Download Is a “Discussion” on “Are Oservational Studies Any Good

Is a “Discussion”
on
“Are Observational Studies Any Good”
Any Good
Don Hoover
May 2, 2014
1
Everyone Already Knows Observational
Studies Are Not Perfect … Right?
• But who thinks
– the real type 1 error is 0.55 when the nominal is 0.05?
– The real coverage of a 95% confidence interval is 25%?
– That’s what David Madigan and the OMAP team find
• This obviously makes such results meaningless
• But how many papers with these properties are
being (and will continue to be) published ???
2
But Does David’s Talk Really Apply to
ALL Observational Studies?
• They Only Look at Observational Studies of Drug
Use and Adverse Consequences
• There’s other kinds of Observational Studies … on
HIV, Epi, Health Behaviors, Nutrition, etc.
– No one has looked at these types of studies
• These other studies must have similar problems
• Maybe at a smaller magnitude
– But there are no “negative controls” for these settings
… so no one can check this
3
The Approach here is Creative and
Innovative
• Finding Negative Control Exposures or Outcomes to derive
empirical distribution of the test statistic somewhat equalizes
assumptions and unmeasured confounding
• With a given Drug Use as the exposure and a given Disease
the outcome, such negative controls are readily available in
many data sets
• So maybe something like it should be used when possible
• But now some questions ……
4
Q1- Why were Negative Control Drugs More
Associated With Outcomes than by Chance?
• People put on Any Drug are Sicker?
• Those receiving a negative (control) drug are
more likely to receive some other positive drug?
• Those apriori more likely to have a given disease
outcome are steered to the negative drugs?
• Incorrect statistical models used?
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Q2- Is this Approach Practical?
• A lot more work to fit many models than the
standard approach which only fits one
– More money as well - A grant application using it
would be less likely to get funded
– More work also means more chance for error in
implementation
6
Q3 – How does one interpret a positive
drug with empirical P < 0.05?
Calibrated Normal Scores of
Negative Controls
Positive Drug with empirical P < 0.05
The use of an “empirical” approach acknowledges we do not know what
is going on so maybe the P < 0.05 is from model artifact not causal
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Q4 – What is done with “Negative Drugs”
more extreme than the Positive One
Calibrated Normal Scores of
Negative Controls
Positive Drug with P < 0.05
Should these Negative
Controls all be
Examined for Causal
Association as their
Signal is larger than the
positive drug?
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Q5 - How to handle Heterogeneity in
Denominator of Calibration Statistic
From Schumie … Madigan Stat Med 2014 33; 209-18


Variance Log ( RR) may introduce Apples to Oranges comparisons especially if
τ 2n1<σˆ 2 , and other τ 2's although such does not appear to be the case in the examples
David used
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