1 / 40

Ethics, informed consent and statistics

tad
Download Presentation

Ethics, informed consent and statistics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

    1. Ethics, informed consent and statistics Paul S. Mueller, MD, MPH, FACP Division of General Internal Medicine Mayo Clinic Rochester

    2. Questions we will cover today What are the elements of informed consent? Do people perceive risk similarly? If not, why not? My test is “positive” (“negative”). What does that mean? The treatment prevents (cures, etc.) a disease by 50%. Is it a good treatment?

    3. "There are lies, damned lies and statistics."

    4. Informed consent Underlying ethical principle: respect for patient autonomy Elements of informed consent: Information Patient voluntarily agrees with plan Patient has decision-making capacity

    5. AMA code on informed consent 2000-2001 (8.08)

    6. Informed consent: legal aspects Based upon negligence principles State law governs malpractice Differing state standards shaped by case law: Professional practice standard: customary for other clinicians to do Reasonable person standard: what a reasonable person needs to know (most states)

    7. Information: what would a reasonable patient want to know? Nature of the intervention Benefits of intervention Risks Alternatives (and their benefits and risks)

    8. Risk What is risk? Webster’s (1999): “ The chance of injury, damage, or loss; dangerous chance; hazard” Understanding risk is complex: Objective: quantitative; patients and clinicians have limited comprehension of the quantitative aspects of risk Subjective: how important is it to the patient?

    11. Situations during which risk is commonly discussed with patients Diagnostic tests: “If test X is positive, the chance of disease Y is…” Treatments: “The chance disease Y is cured by treatment X is...” “Treatment X reduces the risk (or recurrence) of disease Y by…”

    12. If the test is positive, do I have the disease?

    14. The chance a patient with a positive test has the disease is: True positives ? (true positives + false positives) Here: 24 ? (24 + 14) = 63% This is known as positive predictive value; what we usually want to know! Similar concept: negative predictive value (probability patient doesn’t have disease if test is negative) Here: 56 ? (56 + 6) = 90%

    16. The chance a test will be positive if the patient has the disease is: True positives ? (true positives + false negatives) Here: 24 ? (24 + 6) = 80% This is known as sensitivity Measures the effectiveness of a test Similar concept: specificity (probability test is negative if the patient doesn’t have the disease) Here: 56 ? (56 + 14) = 80%

    18. Real example: screening mammography For women with no FH of breast cancer: Sensitivity: 70-88% Specificity: 89-91% Positive predictive value: 1-6% Sensitivity and specificity increase with age Mammography is not a perfect screening test for breast cancer

    19. The bottom line Unfortunately, no test is perfect When a test is positive, the chance the patient has the disease is almost never 100% Not all patients with positive tests have the disease the test is intended to detect

    20. When a test is negative, and the disease is still suspected, what do clinicians usually do next?

    21. How do I know if the treatment that my doctor suggests is good?

    22. Hierarchy of evidence

    24. Worth noting: Almost every trial demonstrates: Some people get better on their own without treatment Not all people who are treated get better

    25. By how much is treatment new better than treatment old? Absolute risk reduction = risk old treatment - risk new Here: 60% - 30% = 30% “The risk of the bad outcome on treatment new is 30% less than on treatment old.” Relative risk = (risk old treatment - risk new) ? risk old Here: (60% - 30%) ? 60% = .50 “On treatment new, the chance of the bad outcome is 50% the risk of being on treatment old.”

    26. Expressed as graph

    27. What is the problem with relative risk?

    28. For each scenario, relative risk reduction is 50%, but the absolute risk reduction is much different

    31. Physician’s Health Study New Engl J Med 1989;321:129-135 N = 22,071 11,037 received aspirin (ASA) and 11,034 placebo Incidence (risk) of MI in ASA group was 255/100,000 per year or 0.26% per year Incidence (risk) in placebo group was 440/100,000 or 0.44% per year Absolute risk reduction of MI with ASA: 0.18% per year Relative risk reduction: 44%

    33. “4S” Trial Lancet 1994;344:1383-1389

    36. Be careful when contemplating risk What are the characteristics of the patients enrolled in the study? Which risk? Absolute or relative? Physicians, the media, medical journals, industry and lawyers often talk in terms of relative risk However, absolute risk reduction is often more relevant to patients

    37. Number needed to treat (NNT): a better way of communicating risk? NNT = number of persons needed to treat to prevent (cure, etc.) one case NNT = 1/absolute risk reduction Physicians Health Study: NNT = 1/0.0018 = 556 Physicians Health Study: would have to treat 556 patients with ASA to prevent one MI 4S trial: NNT = 1/0.035 = 29; would have to treat 29 patients to prevent one coronary death

    39. Conclusions Medical statistics can be are complex and can be confusing No test is perfect Results of trials are often presented in terms of relative risk, which may be irrelevant to patients Effective communication of risk is essential for informed decision-making

    40. Acknowledgements Amit K. Ghosh, MD, FACP BMJ 9/27/2003; a large portion of this issue is devoted to communicating risk to patients

More Related