Observational Studies in a Learning Health System Workshop Summary (2013) / Chapter Skim
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3 Engaging the Issue of Bias
3 Engaging the Issue of Bias
Pages 17-30
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From page 17... ...
• Weak instrumental variables are useful for reliably detecting only large effects because of their sensitivity to even small biases.
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From page 18... ...
Selection bias, for example, arises when a study population is not randomly selected from the target population, and measurement or information-related bias can result when data are missing from or misclassified in an electronic health record. To start the discussion on how to manage and control for bias in observational studies, Sebastian Schneeweiss, professor of medicine and epidemiology at Brigham and Women's Hospital and Harvard Medical School, provided an introduction to the issue of bias, laying the groundwork for presentations by Dylan Small, associate professor of statistics at the University of Pennsylvania, on instrumental variables, and Patrick Ryan, head of epidemiology analytics at Janssen Research and Development and participant in the Observational Medical Outcomes Partnership, on an empirical attempt to measure and calibrate for error in observational analyses.
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From page 19... ...
It should be possible, he said, to screen for naturally occurring variation through the use of propensity score analyses and apply instrumental variable analyses to use this variation for unbiased estimation, when appropriate (see the description of the presentation of Dylan Small on p.
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INSTRUMENTAL VARIABLES AND THEIR SENSITIVITY TO UNOBSERVED BIASES Dylan Small's presentation focused on the use of the instrumental variable method as one approach to controlling for unmeasured confound
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, neither health nor criminal behavior was measured in the census, and as a result, a regression of earnings on the basis of veteran status would produce a biased estimate. The instrumental variables strategy is one approach to addressing unmeasured confounders.
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From page 22... ...
"Conversely, strong instrumental variables that might be moderately biased can be useful, as long as we do a sensitivity analysis to see if we have inferences that are robust enough to allowing for a moderate amount of bias." In closing, he listed some potential instrumental variables for health outcomes research (see Table 3-1) , and he encouraged the community to work on creating useful instrumental variables.
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From page 23... ...
The intent of this framework, he explained, is to use the information that it produces as the context for interpreting observational studies and to adjust and calibrate analytical estimates to be more in line with expectations. As an example, Ryan discussed a study described in a paper published in the British Journal of Clinical Pharmacology that examined the risk of gastrointestinal bleeding in association with the drug clopidogrel (Opatrny et al., 2008)
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From page 24... ...
For gastrointestinal bleeding, the OMOP team specifically identified 24 drugs that it believed were associated with bleeding and 67 drugs for which they could find no evidence to suggest that the drug might be related to gastrointestinal bleeding, on the basis of product labeling and information in the literature. For these negative controls, if the 95 percent confidence interval was properly calibrated, then 95 percent, or 62 of 65, of the relative risk estimates would cover a relative risk of 1.
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From page 25... ...
He said that from here one can take one of two directions: either improve the methods to reduce the magnitude of error or quantify the error and use it to adjust the estimates of the effect through empirical calibration. Instead of having a theoretical null distribution based only on sampling variability, it should be possible to use the empirical null distribution based on the uncertainty measured from negative controls when the method is applied to the data source.
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From page 26... ...
He also remarked on the importance of Small's work with instrumental variables and their use in quantifying the degree of bias associated with unmeasured confounding. He added that this work demonstrated that two-stage least-squares analysis should not be used with weak instrumental variables, that an increased sample size can produce useful results if the instrumental variable is valid, and that it is important to explicitly examine the sensitivity of a weak instrumental variable to biases.
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From page 27... ...
Ryan said that investment in methods development is needed and that PCORI should invest in new approaches to the use of instrumental variables and large-dimensional regressions, as well as in methods to evaluate the methods that are being developed. As an example, he said that he would like to see instrumental variable methods implemented, along with an assessment of how they work in practice.
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From page 28... ...
Dean Follmann, branch chief and associate director for biostatistics at the National Institutes of Health, thought that with many of the new databases being developed an opportunity exists to also consider new types of study designs that build context-specific instrumental variables into the studies. As an example, he proposed a hypothetical trial of a human immunodeficiency virus vaccine in Malawi in which the country's 12 provinces would be randomized and then the vaccine would be administered to individuals in the randomized provinces at different times of the year.
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From page 29... ...
The issue, he continued, is to decide how to create the reference set of positive and negative controls in the context of comparative effectiveness rather than safety. REFERENCES Angrist, J., and A
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