### Department of Biostatistics Seminar/Workshop Series

# Targeted Maximum Likelihood Learning Applied to Clinical Trials and Discovery

## Mark van der Laan, Ph.D.

### Professor of Biostatistics and Statistics,

UC Berkeley

### Wednesday, February 20, 1:30-2:30pm,

MRBIII Conference Room 1220

### Intended Audience: Persons interested in applied statistics, statistical theory, epidemiology, health services research, clinical trials methodology, statistical computing, statistical graphics, R users or potential users

An important point of this presentation is that choice of a statistical model and method must be based on careful consideration of the scientific question of interest in order to provide robust tests of the null hypothesis and to minimize bias in the parameter estimate. For this purpose we developed a generally applicable targeted maximum likelihood estimation methodology.

As an example, I will distinguish between scientific questions concerned with prediction of an outcome based on a set of input variables versus scientific questions in which the goal is to estimate the variable importance or causal effect of one particular variable/treatment.

I will discuss the limitations of fitting regression models for the purpose of learning about a causal effect or variable importance, and present the alternative targeted maximum likelihood approach. Both observational studies and randomized trials will be used to illustrate the advantages of the targeted approach. I will present results from data analyses in which the targeted approach is used to 1) analyze the importance of each of a set of HIV mutations for protease inhibitor resistance, and 2) estimate the causal effect of an intervention based on data generated in a randomized clinical trial.

The differences between prediction and causal effect estimation are further highlighted by the additional assumptions needed for the estimation of the causal effect of an intervention in an observational study. Beyond the familiar ''no unmeasured confounding'' assumption, causal effect estimation also requires an experimental treatment assignment assumption, violation (as easily occurs in observational studies) of which can cause severe bias and increased variance in a causal effect estimate.

To address this problem,I will show that estimation of the causal effect of a "realistic" intervention (similar to the parameter one estimates in an intention-to-treat analysis) provides an important generalization which can always be fully identified from the data. Targeted maximum likelihood estimators of this realistic parameter are also available.

Finally, I will discuss the advantages of applying targeted estimation in the context of a randomized trial. Like standard approaches, the targeted approach relies only on the randomization assumption. However, the targeted approach yields an improved estimate of the causal effect of a treatment in a randomized trial relative to the commonly used marginal estimate of the treatment effect.

Presenter Information