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. Also see Category: Covariate adjustment, Category: Randomization in
research. Other entries about observational
studies can be found in the
observational studies page at the
StATS website.
2009
- P.Mean: Is this a case-control design
(created 2009-04-28). I have a stats study design question. If I were to look at the association
of curly hair for instance with a rash on the forehead, I pick a case control
study design. When I analyze this I find that 45% of kids in the clinic
(surprise) had curly hair. But I look at two groups curly vs non curly and the
outcome of interest is the rash on the forehead, instead of cases vs controls
so now, has this become an observational study instead of case control? Hope I
am making sense, this is only a theoretical question.
2008
- P.Mean: Comparisons involving distinct
groups collected at different times and with different methods (created
2008-09-12). I have a data set of 100 children with a specific health
problem. In this set I have medical histories of the children. In another study,
I have collected a data set of 65 children without that specific health problem.
In this set I also have medical histories of the children. Is it possible to
compare the two samples in some way to determine whether there are significant
differences in the medical histories in the two sets of children?
Outside resources:
- Medical University of South Carolina. Bias Glossary.
Description: This website provides concise definitions of thirteen types of
biases that are likely to affect research findings. BROKEN LINK. Former URL
was www.musc.edu/dc/icrebm/bias.html
- Alastair H MacLennan. HRT: a reappraisal of the risks and benefits.
MJA 2007; 186 (12): 643-646
[Full text]
[PDF]. Description: Research goes in cycles.
Ten years ago, hormone replacement therapy (HRT) was recommended for most
women on the basis of observational studies that showed that it reduced the
risk of heart attacks. Two studies published near the turn of the century
indicated that this might not be the case. These were randomized studies and
were thought to be more definitive than the observational studies. There was a
difference, though, in the conduct of the randomized trials and the
observational studies, most notably the age at which HRT was initiated. A
recent analysis of the data seems to suggest that HRT is protective if it is
initiated early. I'm not an expert on HRT, but the lesson to be learned here
is that no trials are capable of producing perfectly accurate results and you
need to react to these trials carefully rather than with a checklist mentality
(randomized=good, observational=bad).
- GA Wells, B Shea, D O'Connell, J Peterson, V Welch,
M Losos, P Tugwell. The
Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised
studies in meta-analyses. Description: If you are conducting a systematic overview of nonrandomized
studies, you need an objective method for evaluating the quality of these
studies. The Newcastle-Ottawa scale provides a numeric score that you can use
for excluding low quality studies, giving greater weight to higher quality
studies, or for sensitivity analysis. This website was last verified on
August 7, 2007. URL:
www.ohri.ca/programs/clinical_epidemiology/oxford.htm
- Jacqueline A French. When Should We Pay Attention to Unfavorable News
from Pregnancy Registries? Epilepsy Curr. 2007
March; 7(2): 36–37. doi: 10.1111/j.1535-7511.2007.00161.x. [Medline]
[Abstract]
[Full text]
[PDF]. Description: Coming soon!
All of the material above this paragraph is licensed under a
Creative Commons Attribution 3.0 United States License. This page was written by
Steve Simon and was last modified on
2010-04-11. The material
below this paragraph links to my
old website, StATS. Although I wrote all of the material
listed below, my ex-employer, Children's Mercy Hospital, has claimed copyright
ownership of this material. The brief excerpts shown here are included under
the fair use provisions of U.S. Copyright laws.
2007
- 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".
2006
- 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.
2005
- 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.
What now?
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