1.1 Key Areas to demonstrate why I’m a good fit for this role and what I understand a successful candidate should possess.

1.2 Phase 1: Regulatory & Clinical Trial Fundamentals (Weeks 1–3)

Key Learning Areas:

• Clinical Trial Phases (I-IV) – Understand statistical needs at each stage.

  • FDA Statistical Principles for Clinical Trials – Covers statistical methodology for FDA submissions.

  • FDA Clinical Trials Guidance Documents – Comprehensive guidelines on trial design and regulatory compliance.

  • ICH E6(R2) Good Clinical Practice – International standards for clinical trials.

• Study Designs - Randomization, stratification, adaptive trials.

  • ICH Guidance Documents – Covers statistical programming and trial design.

• Endpoints & Statistical Analysis - Primary vs. secondary endpoints, survival analysis.

• FDA Submission Process - Structure of clinical trial data for review.

  • Good Clinical Practice (GCP) – Ethical and scientific quality standards for clinical trials.

    • FDA E6(R3) Good Clinical Practice – Latest updates on GCP compliance.

  • International Council for Harmonisation (ICH) Guidelines – Covers statistical principles (E9) and trial design (E6).

  • FDA Statistical Guidance – Ensures compliance with regulatory expectations.

  • ISO Standards – Relevant for medical device trials.

Action Items:

• Summarize key FDA/ICH/CDISC regulations for compliance.

• Review case studies on statistical analysis in real clinical trials.

1.3 Phase 2: Mastering SAS for Clinical Trial Programming (Weeks 4–6)

Key Learning Areas:

• CDISC Data Standards (SDTM & ADaM) - FDA requires submission in this format.

• Clinical Trial Data Manipulation - Creating datasets that meet FDA requirements.

• Statistical Analysis & Reporting - TLFs (Tables, Listings, Figures)

• Validation & QC Processes - Ensuring accuracy before submission.

Essential SAS Packages & Techniques:

• SAS/STAT - Advanced statistical modeling.

• PROC MIXED, PROC PHREG - Survival analysis & longitudinal data.

• PROC GLM, PROC GENMOD - Regression models.

• PROC REPORT - Creating FDA submission-ready reports.

• PROC SQL - Data manipulation & merging clinical datasets.

Already possessed SAS Clinicial Trial Programmer credentials: Obtained in Nov 2011, Plus, have 8+ years of experience programming in SAS.

Action Items:

• Practice creating SDTM and ADaM datasets using SAS macros.

• Develop FDA-compliant TLFs (Tables, Listings, Figures).

1.4 Phase 3: Leveraging R for Clinical Data Analysis (Weeks 7–9)

Key Learning Areas:

• Survival Analysis (Kaplan-Meier, Cox Models) – Key for clinical trials.

• Longitudinal & Repeated Measures Models - Handling patient follow-ups.

• Data Visualization for FDA Reports – Clear presentation of results.

Essential R Packages:

tidyverse – Data manipulation & visualization.

survival – Kaplan-Meier & Cox proportional hazard models.

lme4 – Linear mixed models for longitudinal data.

ggplot2 – High-quality graphics for clinical data.

Hmisc – Descriptive statistics & reporting.

Have 9+ years of extensive R programming experience ‼️

Action Items:

• Build survival models using R’s survival package.

• Use ggplot2 to visualize patient outcomes.

1.5 Phase 4: Advanced Techniques & Submission Preparation (Weeks 10–12)

Key Learning Areas:

• Automation in Statistical Programming – Using SAS Macros for efficiency.
• Handling Large Clinical Trial Datasets – Parallel computing techniques.
  • Clinical Data Interchange Standards (CDISC)
• FDA Submission Process & Best Practices – Formatting for regulatory review.
  • FDA Submission Formatting Guidelines

Action Items:

• Review real FDA submissions and case studies.

• Build an end-to-end clinical trial analysis project.

1.6 Appendix