survival bias

A bias that occurs when cancer survival confounds analysis of exposure

survival bias

The tendency of people who live longer after an illness or injury to receive more medical care. The medical care may not be the cause of the patients' longevity; rather, the care may be an effect of it.
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Reutfors said this apparent protective effect was probably tied to survival bias.
Muka said, included the possibility of survival bias, since enrollees "may represent survivors of early menopause who did not develop [type 2 diabetes] or die prior to enrollment." However, he said, this would mean that "we have underestimated the association, so the risk would be even higher." Also, all study participants were white, so the results cannot be extrapolated to nonwhite populations.
One important problem is that of "survival bias" that is seen in cases of diseases that have long term survivors where a risk factor associated with survivorship will be over represented and appear to be associated with disease.6
For example, the impact of one of the clinical factors, age, may even introduce a survival bias on genotype frequencies that is difficult to model, and the authors' analysis does not explicitly address the possibility that the clinical characteristics alone might be more precise indicators of prescription threshold than genotype information alone.
The Columbia authors speculate that the inverse association of TC and LDL-C with elder mortality may indicate that low serum lipid levels are a "surrogate for comorbidity, frailty, or subclinical disease." The participants most likely to have been treated with statins could have had higher TC and LDL-C initially compared to the whole group and thus conceivably were in better health (indication bias), or the ones most susceptible to coronary disease had already died at an earlier age (survival bias), or perhaps the statin users just received better medical care, yet a 75% difference of mortality in the elderly associated with statin treatment simply must not be ignored.
The literature on survival bias for stocks is discussed in the second section.
Since student withdrawal data was omitted, the results of the study are subjected to survival bias. The lack of control for such bias is recognized as a limitation of the study.
In the absence of commonality, a sample average can possess substantial survival bias but can still equal the appropriate inference about an asset's expected return.
One procedure to ameliorate the impact of survival bias without encountering the above problem is to match the sample firms with control firms on the basis of survival.
By contrast, a survival bias giving r = 0.67 (which can occur if the generation time is at least 1.5 seasons) has a negligible effect on [N.sub.e].
These differences over time probably are a result of survival bias, the investigators suggested.
To evaluate the survival bias in analyzing the relationship between the Glu298Asp mutation and progression to chronic renal failure, we divided the patients into two groups according to the duration of hemodialysis; among the diabetics, patients who had experienced >5 years of hemodialysis were placed in the long-term survival group, whereas patients with hemodialysis <5 years were placed in the short-term group.