Yeah, so... there is abundant literature on quality assessment. A few samples: - deficiencies in the reporting methodology https://www.ncbi.nlm.nih.gov/pmc/art

Yeah, so... there is abundant literature on quality assessment. A few samples: - deficiencies in the reporting methodology https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3554513/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3554513/ - a bit of incorrect retraction https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3899113/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3899113/ - a fun one on systematic reviews https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4785311/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4785311/ - regarding missing data https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4748550/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4748550/ - a paywalled abstract about power https://www.ncbi.nlm.nih.gov/pubmed/26677241 https://www.ncbi.nlm.nih.gov/pubmed/26677241 - on sample size calculations https://www.ncbi.nlm.nih.gov/pubmed/25523375 https://www.ncbi.nlm.nih.gov/pubmed/25523375 - aaand a fun one on selection bias https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4566301/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4566301/ All found within 10 minutes... We could go on like that for hours with this sizing contest. I do not expect to convince you. You will, if you put the time and effort into it, find other studies saying the opposite (although being less sexy for publishers, they will be harder to find). If you are somewhat knowledgeable in the field of statistics, please take a look a the numbers, as I am quite sure you will find them appalling (19% of study population missing outcome data and 27.9% underpowered studies, anyone?)