| Literature DB >> 33311448 |
Alec P Christie1, David Abecasis2, Mehdi Adjeroud3, Juan C Alonso4, Tatsuya Amano5, Alvaro Anton6, Barry P Baldigo7, Rafael Barrientos8, Jake E Bicknell9, Deborah A Buhl10, Just Cebrian11, Ricardo S Ceia12,13, Luciana Cibils-Martina14,15, Sarah Clarke16, Joachim Claudet17, Michael D Craig18,19, Dominique Davoult20, Annelies De Backer21, Mary K Donovan22,23, Tyler D Eddy24,25,26, Filipe M França27, Jonathan P A Gardner26, Bradley P Harris28, Ari Huusko29, Ian L Jones30, Brendan P Kelaher31, Janne S Kotiaho32,33, Adrià López-Baucells34,35,36, Heather L Major37, Aki Mäki-Petäys38,39, Beatriz Martín40,41, Carlos A Martín8, Philip A Martin42,43, Daniel Mateos-Molina44, Robert A McConnaughey45, Michele Meroni46, Christoph F J Meyer34,35,47, Kade Mills48, Monica Montefalcone49, Norbertas Noreika50,51, Carlos Palacín4, Anjali Pande26,52,53, C Roland Pitcher54, Carlos Ponce55, Matt Rinella56, Ricardo Rocha34,35,57, María C Ruiz-Delgado58, Juan J Schmitter-Soto59, Jill A Shaffer10, Shailesh Sharma60, Anna A Sher61, Doriane Stagnol20, Thomas R Stanley62, Kevin D E Stokesbury63, Aurora Torres64,65, Oliver Tully16, Teppo Vehanen66, Corinne Watts67, Qingyuan Zhao68, William J Sutherland42,43.
Abstract
Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs.Entities:
Year: 2020 PMID： 33311448 DOI： 10.1038/s41467-020-20142-y
Source DB: PubMed Journal: Nat Commun ISSN： 2041-1723 Impact factor: 14.919