This document discusses CDISC standards for representing survival data from oncology clinical trials. It provides an overview of CDISC and describes the SDTM and ADaM domains that are useful for capturing efficacy endpoints involving survival, such as overall survival, progression-free survival and tumor response. Examples are given of how survival data from different patients would be represented in an ADTTE (Analysis Dataset for Time to Event) dataset according to CDISC ADaM standards.
SDTM (Study Data Tabulation Model) defines a standard for organizing and formatting data to streamline processes in collection, management, analysis and reporting of human clinical trial data tabulations and for non-clinical study data tabulations which are to be submitted as part of a product application(IND and NDA) to a regulatory authority such as the United States Food and Drug Administration (FDA) and PMDA (Japan)
In this presentation, Principal Statistical Scientist Ben Vaughn explains how clinical trial data moves from collection in the case report form to its presentation to FDA.
SDTM (Study Data Tabulation Model) defines a standard for organizing and formatting data to streamline processes in collection, management, analysis and reporting of human clinical trial data tabulations and for non-clinical study data tabulations which are to be submitted as part of a product application(IND and NDA) to a regulatory authority such as the United States Food and Drug Administration (FDA) and PMDA (Japan)
In this presentation, Principal Statistical Scientist Ben Vaughn explains how clinical trial data moves from collection in the case report form to its presentation to FDA.
SDTM (Study Data Tabulation Model) defines a standard structure for human clinical trial (study) data tabulations and for nonclinical study data tabulations that are to be submitted as part of a product application to a regulatory authority such as the United States Food and Drug Administration (FDA).
CDISC's CDASH and SDTM: Why You Need Both!Kit Howard
CDISC's clinical data standards are widely used for clinical research, but many people wonder why there seem to be two standards for collected data: the Clinical Data Acquisition Standards Harmonization (CDASH) standard and the Study Data Tabulation Model (SDTM) standard. This poster steps through four significant reasons that reflect the differences in philosophy, intermediate goals and broad-scale uses. Examples illustrate each reason and how they affect your studies.
SDTM Training for personnel with Junior and Intermediate level Clinical Trial Experience. Covers summary of most domains. Salient features include order of domain creation, importance of making programming Data/Metadata Driven, Nature of Clinical Raw Data, Summary of the Clinical Trial process with regards to the data flow to arrive at the Study data to be submitted to regulatory authorities like FDA, Importance of deriving ADAM from SDTM and not directly from raw data, Information has been put together from variety of sources including my own programming work.
A complex ADaM dataset - three different ways to create oneKevin Lee
The paper is intended for Clinical Trial SAS® programmers who create and validate a complex ADaM dataset. Some ADaM datasets require the use of complex algorithms. These algorithms could require several steps of data manipulation and more than one SDTM datasets. It can be very challenging to create a complex ADaM dataset in accordance with ADaM data structures and standards. Furthermore, it can be equally as challenging to validate those ADaM datasets. The paper will introduce three different ways to create a complex ADaM dataset. The first way is to create ADaM from SDTM directly without any intermediate permanent datasets. The second way is to create ADaM through the intermediate permanent datasets like SDTM+ or ADaM+ from SDTM. The third way is to create the final ADaM through the intermediate ADaM from SDTM. The paper will discuss the benefits and limitations of each method and also show some examples.
According to FDA Draft Guidance for Industry in Electronic Submission and Study Data Technical Conformance Guide, the pharmaceutical companies will need to provide CDISC Electronic submission to FDA. The paper will explain Data Standard Catalog which will dictate FDA Standards. The paper will discuss how to prepare CDISC electronic submission and what to prepare in CDISC electronic submission.
Presented at PhUSE 2013
The evaluation of efficacy in oncology studies, in particular for solid tumors, is pretty standard and well defined by several regulatory guidance (e.g. EMA and FDA), including some specific cancer type guidance (e.g. NSCLC from FDA).
Although some references will be also given for non-solid tumors, the paper will mainly focus on solid tumors efficacy
endpoints.
Overall Survival, Best Overall Response as per RECIST criteria, Progression Free Survival (PFS), Time to Progression (TTP), Best Overall Response Rate are some of the key efficacy indicators that will be discussed.
