About this Research Topic

Background

Good research with negative results or null-hypothesis findings is not easily published, even if the studies have a sound biological basis, appropriate sample size, and are well designed. This contributes to publication bias because studies reporting significant results are more easily published than studies with negative results.

This bias prevents sound science from being published, thus causing an artificial disbalance between positive and negative finds that is often misleading. There is a great amount of crucial negative information that is being missed by the scientific community. When sound negative results are published, the scientific community can learn from these, avoid repeating experiments, the waste of public funds and manpower, and a delay in genuine scientific progress. This RT would help to have part of this information published.

The goal of this Research Topic is to encourage the publication of well-designed, statistically well-powered studies that provide conclusive negative or null-hypothesis evidence in the field of Pharmacogenetics and Pharmacogenomics. Hopefully, this will help to gather relevant negative information that could balance publication bias and provide negative evidence that can be eventually used in the design of procedures, or the formulation of recommendations for pharmacogenetics or pharmacogenomics implementation.

We encourage the submission of original studies or meta-analyses presenting negative or null-hypothesis findings related to the field of pharmacogenetics, pharmacogenomics, pharmacokinetics, polymorphic drug metabolism, pharmacodynamics, therapeutic effect, adverse drug events, or drug-drug interactions. Studies focusing on the in vitro effect of genetic variants, association studies of Pharmacogenetics or Pharmacogenomics variability with disease risk, as well as other topics related to Pharmacogenetics and Pharmacogenomics are also welcome.

Studies must be well-designed, have a plausible hypothesis, have good statistical power, rigorous data analysis methods, and assess confounders factors that might influence the results. These could include, for instance, case-control studies, GWAS or NextGen sequencing data for pharmacogenes that have not been reported, because of being negative, or meta-analyses with negative findings that make a substantial contribution to the working hypothesis, provided that the working hypothesis is solid and based on sound published evidence.

Keywords: Pharmacogenetics, pharmacogenomics, null hypothesis, negative, statistical power

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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