© 2008 octagon research solutions, inc. all rights reserved. 2 octagon research solutions, inc....

© 2008 Octagon Research Solutions, Inc. All Rights Reserved.1

Octagon Research Solutions, Inc.Leading the Electronic Transformation of Clinical R&D

© 2008 Octagon Research Solutions, Inc. All Rights Reserved.

Dan Crawford

Director, Clinical Data Strategies

March 12, 2008


Basic Concepts of SDTM

• Captures all the submitted tabulation data as a series of observations in domains based on standard specified structure – SDTM does not specify content!

• Raw Collected Data• No imputed Values• Defines specific rules for variable names and structure

within each domain• No derived or analysis variables except for those in

SDTM– RFSTDTC (Reference Start Date)– RFENDTC (Reference End Date)


Some Common Mistakes when Converting to SDTM

• Adding Derived Variables to CRT Datasets• Imputing Data

– Completing Partial dates– Example: AE Start date is 06/2005. Do not record as

06/01/2005. That work is done in analysis datasets

• Plugging Holes in the data– If you didn’t collect it - Don’t try to create it now

– Example: If collection date is missing, do not create an

algorithm to populate. Leave it blank


SDTM and ADaM

• SDTM– Source or raw data– Vertical

– No redundancy

– Character variables– Each domain is specific to itself– Dates are ISO8601 character

strings

• ADaM– Derived data– Structure may not

necessarily be vertical– Redundancy is needed for

easy analysis– Numeric variables– Combines variables across

multiple domains– Dates are formatted as

numeric (e.g. SAS dates) to allow manipulation

Source: Susan Kenny, Inspire Pharmaceuticals Inc

BOTH ARE NEEDED FOR FDA REVIEW !


Process Flow

1. Source Data Evaluation

2. Author Data Conversion Specifications

3. Migrate Data from Source to SDTM Target

4. Data Pooling to Create Integrate Database

5. Data Standardization


Required Items

•Normalized datasets

•All inclusive lab data

•Gaps between record content and formats catalog will be identified

•Verification that all fields on CRF are captured in datasets

•Supporting documentations on study design and data collection


Source Data Evaluation

• Source Data Review is the key to a successful SDTM Conversion Project. Due to the granularity of SDTM, it requires a thorough knowledge of legacy data and supporting documentation.


Legacy Data Challenges

• Issue: Missing data/documentation• Resolution: Perform QC data audits, may include individual case report forms and/or

utilize CSR listings. Work with Sponsor/vendor to identify and locate missing documentation (if it exists)

• Issue: Non-English databases and/or documentation• Resolution: Identify early and perform translation

• Issue: Incomplete/incorrect formats catalogs• Resolution: Identify discrepancies and update format catalogs/ manually link metadata with proper formats and then programmatically update data and

apply decodes

• Issue: Data discrepancies/oddities• Resolution: Indicate data anomalies in “Reviewers Guide” or create “Notes to Reviewer" in the define file


Source Data Evaluation

• Metadata Analyses: conduct a series of metadata analyses to scan for common attributes and structures against the clinical data.

The results will allow you to create groups of similar studies to reduce units of work and maximize efficiency.


Project Design

• Group similar studies based on review of automated report, source documentation, and source data.

Example: Studies coming from the same CDM system.

Example: Studies with the same phase or conducted by the same CRO

• Data conversion specifications are developed based on similarities within groups of studies.

Example: Data conversion specifications created for the first study in a group will serve as template for subsequent studies in that group.


Project Design

• Data conversion specifications will be created based on typetype of domain: Standard or Custom, and then by classclass: Interventions, Events and Findings.

Interventions

Standard Custom

Events Findings


Project Design

• To ensure full accountability for all data points, each study should include a Mapping Specifications document, detailing the CDISC SDTM target for each source dataset and variable.

• Utilizing a database (excel or access) that stores these instructions will allow you to replicate the process for studies that have identical

(or, similar) structures.

One

Many

One

Many

SDTM

One

One

Many

Many

CRF


• Extraction Transfer Loading (ETL) tool is used to migrate data from source to target datasets

• Graphical modeling of data flow

• Pluggable maps for reusability of logic

Data Migration Process


Quality Control

Automated Quality Control

– Mapping specification utility: Built-in SDTM compliance wizard

– CDISC SDTM compliance verified using software developed in-house or

manual review.