Implementation of CDISC ADAM in The Pharmacokinetics DepartmentSGS
SGS Life Science Services as a leading CRO, is one of the pioneers in the implementation of CDISC standards. Given the positive experiences in the SGS Data Management and Biostatistics Departments (implementation of SDTM and ADaM respectively), the Pharmacokinetics (PK) Department recently decided to adopt the CDISC standards as well.
In an SDTM database, pharmacokinetic data is stored as one record per subject, per time point (PC domain) or per pharmacokinetic parameter (PP domain). For the PK analysis, the generation of Tables, Listings and Figures, and the statistical analysis on PK parameters, ‘analysis ready’ datasets are created.
SDTM (Study Data Tabulation Model) defines a standard structure for human clinical trial (study) data tabulations and for nonclinical study data tabulations that are to be submitted as part of a product application to a regulatory authority such as the United States Food and Drug Administration (FDA).
CDISC's CDASH and SDTM: Why You Need Both!Kit Howard
CDISC's clinical data standards are widely used for clinical research, but many people wonder why there seem to be two standards for collected data: the Clinical Data Acquisition Standards Harmonization (CDASH) standard and the Study Data Tabulation Model (SDTM) standard. This poster steps through four significant reasons that reflect the differences in philosophy, intermediate goals and broad-scale uses. Examples illustrate each reason and how they affect your studies.
SDTM Training for personnel with Junior and Intermediate level Clinical Trial Experience. Covers summary of most domains. Salient features include order of domain creation, importance of making programming Data/Metadata Driven, Nature of Clinical Raw Data, Summary of the Clinical Trial process with regards to the data flow to arrive at the Study data to be submitted to regulatory authorities like FDA, Importance of deriving ADAM from SDTM and not directly from raw data, Information has been put together from variety of sources including my own programming work.
A complex ADaM dataset - three different ways to create oneKevin Lee
The paper is intended for Clinical Trial SAS® programmers who create and validate a complex ADaM dataset. Some ADaM datasets require the use of complex algorithms. These algorithms could require several steps of data manipulation and more than one SDTM datasets. It can be very challenging to create a complex ADaM dataset in accordance with ADaM data structures and standards. Furthermore, it can be equally as challenging to validate those ADaM datasets. The paper will introduce three different ways to create a complex ADaM dataset. The first way is to create ADaM from SDTM directly without any intermediate permanent datasets. The second way is to create ADaM through the intermediate permanent datasets like SDTM+ or ADaM+ from SDTM. The third way is to create the final ADaM through the intermediate ADaM from SDTM. The paper will discuss the benefits and limitations of each method and also show some examples.
According to FDA Draft Guidance for Industry in Electronic Submission and Study Data Technical Conformance Guide, the pharmaceutical companies will need to provide CDISC Electronic submission to FDA. The paper will explain Data Standard Catalog which will dictate FDA Standards. The paper will discuss how to prepare CDISC electronic submission and what to prepare in CDISC electronic submission.
Presented at PhUSE 2013
The evaluation of efficacy in oncology studies, in particular for solid tumors, is pretty standard and well defined by several regulatory guidance (e.g. EMA and FDA), including some specific cancer type guidance (e.g. NSCLC from FDA).
Although some references will be also given for non-solid tumors, the paper will mainly focus on solid tumors efficacy
endpoints.
Overall Survival, Best Overall Response as per RECIST criteria, Progression Free Survival (PFS), Time to Progression (TTP), Best Overall Response Rate are some of the key efficacy indicators that will be discussed.
Implementation of CDISC ADAM in The Pharmacokinetics DepartmentSGS
SGS Life Science Services as a leading CRO, is one of the pioneers in the implementation of CDISC standards. Given the positive experiences in the SGS Data Management and Biostatistics Departments (implementation of SDTM and ADaM respectively), the Pharmacokinetics (PK) Department recently decided to adopt the CDISC standards as well.
In an SDTM database, pharmacokinetic data is stored as one record per subject, per time point (PC domain) or per pharmacokinetic parameter (PP domain). For the PK analysis, the generation of Tables, Listings and Figures, and the statistical analysis on PK parameters, ‘analysis ready’ datasets are created.
The use of Adaptive designs is becoming quite popular and well-perceived by the regulatory agencies such as the FDA in the US. “Adaptation” can occur in different fashion and potentially make studies more efficient (e.g. shorter duration, fewer patients) more likely to demonstrate an effect of the drug if one exists, or more informative (see “Adaptive Design Clinical Trials for Drugs and Biologics” FDA guidance).
