Abstract We evaluated what guidance exists in the literature to improve the transparency of studies that make secondary use of health data. To find relevant literature, we searched PubMed and Google Scholar and drafted a list of health organizations based on our personal expertise. We quantitatively and qualitatively coded different types of research transparency: registration, methods reporting, results reporting, data sharing, and code sharing. We found 54 documents that provide recommendations to improve the transparency of studies making secondary use of health data, mainly in relation to study registration (n = 27) and methods reporting (n = 39). Only three documents made recommendations on data sharing or code sharing. Recommendations for study registration and methods reporting mainly came in the form of structured documents like registration templates and reporting guidelines. Aside from the recommendations aimed directly at researchers, we found 31 recommendations aimed at the wider research community, typically on how to improve research infrastructure. Limitations or challenges of improving transparency were rarely mentioned, highlighting the need for more nuance in providing transparency guidance for studies that make secondary use of health data.
• The rapid and continuing progress in gene discovery for complex diseases is fuelling interest in the potential application of genetic risk models for clinical and public health practice. • The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting vary. • A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by prior reporting guidelines. • These recommendations aim to enhance the transparency of study reporting and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis. • A detailed Explanation and Elaboration document is published as an accompanying article [1].
A typical rule that has been used for the endorsement of new medications by the Food and Drug Administration is to have two trials, each convincing on its own, demonstrating effectiveness. "Convincing" may be subjectively interpreted, but the use of p-values and the focus on statistical significance (in particular with p < .05 being coined significant) is pervasive in clinical research. Therefore, in this paper, we calculate with simulations what it means to have exactly two trials, each with p < .05, in terms of the actual strength of evidence quantified by Bayes factors. Our results show that different cases where two trials have a p-value below .05 have wildly differing Bayes factors. Bayes factors of at least 20 in favor of the alternative hypothesis are not necessarily achieved and they fail to be reached in a large proportion of cases, in particular when the true effect size is small (0.2 standard deviations) or zero. In a non-trivial number of cases, evidence actually points to the null hypothesis, in particular when the true effect size is zero, when the number of trials is large, and when the number of participants in both groups is low. We recommend use of Bayes factors as a routine tool to assess endorsement of new medications, because Bayes factors consistently quantify strength of evidence. Use of p-values may lead to paradoxical and spurious decision-making regarding the use of new medications.
Funnel plots, and tests for funnel plot asymmetry, have been widely used to examine bias in the results of meta-analyses. Funnel plot asymmetry should not be equated with publication bias, because it has a number of other possible causes. This article describes how to interpret funnel plot asymmetry, recommends appropriate tests, and explains the implications for choice of meta-analysis model
<p>PDF - 140KB, Total number and total direct cost of CEC grants by year.</p>
Many fields face an increasing prevalence of multi-authorship, and this poses challenges in assessing citation metrics. Here, we explore multiple citation indicators that address total impact (number of citations, Hirsch H index [H]), co-authorship adjustment (Schreiber Hm index [Hm]), and author order (total citations to papers as single; single or first; or single, first, or last author). We demonstrate the correlation patterns between these indicators across 84,116 scientists (those among the top 30,000 for impact in a single year [2013] in at least one of these indicators) and separately across 12 scientific fields. Correlation patterns vary across these 12 fields. In physics, total citations are highly negatively correlated with indicators of co-authorship adjustment and of author order, while in other sciences the negative correlation is seen only for total citation impact and citations to papers as single author. We propose a composite score that sums standardized values of these six log-transformed indicators. Of the 1,000 top-ranked scientists with the composite score, only 322 are in the top 1,000 based on total citations. Many Nobel laureates and other extremely influential scientists rank among the top-1,000 with the composite indicator, but would rank much lower based on total citations. Conversely, many of the top 1,000 authors on total citations have had no single/first/last-authored cited paper. More Nobel laureates of 2011-2015 are among the top authors when authors are ranked by the composite score than by total citations, H index, or Hm index; 40/47 of these laureates are among the top 30,000 by at least one of the six indicators. We also explore the sensitivity of indicators to self-citation and alphabetic ordering of authors in papers across different scientific fields. Multiple indicators and their composite may give a more comprehensive picture of impact, although no citation indicator, single or composite, can be expected to select all the best scientists.
A table presenting all meta-analyses done. 'Analysis' refers to the the type of data used in each meta-analysis, as this was explained in Methods. 'Studies' refers to the number of studies included within each meta-analysis. Columns 'HR' and '95 % CI' refer to the summary Hazard Ratio (HR) of each meta-analysis with its 95 % Confidence Interval (CI) (lower and upper limit). 'Tau' refers to the squared root of the estimate of between-study variance in each of our random-effects meta-analyses. Columns 'I and '95 % CI' refer to a measure of between-study heterogeneity and its corresponding 95 % CI. 'Q-statistic' and its 'P-value' refer to Cochran's Q measure of heterogeneity with its p-value. 'Observed', 'Expected' and 'P-value (binomial)' refer to the observed and expected amount of statistically significant results and the comparison between the two, as this was described in Methods. (XLSX 50.7 kb)
<i>Most gracious readers, two boring researchers present a series of pitiful tales—covert and overt fraud, petty misdeeds, and misconceptions by honest and not so honest researchers, professors, industry sponsors, bureaucrats, and other legendary adventurers of modern science</i>
