Epidemiology and evidence-based medicine

Chapter 20 focuses on epidemiology and evidence-based medicine. It covers study design, types of data and descriptive statistics, from samples to populations, relationships, relative risk, odds ratios, and 'number needed to treat', survival analysis, sample size, diagnostic tests, meta-analysis, before concluding with advice on how to read a paper.

2016 ◽  
Vol 27 (6) ◽  
pp. 1785-1805 ◽  
Author(s):  
Dehui Luo ◽  
Xiang Wan ◽  
Jiming Liu ◽  
Tiejun Tong

The era of big data is coming, and evidence-based medicine is attracting increasing attention to improve decision making in medical practice via integrating evidence from well designed and conducted clinical research. Meta-analysis is a statistical technique widely used in evidence-based medicine for analytically combining the findings from independent clinical trials to provide an overall estimation of a treatment effectiveness. The sample mean and standard deviation are two commonly used statistics in meta-analysis but some trials use the median, the minimum and maximum values, or sometimes the first and third quartiles to report the results. Thus, to pool results in a consistent format, researchers need to transform those information back to the sample mean and standard deviation. In this article, we investigate the optimal estimation of the sample mean for meta-analysis from both theoretical and empirical perspectives. A major drawback in the literature is that the sample size, needless to say its importance, is either ignored or used in a stepwise but somewhat arbitrary manner, e.g. the famous method proposed by Hozo et al. We solve this issue by incorporating the sample size in a smoothly changing weight in the estimators to reach the optimal estimation. Our proposed estimators not only improve the existing ones significantly but also share the same virtue of the simplicity. The real data application indicates that our proposed estimators are capable to serve as “rules of thumb” and will be widely applied in evidence-based medicine.


Author(s):  
Ann Merete Møller

Evidence-based medicine (EBM) is defined as ‘The judicious use of the best current evidence in making decisions about the care of individual patients’. Evidence-based medicine (EBM) is meant to integrate clinical expertise with the best available research evidence and patient values. The purpose of EBM is to assist clinicians in making the best decisions. Practising EBM includes asking an answerable, well-defined clinical question, searching for information, critically appraising information retrieved, extracting data, synthesizing data, and making conclusions about the overall effect. The clinical question includes information of the following elements: the population, the intervention, and the clinically relevant outcomes in focus. The clinical question is a tool to make the focus of the question clearer, and an aid to build the following search strategy. A comprehensive and reproducible literature search is essential for conducting a high-quality and up-to-date search. The search should include all relevant clinical databases. Papers retrieved after the search must be critically appraised and evaluated for the risk of bias. Evidence-based methods are used in the production of systematic reviews, and the development of clinical guidelines. Whether a meta-analysis should be performed depends on the quality and nature of the extracted data. Practising EBM may be challenged by a lack of well-performed trials, various types of bias (including publication bias), and heterogeneity between existing trials. Several tools have been constructed to help the process; examples are the CONSORT statement, the PRISMA statement, and the AGREE instrument.


2012 ◽  
Vol 21 (2) ◽  
pp. 151-153 ◽  
Author(s):  
A. Cipriani ◽  
C. Barbui ◽  
C. Rizzo ◽  
G. Salanti

Standard meta-analyses are an effective tool in evidence-based medicine, but one of their main drawbacks is that they can compare only two alternative treatments at a time. Moreover, if no trials exist which directly compare two interventions, it is not possible to estimate their relative efficacy. Multiple treatments meta-analyses use a meta-analytical technique that allows the incorporation of evidence from both direct and indirect comparisons from a network of trials of different interventions to estimate summary treatment effects as comprehensively and precisely as possible.