The aim of this presentation is to illustrate a case where an adaptive design was used in a Phase III oncology pivotal study having Overall Survival as a primary end-point. The particular adaptation implemented was an un-blinded SSR that applied a promising zone approach.
The main focus will be how the adaptive design impacted the SDTM modelling, the design of some ADaM datasets (e.g. those containing the time-to-event endpoints and therefore using ADTTE ADaM model) and later on how some mapping and analysis decisions were described in both the study and analysis reviewer guide.
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At Axtria, we provide world-class training, support and growth prospects - all crafter to build on your unique skills and outline your success. You will be in a highly collaborative culture among a bunch of the most talented and visionary folks in the industry.
Audio and slides for this presentation are available on YouTube: http://youtu.be/6W_xoH4s-Yk
Dr. Patrick Wen, of Dana-Farber Cancer Institute's Center for Neuro-Oncology, discusses current clinical trial options for brain tumor patients and some of the new therapies available in neuro-oncology. This presentation was originally given at Dana-Farber Cancer Institute on Dec. 4, 2013.
Cervical Total Disc Replacement Device Market Competitive Research And Precis...subishsam
The research firm Contrive Datum Insights has just recently added to its database a report with the heading global Cervical Total Disc Replacement Device Market .Both primary and secondary research methodologies have been utilised in order to conduct an analysis of the worldwide Cervical Total Disc Replacement Device Market . In order to provide a comprehensive comprehension of the topic at hand, it has been summed up using appropriate and accurate market insights. According to Contrive Datum Insights, this worldwide comprehensive report is broken up into several categories in order to present the data in a way that is understandable, succinct, and presented in a professional
ICD-10 for physicians: its about good patient care and clinical documentationMichael Arrigo
They key thing for physicians to know about ICD-10 is that if they are using good clinical documentation practices, the coders will do the hard work. Much of the burden of ICD-10 comes to those physicians who currently do not document the details of the patient condition. Those that do will feel less pain from the ICD-10 transition.
The number and type of new concepts required for ICD-10 are not foreign to clinicians. The focus of the documentation should really be about good patient care. Patients deserve to have accurate and complete documentation of their conditions.
If other industries understand the value of accurate and complete documentation of data about encounters, shouldn't healthcare?
ICD-10 reimbursement will introduce changes based on what was done and why. Certainly any physician interested providing good care cannot argue with this?
Actividad Preparatoria del Seminario de Prevención Cuaternaria del 4° Congreso Iberoamericano de Medicina Familiar y Comunitaria, Wonca Ibemeroamericana, CIMF http://www.montevideo2015wonca-cimf.org/
Seminario: Codificación y clasificación de diagnósticos en atención primaria y Prevención Cuaternaria.
Auditorio del Instituto Universitario Hospital Italiano Buenos Aires. Miércoles 11 Marzo 2015
Each therapeutic area has its own unique data collection and analysis. Especially, Oncology has a unique way to collect and analyze the data. Response criteria of oncology studies dictate what to collect and analyze data. Three oncology studies follow the different guidelines standards. The Solid Tumor studies usually follow RECIST (Response Evaluation Criteria in Solid Tumor), Lymphoma studies usually follow Cheson and Leukemia studies follow study specific guidelines (IWCLL for Chronic Lymphocytic Leukemia, IWAML for Acute Myeloid Leukemia, NCCN Guidelines for Acute Lymphoblastic Leukemia and ESMO clinical practice guides for Chronic Myeloid Leukemia).
CDISC also introduced data collection, analysis and coding standards. Oncology data are collected and stored as SDTM domains - TU (Tumor Identification), TR (Tumor Results) and RS (Response). CDISC also introduces oncology terminology. For example, tumor responses are CR (Complete Response), Partial Response (PR), Stable Disease (SD), Progression Disease (PD) and Not Evaluable (NE). Oncology studies also have different efficacy end-points. CDISC introduces oncology specific ADaM data set – Time to Event (--TTE) data set for OS (Overall Survival) and PFS (Progression Free Survival).
Using response criteria and CDISC standards, oncology-specific standards will be define and integrated from protocol to analysis. These standards will also derive the automated oncology artifiact developments.
