CDISC A complete course

4 out of 5
4
6 reviews

What is SDTM?

SDTM provides a standard for organizing and formatting data to streamline processes in collection, management, analysis and reporting. Implementing SDTM supports data aggregation and warehousing; fosters mining and reuse; facilitates sharing; helps perform due diligence and other important data review activities; and improves the regulatory review and approval process.

Highlights of SDTM :

  1. SDTM Domains
  2. aCRF Desigining
  3. Trail Desigining
  4. Spec Desigining
  5. SDTM Conversions
  6. Validation
  7. Migration
  8. Submission

What is ADAM?

ADaM defines dataset and metadata standards that support:

  • efficient generation, replication, and review of clinical trial statistical analyses, and
  • traceability among analysis results, analysis data, and data represented in the Study Data Tabulation Model (SDTM).​

ADaM is one of the required standards for data submission to FDA (U.S.) and PMDA (Japan).

Highlights of Course :

  1. Introduction of ADaM
  2. ADSL
  3. OCCDS
  4. BDS
  5. Business logics
  6. Industry Examples with Interview questions
  7. Additional Concepts for above 3 + yrs exp.

What is TLFs?

Tables, Listings and Figures plays vital role in SAS programming to display the data in a readable format to display the data in the form of Charts and Graphs  which helps in Data Analysis.

 

Tables are  created as per SAP(statistical Analysis Plan) document which is prepared to assist SAS programmers to  detail on the scope of planned analyses, population definitions, and methodology on how prospective decisions are to be made for presenting study results.

 

Listings are the reports generated in the file formats like rtf,the difference between tables and listings is that listing does not involves any manipulation of data where as tables are derived from source data by applying different SAS procedures such as PROC SORT, PROC FORMAT, PROC TRANSPOSE, and PROC REPORT and transformed into a format as per SAP.

 

Graphs are prepared using  procedures such as PROC CHART, PROC PLOT, PROC GCHART, PROC GPLOT,PROC SGPLOT, PROC SGPANEL, PROC SGPIE.

 

TLF’s helps in submission of Final document to FDA And sponsors.

Highlights of Course :

  1. Introduction of TLF’s
  2. Demographics
  3. Adverse Event
  4. Dispostion
  5. Conmed
  6. Exposure
  7. Vitals
  8. Lab
  9. Industry Examples with Interview questions

Software Installation : Currently software installation is a paid one. You need to do it from system administrator. The total software which consists of around 17 Gb. It will take 2 Hours to do the installation. For SDTM there is no software you need use SAS for SDTM Programming.

System requirements :

Processor : i3 and above

RAM : 4GB and above [preferable 8 GB Best ]

Harddisk : SSD Harddisk with 250 GB OR Above  / other harddisk also its ok

 

Target Audience : To do these SDTM Course you need have the knowledge on

  1. Base sas
  2. Advanced SAS
  3. Clinical Reseach just basic knowlegde

Materials : Books and Daily notes will be provided by us along with that recording also will be provided for reference . Step by step notes will be provided and excersises also.

 

Sample Videos : We request you that first don’t join the course directly as we provide the sample videos in the cirriculam first watch that try to understand. If you understand and feel comfortable with trainer explanation then gohead for classes. Join the course.

Working hours  : 9:00 am ist  to 8 pm ist .

SDTM : Study data tabulation model Introduction to clinical

Introduction to Clinical

1
Adverse event
2
SAE
3
ARM
4
Baseline
5
Randomization
6
Parallel
7
Crossover
8
DMC [Data Monitoring Committee ]
9
Eligible Criteria
10
Inclusion/Exclusion Criteria
11
Inform consent
12
Masking
13
Study Design
14
Study Documents
15
Study Start date
16
Study Type
17
Title
18
Blinded Trails

ICH/GCP Guidelines

21 CRF Part 11

Clinical Research Methodology

1
Phase 1
2
Phase 2
3
Phase 3
4
Phase 4

aCRF Desigining

1
Rawdatasets
2
Blank CRF
3
SDTM Control Terminology Protocol
4
Blank Annotated aCRF
5
SDTM IG Guide
6
CDISC Annotation Rules.

