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When Is Enough, Enough? When Are More Observational Epidemiologic Studies Needed to Resolve a Research Question: Illustrations Using Biomarker-Cancer Associations.

ABSTRACT: BACKGROUND:Research reproducibility is vital for translation of epidemiologic findings. However, repeated studies of the same question may be undertaken without enhancing existing knowledge. To identify settings in which additional research is or is not warranted, we adapted research synthesis metrics to determine number of additional observational studies needed to change the inference from an existing meta-analysis. METHODS:The fail-safe number (FSN) estimates number of additional studies of average weight and null effect needed to drive a statistically significant meta-analysis to null (P ? 0.05). We used conditional power to determine number of additional studies of average weight and equivalent heterogeneity to achieve 80% power in an updated meta-analysis to detect the observed summary estimate as statistically significant. We applied these metrics to a curated set of 98 meta-analyses on biomarkers and cancer risk. RESULTS:Both metrics were influenced by number of studies, heterogeneity, and summary estimate size in the existing meta-analysis. For the meta-analysis on Helicobacter pylori and gastric cancer with 15 studies [OR = 2.29; 95% confidence interval (CI), 1.71-3.05], FSN was 805 studies, supporting futility of further study. For the meta-analysis on dehydroepiandrosterone sulfate and prostate cancer with 7 studies (OR = 1.29; 95% CI, 0.99-1.69), 5 more studies would be needed for 80% power, suggesting further study could change inferences. CONCLUSIONS:Along with traditional assessments, these metrics could be used by stakeholders to decide whether additional studies addressing the same question are needed. IMPACT:Systematic application of these metrics could lead to more judicious use of resources and acceleration from discovery to population-health impact.

SUBMITTER: Marrone MT

PROVIDER: S-EPMC6363830 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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When Is Enough, Enough? When Are More Observational Epidemiologic Studies Needed to Resolve a Research Question: Illustrations Using Biomarker-Cancer Associations.

Marrone Michael T MT Tsilidis Konstantinos K KK Ehrhardt Stephan S Joshu Corinne E CE Rebbeck Timothy R TR Sellers Thomas A TA Platz Elizabeth A EA

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20181030 2

<h4>Background</h4>Research reproducibility is vital for translation of epidemiologic findings. However, repeated studies of the same question may be undertaken without enhancing existing knowledge. To identify settings in which additional research is or is not warranted, we adapted research synthesis metrics to determine number of additional observational studies needed to change the inference from an existing meta-analysis.<h4>Methods</h4>The fail-safe number (FSN) estimates number of addition ...[more]

PMID: 30377205

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Project description:BackgroundPhylogenies often contain both well-supported and poorly supported nodes. Determining how much additional data might be required to eventually recover most or all nodes with high support is an important pragmatic goal, and simulations have been used to examine this question. Most simulations have been based on few empirical loci, and suggest that well supported phylogenies can be determined with a very modest amount of data. Here we report the results of an empirical phylogenetic analysis of all 10 genera and 25 of 48 species of the new world pond turtles (family Emydidae) based on one mitochondrial (1070 base pairs) and seven nuclear loci (5961 base pairs), and a more biologically realistic simulation analysis incorporating variation among gene trees, aimed at determining how much more data might be necessary to recover weakly-supported nodes with strong support.ResultsOur mitochondrial-based phylogeny was well resolved, and congruent with some previous mitochondrial results. For example, all genera, and all species except Pseudemys concinna, P. peninsularis, and Terrapene carolina were monophyletic with strong support from at least one analytical method. The Emydinae was recovered as monophyletic, but the Deirochelyinae was not. Based on nuclear data, all genera were monophyletic with strong support except Trachemys, and all species except Graptemys pseudogeographica, P. concinna, T. carolina, and T. coahuila were monophyletic, generally with strong support. However, the branches subtending most genera were relatively short, and intergeneric relationships within subfamilies were mostly unsupported. Our simulations showed that relatively high bootstrap support values (i.e. >or= 70) for all nodes were reached in all datasets, but an increase in data did not necessarily equate to an increase in support values. However, simulations based on a single empirical locus reached higher overall levels of support with less data than did the simulations that were based on all seven empirical nuclear loci, and symmetric tree distances were much lower for single versus multiple gene simulation analyses.ConclusionOur empirical results provide new insights into the phylogenetics of the Emydidae, but the short branches recovered deep in the tree also indicate the need for additional work on this clade to recover all intergeneric relationships with confidence and to delimit species for some problematic groups. Our simulation results suggest that moderate (in the few-to-tens of kb range) amounts of data are necessary to recover most emydid relationships with high support values. They also suggest that previous simulations that do not incorporate among-gene tree topological variance probably underestimate the amount of data needed to recover well supported phylogenies.

Dataset Information

When Is Enough, Enough? When Are More Observational Epidemiologic Studies Needed to Resolve a Research Question: Illustrations Using Biomarker-Cancer Associations.

Publications

When Is Enough, Enough? When Are More Observational Epidemiologic Studies Needed to Resolve a Research Question: Illustrations Using Biomarker-Cancer Associations.

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