PubMed · 7823656
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C P Duvall. 1995. How will physicians respond?. https://doi.org/10.1097/00005650-199501001-00006
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OBJECTIVE: To discover cancer patients' views about disclosure of information to their family, their family's influence over the information given them, and their preferences for doctors' behaviour if they and their family disagree, as a complement to applied ethical theory. DESIGN: Semistructured interviews followed by qualitative content analysis. SETTING: Mainly urban British general practice. SUBJECTS: 30 patients in whom cancer, excluding basal and squamous cell skin carcinomas, was diagnosed 1-7 years earlier. RESULTS: All subjects wished doctors to respect their views rather than those of their family, should they differ. With their consent, subjects favoured close family receiving information about their illness, all but one mentioning advantage to their family. Without such consent, six unconditionally favoured disclosure of information to their family while seven unconditionally opposed disclosure. Seventeen participants restricted their approval for such disclosure to specific circumstances. Only two participants approved of their family influencing the information that they received about their illness; all but seven wished to receive full information, the exceptions relating to information about bad prognosis. CONCLUSIONS: Subjects favoured openness with their family but most rejected unconditional disclosure of information without their consent and their family influencing what information they would be given. They valued respect for their autonomy more highly than beneficence and considered that their own needs took priority over those of their family.
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The recent controversy over the increased risk of venous thrombosis with third generation oral contraceptives illustrates the public policy dilemma that can be created by relying on conventional statistical tests and estimates: case-control studies showed a significant increase in risk and forced a decision either to warn or not to warn. Conventional statistical tests are an improper basis for such decisions because they dichotomise results according to whether they are or are not significant and do not allow decision makers to take explicit account of additional evidence--for example, of biological plausibility or of biases in the studies. A Bayesian approach overcomes both these problems. A Bayesian analysis starts with a "prior" probability distribution for the value of interest (for example, a true relative risk)--based on previous knowledge--and adds the new evidence (via a model) to produce a "posterior" probability distribution. Because different experts will have different prior beliefs sensitivity analyses are important to assess the effects on the posterior distributions of these differences. Sensitivity analyses should also examine the effects of different assumptions about biases and about the model which links the data with the value of interest. One advantage of this method is that it allows such assumptions to be handled openly and explicitly. Data presented as a series of posterior probability distributions would be a much better guide to policy, reflecting the reality that degrees of belief are often continuous, not dichotomous, and often vary from one person to another in the face of inconclusive evidence.