## Clinical Trials Homework Help

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**The Domain**

- Historical remarks, some diseases and discoveries
- Steps(phases)in drug development
- Scope of clinical trials: new drugs, generics, devices, psychiatric therapy, alternative medicine.
- Role of statistics

**Planning a Clinical Trial – Statistican’s Inputs**

- Principles of design of experiments (replication, local control, randomization)
- Power and sample size
- Bias reduction (blinding)
- Commonly used designs

**Statistical Analysis Plan (SAP) of Clinical Trial**

- Trial objectives, hypotheses, choice of techniques, nature of endpoints
- End point Binary:
*A Randomizated Evaluation of First-Dollar Coverage for Post-MI Secondary Preventive Therapies (Post-MI FREEE)* - End point – Normal:
*accuracy study of SoftTouch (a non-invasive device for measurement of peripheral blood biomarkers)* - End point – count data:
*Pediatric Asthma Alert Intervention for Minority Children With Asthma (PAAL)* - End point – non-Normal:
*TBTC Study 27/28 PK: Moxifloxacin Pharmacokinetics During TB Treatment*

**Illustrative Statistical Analysis of Clinical Trial Data**

- One sample problem – reduction in blood pressure
- Two sample problem – anorexia
- K-sample problem – drowsiness due to antihistamines
- Cochrane’s Q test – allergic response
- Analysis of Time-concentration data in pharmacokinetic study

**Pharmacokinetics (PK) and Bioavailability**

- Basic concepts of PK
- PK analysis of time-concentration data (bioavailability assessment)
- Oral administration
- Estimation of C
_{max}, T_{max}, AUC, K_{e}, K_{a} - Intravenous administration

- Dose-response modeling
- Types of dose-response relationships
- Michaelis-Menton model for saturating relationship
- Power model: A model that includes three shapes

- Types of dose-response relationships

**Inference for Pharmocokinetic (PK) data**

- Normality testing of PK parameters (AUC, C
_{max}) - Transformations for achieving normality (AUC, C
_{max}) - Parametric (AUC, Cmax) and Non-parametric tests (T
_{max}) - Bootstrap confidence interval for t
_{1/2}

**Analysis of Dose-Response Data**

- Estimation of median effective dose
- Testing of dose proportionality in power model

**Bioequivalence Studies-Parallel Design**

- Statistical equality vs. clinical equivalence
- Testing bioequivalence (AUC)
- CI approach (AUC)
- Testing bioequivalence (C
_{max}) - CI approach (C
_{max})

**Bioequivalence Studies 2 x 2 (Crossover Design)**

- What is crossover design?
- Analysis of illustrative data using two sample tests
- Test for carry over effect
- Test for period effect
- Test for treatment difference

- Testing equivalence using CI
- Parallel vs. crossover design

**Treatment Comparisons**

- R fundamentals associated with clinical trials
- A simple simulated clinical trial
- Statistical models for treatment comparisons
- Incorporating covariates

**Survival Analysis**

- Time-to-event data structure
- Statistical models for survival data
- Right-censored data analysis
- Interval-censored data analysis

**Analysis of Data from Longitudinal Clinical Trials**

- Trial designs and data structure
- Statistical models and analysis

**Analysis of Bioequivalence Clinical Trials**

- Data from bioequivalence clinical trials
- Bioequivalence clinical trial endpoints
- Statistical methods to analyze bioequivalence

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