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Background: Research results can be communicated in various ways, for instance through numbers, words, and combinations of these. Within clinical research, in addition to presenting the estimate of effect, it is also desirable to say something about our confidence in this result, for example by presenting a confidence interval and making an assessment of the quality of the evidence. The purpose of this study was to assess how the ways in which we communicate results are perceived by our readers. Material and methods: We selected 17 results-statements taken from reports published by the Norwegian Knowledge Centre for the Health Services, and conducted an online survey. Participants were asked to respond to what they thought the writers were trying to convey. An open invitation to participate in the survey was sent with the biweekly newsletter for the Norwegian Knowledge Centre for the Health Services, which is delivered to over 3,000 e-mail recipients. Results: We received 173 completed questionnaires. For all statements, the majority (over 50 %) chose the interpretation that in our assessment was most in line with the authors' intentions, but the proportion varied from 54.1 % to 93.4 %. Conclusion: Respondents' interpretations of the Knowledge Centre's statements of results corresponded largely with the authors' intentions. The variation in interpretations shows that we must continue to work to find formulations that are easy to understand, yet sufficiently precise. [ABSTRACT FROM AUTHOR]

What is meant by the claim that something is "well documented"? How much confidence can we have in the results? The most commonly used method to grade the quality of the evidence is GRADE (Grading of Recommendations Assessment, Development and Evaluation). GRADE is used in systematic reviews, where the effects of different treatments or options are being compared, and in guidelines with recommendations about the competing options. GRADE makes a clear distinction between the quality of the evidence and the strength of recommendation. When grading the quality of the evidence with GRADE, the starting point is a well conducted systematic review of research on the question that is being assessed. Both randomized controlled trials and observational studies can be included in the evaluation. GRADE uses five criteria to judge for possible downgrading of the quality of the evidence: risk of bias, consistency, directness, precision and reporting bias. Observational studies may in addition be assessed for possible upgrading of the quality of evidence using the following three criteria: strong association, dose response relationship, and plausible confounding. All the judgments are noted and presented together with the results in tables. [ABSTRACT FROM AUTHOR]

Meta-analysis is a quantitative method for summarizing single studies. In a meta-analysis, one tries to quantify the treatment effect, assigning more weight to large studies than to small studies. A much used method for weighting is the inverse variance method. If all studies have measured the results in the same way, the results can be used directly in the meta-analysis, but if the same outcome is measured in different ways across different studies, one has to use a standardized effect size where results are converted to a common scale. If it is believed that the effect is consistent across various populations and settings, one can employ a fixed-effect model. If systematic differences in effect can be expected, a random-effects model is used. Meta-analyses are often depicted as forest plots. Each line represents one study where the effect estimate is marked as a point on a line, with each end of the line representing the confidence interval around it. The meta-analysis is shown as a diamond where the width illustrates the uncertainty around the estimate. If all study results point in the same direction, the meta-analysis is considered "homogeneous". But if the studies vary in their effect size and direction, the findings are "heterogeneous". The strength of meta-analysis is that it can be used to summarize a large body of information in one number. This is also its limitation. One number cannot describe the variation that exists across different studies. [ABSTRACT FROM AUTHOR]

Introduction: Literally hundreds of methods for hypothesis tests and confidence intervals for contingency tables are described in the literature. This is the case even for the seemingly simple 2 × 2 table. The Pearson chi squared test, the Fisher exact test, and the Wald confidence interval are widely used methods. Unfortunately, these methods are also commonly used in situations when they perform poorly, and better alternatives exist. Aims: To show how to evaluate and choose appropriate methods for hypothesis testing and confidence interval estimation for 2x2 tables. Methods: Important properties of a hypothesis test are the actual significance level and power. Important properties for a confidence interval are coverage probability, expected interval width, and symmetry. We have studied these properties for alternative methods. Conclusion: For large samples, the asymptotic Pearson chi squared test performs well. Yates's continuity correction should never be used. In small samples, the traditional advice is the Fisher exact test. But unconditional tests are generally more powerful than Fisher's exact test for small samples. Unconditional tests also preserve the significance level. That is, the actual significance level does not exceed the nominal significance level, which is often 5%. Unconditional tests were but previously disadvantaged by being computationally demanding. Alternatively, Fisher's exact test with mid-p adjustment is easy to compute, and gives approximately the same results as an unconditional test. The Wald confidence interval is a traditional choice for the difference between two proportions. But its coverage can dip substantially below the nominal coverage, which is usually 95%. The Agresti-Caffo interval and the Newcombe hybrid score interval are also easy to compute with closed form expressions. They have better coverage, and also narrower expected interval width, and better symmetry than the Wald interval. [ABSTRACT FROM AUTHOR]