How to End Selective Reporting in Animal Research
Year: 2016
Type: book-chapter
Abstract: Reporting bias occurs if the probability of publication depends on the magnitude, direction or statistical significance of the study results. The widespread misinterpretation and misuse of statistical significance testing along with intellectual or financial conflicts of interest appear to be major drivers of reporting bias. Reporting bias invalidates systematic reviews and meta-analyses and corrupts the cumulative scientific record. Increasingly, selective reporting of research outcomes is seen as a form of research misbehaviour. More generally, the wasteful aspects of our current system of producing scientific evidence are being criticized. The grave consequences of selective reporting in clinical research can be illustrated by the pernicious chain from selectively reporting positive findings, to a biased evidence base, to biased systematic reviews and misleading treatment guidelines, finally resulting in flawed healthcare decisions and substantial loss of quality and duration of human life. It is plausible and there is evidence that the experimental animal literature too is substantially biased due to selective reporting practices. The economic, scientific and ethical impact of selective reporting in animal research is an under-researched topic. Nevertheless, the general issues are the same as in clinical research: misguided follow-up studies and potential harm through needless replication attempts and premature first-in-man studies. In this chapter, we argue that a future free of reporting bias can be achieved through extension of the Institutional Animal Care and Use Committees' (IACUC) remit with comprehensive monitoring responsibilities and closer collaboration with funding bodies. We present the outlines of a smart, textmining-technology-employing electronic system that monitors the progress of all IACUC-approved experiments. Combined with appropriate sanctions, such a system could put an end to outcome-conditional reporting. The retrospective and complete removal of bias from the aggregate currently existing literature requires other initiatives.
Authors: Gerben ter Riet, Lex M. Bouter
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2548936888