{"id":2539,"date":"2014-02-13T14:33:40","date_gmt":"2014-02-13T19:33:40","guid":{"rendered":"http:\/\/blogs.cdc.gov\/genomics\/?p=2539"},"modified":"2024-04-08T16:19:22","modified_gmt":"2024-04-08T20:19:22","slug":"is-evidence-based","status":"publish","type":"post","link":"https:\/\/blogs.cdc.gov\/genomics\/2014\/02\/13\/is-evidence-based\/","title":{"rendered":"Is Evidence-based Medicine the Enemy of Genomic Medicine?"},"content":{"rendered":"

\"hands<\/a>A general practitioner recently writing in the BMJ<\/a>, \u00a0said that evidence-based medicine is polluted with \u201cfraud, sham diagnosis, short term data, poor regulation, surrogate ends, questionnaires that can\u2019t be validated, and statistically significant but clinically irrelevant outcomes\u201d, all leading to \u201coverdiagnosis and misery\u201d. In more temperate tones, Goldberger and Buxton recently suggested \u00a0in a JAMA Viewpoint<\/a>\u00a0\u00a0article that personalized medicine and guideline-based medicine \u201cpresent conflicting priorities\u201d, with evidence-based guidelines derived from clinical trial data failing to recognize the heterogeneity of the patient population to which they will be applied. The existence of these guidelines then acts, they say, as a barrier to the development of personalized approaches that would be more appropriate for different population subgroups \u2013 including those likely to gain no benefit from the intervention.\u00a0<\/p>\n

These are forceful arguments, but are they aimed at the right target? Is it really \u201cevidence\u201d that\u2019s to blame for overtreatment and spiralling pharmaceutical usage, and are evidence-based guidelines standing in the way of the development of personalized medicine? This is especially pertinent in an era of breathtaking developments in genomics that promise a new era of precision or personalized medicine<\/a>.<\/p>\n

We have previously argued<\/a> that an evidence-based approach is essential if we are to reap the promised benefits of genomic medicine \u2013 including the goal of more precisely targeted or \u201cpersonalized\u201d approaches to prevention and treatment. The methods of the population sciences<\/a> are needed to enable us to integrate the complex set of biologic, social, economic, cultural and physical factors that interact to determine each individual\u2019s health, disease susceptibility, and response to treatment or preventive interventions. The analytic frameworks of epidemiology and health services research provide methodologies for evaluating the benefits, harms, and costs (including opportunity costs) of implementing diagnostic and therapeutic interventions based on genomic factors, and of comparing genomic approaches to existing clinical practice.<\/p>\n

We must also keep in mind that, however \u201cpersonalized\u201d at the level of individual patient and medical practitioner, integration of genomics into clinical care takes place within the wider context of healthcare organizations, families, communities and state and federal policies<\/a>. The public health \u00a0imperative is to ensure that validated applications can reach all segments of the population, \u00a0to protect patients and the wider community from the premature implementation of tests or interventions that are minimally effective, ineffective, or even harmful. It\u2019s not \u201cevidence\u201d that leads to overtreatment and harm, but poor evidence and overinterpretation of evidence.<\/p>\n

No-one would deny, though, that there are serious challenges to face in assembling and appraising the evidence base to support personalized medicine in the era of genomics. As disease entities become more finely subdivided on the basis of histologic and molecular features, and genomic factors join \u201ctraditional\u201d characteristics such as age and sex in defining population risk subgroups, it becomes more challenging to design clinical and population studies with enough power to yield statistically significant results. New ways of adapting or applying evidentiary standards will be needed, and consideration of the relative weight given to different types of evidence derived from comparative RCTs, observational studies, natural experiments, adaptive trials, pragmatic trials, evidence synthesis, and modeling<\/a>.<\/p>\n

In the meantime, should we abandon the concept of the \u201cevidence-based guideline\u201d? We think this would be throwing the baby out with the bath water. Guidelines will never be perfect, and should always be supplemented by patient empowerment and clinician knowledge of contextual factors including \u00a0\u00a0personal characteristics, social circumstances, values and preferences. If viewed in this way, guidelines developed according to sound principles of evidence-gathering and appraisal, explicit statement of the processes by which they were formulated (including steps taken to avoid bias and conflicts of interest), and which clearly set out the clinical scenario and patient population to which they apply, can serve as an important reference point for clinical decision-making. Guideline developers can also play a valuable role in flagging areas in which evidence is lacking or limited, and in identifying issues that need further research. \u00a0\u00a0Even in the rapidly developing field of genomic medicine, evidence on the balance of benefits and harms will always be required to make informed health related decisions by healthcare providers, patients and\u00a0policy makers.<\/p>\n","protected":false},"excerpt":{"rendered":"

A general practitioner recently writing in the BMJ, \u00a0said that evidence-based medicine is polluted with \u201cfraud, sham diagnosis, short term data, poor regulation, surrogate ends, questionnaires that can\u2019t be validated, and statistically significant but clinically irrelevant outcomes\u201d, all leading to \u201coverdiagnosis and misery\u201d. In more temperate tones, Goldberger and Buxton recently suggested \u00a0in a JAMA<\/p>\n","protected":false},"author":122,"featured_media":2573,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5761,5236],"tags":[5726,31856],"_links":{"self":[{"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/posts\/2539"}],"collection":[{"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/users\/122"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/comments?post=2539"}],"version-history":[{"count":26,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/posts\/2539\/revisions"}],"predecessor-version":[{"id":6477,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/posts\/2539\/revisions\/6477"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/media\/2573"}],"wp:attachment":[{"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/media?parent=2539"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/categories?post=2539"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/tags?post=2539"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}