The presentation is intended for Clinical Trial programmers or statisticians who are working on the solid tumor studies in oncology. There are three types of studies in oncology: Solid Tumor, Lymphoma and Leukemia. The solid tumor study usually follow RECIST (Response Evaluation Criteria in Solid Tumor) while Lymphoma follows Cheson and Leukemia follows study-specific criteria. The presentation will provide the brief introduction of RECIST 1.1 such as lesions (target, non target and new) and their selection criteria (size, number and etc). It will also discuss how the changes in tumor measurements will lead to responses (Complete Response, Partial Response, Stable Disease, Progression Disease and Not Evaluable).
Then, the presentation will introduce how RECIST 1.1 data are streamlined in CDISC – mainly in SDTM and ADaM. The presentation will introduce the new oncology SDTM domains - TU (Tumor Identification), TR (Tumor Results) and RS (Response) according to RECIST 1.1. The presentation will also show how ADaM datasets can be created for the tumor response evaluation and analysis in ORR (Objective Response Rate), PFS (Progression Free Survival) and TTP (Time to Progression).
The use of Adaptive designs is becoming quite popular and well-perceived by the regulatory agencies such as the FDA in the US. “Adaptation” can occur in different fashion and potentially make studies more efficient (e.g. shorter duration, fewer patients) more likely to demonstrate an effect of the drug if one exists, or more informative (see “Adaptive Design Clinical Trials for Drugs and Biologics” FDA guidance).
The aim of this presentation is to illustrate a case where an adaptive design was used in a Phase III oncology pivotal study having Overall Survival as a primary end-point. The particular adaptation implemented was an un-blinded SSR that applied a promising zone approach.
The main focus will be how the adaptive design impacted the SDTM modelling, the design of some ADaM datasets (e.g. those containing the time-to-event endpoints and therefore using ADTTE ADaM model) and later on how some mapping and analysis decisions were described in both the study and analysis reviewer guide.
While the evolution of information technology is bringing the data closer to customers for their own exploration, the need of a comprehensive understanding of the therapeutic area knowledge for programmers in clinical development is increasing. Starting with a basic understanding on the medical background, special assessment methods, ways of statistically analyzing and displaying the data, to name a few essential ones enables programmers to interact with partners (e.g. scientist, statisticians etc.) on equal par.
In this intent, activities to collect and provide comprehensive information around the Oncology and Rheumatoid Arthritis Therapeutic Areas (TA) via the PhUSE Wiki had started in February 2013 and continued throughout the year. Various PhUSE members have spent time and energy to provide and expand their knowledge and make it available to the entire community.
Today, although there is still much to do to complete and maintain the collected material, the two TA Wikis are a useful tool for Statistical Programmers approaching these TA for the first time or who want to improve their knowledge. Moreover the PhUSE Wiki can be seen as a basic tool for future developments to improve the way professionals in the different TA work. An established working relationship across organizations, pharmaceutical companies or external service providers, will help to support implementation of TA-specific standards from mapping raw data in SDTM, data analysis using ADaM and finally data presentation in standardized outputs. The PhUSE Wiki can be the central place to share important updates such as new CDISC TA standards or the availability of new TA regulatory guidance. On the other hand we see the Wiki as a place to discuss, to stimulate and inspire new initiatives among the “SAS-Programming Community”, be it Statisticians, Programmers, Data Managers or everyone else involved; this may include specific TA working related white papers and/or scripts being part of the FDA Working Groups WG5 “Development of Standard Scripts for Analysis and Programming” Project 08 “Create white papers providing recommended display and analysis including Table, List and Figure shells”.
Presented at PhUSE/FDA CSS 2014 in Silver Spring (US)
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
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Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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CDISC SDTM and ADaM for survival data
1. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
CDISC SDTM and ADaM
for survival data
VI BIAS Annual Conference
Analisi della sopravvivenza ed applicazioni in Oncologia:
un percorso dalle basi agli ultimiaggiornamenti
Genoa – 30-31/10/2014
Angelo Tinazzi
Cytel Inc., Wilmington Del. USA
Succursale de Meyrin – Geneva – Switzerland
angelo.tinazzi@cytel.com
1
2. Cytel Inc. - Confidential
[A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
2
The information contained in this
presentation is based on personal
research of the author and does not
necessarily represent Cytel Inc.
3. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
3
CDISC Intro
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
CDISC is a global, open, multidisciplinary, non-profit
organization that has established standards
to support the acquisition, exchange, submission
and archive of clinical research data and
metadata.
