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SAS Clinical Trials

1. UNIX Programming / Operating Systems Basis Review.
2. Statistical Analytical Tools Basics - Refresh course
3. Clinical Trial Terminology and definitions.
4. Clinical Trial Process:

(A) Data Analysis & Cleaning. (Conceptual Study)-10h

  • (i) Data Extraction
  • (ii) Transformation
  • (iii) Data Validation
  • (iv) Reporting (list and table reports)
  • (v) Documentation

(B) Clinical Trials Processing. (Conceptual Study)-10 h

(a) Pharmogenology: Introduction to New Drug Development Process.

  • (i) Pre Clinical Trials Analysis.
  • (ii) Drug Safety Evaluations.
  • (iii) Clinical Trial - Phase I, II & III Studies (Placebo, Efficacy, Adverse Effects, Drug Safety and Evaluation)
  • (iv) Clinical Trial - Phase-IV (Post Marketing Surveillance of Drugs -Therapy)

(C) Clinical Project Coordination:-10h

  • (i) Introduction - Clinical Project Coordination.
  • (ii) Organization chart preparation, Planning - Clinical Operating Plans
  • (iii) Clinical study schedule budgets and forecast.

(D) Protocols and SOP's- 10h

  • (i) Protocol and Research reports.
  • (ii) Clinical Investigator Profile.
  • (iii) Protocol and Case report form (CRF).
  • (iv) Design of Clinical Protocol.
  • (v) Design of Case Report Form.

(E) Clinical Data Management and Statistical Design in the Clinical Research process.-10h

  • (i) Organization of Support.
  • (ii) Planning of Clinical Trials.
  • (iii) Data Management.
  • (iv) Clinical Data system.
  • (v) Inferential Analysis of Clinical Trials.
    (Statistical Analysis of Data and Reporting)

Statistical Analysis. (Conceptual Study)- 150 hours

(a) Hypothesis Testing This covers the scope of testing the available data and determines the sample size. Various elements such as probability factor mean populations who qualify to receive drug are calculated based on null hypothesis and alternate hypothesis values. In this process we also determine the p-value (Actual Probability) with respect to Z - test statistic value.

(b) Perform sample tests Data collection and validation. Perform t-test distributions using PROC for hypothesis value.

(c) Creating Trial Dataset and performing statistical summarization using PROC UNIVARIATE, PROC MEANS and trying to understand the correctness and trueness of the data available for use for analysis and testing.

(d) One Sample t-test and two-sample t-test are performed for identifying the probability of determining the right kind of test samples. One way ANOVA (Analysis of Variance) - Compares two or more group means based on independent samples from each group.

(e) Two way ANOVA - Simultaneously analyzing two factors that affect a response and includes another source of variation - blocking factor. This is mostly used in clinical trial.

(f) PROC GLM (General linear model) and PROC Compare.

(g) Repeated Measures Analysis: Multiple measurements taken from the same experimental unit.

  • Perform f-test - comparison of response profiles.
  • PROC MIXED - can handle missing data more efficiently than GLM.
  • PROC GENMOD - uses generalized estimated equations (GEE) for analyzing repeated measures datasets that have missing values.

(h) Linear Regressions: PROC GLM (Used for linear regression analysis using single MODEL variable). PROC REG (Used for multiple linear regression analysis using multiple MODEL variables.

(i) Wilcoxon Signed - Rank test - using PROC UNIVARIATE.

(j) Analysis of covariance using ANCOVA.

(k) Wilcoxon Rank-sum test: PROC NPAR1WAY - used to sum of ranks for either group. Can be used to compute the test statistic value.

(l) Kruskal Wallis test: PROC rank & PROC GLM (uses output from proc rank)

(m) Chi-square test: proc freq Fisher's exact test: comparing two binomial proportions p1 & p2. Useful for small cell sizes or extreme proportions.


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