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The order may be slightly different but the core content of the recordings remain the same.

Session 1
Introduction to R
Introduction to R tidyverse
Some basic differences between SAS and R
Quick introduction to the tasks handled by a clinical trials programmer

Session 2
Some rules regarding statements
Some rules regarding dataset and variable naming conventions
Vector vs data frame (dataset)
Introduction to programming interface in R studio in comparison to SAS
Start of programming concept equivalents of SAS in R
Creating sample data
Create a copy of an existing  dataset
Subset observations
Note that in these introductory sessions I am introducing bare minimum conventions required to get started with R programming

Session 3
Start of programming concept equivalents of SAS in R
Subset variables
Subset observations and variables
Renaming variables
Note that in these introductory sessions I am introducing bare minimum conventions required to get started with R programming

Session 4
Start of programming concept equivalents of SAS in R
Adding new variables
Note that in these introductory sessions I am introducing bare minimum conventions required to get started with R programming

Session 5
Programming concept equivalents of SAS in R
Appending datasets
Reorder observations
Merging datasets

Session 6
Programming concept equivalents of SAS in R
Merging data
Fetch frequencies
Descriptive statistics for numeric variables
Summarize – count distinct
Summarize – all statistics that are typically needed in TFL programming
First dot and last dot concept replication
Note that in these introductory sessions I am introducing bare minimum conventions required to get started with R programming

Session 7
Programming concept equivalents of SAS in R
Descriptive statistics for numeric variables
Summarize – count distinct
Summarize – all statistics that are typically needed in TFL programming
Note that in these introductory sessions I am introducing bare minimum conventions required to get started with R programming

Session 8
Programming concept equivalents of SAS in R
First dot last dot concept
Handling duplicates
Note that in these introductory sessions I am introducing bare minimum conventions required to get started with R programming

Session 9
Programming concept equivalents of SAS in R
Restructuring the data
Long to wide
Wide to long
Arrays equivalent
Executing the source code from another file in current program
Character functions
Note that in these introductory sessions I am introducing bare minimum conventions required to get started with R programming

Session 10
Programming concept equivalents of SAS in R
Character functions demo
Functions to convert the data type
A few numeric functions
Introduction to the concept of column-wise vs row-wise processing
Importing data from external files
Replicating the concept of library
Note that in these introductory sessions I am introducing bare minimum conventions required to get started with R programming

Session 11
Introduction to the concept of vectors
Replicating the concept of user defined formats using named vectors
Note that in these introductory sessions I am introducing bare minimum conventions required to get started with R programming

Session 12
Basics of converting collected data values to standard notation - TASKS_SDTMGEN_L040
Quick introduction to 'comparedf' function from arsenal package
Note that any programming concepts that are needed in individual lessons but not covered as part of general SAS to R equivalent sessions will be discussed in detail during these sessions on need basis

Session 13
Conversion of raw date values to ISO8601 format - TASKS_SDTMGEN_L050
Creation of planned arm related variables in SDTM.DM dataset - TASKS_SDTMGEN_L071
Note that any programming concepts that are needed in individual lessons but not covered as part of general SAS to R equivalent sessions will be discussed in detail during these sessions on need basis

Session 14
Creation of actual arm related variables in SDTM.DM dataset - TASKS_SDTMGEN_L072
Creation of exposure start date variables in SDTM.DM dataset - TASKS_SDTMGEN_L073
Creation of RFICDTC, RFSTDTC, RFENDTC in SDTM.DM dataset - TASKS_SDTMGEN_L074
Each of these concepts plays an important role in strengthening our R programming foundation.
While they may seem simple at first glance—especially for those with SAS experience—they offer valuable opportunities to build fluency and confidence in R.

Session 15
Creation of DTHFL and DTHDTC in SDTM.DM dataset - TASKS_SDTMGEN_L075
Creation of study day variable in SDTM datasets - TASKS_SDTMGEN_L101
Creation of RFPENDTC in SDTM.DM dataset - TASKS_SDTMGEN_L070a

Session 16
Creating tests as rows in a findings domain - TASKS_SDTMGEN_L108
Mapping collected visits to standardized visit values - TASKS_SDTMGEN_L104
Remapping unscheduled visits in a findings domain using SV dataset - TASKS_SDTMGEN_L109

Session 17
Convert results in original units to standard units - TASKS_SDTMGEN_L150
Derivation of baseline flag in SDTM - TASKS_BL_L104
Creation of SEQ variable - TASKS_SDTMGEN_L112

