hidden
  • The workaround would be to use the Category section of Object Management tool to remove these hidden folders

Navigating iReview Windows

  • If you have hundreds of saved objects, typing the first few letters (similar to Windows Explorer) will help with easy scrolling and navigation in the Object Specifications window

Reference: Integrated Clinical Systems, Inc.

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.

What is Clinical Reviewer?

Clinical Review on iPad

  1. CRF Images View CRF (Case Report Form) data with pinch zoom.
  2. SAS Datasets Search and view data exported from SAS datasets.
  3. DEFINE.XML Import meta data directly from DEFINE.XML.
  4. Control Terminology View all metadata including coded terms defined in DEFINE.XML
  5. Secure Data – Data transferred to local memory viewed in “Airplane” mode with self deleting expiration.

Clinical data can be imported from standard CDISC DEFINE.XML format directly onto Clinical Reviewer app on iPad. Take advantage of the multi-touch interface to view Case Report Form or SAS datasets directly. Metadata including variable and value level metadata is viewable as defined in DEFINE.XML.

Watch this tutorial and see for yourself…

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“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.”

Source: Meta-x

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.

How to Use SAS – Lesson 5 – Data Reduction and Data Cleaning

This video series is intended to help you learn how to program using SAS for your statistical needs. Lesson 5 introduces the concept of data reduction (also known as subsetting ;data sets). I discuss how one can subset a data set (i.e. reduce a data set’s number of observations) based on some criteria using the IF statement in the DATA STEP, or using the WHERE statement in a PROC STEP. I also discuss using the KEEP, DROP, and RENAME statements for reducing data to only a handful of the original variables (i.e. reduce a data set’s number of variables). Furthermore, I show how one can label variables so that descriptive information can be presented in output and value formats so that specific values are easy to understand. Finally, I provide basic examples of each of these for three hypothetical data sets.

Helpful Notes:

1. There are two places you can reduce the data you analyze; in the DATA STEP, and in the PROC STEP.

2. To subset data in the DATA STEP, use the IF statement.

3. To subset data in the PROC STEP, use the WHERE statement.

4. Another way to reduce data is to eliminate variables using a KEEP or DROP statement. This method is useful if you are creating a second data set or analytic version of your main dataset.

5. The RENAME statement simply changes a variables name.

Today’s Code:

data main;
input x y z;
cards;
1 2 3
7 8 9
;
run;

proc contents data=main; run;
proc print data=main; run;

/* 1. Reduce data in the DATA STEP using a simple IF statement */
data reduced_main; set main;
if x = 1;
run;

proc print data=main; run;
proc print data=reduced_main; run;

/* 2. Reduce data in the PROC STEP using a simple WHERE statement */
proc print data=main;
where x = 1;
run;

proc print data=main; run;
proc print data=reduced_main; run;

/* 3. Reduce data in the DATA STEP by KEEPing only the variables you do want */
data reduced_main; set main;
KEEP x y;
run;

proc print data=main; run;
proc print data=reduced_main; run;

/* 4. Reduce data in the DATA STEP by DROPing the variables you don’t want */
data reduced_main; set main;
DROP y;
run;

proc print data=main; run;
proc print data=reduced_main; run;

/* 5. Clean up variables using the RENAME statement within a DATA STEP */
data clean_main; set main;
rename x = ID y = month z = day;
run;

proc contents data=main; run;
proc contents data=clean_main; run;

/* 6. Clean up variables using a LABEL statement within a DATA STEP */
data clean_main; set clean_main;
label ID = “Identification Number” month = “Month of the Year” day = “Day of the Year”;
run;

proc contents data=main; run;
proc contents data=clean_main; run;

/* 7. FORMAT value labels using the PROC FORMAT and FORMAT statements */
PROC FORMAT;
value months 1=”January” 2=”February” 3=”March” 4=”April” 5=”May” 6=”June” 7=”July” 8=”August” 9=”September” 10=”October” 11=”November” 12=”December”;
run;

data clean_main; set clean_main;
format month months.;
run;

proc ;freq data=clean_main;
table month;
run;

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“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.”

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.

Assigning Libraries to Access and Store SAS® Data

Use SAS learning software to learn how to assign libraries to access and store SAS data.

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“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.”

Source: http://support.sas.com/learn/ondemand/professionals

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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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

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“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.