Evidence Based Medicine

Evidence Based Medicine Sensitivity - represents the proportion of truly diseased persons in a screened population who are identified as being diseased by the test. It is a measure of the probability of correctly diagnosing a condition Specificity - is the proportion of truly nondiseased persons who are so identified by the screening test. It is a measure of the probability of correctly identifying a nondiseased person. Relative risk - The proportion of diseased people amongst those exposed to the relevant risk factor divided by the proportion of diseased people amongst those not exposed to the risk factor. This should be used in those cohort studies where those with and without disease are followed to observe which individuals become diseased. Absolute risk - The excess risk due to exposure to a specific hazard (disease, injury, etc.) Attributable risk - The rate of a disease or other outcome in exposed individuals that can be attributed to the exposure. Type I error, ("false positive") - the error of rejecting a null hypothesis when it is actually true. In other words, this is the error of accepting an alternative hypothesis (the real hypothesis of interest) when the results can be attributed to chance. Plainly speaking, it occurs when we are observing a difference when in truth there is none. A false positive normally means that a test claims something to be positive, when that is not the case. For example, a pregnancy test with a positive result (indicating that the person taking the test is pregnant) has produced a false positive in the case where the person is not pregnant. Type II error ("false negative") - the error of failing to reject a null hypothesis when the alternative hypothesis is the true state of nature. In other words, this is the error of failing to observe a difference when in truth there is one. This type of error can only occur when the statistician fails to reject the null hypothesis. • Screening The intention of screening is to identify disease in a community early, thus enabling earlier intervention and management in the hope to reduce mortality and suffering from a disease. Although screening may lead to an earlier diagnosis, not all screening tests have been shown to benefit the person being screened; overdiagnosis, misdiagnosis, and creating a false sense of security are some potential adverse effects of screening. For these reasons, a test used in a screening program, especially for a disease with low incidence, must have good specificity in addition to acceptable sensitivity. World Health Organization guidelines for screening: 1. The condition should be an important health problem. 2. There should be a treatment for the condition. 3. Facilities for diagnosis and treatment should be available. 4. There should be a latent stage of the disease. 5. There should be a test or examination for the condition. 6. The test should be acceptable to the population. 7. The natural history of the disease should be adequately understood. 8. There should be an agreed policy on who to treat. 9. The total cost of finding a case should be economically balanced in relation to medical expenditure as a whole. 10. Case-finding should be a continuous process, not just a "once and for all" project. EBM 1 Screening Bias Lead time bias – Will the patient survive longer if the condition is diagnosed earlier (by screening)? If not then screening only à increased anxiety Length bias - Many screening tests involve the detection of cancers. It is often hypothesized that slower growing tumors have better prognosis than tumors with high growth rates. Screening is more likely to detect slower growing tumors (due to longer pre-clinical sojourn time), which may be less deadly. Thus screening may tend to detect cancers that would not have killed the patient or even been detected prior to death from other causes. Selection bias - Not everyone will partake in a screening program. There are factors that differ between those willing to get tested and those who are not. If people with a higher risk of a disease are more eager to be screened, for instance a woman with a family history of breast cancer joining a mammography program, then a screening test will look worse than it really is. This is because there's going to be more people with the illness joining, and a higher chance of people dying of that illness. Selection bias may also make a test look better than it really is. If a test is more available to young and healthy people (for instance if people have to travel a long distance to get checked) then fewer people in the screening population will get ill, and the test will seem to make a positive difference. Overdiagnosis bias - Screening may identify abnormalities that would never cause a problem in a person's lifetime. An example of this is prostate cancer screening. It has been said that "most men die with prostate cancer, not of it". Autopsy studies have shown that a high proportion of men who have died in other ways, have prostate cancer when the prostate is examined under a microscope. - Aside from issues with unnecessary treatment (prostate cancer treatment is by no means without risk), overdiagnosis makes a study look good at picking up abnormalities, even though they are sometimes harmless. Quality-adjusted life years, or QALYs, is a way of measuring both the quality and the quantity of life lived, as a means of quantifying in benefit of a medical intervention. They are based on the number of years of life that would be added by the intervention. Each year in perfect health is assigned the value of 1.0 down to a value of 0 for death. If the extra years would not be lived in full health, for example if the patient would lose a limb, or be blind or be confined to a wheelchair, then the extra life-years are given a value between 0 and 1 to account for this. The "weight" values between 0 and 1 are usually determined by methods such as: Time-trade-off (TTO) - In this method, respondents are asked to choose between remaining in a state of ill health for a period of time, or being restored to perfect health but having a shorter life expectancy. Standard gamble (SG) - In this method, respondents are asked to choose between remaining in a state of ill health for a period of time, or choosing a medical intervention which has a chance of either restoring them to perfect health, or killing them. Visual analogue scale (VAS) - In this method, respondents are asked to rate a state of ill health on a scale from 0 to 100, with 0 representing death and 100 representing perfect health. This method has the advantage of being the easiest to ask, but is the most subjective. EBM 2