The CDISC mission is to develop and support
global, platform-independent data standards
that enable information system
interoperability to improve medical research
and related areas of healthcare. CDISC
standards are vendor-neutral, platform-independent
and freely available via the CDISC
website.
4. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
4
CDISC Intro
Available Standards
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
5. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
5
CDISC Intro
Standards in Submission
Data
Analysis
Data
Tabulations
Data
Collection
Planning
SDTM ADaM
SEND
CDASH
LAB
Protocol
Study
Design
NCI CT
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
6. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
6
CDISC Intro
A World of Clinical Standards
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
7. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
7
CDISC Intro
Traceability
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
Primary Overall Survival Analysis
ADTTE
ADaM define.xml
DM
aCRF
SDTM define.xml
8. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
8
Efficacy Endpoints in Oncology
Solid Tumors
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
Clinical Trial
Endpoint
Definition Source
Overall Survival
(OS)
Time from randomization to death from any cause. FDA
Objective
Response Rate
(ORR)
Proportion of patients achieving either a partial or
complete response for a minimum duration of time.
FDA
Disease-free
survival (DFS)
Time from randomization until recurrence of tumor
or death from any cause. DFS is typically used in
clinical trials of adjuvant cancer therapy.
FDA
Progression-free
survival (PFS)
Time from randomization until objective tumor
progression or death.
FDA
Time to
progression (TTP)
Time from randomization until objective tumor
progression (does not include deaths)
FDA
Time to treatment
failure (TTF)
Time from randomization to treatment
discontinuation for any cause, including drug
toxicity.
FDA
Progression-free
survival 2 (PFS2)
Same as PFS with some indication variant. E.g. in
Prostate cancer
-
Duration of
Response (DOR)
Time from documentation of tumor response to
disease progression
EMA
Clinical Benefit
Response Rate
(CBR)
Patients achieving either a complete response,
partial response or absence of progression at 6
months
EMA
9. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
9
Efficacy Endpoint in Oncology
Solid Tumors
RAN SD SD PR CR PD
DOR
ORR
OS
PFS TTP
Death /
Alive
TTF
Off TRT
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
10. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
10
CDISC SDTM
SDTM Domains (as per Version 3.2)
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
11. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
11
CDISC SDTM
Domains/Items Useful for Overall Survival
Date of Origin / Starting point
Randomization date
Treatment Start date
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
DS
DM
12. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
12
CDISC SDTM
Domains/Items Useful for Overall Survival
Date of death
DM
DS
Patient 101001 died (DTHFL=Y) the 17JAN2013 (DTHDTC)
Patient 101006 is alive (DTHFL=NULL)
Patient died (DSSCAT=REPORT OF DEATH) because of Progressive Disease (DSTERM) on
14DEC2010 (DSSTDTC)
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
13. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
13
CDISC SDTM
Domains/Items Useful for Overall Survival
Date of last follow-up
Any date documenting patient is still alive
Visit date, AEs, CM, Lab Samples, ECG, etc.
In SDTM we have a lot of «administrative» date that should be
not considered (--DTC)
Investigate for date in Supplemental Qualifiers
SUBJID SSORRES SSSTR
ESC
VISIT SSDTC
001001 ALIVE ALIVE FUP 1 07JAN13
001001 ALIVE ALIVE FUP 2 25MAY13
001002 DECEASED DEAD FUP 1 08JUN13
001003 ALIVE ALIVE FUP 1 01JAN12
001003 ALIVE ALIVE FUP 2 05AUG12
001003 PATIENT LOST
TO FOLLOW UP
LOST FUP 3 20NOV12
Last follow-up
Date when death was reported.
Date of death in DM
At this follow-up the patient was
known to be lost, date of last
follow-up is therefore 05AUG12
(previous visit)
Survival SS Status (available from version 3.2)
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
14. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
14
CDISC SDTM
OncologyDomains for Tumor Response TU/TR/RS
Tumor Identification
Unique identification of tumors for that patient
Tumor Results
Quantitative measurements and/or qualitative
assessments of the tumors identified in the TU
Disease Response
Clinical response evaluations determined from the TR
data and other SDTM domains
TU
TR
RS
Overcoming Difficulties in Implementing RECIST criteria, PhUSE 2013, G. Ruhnke
CDISC Journey on Solid Tumor Studies using RECIST 1.1., PhUSE 2013, K. Lee
SDTM Oncology Domains From Patients to Data to Narrative, PhUSE 2013, K. Stoltzfus
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
15. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
15
CDISC SDTM
OncologyDomains for Tumor Response TU/TR/RS
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
16. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
16
CDISC SDTM
OncologyDomains for Tumor Response TU/TR/RS
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
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CDISC SDTM
OncologyDomains for Tumor Response TU/TR/RS
Source: Oncology Legacy Data => SDTM.