Trail Designing

Purpose of Trail designing

1
Clearly and quickly grasp the design of a clinical trial
2
Compare the designs of different trials
3
Search a data warehouse for clinical trials with certain features
4
Compare planned and actual treatments and visits for subjects in a clinical trial

TA :Trail Arm

1
describes the sequences of Elements in each Epoch for each Arm, and thus describes the complete sequence of Elements in each Arm

TV : Trial Visits

1
describes the planned schedule of Visits

TI : Trial Inclusion/Exclusion Criteria

1
describes the inclusion/exclusion criteria used to screen subjects

TE : Trial Elements

1
describes the Elements used in the trial

Trial Summary

1
lists key facts (parameters) about the trial that are likely to appear in a registry of clinical trials.

TD: Trial Disease Assessment

1
provides information on the protocol-specified disease assessment schedule, and will be used for comparison with the actual occurrence of the efficacy assessments in order to determine whether there was good compliance with the schedule.

TM: Trial Disease Milestones

1
describes observations or activities identified for the trial which are anticipated to occur in the course of the disease under study and which trigger the collection of data.

Spec Designing

1
An SDTM Spec designing will be explained based on aCRF designing and docs

SDTM Domain in-depth domain explanation and programming conversion Techniques.

Models for Special Purpose Domains

DM Demographics

1
A special purpose domain that includes a set of essential standard variables that describe each subject in a Clinical study.

SV Subject Visits

1
A special purpose domain that contains the actual start and end data/time for each visit of each individual subject.

Models for Interventions Domains

CM Concomitant and Prior Medications

1
An interventions domain that contains concomitant and prior medications used by the subject, such as those given on an as needed basis or condition-appropriate medications

EX Exposure

1
An interventions domain that contains the details of a subject’s exposure to protocol-specified study treatment.

Models for Events Domains

AE Adverse Events

1
An events domain that contains data describing untoward medical occurrences in a patient…

DS Disposition

1
An events domain that contains information encompassing and representing data related to subject disposition

An events domain that contains information encompassing and representing data related to subject disposition

Findings About Events or Interventions FA

1
– A findings domain that contains the findings about an event or intervention that cannot be represented within an events or interventions domain record or as a supplemental qualifier.

Models for Findings Domains

LB Laboratory Test Results

1
A findings domain that contains laboratory test data such as hematology, clinical chemistry and urinalysis.

Cardiovascular System Findings (CV)

1
A findings domain that contains physiological and morphological findings related to the cardiovascular system, including the heart, blood vessels and lymphatic

Vital Signs

1
A findings domain that contains measurements including but not limited to blood pressure, temperature,respiration, body surface area, body mass index, height and weight.

Models for Relating Peer Records

RELREC Dataset

1
A Relating Peer Records domain that represents data of relationship records.

Migration

1
A brief explanation of migration techniques in real world with unix commands via unix putty

ADaM : Analysis data model

Fundamentals of the ADaM Standard

Standard ADaM Variables

1
ADaM Variable Conventions
2
General Variable Conventions
3
Timing Variable Conventions
4
Date and Time Imputation Flag Variables
5
Flag Variable Conventions
6
Variable Naming Fragments

The ADaM Subject-Level Analysis Dataset (ADSL)

1
ADSL Identifier Variables
2
Subject Demographics Variables
3
ADSL Population Indicator Variables
4
ADSL Treatment Variables
5
ADSL Dose Variables
6
Treatment Timing Variables
7
Subject-Level Period, Subperiod, and Phase Timing Variables
8
ADSL Subject-Level Trial Experience Variables
9
More concepts on Extra variables

The ADaM (OCCDS)

1
Working with ADAE Conversions with additional variables
2
Working with ADDS Conversions with additional variables
3
Working with ADCM Conversions with additional variables
4
Working with ADEX Conversions with additional variables

The ADaM Basic Data Structure (BDS)

1
1.Identifier Variables for BDS Datasets
2
2. Record-Level Treatment and Dose Variables for BDS Datasets
3
Record-Level Dose Variables for BDS Datasets
4
Timing Variables for BDS Datasets
5
Period, Subperiod, and Phase Start and End Timing Variables
6
Suffixes for User-Defined Timing Variables in BDS Datasets
7
Analysis Parameter Variables for BDS Datasets
8
PARAM, AVAL, and AVALC
9
Analysis Parameter Criteria Variables for BDS Datasets
10
Analysis Descriptor Variables for BDS Datasets
11
Analysis Visit Windowing Variables for BDS Datasets
12
Time-to-Event Variables for BDS Datasets
13
Toxicity and Range Variables for BDS Datasets
14
Flag Variables for BDS Datasets
15
BDS Population Indicators
16
Datapoint Traceability Variables
17
SDTM and ADaM Population and Baseline Flags diference
18
Creation of Derived Columns versus Creation of Derived Rows
19
Rules for the Creation of Rows and Columns