1998 ◽  
Vol 3 (1) ◽  
pp. 44-49 ◽  
Author(s):  
Jack Dowie

Within ‘evidence-based medicine and health care’ the ‘number needed to treat’ (NNT) has been promoted as the most clinically useful measure of the effectiveness of interventions as established by research. Is the NNT, in either its simple or adjusted form, ‘easily understood’, ‘intuitively meaningful’, ‘clinically useful’ and likely to bring about the substantial improvements in patient care and public health envisaged by those who recommend its use? The key evidence against the NNT is the consistent format effect revealed in studies that present respondents with mathematically-equivalent statements regarding trial results. Problems of understanding aside, trying to overcome the limitations of the simple (major adverse event) NNT by adding an equivalent measure for harm (‘number needed to harm’ NNH) means the NNT loses its key claim to be a single yardstick. Integration of the NNT and NNH, and attempts to take into account the wider consequences of treatment options, can be attempted by either a ‘clinical judgement’ or an analytical route. The former means abandoning the explicit and rigorous transparency urged in evidence-based medicine. The attempt to produce an ‘adjusted’ NNT by an analytical approach has succeeded, but the procedure involves carrying out a prior decision analysis. The calculation of an adjusted NNT from that analysis is a redundant extra step, the only action necessary being comparison of the results for each option and determination of the optimal one. The adjusted NNT has no role in clinical decision-making, defined as requiring patient utilities, because the latter are measurable only on an interval scale and cannot be transformed into a ratio measure (which the adjusted NNT is implied to be). In any case, the NNT always represents the intrusion of population-based reasoning into clinical decision-making.


2008 ◽  
Vol 5;12 (5;9) ◽  
pp. 819-850
Author(s):  
Laxmaiah Manchikanti

Observational studies provide an important source of information when randomized controlled trials (RCTs) cannot or should not be undertaken, provided that the data are analyzed and interpreted with special attention to bias. Evidence-based medicine (EBM) stresses the examination of evidence from clinical research and describes it as a shift in medical paradigm, in contrast to intuition, unsystematic clinical experience, and pathophysiologic rationale. While the importance of randomized trials has been created by the concept of the hierarchy of evidence in guiding therapy, much of the medical research is observational. The reporting of observational research is often not detailed and clear enough with insufficient quality and poor reporting, which hampers the assessment of strengths and weaknesses of the study and the generalizability of the mixed results. Thus, in recent years, progress and innovations in health care are measured by systematic reviews and meta-analyses. A systematic review is defined as, “the application of scientific strategies that limit bias by the systematic assembly, clinical appraisal, and synthesis of all relevant studies on a specific topic.” Meta-analysis usually is the final step in a systematic review. Systematic reviews and meta-analyses are labor intensive, requiring expertise in both the subject matter and review methodology, and also must follow the rules of EBM which suggests that a formal set of rules must complement medical training and common sense for clinicians to integrate the results of clinical research effectively. While expertise in the review methods is important, the expertise in the subject matter and technical components is also crucial. Even though, systematic reviews and meta-analyses, specifically of RCTs, have exploded, the quality of the systematic reviews is highly variable and consequently, the opinions reached of the same studies are quite divergent. Numerous deficiencies have been described in methodologic assessment of the quality of the individual articles. Consequently, observational studies can provide an important complementary source of information, provided that the data are analyzed and interpreted in the context of confounding bias to which they are prone. Appropriate systematic reviews of observational studies, in conjunction with RCTs, may provide the basis for elimination of a dangerous discrepancy between the experts and the evidence. Steps in conducting systematic reviews of observational studies include planning, conducting, reporting, and disseminating the results. MOOSE, or Meta-analysis of Observational Studies in Epidemiology, a proposal for reporting contains specifications including background, search strategy, methods, results, discussion, and conclusion. Use of the MOOSE checklist should improve the usefulness of meta-analysis for authors, reviewers, editors, readers, and decision-makers. This manuscript describes systematic reviews and meta-analyses of observational studies. Authors frequently utilize RCTs and observational studies in one systematic review; thus, they should also follow the reporting standards of the Quality of Reporting of Meta-analysis (QUOROM) statement, which also provides a checklist. A combined approach of QUOROM and MOOSE will improve reporting of systematic reviews and lead to progress and innovations in health care. Key words: Observational studies, evidence-based medicine, systematic reviews, metaanalysis, randomized trials, case-control studies, cross-sectional studies, cohort studies, confounding bias, QUOROM, MOOSE


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