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However, using observational data to compare outcomes associated with antipsychotic polypharmacy may result in biased estimates [ 15].
However, greater attention to innovative methods for using observational data to study rare disease health outcomes is needed.
Investigators using observational data to make comparisons of treatment outcomes associated with these medications should take these important differences in patient characteristics into consideration.
Patients undergoing bone marrow transplantation (BMT) constitute an excellent study population for using observational data to evaluate whether inpatient hyperglycemia predicts infections.
As for using observational data to identify the effect of such interventions, numerous attempts have being made, see e.g. [ 21– 21].
There are multiple reasons for the increasing interest in using observational data to estimate the effects of treatments, interventions, and exposures.
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Cosma Shalizi and Andrew Thomas, statisticians at Carnegie Mellon University, contend that it is mathematically impossible to use observational data to establish that contagion is a major reason behaviors spread.
Chen and Tung used observational data to trace the pathways of recent ocean heating.
One way to decide whether a theory or a theoretical claim is true, close to the truth, or acceptably probable is to derive predictions from it and use observational data to evaluate them.
Reanalyses products are widely used in the energy, agricultural, insurance (and re-insurance), and water sectors.4 It is also possible to use observational data to reconstruct extreme weather events and/or nature-induced disasters (Brohan et al. 2016).
The participants thought it valuable to use observational data to generate hypotheses regarding patient baseline characteristics that are likely to predict clinically important changes in PROs in response to anemia treatment and to design adequately powered blinded randomized controlled trials of anemia treatment using PROs as primary rather than secondary end points.
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