Observational Studies

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Summary: up-to-date account of methods for observational studies
Review: Many years ago the famous statistician Ronald Aylmer Fisher criticized analyses that linked lung cancer to smoking. He argued that these studies had hidden biases because they were not controlled experiments. He proposed that smokers could be different from non-smokers because of a genetic propensity to desire cigarettes and that this genetic trait could be correlated with a higher incidence of lung cancer. Thus it would be possible to see a higher frequency of lung cancer among smokers because of this genetic trait rather than because the smoking itself causes the cancer. As far-fetched as this argument may seem to us today, it is based on sound statistical principles and points out some of the potential problems that occur with observational studies.

Although randomized control trials are the best way to determine differences in treatment effects, they are not always practical or ethical. It would be wrong to randomly choose subjects and force some of them to smoke.

Getting around issues of bias in observational studies was first addressed by Cochran who published a book on the subject in 1983. Rosenbaum came out with his own book in 1995 and this second edition expands and updates that popular text.

In Chapter 1 he present examples of observational studies and raises many important issues. Chapter 2 explains the principles of randomized controlled experiments. In Chapter 3 he covers overt bias and some of the basic methods to adjust for it. The sensitivity of observational studies to hidden biases is covered in Chapter 4.

This book is well written, authoritative and contains numerous references and examples. It also includes a chapter on how to plan an observational study.

Such studies are very important to epidemiologists who want to discover the cause of an epidemic or a disease. With large data base it is possible to remove or adjust biases by matching subjects using propensity scores. Rosenbaum effectively describes how propensity scorng and stratification can be used to make observational studies behave more like randomized control trials.