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
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CDISC ADaM
FoundationModels
• One record per subject
• Demographics, Baseline Chars
• Study Population (ITTFL, SAFFL, ..)
• Study Arm, Treatment Periods
ADSL
Analysis Subject Level
Dataset
• Vertical Structure
• One or more record per subject/time-point
BDS
Basic Data Structure
• Counting of subjects with a record for
each term
• It often includes a structured
hierarchy of dictionary
ODS
Occurrence Data
Structure
• Based on BDS
• Time to the Event of Interest
• One or more time-to-event endpoint
per ADTTE dataset
TTE
Time to Event
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
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CDISC ADaM
ADTTE – Analysis Dataset for Time to Event – Variables of Interest
Description of time-to-event (PARAMCD/PARAM)
E.g. OS/Overall Survival
Date Origin (STARTDT)
E.g. Randomization Date
Censor (CNSR) 0=Event
Analysis date of event or censoring (ADT)
E.g. Death Date / Last follow-up
Elapsed time to the event of interest from the origin
(AVAL)
E.g. (ADT-STARTDT)+1 (days)
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
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CDISC ADaM
ADTTE – Analysis Dataset for Time to Event – Variables of Interest
Event or Censoring Description (EVNTDESC)
E.g. DEATH
Censor Date Description (CNSDTDSC)
E.g. LAST FOLLOW-UP DATE
Imputation Date Flag (ADTF)
E.g. D/M/Y
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
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ADTTE Examples
Case 1: Single Endpoint with Multiple Values for Censoring
A studywith Overall Survival as Primary Endpoint
Definition Time from randomization until death
from any cause
Censor Last date subject was seen alive
Cut-off Date Applied: 08APR2014
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
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ADTTE Examples
Case 1: Single Endpoint with Multiple Values for Censoring
SUBJID TR
TP
PARA
MCD
PARAM AVAL START
DT
ADT A
D
T
F
C
N
S
R
EVNTDESC
001001 PBO OS Overall Survival
(Months)
16.8 15AUG11 07JAN13 0 DEATH
001002 EXP OS Overall Survival
(Months)
8.4 12SEP11 25MAY12 D 1 LAST FOLLOW-UP
DATE
001003 EXP OS Overall Survival
(Months)
7.2 02SEP13 08APR14 1 FINAL
ANALYSIS CUT-OFF
DATE
Endpoint: Overall Survival ; Cut-off Date Applied: 08APR2014
Treatment
Received
Time to Event
Parameter
− Start Date Randomisation
- Event/Censor Date
Time part of the TTE event
(ADT-STARTDT+1)/30.42
*30.42 Standard for nr. of days in a month
Event/Censor
Description
0=Event
1..n=Censor
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
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ADTTE Examples
Case 1: Single Endpoint with Multiple Values for Censoring
SUBJID TR
TP
PARA
MCD
PARAM AVAL START
DT
ADT A
D
T
F
C
N
S
R
EVNTDESC
001001 PBO OS Overall Survival
(Months)
12.1 01APR13 07JAN13 0 DEATH
001002 EXP OS Overall Survival
(Months)
17.2 04APR13 25MAY12 D 1 LAST FOLLOW-UP
DATE
001003 EXP OS Overall Survival
(Months)
17.5 08APR13 08APR14 1 FINAL
ANALYSIS CUT-OFF
DATE
Endpoint: Overall Survival ; Cut-off Date Applied: 08APR2014
A partial date (--MAY12) where the day part has been imputed. Valid values are D,M,Y
More details about censoring date. This patient either die or had a follow-up>cut-off date
and therefore censored at the time of analysis cut-off date
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
This is also traceability!!!!!!
