Reading posts

• New reading posted, note that we're using the stats-based textbook now

• The count of question and answer posts has reset for the second half of the semester

• 5 question posts (2 questions each), 5 answer posts (1 answer each)

• Objective: Evaluate the effectiveness of cognitive-behavior therapy for chronic fatigue syndrome.

• Participant pool: 142 patients who were recruited from referrals by primary care physicians and consultants to a hospital clinic specializing in chronic fatigue syndrome.

• Actual participants: Only 60 of the 142 referred patients entered the study. Some were excluded because they didn't meet the diagnostic criteria, some had other health issues, and some refused to be a part of the study.

Reference: Deale et. al. Cognitive behavior therapy for chronic fatigue syndrome: A randomized controlled trial. The American Journal of Psychiatry 154.3 (1997).

• Patients randomly assigned to treatment and control groups, 30 patients in each group:

  • Treatment: Cognitive behavior therapy – collaborative, educative, and with a behavioral emphasis. Patients were shown on how activity could be increased steadily and safely without exacerbating symptoms.

  • Control: Relaxation – No advice was given about how activity could be increased. Instead progressive muscle relaxation, visualization, and rapid relaxation skills were taught.

The table below shows the distribution of patients with good outcomes at 6-month follow-up. Note that 7 patients dropped out of the study: 3 from the treatment and 4 from the control group.

• Proportion with good outcomes in treatment group: \(19 / 27 \approx 0.70 \rightarrow 70\%\)

• Proportion with good outcomes in control group: \(5 / 26 \approx 0.19 \rightarrow 19\%\)

Do the data show a "real" difference between the groups?

• Suppose you flip a coin 100 times. While the chance a coin lands heads in any given coin flip is 50%, we probably won't observe exactly 50 heads. This type of fluctuation is part of almost any type of data generating process.

• The observed difference between the two groups (70 - 19 = 51%) may be real, or may be due to natural variation.

• Since the difference is quite large, it is more believable that the difference is real.

• We need statistical tools to determine if the difference is so large that we should reject the notion that it was due to chance.

Are the results of this study generalizable to all patients with chronic fatigue syndrome?

These patients had specific characteristics and volunteered to be a part of this study, therefore they may not be representative of all patients with chronic fatigue syndrome. While we cannot immediately generalize the results to all patients, this first study is encouraging. The method works for patients with some narrow set of characteristics, and that gives hope that it will work, at least to some degree, with other patients.

Research question: Can people become better, more efficient runners on their own, merely by running?

Source: http://well.blogs.nytimes.com/2012/08/29/finding-your-ideal-running-form

• Research question: Can people become better, more efficient runners on their own, merely by running?

• Question: What is the population of interest?
• Answer: All people

• Study Sample: Group of adult women who recently joined a running group

• Question: Population to which results can be generalized?
• Answer: Adult women, if the data are randomly sampled

• Anti-smoking research started in the 1930s and 1940s when cigarette smoking became increasingly popular. While some smokers seemed to be sensitive to cigarette smoke, others were completely unaffected.

• Anti-smoking research was faced with resistance based on anecdotal evidence such as "My uncle smokes three packs a day and he's in perfectly good health", evidence based on a limited sample size that might not be representative of the population.

• It was concluded that "smoking is a complex human behavior, by its nature difficult to study, confounded by human variability."

• In time researchers were able to examine larger samples of cases (smokers), and trends showing that smoking has negative health impacts became much clearer.

Source: Brandt, The Cigarette Century (2009), Basic Books.

• Wouldn't it be better to just include everyone and "sample" the entire population?

• This is called a census.

• There are problems with taking a census:

• It can be difficult to complete a census: there always seem to be some individuals who are hard to locate or hard to measure. And these difficult-to-find people may have certain characteristics that distinguish them from the rest of the population.
• Populations rarely stand still. Even if you could take a census, the population changes constantly, so it's never possible to get a perfect measure.
• Taking a census may be more complex than sampling.

Source: http://www.npr.org/templates/story/story.php?storyId=125380052

A historical example of a biased sample yielding misleading results:

In 1936, Landon sought the Republican presidential nomination opposing the re-election of FDR.

• The magazine had surveyed

• its own readers,
• registered automobile owners, and
• registered telephone users

• These groups had incomes well above the national average of the day (remember, this is Great Depression era) which resulted in lists of voters far more likely to support Republicans than a truly typical voter of the time, i.e. the sample was not representative of the American population at the time.

• The Literary Digest election poll was based on a sample size of 2.4 million, which is huge, but since the sample was biased, the sample did not yield an accurate prediction.

• Back to the soup analogy: If the soup is not well stirred, it doesn't matter how large a spoon you have, it will still not taste right. If the soup is well stirred, a small spoon will suffice to test the soup.

• To identify the explanatory variable in a pair of variables, identify which of the two is suspected of affecting the other:

• explanatory variable \(\xrightarrow{might~affect}\)response variable

• Labeling variables as explanatory and response does not guarantee the relationship between the two is actually causal, even if there is an association identified between the two variables. We use these labels only to keep track of which variable we suspect affects the other.

• Observational study: Researchers collect data in a way that does not directly interfere with how the data arise, i.e. they merely "observe", and can only establish an association between the explanatory and response variables.

• Experiment: Researchers randomly assign subjects to various treatments in order to establish causal connections between the explanatory and response variables.

• If you're going to walk away with one thing from the second half of this class, let it be "correlation does not imply causation".