Working with BDS Conversion specs

1
Working with ADVS Conversion with additional Variables
2
Working with ADLB Conversion with additional variables
3
Working with ADTTE Conversion with additional Variables

Business Logics

1
A parameter-invariant function of AVAL and BASE on the same row that does not involve a transform of BASE should be added as a new column.
2
Creation of a New Parameter to Handle a Transformation
3
Creation of a New Parameter to Handle a Second System of Units
4
Creation of a New Row to Handle a Derived Analysis Timepoint
5
Creation of New Rows to Handle a Derived Analysis Timepoint When There is Value-Level Population Flagging
6
Creation of New Rows to Handle Imputation of Missing Values by Last Observation Carried Forward and Worst Observation Carried Forward
7
Creation of New Rows to Handle Imputation of Missing Values by Baseline Observation Carried Forward and Last Observation Carried Forward
8
Creation of Endpoint Rows to Facilitate Analysis of a Crossover Design

ADaM Methodology and Examples When the Criterion Has Multiple Responses

Extra Topics

1
Paired lab variables
2
Lab visit window techniques
3
Raw/Standard Names/Units
4
ADaM – DTYPE
5
ADAM Metadata Excel file
6
ISO8601 Dates, Partial Dates, Durations and Periods
7
Study Validation Checklists
8
Baseline Values
9
Change, Percent Change from Baseline Imputation Methods

TLFS: Tables Listings and Figures

Listing

1
Demographics and Baseline Characteristics safety population
2
Ae listing of treatment emergent adverse event
3
Subject Disposition
4
Vital Signs Safety Population
5
Exposure to Study Medication Safety Population

Summary tables

1
Demographic: Subject Demographics and Baseline Characteristics Safety Population
2
Adverse event
3
Summary of Overall Treatment Emergent Adverse Event (TEAE) Information Safety Population
4
Summary of Treatment Emergent Adverse Events (TEAE) by System Organ Class and Preferred Term Safety Population
5
Concomitant medication: Concomitant Treatment Phase Medications Safety Population
6
Exposure: Exposure to Study Medication Safety Population
7
Laboratory: Laboratory shift table
8
Vitals: Summary of Change from Baseline in Vital Signs Safety Population
9
Disposition: Subject Disposition.

Figures

1
Summary of Treatment-Emergent Adverse Events by Maximum CTCAE Grade
2
Summary of vitals sign safety population
3
Coagulation values by (mean of +/- ) values by visit
4
Kaplan-meier plot of time to first abnormal laboratory test values
5
% change in tumor size from baseline
Faq Content 1
Faq Content 2

Productivity Hacks to Get More Done in 2018

— 28 February 2017

  1. Facebook News Feed Eradicator (free chrome extension) Stay focused by removing your Facebook newsfeed and replacing it with an inspirational quote. Disable the tool anytime you want to see what friends are up to!
  2. Hide My Inbox (free chrome extension for Gmail) Stay focused by hiding your inbox. Click "show your inbox" at a scheduled time and batch processs everything one go.
  3. Habitica (free mobile + web app) Gamify your to do list. Treat your life like a game and earn gold goins for getting stuff done!


4
4 out of 5
6 Ratings

Detailed Rating

Stars 5
3
Stars 4
0
Stars 3
3
Stars 2
0
Stars 1
0

{{ review.user }}

{{ review.time }}
 

Show more
Please, login to leave a review
Add to Wishlist
Get course
Enrolled: 187 students
Duration: 10 Weeks
Lectures: 129
Video: Available
Level: Advanced

Archive

Working hours

Monday 9:00 am to 8 pm ist
Tuesday 9:00 am to 8 pm ist
Wednesday 9:00 am to 8 pm ist
Thursday 9:00 am to 8 pm ist
Friday 9:00 am to 8 pm ist
Saturday 9:00 am to 8 pm ist
Sunday Closed