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ADTTE Examples
Case 1: Single Endpoint with Multiple Values for Censoring
Endpoint: Overall Survival ; Cut-off Date Applied: 08APR2014
SUBJ
ID
TR
TP
PARA
MCD
… ADT A
D
T
F
CN
SR
EVNTDESC SRC
DOM
SRCVAR SRCS
EQ
001001 PBO OS … 07JAN13 0 DEATH DM DTHDTC .
001002 EXP OS … 25MAY12 D 1 LAST FOLLOW-UP
DATE
ADSL LASTVSDT .
001003 EXP OS … 08APR14 1 FINAL
ANALYSIS
CUT-OFF DATE
ADSL LASTVSDT .
SDTM.DM ADAM.ADSL
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
This is also traceability!!!!!!
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ADTTE Examples
Case 2: Composite Endpoint with multiple value for Event and Censoring
Progression Free Survival
Definition Time from randomization until radiological
tumor progression or death which ever come
first
Censor Last date radiological tumor assessment
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
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ADTTE Examples
Case 2: Composite Endpoint with multiple value for Event and Censoring
Endpoint: Progression Free Survival
SUBJ
ID
T
R
T
P
PA
RA
MC
D
PARAM AVAL START
DT
ADT C
N
S
R
EVNTDESC CNSDTDSC
001001 P
B
O
PFS Progression
Free
Survival
(Months)
16.8 15AUG11 07JAN13 0 RADIOLOGI
CAL
PROGRESSI
ON
001002 E
X
P
PFS Progression
Free
Survival
(Months)
8.4 12SEP11 25MAY12 1 STUDY
COMPLETED
LAST
RADIOLOGIC
AL
ASSESSMENT
001003 E
X
P
PFS Progression
Free
Survival
(Months)
7.2 02SEP13 08APR14 2 NO
BASELINE
ASSESSMEN
T
RANDOMIZAT
ION
001004 P
B
O
PFS Progression
Free
Survival
(Months)
8.4 12SEP11 25MAY12 0 DEATH
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
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ADTTE Examples
Case 3: One ADTTE with several time-to-event Endpoints
Phase III study
First Relapsed or Refractory Acute Myeloid
Leukemia
Primary Endpoint: Overall Survival
Sensitivity Analysis
Overall Survival censored for Susbsequent AML Therapies
Overall Survival censored for Post Treatment
Transplantation
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
28. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
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ADTTE Examples
Case 3: One ADTTE with several time-to-event Endpoints
SUBJ
ID
T
R
T
P
PAR
AMC
D
PARAM AVAL ADT C
N
S
R
EVNTDESC CNSDTDSC
001001 P
B
O
AML Susbsequent AML
Therapies (Months)
0.6 31AUG11 0 SUBSEQUENT
AML NON
PROTOCOL
THERAPY
001001 P
B
O
TRA Post Treatment
Transplantation
(Months)
16.8 07JAN13 1 NO EVENT LAST VISIT
DATE
001001 P
B
O
OS Overal Survival
(Months)
16.8 07JAN13 0 DEATH
001001 P
B
O
OST
R
OS Censored for
Transpl. (Months)
16.8 07JAN13 0 DEATH
001001 P
B
O
OSA
ML
OS Censored for
Subsequent AML
Therapy (Months)
0.6 31AUG11 1 NO EVENT SUBSEQUENT
AML NON
PROTOCOL
THERAPY DATE
The Subsequent AML therapy started on 31st August 2011. The death occurred the 7th
January 2013 was censored for OSAML
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
29. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
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ADTTE Examples
ADaMDefine.xml
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
Variable Value Label Type Format Source/Computational Method
PARAMCD PFS Progression
Free Survival
(Months)
FLOAT 8.1 Analysis Date (ADT) used for Progression
Free Survival will be the earliest date of
radiologically documented disease
progression (RS.RSDTC where
RS.RSTESTCD=OVRLRESP and
RSORRES=PD) or death (DM.DTHDTC)
If none of these events occurred Analysis
Date (ADT) is the last post-baseline
tumour assessment (Max RS.RSDTC).
