Bias in genetic association studies and impact factor

Munafò, Marcus R; Stothart, George; Flint, Jonathan

doi:10.1038/mp.2008.77

Cited by 65 publications

(49 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quantification of the likelihood of bias in genetic studies of MDD is a necessary subsequent step. A preliminary report suggests that a selective reporting bias may take place in these studies [60]. Third, hints of bias cannot exclude the possibility that some of the biomarkers included in this study may have good discriminatory power because they only suggest the presence of bias and are not proof thereof.…”

Section: Discussionmentioning

confidence: 99%

Bias in Peripheral Depression Biomarkers

Carvalho

Köhler

Brunoni

et al. 2016

Psychother Psychosom

View full text Add to dashboard Cite

Background: To aid in the differentiation of individuals with major depressive disorder (MDD) from healthy controls, numerous peripheral biomarkers have been proposed. To date, no comprehensive evaluation of the existence of bias favoring the publication of significant results or inflating effect sizes has been conducted. Methods: Here, we performed a comprehensive review of meta-analyses of peripheral nongenetic biomarkers that could discriminate individuals with MDD from nondepressed controls. PubMed/MEDLINE, EMBASE, and PsycINFO databases were searched through April 10, 2015. Results: From 15 references, we obtained 31 eligible meta-analyses evaluating biomarkers in MDD (21,201 cases and 78,363 controls). Twenty meta-analyses reported statistically significant effect size estimates. Heterogeneity was high (I² ≥50%) in 29 meta-analyses. We plausibly assumed that the true effect size for a meta-analysis would equal the one of its largest study. A significant summary effect size estimate was observed for 20 biomarkers. We observed an excess of statistically significant studies in 21 meta-analyses. The summary effect size of the meta-analysis was higher than the effect of its largest study in 25 meta-analyses, while 11 meta-analyses had evidence of small-study effects. Conclusions: Our findings suggest that there is an excess of studies with statistically significant results in the literature of peripheral biomarkers for MDD. The selective publication of ‘positive studies' and the selective reporting of outcomes are possible mechanisms. Effect size estimates of meta-analyses may be inflated in this literature.

show abstract

Section: Discussionmentioning

confidence: 99%

Bias in Peripheral Depression Biomarkers

Carvalho

Köhler

Brunoni

et al. 2016

Psychother Psychosom

View full text Add to dashboard Cite

show abstract

“…Moreover, the situation is surely much worse than what the discussion above would suggest because in addition to testing many hypotheses with a low likelihood of effects, investigators often exploit hidden flexibility in their data analysis strategies, allowing the true alpha level to rise well above the nominal alpha level (Simmons, Nelson, & Simonsohn, 2011; see also discussion by Ioannidis, 2005b, of "bias" andWagenmakers et al, 2012, this issue, on "fairy tale factors"). Moreover, the highest impact journals famously tend to favor highly surprising results; this makes it easy to see how the proportion of false positive findings could be even higher in such journals than it would be in less career-enhancing outlets (Ioannidis, 2005a;Munafò, Stothart, & Flint, 2009). Naturally, those areas within psychology that lend themselves to performing a great number of tests on a variety of variables in any given study, as well as areas in which underpowered studies are more common, are likely more prone to false findings than are other areas.…”

mentioning

confidence: 99%

Is the Replicability Crisis Overblown? Three Arguments Examined

Pashler

Harris

2012

Perspect Psychol Sci

629

489

View full text Add to dashboard Cite

We discuss three arguments voiced by scientists who view the current outpouring of concern about replicability as overblown. The first idea is that the adoption of a low alpha level (e.g., 5%) puts reasonable bounds on the rate at which errors can enter the published literature, making false-positive effects rare enough to be considered a minor issue. This, we point out, rests on statistical misunderstanding: The alpha level imposes no limit on the rate at which errors may arise in the literature (Ioannidis, 2005b). Second, some argue that whereas direct replication attempts are uncommon, conceptual replication attempts are common-providing an even better test of the validity of a phenomenon. We contend that performing conceptual rather than direct replication attempts interacts insidiously with publication bias, opening the door to literatures that appear to confirm the reality of phenomena that in fact do not exist. Finally, we discuss the argument that errors will eventually be pruned out of the literature if the field would just show a bit of patience. We contend that there are no plausible concrete scenarios to back up such forecasts and that what is needed is not patience, but rather systematic reforms in scientific practice.

show abstract

“…The SNP8NRG243177 variant is of special relevance. Using a bioinformatic approach, Law et al 9 showed that this variant affects the binding of transcription factors to the 5 0 promoter region of the gene and is associated with the expression of a newly described isoform of the protein. Further studies are necessary to elucidate how this variant leads to pathological processes that increase the risk of psychosis, and how this genetic trait may interact with environmental factors.…”

mentioning

confidence: 99%