Program Schedule


This is the 2019 schedule for the 3-hour weekly lectures in the distance-learning Main Course component of the PPCR program.

 

Module 1 - BASICS OF CLINICAL RESEARCH

Tutorial Lecture, 28 March 2019 – Program Staff and PPCR Program Director – Felipe Fregni

• Opening remarks
• Syllabus and discussion of the program format
• Team introduction
• Sites introduction
• Using the Collaborative Learning Method: An overview
• Website tutorial
• Discussion

Lecture 1, 11 April 2019 – Albert Hofman

Introduction to Clinical Trials:

• Overview of clinical research

(2nd lecture) Steven Freedman & Camilia Martin

Introduction to Clinical Trials:

• Why perform a clinical trial?
• What is a clinical trial?
• Phase I studies; Phase II studies; Phase III/IV studies
• Introduction to ethics of clinical trials

Lecture 2, 18 April 2019 – Jonathan S. Williams

Selection of the Questions:

• Primary question
• Secondary question
• Adverse effects
• Ancillary questions
• Natural history
• Frequent errors

Lecture 3, 25 April 2019 – Michele Hacker

Study Population:

• Definition of study population
• Issues on generalization
• Trade-off: Internal validity vs. external generalizability
• Examples from landmark studies

Lecture 4, 2 May 2019 – David Wypij

Basic Study Design:

• Observational studies
• Randomized control studies
• Nonrandomized concurrent control studies
• Historical controls/databases
• Cross-over designs
• Factorial design
• Studies of equivalence
• Large clinical trials

Lecture 5, 9 May 2019 – Joseph Massaro

Study Blinding:

• Unblinded trials
• Single blind trials
• Double blind trials
• Triple blind trials
• Special problems in double blind studies – matching of drugs, coding of drugs, and assessment of blindness

Lecture 6, 16 May 2019 – David Wypij

The Randomization Process:

• Fixed allocation randomization
• Simple randomization
• Blocked randomization
• Stratified randomization
• Adaptive randomization procedures (baseline adaptive randomization procedures)
• Mechanisms of randomization

Lecture 7, 23 May 2019 – Roger Davis

Statistics – Basics:

• Data classification
• Data distribution
• Descriptive methods for categorical data
• Descriptive methods for continuous data

Lecture 8, 30 May 2019 – Felipe Fregni

Safety, Clinical, and Surrogate Outcomes:

• Reliability of measurements
• Validity of measurements
• Introduction to safety, clinical, and surrogate outcomes


Module 2 - BASIC STATISTICS

Lecture 9, 6 June 2019 – John Orav

Statistical Tests I:

• Estimation of parameters
• Comparison of population means (student t-test, ANOVA)

Lecture 10, 13 June 2019 – John Orav

Statistical Tests II:

• Chi-square and Fisher’s exact test
• Trend test for categorical data

Lecture 11, 20 June 2019 – Jessica Paulus

Sample Size Calculation:

• Dichotomous response variables
• Sample size for continuous response variables
• Sample size for equivalency of interventional studies
• Estimating sample size parameters
• Practical example: How to calculate sample size for a grant application

Lecture 12, 27 June 2019 – Felipe Fregni

Statistical Tests III:

• Parametric and Nonparametric tests for more than two group comparisons (Analysis of variance (ANOVA) and Kruskal-Wallis)
• Correlation (Pearson and Spearman correlation coefficient)

 

28 June – 8 August 2019 – GROUP PROJECT


Module 3 - APPLIED STATISTICS

Lecture 13, 15 August 2019 – Roger Davis

Survival Analysis:

• Estimation of the survival curve (Kaplan Meier estimate)
• Comparison of two survival curves
• Covariate adjusted analysis
• Use of survival analysis in clinical research

Lecture 14, 22 August 2019 – Felipe Fregni

Missing Data and Covariate Adjustment:

• Missing data
• Intention-to-treat analysis
• Covariate adjustment

Lecture 15, 29 August 2019 – Felipe Fregni

Meta-analysis and Subgroup Analysis:

• Subgroup analysis
• Comparison of multiple variables
• Meta-analysis of multiple studies

Lecture 16, 5 September 2019 – Armando Teixeira-Pinto

Introduction to Regression Modeling:

• Adjusted analysis and predictors
• Introduction to multivariate analysis (analysis with more than one independent variable)


Module 4 - PRACTICAL ASPECTS OF CLINICAL RESEARCH

Lecture 17, 12 September 2019 – Lotfi Merabet

Recruitment of Study Participants & Participant Adherence:

• Monitoring
• Reasons for participation
• Participant adherence
• Reducing dropout rates
• Considerations before participant enrollment
• Maintaining good participant adherence
• Adherence monitoring
• Special population of patients: The “skeptic”, the “pleaser”, “the information seeker”, “the hopeless”, “the money seeker”, “the professional research participant”, “the high maintenance participant”, “the noncompliant participant”

Lecture 18, 19 September 2019 - Laura Parrott

Leadership in Clinical Trials

• Leadership skills
• Leadership and Teamwork

(2nd lecture) Felipe Fregni

Clinical Research in the Context of Individualized Medicine (N-of-1 Designs)

• Basics and goals of N-of-1 designs: When to use
• Basics and goals of adaptive designs: When to use
• Introduction to individualized medicine
• Statistical issues
• Examples and discussion

Lecture 19, 26 September 2019 – Mark Barnes

Integrity in Research:

• Disputes about authorship - when authorship fails
• The right or otherwise to publish data, patents, and grant funding
• Scientific integrity and misconduct
• Publication practices
• Conflict of interest

Lecture 20, 3 October 2019 – Donald Halstead

Effective Communication in Clinical Research:

• Principles of good writing
• How to write a paper
• Oral presentations
• What is your brand?


Module 5 - STUDY DESIGNS

Lecture 21, 10 October 2019 – David Wypij

Non-Inferiority Designs:

• Superiority trials
• Non-inferiority designs
• Goals of non-inferiority designs
• Choosing the non-inferiority margin

Lecture 22, 17 October 2019 – Elizabeth Mostofsky

Phase III & Multicenter Trials:

• Challenges for multicenter trials
• Data monitoring
• Site selection
• Funding and regulatory issues

(2nd lecture) Felipe Fregni

Adaptive Designs & Interim Analysis:

• Clinical trials with medical devices
• Interim analysis
• Adaptive (flexible) design

Lecture 23, 24 October 2019 – Heather Baer

Observational Studies:

• Basic designs of observational studies
• Retrospective studies or cohort studies
• Sample size for observational studies
• Bias and confounding
• Control of bias
• Control of the phenomenon of confounding

Lecture 24, 31 October 2019 – Heather Baer

Confounders in Observational Studies: Using the Method of Propensity Score:

• The issue of confounders in observational studies
• Methods to control for confounders
• Method of propensity score


Lecture 25, 7 November 2019 – Felipe Fregni

Special Panel: RCT vs. Observational Designs – How to choose?

• RCTs – why should we choose this design?
• Observational studies – why should we choose this design?

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