Session 18
Create a supplementary domain - TASKS_SDTMGEN_L111
Merge supplementary dataset to a parent domain - TASKS_SDTMGEN_L201
Create an xpt file - TASKS_SDTMGEN_L221

Session 19
Creation of EPOCH variable in SDTM domains - TASKS_SDTMGEN_L110
Handling multiple races in SDTM.DM domain - TASKS_SDTMGEN_L080

Session 20
Create a SDTM findings domain – CV - SDTM_CV_L101

Session 21
Create a SDTM events domain – DS - SDTM_DS_LCSG001

Session 22
Create a SDTM domain – DM - SDTM_DM_LCSG001

Session 23
Create a SDTM domain – SE - SDTM_SE_LCSG001

Session 24
Concatenate values in  a variable across observations
Retaining non-missing value from a previous observation onto current record
Create a SDTM domain - SV

Session 25
ADaM - Subject Level Analysis Dataset
Key Date Variables - ADaM_C1001_L101
Subject Level Population Flags - ADaM_C1001_L102

Session 26
ADaM - Subject Level Analysis Dataset
Treatment related variables - Cross-over study - ADaM_C1001_L103a
Creation of baseline variables - ADaM_C1001_L104

Session 27
ADaM - Subject Level Analysis Dataset
ADSL dataset for a parallel arm study - MYCSG-001 (ADaM_ADSL_L1101)

Session 28
ADaM - Dates and Date imputations
Conversion of character dates in ISO 8601 format to numeric date, time, datetime values -   ADaM_C1002_L101
Imputation of missing date components – start date and end dates - ADaM_C1002_L102a

Session 29
ADaM programming
Concept of treatment emergence in adverse events - ADaM_C1003_L102a
Derived parameters creation in ADaM BDS datasets - ADaM_C1004_L103

Session 30
ADaM programming
BDS dataset – function of more than one row within a same parameter
Creating rows within same parameter – Rule 3 - ADaM_C1005_L101
Creating a new parameter – Rule 4  - ADaM_C1004_L111

Session 31
Replicate the concept of retaining a value – fill function
ADaM programming
BDS dataset – data imputation
LOCF - ADaM_C1010_L101
WOCF - ADaM_C1010_L121
BOCF - ADaM_C1010_L131
Interpolation - ADaM_C1010_L141

Session 32
ADaM programming
BDS dataset
Visit windowing - ADaM_C1007_L101
Analysis Flags creation – example 1 - ADaM_C1009_L101
Analysis Flags creation – example 2 - ADaM_C1009_L103

Session 33
ADaM programming
BDS dataset
ADIS - ADaM_ADIS_LVAC01

Session 34
ADaM programming
BDS dataset
ADEXSUM (Exposure Summary) - ADaM_ADEXSUM_L1101

Session 35
Table programming
Descriptive statistics for a single numeric variable (TFL_TABGEN_L201)

Session 36
Table programming
Descriptive statistics for two numeric variables (TFL_TABGEN_L202)

Session 37
Table programming
Summary for categorical variables – one variable (TFL_TABGEN_L301)
Summary for categorical variables – two variables (TFL_TABGEN_L302)

Session 38
Table programming
Demographic characteristics (TFL_TABGEN_L401)
Overview of Treatment-Emergent Adverse Events (TFL_TAE_L100)

Session 39
Table programming
TEAE by PT (TFL_TAE_L105)
TEAE by SOC and PT (TFL_TAE_L101)

Session 40
Table programming
Vital signs summary by visit (TFL_TVS_L101)
Potentially clinically significant vital signs (TFL_TVS_L102)

Session 41
Creation of user-defined functions
Basics
Function for study day creation - MACROS_SDTM_L261

Session 42
Creation of user-defined functions
Function for sequence variable creation - MACROS_SDTM_L271
Function for baseline flag and related variables - MACROS_ADaM_L130
Function for iso dates creation - MACROS_SDTM_L251

Session 43
Creation of user-defined functions
Function for demographics table layout (TFL_TABGEN_L401)
Function for creating treatment totals
Types
safety_with_total
safety_without_total
safety_with_alldoselevels

Session 44
Quick introduction to factor datatype
Introduction to graphs
Creation of a bar chart - TFL_FGN_LBAR101

Session 45
Creation of a scatter plot (TFL_FGN_LSCATTER101)
Creation of a line plot (TFL_FGN_LLINE101)

