Category: Observational studies. Observational studies are studies where the experimenter does not choose who gets into the control group and the treatment/exposure group. Rather the patients and/or their physicians make this choice, or the groups were intact prior to the start of the research. Observational studies raise some important methodological challenges, but when they are used carefully, they provide valuable insights that are not possible with other research designs. Articles are arranged by date with the most recent entries at the top. You can find the theme and closely related categories and other resources at the bottom of this page.

Stats: I don't want to use a randomized trial (July 18, 2007). An email on the MedStats group outlines a new treatment that is: 1. without any significant competing treatments, 2. utilized in a heterogenous patient population, and 3. difficult to study in a randomized trial. There are a variety of alternatives to a randomized study, but I suspect that this person wants to use a historical control study. It sounds like he wants an informal endorsement from a group of professional statisticians to use a historical control study instead of a randomized study.

Stats: How two bad control groups can add up to one good comparison (June 28, 2007). Many observational studies are criticized (often deservedly) for having a bad control group. If you choose a bad control group, you create an unfair (apples to oranges) comparison. But surprisingly, two controls groups, even if both are imperfect, can lead to a strong conclusion. The trick is to recognize that if one control group has a positive bias (it makes the treatment group look better than it should) and the other one has a negative bias (it makes the treatment group look worse than it should), then these two control groups bracket the ideal control group.

Stats: The debate about historical control groups (June 27, 2007). Someone on the Evidence Based Medicine email discussion group asked about how to appraise a "before and after" design. This is effectively the same as using a historical control group.  Historical control groups have a bad reputation.

Stats: The trouble with apples and oranges (June 25, 2007). I am still working on the details of a presentation for the Kansas City University of Medicine and Biosciences. They want me to talk at lunch during the 2007 Homecoming CME and Reunion weekend. The new title is "Medical Journals - The Trouble with Apples and Oranges."

Stats: When bad control groups happen to good researchers (June 15, 2007). The Kansas City University of Medicine and Biosciences wants me to give a light humorous talk at lunch during the 2007 Homecoming CME and Reunion weekend. Somehow, they provided me with a title for my talk, "Humor, Databases and Grant Proposals: What Strange Bedfellows" which is a fine title, but not the one I would have chosen. I'll talk it over with the organizers, but here's a possible choice: "When bad control groups happen to good researchers".

Stats: Abstainer errors in study of alcohol abuse (April 19, 2006). A correspondent in the MedStats email discussion group (RR), mentioned an interesting example of problems in defining groups in observational studies. The actual publication is Kaye Fillmore et al. "Moderate alcohol use and reduced mortality risk: systematic error in prospective studies." Addiction Research and Theory. Advanced online publication March 30, 2006.

Stats: Case cohort design (August 11, 2005). During a consultation about an NIH research grant, the term "case cohort design" came up. The Case Cohort design is similar to a nest Case Control design, but also has some important differences.

Stats: The paired availability design (May 31, 2005). In the quest to finish my book on Statistical Evidence, I had to leave some material on the cutting room floor. One of the nicer descriptions was about the paired availability design.

Stats: Non-random samples (March 25, 2005). Someone sent me an email asking about a project that involved interviews of women at higher levels of management in an organization. This is a rather small group, and might require a non-random selection process. What are the limitations of a non-random sample?

Stats: A collection of randomized and non-randomized studies (March 22, 2005). I'm updating some of my training classes to use examples from open source journals, because it is easier for me to include content of these articles directly in the web pages. An example of this is practice exercises for my training class Statistical Evidence: Apples or Oranges? But the previous practice exercise, which used a wider range of journals had some cute articles in the mix. I'll especially miss the article on episiotomy.

Stats: Spectrum Bias (January 4, 2005). I tried to start a page on diagnostic tests a while back, but have not had the time to fully develop it. One of the important issues for diagnostic tests is spectrum bias. The sensitivity and specificity of a diagnostic test can depend on who exactly is being tested. Think of disease as a range of possibilities from slight to moderate to extreme. If only a portion of the disease range is included, you may get an incorrect impression of how well a diagnostic test works. This is known as spectrum bias.

Theme and closely related categories:

Other resources:

[Return to full topic list] [Read current weblog entries]

This webpage was written by Steve Simon on 2007-06-26, edited by Steve Simon, and was last modified on 2008-07-14. Send feedback to ssimon at cmh dot edu or click on the email link at the top of the page.