Manual Quality Control

– Completion of quality control checklists:

• 100 % QC of converted data against mapping specifications

• 2 subject per Domain QC for all data points against Raw Data


Recommendations

• Adopt and move SDTM standards as far “upstream” as possible

• Design CRFs with SDTM in mind

• Standardize Controlled Terminology

• Convert Datasets to SDTM

• Generate Analysis datasets and CSR from SDTM


Data Pooling ChallengesData Pooling Challenges


Data pooling

• During pooling, data content is standardized Unique Subject Identifiers

• Terms are mapped to common standard Laboratory Data Any collected data with “controlled terminology”

» AE Outcome» AE Relationship» Race

• Dictionary encoding of Adverse Events and Concomitant medication and possibly Medical History


Challenge #1: Standardization of Data

• Laboratory Data– Standardization of units

• Impacts results and normal range values

– Normal Ranges• Many times the normal ranges are not incorporated into the

laboratory datasets. Find them• Some Laboratory normal ranges are based on Gender and

Age.

– Create library of Standard Analyte names, units (SI) along with all conversion factors


Laboratory Standardization Example

LBTEST LBORRES LBORRESU Conversion Factor

LBSTRESN LBSTRESU

Albumin 36 g/L (SI) N/A 36 g/L

Albumin 3.4 g/dL 10 34 g/L

Albumin 612 µmol/L .06600 40 g/L


Challenge # 2: USUBJID

• Do you have the same subjects enrolled in more than one trial?

• If so, do you have a database that tracks these subjects?


Challenge # 2: USUBJID

• When combining studies to create a pooled database for an ISS/ISE – those subjects will need to have the same USUBJID across all studies.

• USUBJID Database:– Pool necessary variables from all studies (most likely will

come from different source datasets DM, VS, MH)– Output all Subjects with matching DOB and Gender– Use additional information to determine if subject is a match– Assign USUBJID


Gender=Female Date of Birth=11JUL1947

OBS STUDYID

USUBJID

SITEID

PID PIN RACE HGT Diagnosis Date

COUNTRYCD

MCHD_OBS

REVIEWE

R

COMMENTS

59 DC2005-015-0023

000459

15 23 -NP Asian 146 NDJUN97 UK . DK NO MATCH

205 DC2005-010-0045

000620

10 45 MP Caucasian

157 NDJUL97 UK . DK NO MATCH

Gender=Male Date of Birth=10MAY1955

OBS STUDYID

USUBJID

SITEID

PID PIN RACE HGT Diagnosis Date

COUNTRYCD

MCHD_OBS

REVIEWE

R

COMMENTS

87 DC2005-005-0141

000041

5 141 PAC Caucasian

160 -8MAY1997USA 143 DK MATCH

143 DC2005-001-0097

000041

1 97 PAC Caucasian

160 XXMAY1997

USA 87 DK MATCH


Challenge # 3:Recoding – Medical History

• Does your ISE require analysis based on a subset of the population – i.e. subjects with Cardiovascular disease?

• Medical History is not coded in many studies and can be problematic to code for an ISS/ISE

• Some CRFs may be designed to allow for more than one term per line

• Coding Medical History typically involves the splitting of many terms


MHTERM MHMODIFY MHBODSYS MHDECOD

HIP DYSPLASIA OPERATED IN 1976. TORN MENISCUS (R) OPERATED ON 1994. OCCASIONAL LEFT KNEE PAIN

HIP DYSPLASIA CONGENITAL, FAMILIAL AND GENETIC DISORDERS

HIP DYSPLASIA


HIP DYSPLASIA OPERATED IN APPROXIMATELY 1976.

SURGICAL AND MEDICAL PROCEDURES

HIP SURGERY


TORN MENISCUS (R) OPERATED ON 1994

SURGICAL AND MEDICAL PROCEDURES

MENISCUS OPERATION


TORN MENISCUS (R)

INJURY, POISONING AND PROCEDURAL COMPLICATIONS

MENISCUS LESION


OCCASIONAL LEFT KNEE PAIN

MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS

ARTHRALGIA


Challenge # 3:Coding – “Splits”

Dataset USUBJID AESEQ AETERM AEMODIFY AEGRPID

CRT 000146 2 Nausea/Vomiting

Pool 000146 2.1 Nausea/Vomiting Nausea 2

Pool 000146 2.2 Nausea/Vomiting Vomiting 2

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