Session 46
Creation of a spider plot (TFL_FGN_LSPIDER101)
Creation of a forest plot (TFL_FGN_LFOREST101)

Session 47
Adding table of numbers to bar chart ( modified TFL_FGN_LBAR101)
Line plot – PK concentration summary over time (TFL_FGN_LLINE111a)
Box plot – manually computed statistics ( modified TFL_FGN_LBOXPLOT101)

Session 48
Introduction to inferential analysis
P-value for t-test (TFL_TIS_LTTEST101)
P-value for ANOVA (TFL_TIS_L121)

Session 49
ANCOVA (TFL_TIS_L151)
Introduction to a few concepts
List
Map functions
Nest function
Unnest function
Fisher test (TFL_TIS_L101)

Session 50
Generating rtf reports using r2rtf package
Demographics summary (TFL_TABGEN_L401)
All customizations that we do in reporting using SAS may not be possible with R
So, there is a trade-off one has to make with rtf reporting/layouts
There are other packages like huxtable, gt for report customization which can be explored too

Session 51
Generating rtf reports using r2rtf package
Adding capital N counts (TFL_TAE_L101)
Using subline_by (TFL_TVS_L101)
Figure(TFL_FGN_LSCATTER101)

Session 52
Inferential statistics
Chi-square test (TFL_TIS_L111)
Binomial test (TFL_TIS_L131)
Some other inferential stats… reference codes available on website
Closing remarks

The order may be slightly different but the core content of the recordings remain the same.

Session 01
    - Course introduction

Session 02
    - Quick recap of session 01
    - What does a clinical trials programmer typically do
    - Clinical trial terminology

Session 03
    - Recap of clinical trial terminology
    - Trial design aspects

Session 04
    - Quick recap of trial design aspects
    - Introduction to SDTM
    - Domains classification in SDTM
    - Easily remember the flow and terminology

Session 05
    - Quick recap
    - Key variables seen in each of the domain classes
    - Variables in Interventions domains
    - Variables in Events domains
    - Variables in Findings domains
    - Variables classification - two types
    - Core designation
    - Role of the variable

Session 06
    - Quick recap
    - Key variables seen in each of the domain classes
    - Variables in Findings domains
    - Variables classification - two types
    - Core designation
    - Role of the variable
    - Value-level standardization
    - Advantages of having a standard

Session 07
    - Full list of variables allowed as per standard for 3 general observation classes
    - Introduction to SDTM model and Implementation Guide documents
    - Examples of some common identifier variables

Session 08
    - Quick recap
    - Variables in demographics domain
    - Variables in Subject Elements domain
    - Variables in Subject Visits domain
    - Common identifier variables

Session 09
    - Programming concepts
    - Conversion of raw values to standard notation - TASKS_SDTMGEN_L040

Session 10
    - Programming concepts
    - Conversion of raw date values to standard notation - TASKS_SDTMGEN_L050
    - Study day creation - TASKS_SDTMGEN_L101

Session 11
    - An example protocol to refresh trial design concepts
    - Programming concepts
    - Conversion of raw values to standard notation
    - Conversion of raw date values to standard notation
    - Study day creation

Session 12
    - Programming concepts
    - Derivation of planned arm variables in DM dataset

Session 13
    - Programming concepts
    - Derivation of planned arm variables in DM dataset
    - Derivation of actual arm variables in DM dataset

Session 14
    - Programming concepts
    - Derivation of RFXSTDTC, RFXENDTC in DM dataset
    - Derivation of RFICDTC, RFSTDTC, RFENDTC in DM dataset
    - Derivation of DTHFL, DTHDTC in DM dataset

Session 15
    - Programming concepts
    - Derivation of RFICDTC, RFSTDTC, RFENDTC in DM dataset
    - Derivation of DTHFL, DTHDTC in DM dataset
    - SDTM DM dataset - programming for multiple races

Session 16
    - Programming concepts
    - Derivation of RFPENDTC
    - Manual approach
    - Dynamic approach

Session 17
    - Programming concepts
    - Quick recap on variables related to findings domains
    - Creating tests as rows
    - Converting results to standard units
    - Creating Epoch variable
    - Creating Sequence variable

Session 18
    - Programming concepts
    - Converting results to standard units
    - Creating Epoch variable
    - Creating Sequence variable

Session 19
    - Programming concepts
    - Creating Sequence variable
    - Creation of baseline flag

