Dataset Information

Rate and success of study replication in ecology and evolution.

ABSTRACT: The recent replication crisis has caused several scientific disciplines to self-reflect on the frequency with which they replicate previously published studies and to assess their success in such endeavours. The rate of replication, however, has yet to be assessed for ecology and evolution. Here, I survey the open-access ecology and evolution literature to determine how often ecologists and evolutionary biologists replicate, or at least claim to replicate, previously published studies. I found that approximately 0.023% of ecology and evolution studies are described by their authors as replications. Two of the 11 original-replication study pairs provided sufficient statistical detail for three effects so as to permit a formal analysis of replication success. Replicating authors correctly concluded that they replicated an original effect in two cases; in the third case, my analysis suggests that the finding by the replicating authors was consistent with the original finding, contrary the conclusion of "replication failure" by the authors.

SUBMITTER: Kelly CD

PROVIDER: S-EPMC6743472 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Rate and success of study replication in ecology and evolution.

Kelly Clint D CD

PeerJ 20190910

The recent replication crisis has caused several scientific disciplines to self-reflect on the frequency with which they replicate previously published studies and to assess their success in such endeavours. The rate of replication, however, has yet to be assessed for ecology and evolution. Here, I survey the open-access ecology and evolution literature to determine how often ecologists and evolutionary biologists replicate, or at least claim to replicate, previously published studies. I found t ...[more]

PMID: 31565572

Similar Datasets

Project description:Cancer therapy, even when highly targeted, typically fails because of the remarkable capacity of malignant cells to evolve effective adaptations. These evolutionary dynamics are both a cause and a consequence of cancer system heterogeneity at many scales, ranging from genetic properties of individual cells to large-scale imaging features. Tumors of the same organ and cell type can have remarkably diverse appearances in different patients. Furthermore, even within a single tumor, marked variations in imaging features, such as necrosis or contrast enhancement, are common. Similar spatial variations recently have been reported in genetic profiles. Radiologic heterogeneity within tumors is usually governed by variations in blood flow, whereas genetic heterogeneity is typically ascribed to random mutations. However, evolution within tumors, as in all living systems, is subject to Darwinian principles; thus, it is governed by predictable and reproducible interactions between environmental selection forces and cell phenotype (not genotype). This link between regional variations in environmental properties and cellular adaptive strategies may permit clinical imaging to be used to assess and monitor intratumoral evolution in individual patients. This approach is enabled by new methods that extract, report, and analyze quantitative, reproducible, and mineable clinical imaging data. However, most current quantitative metrics lack spatialness, expressing quantitative radiologic features as a single value for a region of interest encompassing the whole tumor. In contrast, spatially explicit image analysis recognizes that tumors are heterogeneous but not well mixed and defines regionally distinct habitats, some of which appear to harbor tumor populations that are more aggressive and less treatable than others. By identifying regional variations in key environmental selection forces and evidence of cellular adaptation, clinical imaging can enable us to define intratumoral Darwinian dynamics before and during therapy. Advances in image analysis will place clinical imaging in an increasingly central role in the development of evolution-based patient-specific cancer therapy.

Project description:IntroductionPatients with differentiated thyroid cancer (DTC) are treated with (near)-total thyroidectomy followed by remnant ablation. Optimal radioiodine-131 (131I) uptake is achieved by withholding thyroid hormone (THW), pretreatment with recombinant human Thyrotropin Stimulating Hormone (rhTSH) is an alternative. Six randomized trials have been published comparing THW and rhTSH, however comparison is difficult because an uniform definition of ablation success is lacking. Using a strict definition, we performed an observational study aiming to determine the efficacy of rhTSH as preparation for remnant ablation.Patients and methodsAdult DTC patients with, tumor stage T1b to T3, Nx, N0 and N1, M0 were included in a prospective multicenter observational study with a fully sequential design, using a stopping rule. All patients received remnant ablation with 131I using rhTSH. Ablation success was defined as no visible uptake in the original thyroid bed on a rhTSH stimulated 150 MBq 131I whole body scan (WBS) 9 months after remnant ablation, or no visible uptake in the original thyroid bed on a post therapeutic WBS when a second high dose was necessary.ResultsAfter interim analysis of the first 8 patients, the failure rate was estimated to be 69% (90% confidence interval (CI) 20-86%) and the inclusion of new patients had to be stopped. Final analysis resulted in an ablation success in 11 out of 17 patients (65%, 95% CI 38-86%).ConclusionAccording to this study, the efficacy of rhTSH in the preparation of 131I ablation therapy is inferior, when using a strict definition of ablation success. The current lack of agreement as to the definition of successful remnant ablation, makes comparison between different ablation strategies difficult. Our results point to the need for an international consensus on the definition of ablation success, not only in routine patient's care but also for scientific reasons.Trial registrationDutch Trial Registration NTR2395.

Dataset Information

Rate and success of study replication in ecology and evolution.

Publications

Rate and success of study replication in ecology and evolution.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets