# Lesson 11: Estimating Clinical Effects

### Introduction

The design of a clinical trial imposes structure on the resulting data. For example, in pharmacologic treatment mechanism (Phase I) studies, blood samples are used to display concentration × time curves, which relate to simple physiologic models of drug distribution and/or metabolism. As another example, in SE (Phase II) trials of cytotoxic drugs, investigators are interested in tumor response and toxicity of the drug or regimen. The usual study design permits estimating the unconditional probability of response or toxicity in patients who met the eligibility criteria.

For every trial, investigators must distinguish between those analyses, tests of hypotheses, or other summaries of the data that are specified a priori and justified by the design and those which are exploratory. Remember, the results from statistical analyses of endpoints that are specified a priori in the protocol carry more validity. Although exploratory analyses are important and might uncover biological relationships previously unsuspected, they may not be statistically reliable because it is not possible to account for the random nature of exploratory analyses. Exploratory analyses are not confirmatory by themselves but generate hypotheses for future research.

### Learning objectives & outcomes

Upon completion of this lesson, you should be able to do the following:

- State the objectives of a pharmacokinetic model.
- Use a SAS program to calculate a confidence interval for an odds ratio
- Use a SAS program to perform a Mantel-Haenszel.analysis to estimate an odds ratio adjusted for strata effects.
- Recognize when odds-ratios or relative risks differ significantly between groups.
- Modify a SAS program to perform JT and Cochran-Armitage tests for trend.
- Interpret a Kaplan-Meier survival curve.
- Interpret SAS output comparing survival curves.
- Describe the process of bootstrapping to estimate variability of an estimator.

#### Reference

Piantadosi Steven. (2005) Counting Subjects and Events. Estimating Clinical Effects. In: Piantadosi Steven. *Clinical Trials: A Methodologic Perspective*. 2nd ed. Hobaken, NJ: John Wiley and Sons, Inc.

Friedman, Furberg, DeMets, Reboussin and Granger. (2015). Survival Analysis. In FFDRG. Fundamentals of Clinical Trials. 5th ed. NY: Springer.