{ "_id": "6a4c962607c3fd5c3260e180", "Package": "testassay", "Type": "Package", "Title": "A Hypothesis Testing Framework for Validating an Assay for\nPrecision", "Version": "0.1.1", "Author": "Michael C Sachs and Michael P Fay", "Maintainer": "Michael C Sachs ", "Description": "A common way of validating a biological assay for is\nthrough a procedure, where m levels of an analyte are measured\nwith n replicates at each level, and if all m estimates of the\ncoefficient of variation (CV) are less than some prespecified\nlevel, then the assay is declared validated for precision\nwithin the range of the m analyte levels. Two limitations of\nthis procedure are: there is no clear statistical statement of\nprecision upon passing, and it is unclear how to modify the\nprocedure for assays with constant standard deviation. We\nprovide tools to convert such a procedure into a set of m\nhypothesis tests. This reframing motivates the m:n:q procedure,\nwhich upon completion delivers a 100q% upper confidence limit\non the CV. Additionally, for a post-validation assay output of\ny, the method gives an ``effective standard deviation\ninterval'' of log(y) plus or minus r, which is a 68% confidence\ninterval on log(mu), where mu is the expected value of the\nassay output for that sample. Further, the m:n:q procedure can\nbe straightforwardly applied to constant standard deviation\nassays. We illustrate these tools by applying them to a growth\ninhibition assay. This is an implementation of the methods\ndescribed in Fay, Sachs, and Miura (2018)\n.", "License": "MIT + file LICENSE", "LazyData": "TRUE", "VignetteBuilder": "knitr", "RoxygenNote": "7.1.0", "Repository": "https://sachsmc.r-universe.dev", "Date/Publication": "2020-06-03 13:04:23 UTC", "RemoteUrl": "https://github.com/sachsmc/testassay", "RemoteRef": "HEAD", "RemoteSha": "2dd44ce296bff8d0ce20022e1aab5593eac2cbcd", "NeedsCompilation": "no", "Packaged": { "Date": "2026-07-07 05:58:35 UTC", "User": "root" }, "_user": "sachsmc", "_type": "src", "_file": "testassay_0.1.1.tar.gz", "_fileid": "https://r2.ropensci.org/9c38b40d7096e63f3d74855b1b92dafc00210c9e637ae58f2c9ab1a999b1b832", "_filesize": 247057, "_sha256": "9c38b40d7096e63f3d74855b1b92dafc00210c9e637ae58f2c9ab1a999b1b832", "_expires": "2026-10-15T06:01:08.000Z", "_created": "2026-07-07T05:58:35.000Z", "_published": "2026-07-07T06:01:09.885Z", "_jobs": [ { "job": 85546873533, "time": 120, "config": "linux-devel-x86_64", "r": "4.7.0", 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5464185, "type": "user", "name": "Michael Sachs", "followers": 93 }, "_downloads": { "count": 224, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/testassay" }, "_devurl": "https://github.com/sachsmc/testassay", "_searchresults": 7, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "extra/testassay.html", "LICENSE", "manual.pdf" ], "_homeurl": "https://github.com/sachsmc/testassay", "_realowner": "sachsmc", "_cranurl": true, "_releases": [ { "version": "0.1.0", "date": "2016-11-29" }, { "version": "0.1.1", "date": "2020-06-03" } ], "_exports": [ "lognormConstCVCI", "normConstCVCI", "testassay" ], "_datasets": [ { "name": "gia", "title": "Growth Inhibition Assay", "object": "gia", "class": [ "data.frame" ], "fields": [ "parasite", "assay", "elisa", "gia", "sample", "meanAAgia", "varAAgia" ], "rows": 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