Session 20
    - Programming concepts
    - Remapping unscheduled visits
    - Epoch handling for partial dates

Session 21
    - Integrating Programming concepts of SDTM
    - Adverse Events

Session 22
    - Integrating Programming concepts of SDTM
    - Adverse Events - continue from previous session
    - Findings domain

Session 23
    - Create Supplementary dataset
    - Converting sas datasets to xpt format
    - Generate P21 report for AE dataset

Session 24
    - Generate P21 report for AE dataset

Session 25
    - SDTM DM dataset programming

Session 26
    - SDTM SE dataset programming

Session 27
    - Creation of Trial Design Domains

Session 28
    - SDTM DS dataset programming

Session 29
    - ADaM introduction

Session 30
    - Two ADaM lessons
    - Derivation of key date variables in ADSL - ADaM_C1001_L101
    - Derivation of new parameter - ADaM_C1004_L103

Session 31
    - Derivation of new parameter - ADaM_C1004_L103

Session 32
    - Derivation of new rows within a parameter - ADaM_C1005_L101b

Session 33
    - BDS dataset baseline related concepts - ADaM_C1006_L101a

Session 34
    - BDS dataset data imputation - LOCF records - ADaM_C1010_L102

Session 35
    - Date imputations - ADaM_C1002_L103
    - Visit windowing - ADaM_C1007_L101

Session 36
    - Visit windowing - ADaM_C1007_L101
    - Creation of an analysis flag - ADaM_C1009_L101

Session 37
    - Creation of analysis flags - ADaM_C1009_L102, ADaM_C1009_L105
    - Shift from baseline - ADaM_C1012_L101

Session 38
    - Population flags in ADSL - ADaM_C1001_L102

Session 39
    - ADaM_C1001_L103a - Treatment related variables
    - ADaM_C1001_L103b - Treatment grouping variables
    - ADaM_C1001_L104 - Baseline variables
    - ADaM_C1001_L105 - Grouping variables

Session 40
    - ADaM_C1003_L102a - Treatment emergence
    - ADaM_C1011_L101c - Variables for criteria evaluation

Session 41
    - Full ADSL programming - ADaM_ADSL_L1101

Session 42
    - Full BDS programming - ADaM_ADEG_L1101

Session 43
    - Introduction to Tables programming
    - TFL_TABGEN_L201

Session 44
    - Introduction to Tables programming - continued
    - TFL_TABGEN_L201

Session 45
    - TFL_TABGEN_L201

Session 46
    - Proc Report Basics

Session 47
    - TFL_TABGEN_L202
    - TFL_TABGEN_L301

Session 48
    - TFL_TABGEN_L302
    - TFL_TABGEN_L401
    - Outputs sharing same layout
    - TFL_TDM_L100
    - TFL_TEX_L101

Session 49
    - TFL_TVS_L101
    - TFL_TDS_L101

Session 50
    - Adverse Event Tables
    - TFL_TAE_L100 - AE Overview table
    - TFL_TAE_L105 - AE by preferred term table
    - TFL_TAE_L101 - AE by system organ class and preferred term table

Session 51
    - Shift Tables
    - TFL_TEG_L106 - Basic example

Session 52
    - Processing of data in Tables programming

Session 53
    - Quick overview of common listings

Session 54
    - Introduction to Inferential analysis in clinical trials
    - One sample t-test
    - Fisher's exact test

Session 55
    - Introduction to Inferential analysis in clinical trials
    - Exact binomial test - TFL_TIS_L131
    - ANOVA - Analysis of Variance - TFL_TIS_L121
    - ANCOVA - Analysis of Covariance - TFL_TIS_L151

Session 56
    - Inferential analysis in clinical trials
    - ANCOVA - Analysis of Covariance - TFL_TIS_L151
    - MMRM - Repeated measures analysis - TFL_TIS_LREPMS101

Session 57
    - Time to event in clinical trials
    - ADaM concepts - ADaM_C1013_L101
    - Table related to TTE - TFL_TTTE_L101

Session 58
    - Line plot - TFL_FGN_LLINE101
    - Scatter plot - TFL_FGN_LSCATTER101

Session 59
    - Scatter plot - TFL_FGN_LSCATTER101
    - Bar chart - TFL_FGN_LBAR101

Session 60
    - Box plot - TFL_FGN_LBOXPLOT101
    - Spider plot - TFL_FGN_LSPIDER101