Progression Free Survival (months)=(ADT
- STARTDT+1)/30.42
30. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
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ADTTE Examples
Analysis ResultsMetadata
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
Analysis Results Metadata
Fields
Description
Display Identifier Tables 14.2.1
Display Name Overall Survival (months) by Treatment
Result Identifier Overall Survival (months)
PARAM Overall Survival (months)
PARAMCD OS
Analysis Variables AVAL, CNSR
Reason Primary Efficacy Endpoint as per Protocol
Dataset ADTTE
Selection Criteria ITTFL=‘Y’ and PARAMCD=‘OS’
Documentation SAP Section 10.1.1
Programming Statements PROC LIFETEST DATA=ADTTE;
TIME AVAL*CNSR(1);
STRATA TRTP;
WHERE PARAMCD=‘OS’;
RUN;
31. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
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ADTTE Examples
The Analysis Reviewer Guide: The ideal place where to clarify
potential source of misinterpretation not enough explained in the SAP
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
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References
RECIST and OTHER NON SOLID TUMOR REFERENCES
P Therasse et al, "New response evaluation criteria in solid tumors: Revised
RECIST guideline (version 1.1)," European Journal of Clinical Oncology, pp.
45: 228-247, 2009.
Overcoming Difficulties in Implementing RECIST criteria, PhUSE 2013, G.
Ruhnke
CDISC Journey on Solid Tumor Studies using RECIST 1.1., PhUSE 2013, K.
Lee
SDTM Oncology Domains From Patients to Data to Narrative, PhUSE 2013, K.
Stoltzfus
D Cheson et al, "Revised Recommendations of the International Working
Group for Diagnosis, Standardization of Response Criteria, Treatment
Outcomes, and Reporting Standards for Therapeutic Trials in Acute Myeloid
Leukemia," Journal of Clinical Oncology, pp. Vol 21, No 24: pp 4642-4649,
2003
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
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References
ONCOLOGY RELATED REGULATORY GUIDANCE
Clinical Trial Endpoints for the Approval of Non-Small Cell Lung Cancer Cancer
Drugs and Biologics, FDA, 2011
Cancer Drug Approval Endpoints
http://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResourc
es/CancerDrugs/ucm094586.htm
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
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CDISC SDTM
CDISC Standards
References
CDISC Study Data Tabulation Model (SDTM) v1.4
Study Data Tabulation Model Implementation Guide (SDTMIG) v3.2
Associated Persons Implementation Guide (SDTMIG-AP) v1.0
Released Therapeutic Area Standards:
Diabetes, Alzheimer, Asthma, Multiple Sclerosis, Pain, Parkinson Disease, Polycystic Kidney Disease,
Tuberculosis and Virology
Oncology for Tumor Response domains (integrated into SDTMIG 3.1.3)
CDISC/NCI-EVE Standard Controlled Terminology
FDA Guidance and Technical Documents:
CDER and CBER: Guidance for Industry - Providing Regulatory Submissions in Electronic Format -
Standardized Study Data (Draft)
Study Data Specifications (soon replaced by Study Data Technical Conformance Guide)
CDER Common Data Standards Issues
C Holland J Shostak - Implementing CDISC Using SAS, SAS 2012
F Wood - Creating SDTM Datasets from Legacy Data - PharmaSUG - 2011
A Tinazzi - Looking for SDTM Migration Specialist – PhUSE - London 2014
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
35. Cytel Inc. - Confidential [A. Tinazzi – CDISC SDTM and ADaM for survival data – BIAS 2014 Genoa 30-31 October 2014]
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CDISC ADaM
References
Analysis Data Model (ADaM) Implementation Guide v1.0
The ADaM Basic Data Structure for Time-to-Event Analyses v1.0
Analysis Data Model (ADaM) Data Structure for Adverse Event Analysis
G Cappellini - ADaM and traceability: Chiesi experience. BIAS Seminar «Data
handling and reporting in clinical trials with SAS» - Milan 2013
A Tinazzi - Interpreting CDISC ADaM IG through Users Interpretation. PhUSE -
Bruxelles 2013
CDISC Intro
Efficacy Endp. Onco
CDISC SDTM
CDSIC ADaM
ADTTE Examples
References
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Thank you for your time!
Angelo Tinazzi – Associate Director – Statistical Programming
angelo.tinazzi@cytel.com
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Cytel Global Reach
USA
• Cambridge, MA
(HQ)
• Philadelphia, PA
• Waltham, MA
Sales Offices
• New York, NY
• San Francisco, CA
France
• Paris (sales
office)
Switzerland
• Geneva
India
• Pune
• Hyderabad
• Bangalore
• >400 FTEs worldwide
• Low turnover rate (7% in
2011, 2012)
UK
• London (sales
office)
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