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Doing All They Can: Physicians Who Deny Medical Futility

Published online by Cambridge University Press:  01 January 2021

Jeffrey W. Swanson  and
S. Van McCrary
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Extract

Why do some physicians continue to treat patients who are clearly dying or persistently unconscious, while others consider medical intervention to be futile past a certain point? No doubt, medical decisions vary in part because clinical information is often ambiguous in individual cases and because it may support more than one reasonable interpretation of a patient's chances for survival or improvement if a particular treatment is administered. Also, cases vary considerably to the extent that a patient's or a family member's preferences for treatment are communicated, understood, and implemented. But, beyond these contingencies, patients at the end of life may receive more, less, or different treatment because physicians themselves are social actors, individuals who bring to bear on their clinical decisions a variety of personal attitudes, values, concerns, and interests. Legal defensiveness, religious vitalism, authoritarianism, intolerance of ambiguity, and other traits may influence physicians’ behavior, but each may be concealed under the rubric of what is “medically indicated” or “medically appropriate.”

Type
Article
Information
Journal of Law, Medicine & Ethics , Volume 22 , Issue 4 , Winter 1994 , pp. 318 - 326
Copyright
Copyright © American Society of Law, Medicine and Ethics 1994

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References

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More specifically, logistic regression makes inferences based on the sampling distribution of the natural logarithm of the odds ratio, which approximates a normal distribution even with fairly small sample sizes. The logistic coefficient yields the estimated odds ratio by which an “event” is associated with some predictor variable that controls for the other predictors in the model. The square of this parameter divided by its standard error yields the Wald chi-square statistic, which can be used to test the statistical significance (or probability of true effect) of each independent variable with respect to the chi-square sampling distribution. Finally, the logistic regression equation yields a predicted probability of the event for every combination of independent variables included in a model—for example, the probability that physicians with a particular profile of attitudes and characteristics will deny futility. Upper and lower confidence bounds for these predicted probabilities are calculated as well. The overall “fit” between predicted probabilities and actual responses in the entire sample allows an assessment of whether the model has been adequately specified, that is, whether relevant predictors have been included, and thus whether the model offers explanatory power for making inferences about true relationships between variables in the population. Predicted probabilities generated by logistic regression are especially useful for examining the estimated magnitude of the effect of combinations of independent variables that may occur in small numbers in a sample, for which a straight percentage rate would be unstable.Google Scholar
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