{ "_id": "6a235e85530b9bc726bd6035", "Package": "CoRpower", "Title": "Power Calculations for Assessing Correlates of Risk in Clinical\nEfficacy Trials", "Version": "1.0.4", "Authors@R": "c(person(\"Stephanie\", \"Wu\", email = \"swu18@uw.edu\", role = \"aut\"), person(\"Michal\", \"Juraska\", email = \"mjuraska@fredhutch.org\", role = c(\"aut\", \"cre\")), person(\"Peter\", \"Gilbert\", email = \"pgilbert@scharp.org\", role = \"aut\"), person(\"Yunda\", \"Huang\", email = \"yunda@scharp.org\", role = \"aut\"))", "URL": "https://github.com/mjuraska/CoRpower", "Description": "Calculates power for assessment of intermediate biomarker\nresponses as correlates of risk in the active treatment group\nin clinical efficacy trials, as described in Gilbert, Janes,\nand Huang, Power/Sample Size Calculations for Assessing\nCorrelates of Risk in Clinical Efficacy Trials (2016,\nStatistics in Medicine). The methods differ from past\napproaches by accounting for the level of clinical treatment\nefficacy overall and in biomarker response subgroups, which\nenables the correlates of risk results to be interpreted in\nterms of potential correlates of efficacy/protection. The\nmethods also account for inter-individual variability of the\nobserved biomarker response that is not biologically relevant\n(e.g., due to technical measurement error of the laboratory\nassay used to measure the biomarker response), which is\nimportant because power to detect a specified correlate of risk\neffect size is heavily affected by the biomarker's measurement\nerror. The methods can be used for a general binary clinical\nendpoint model with a univariate dichotomous, trichotomous, or\ncontinuous biomarker response measured in active treatment\nrecipients at a fixed timepoint after randomization, with\neither case-cohort Bernoulli sampling or case-control\nwithout-replacement sampling of the biomarker (a baseline\nbiomarker is handled as a trivial special case). In a specified\ntwo-group trial design, the computeN() function can initially\nbe used for calculating additional requisite design parameters\npertaining to the target population of active treatment\nrecipients observed to be at risk at the biomarker sampling\ntimepoint. Subsequently, the power calculation employs an\ninverse probability weighted logistic regression model fitted\nby the tps() function in the 'osDesign' package. Power results\nas well as the relationship between the correlate of risk\neffect size and treatment efficacy can be visualized using\nvarious plotting functions. To link power calculations for\ndetecting a correlate of risk and a correlate of treatment\nefficacy, a baseline immunogenicity predictor (BIP) can be\nsimulated according to a specified classification rule (for\ndichotomous or trichotomous BIPs) or correlation with the\nbiomarker response (for continuous BIPs), then outputted along\nwith biomarker response data under assignment to treatment, and\nclinical endpoint data for both treatment and placebo groups.", "BugReports": "https://github.com/mjuraska/CoRpower/issues", "License": "GPL-2", "Encoding": "UTF-8", "RoxygenNote": "7.1.1", "VignetteBuilder": "knitr", "Repository": "https://mjuraska.r-universe.dev", "Date/Publication": "2026-06-05 23:01:09 UTC", "RemoteUrl": "https://github.com/mjuraska/corpower", "RemoteRef": "HEAD", "RemoteSha": "85f10e177a2f4a89053d93e247872efa56a13380", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-05 23:37:48 UTC", "User": "root" }, "Author": "Stephanie Wu [aut],\nMichal Juraska [aut, cre],\nPeter Gilbert [aut],\nYunda Huang [aut]", "Maintainer": "Michal Juraska ", "MD5sum": "e9e5795e9f7f536d11d25791def2679b", "_user": "mjuraska", "_type": "src", "_file": "CoRpower_1.0.4.tar.gz", "_fileid": "b6bade1eb6f4b62b1b39c2e84aa3b3cbe334c96a6728a1e8f5e0b30726b02301", "_filesize": 294342, "_sha256": "b6bade1eb6f4b62b1b39c2e84aa3b3cbe334c96a6728a1e8f5e0b30726b02301", "_created": "2026-06-05T23:37:48.000Z", "_published": "2026-06-05T23:40:52.981Z", "_distro": "noble", "_jobs": [ { "job": 79831315696, "time": 133, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7448888030" }, { "job": 79831315698, "time": 133, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7448887771" }, { "job": 79831315697, "time": 113, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7448884322" }, { "job": 79831315700, "time": 152, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7448891696" }, { "job": 79831081156, "time": 151, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7448863034" }, { "job": 79831315695, "time": 92, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7448880404" }, { "job": 79831315724, "time": 101, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7448882085" }, { "job": 79831315703, "time": 127, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7448886917" }, { "job": 79831315705, "time": 104, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7448882604" } ], "_buildurl": "https://github.com/r-universe/mjuraska/actions/runs/27045683718", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/mjuraska/corpower", "_commit": { "id": "85f10e177a2f4a89053d93e247872efa56a13380", "author": "Michal Juraska ", "committer": "Michal Juraska ", "message": "Fix handling of dichotomous biomarker that caused crashing\n", "time": 1780700469 }, "_maintainer": { "name": "Michal Juraska", "email": "mjuraska@fredhutch.org", "login": "mjuraska", "description": "A biostatistician at Fred Hutch", "uuid": 9420213 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "survival", "role": "Imports" }, { "package": "osDesign", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" } ], "_owner": "mjuraska", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2026-23", "n": 1 } ], "_tags": [], "_stars": 0, "_contributors": [ { "user": "smwu", "count": 89, "uuid": 33609713 }, { "user": "mjuraska", "count": 59, "uuid": 9420213 } ], "_userbio": { "uuid": 9420213, "type": "user", "name": "Michal Juraska", "description": "A biostatistician at Fred Hutch" }, "_downloads": { "count": 190, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/CoRpower" }, "_devurl": "https://github.com/mjuraska/corpower", "_searchresults": 15, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/CoRpower.html", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/mjuraska/corpower", "_realowner": "mjuraska", "_cranurl": true, "_releases": [ { "version": "1.0.0", "date": "2018-10-06" }, { "version": "1.0.1", "date": "2019-04-25" }, { "version": "1.0.2", "date": "2019-06-19" }, { "version": "1.0.3", "date": "2019-09-27" }, { "version": "1.0.4", "date": "2020-11-17" } ], "_exports": [ "computeN", "computePower", "plotPowerCont", "plotPowerTri", "plotROCcurveTri", "plotRRgradVE", "plotVElatCont" ], "_help": [ { "page": "computeN", "title": "Estimation of Size and Numbers of Cases and Controls in the Target Population of Active Treatment Recipients At Risk at the Biomarker Sampling Timepoint", "topics": [ "computeN" ] }, { "page": "computePower", "title": "Power Calculations for Assessing Intermediate Biomarkers as Correlates of Risk in the Active Treatment Group in Clinical Efficacy Trials, Accounting for Biomarker's Measurement Error and Treatment Efficacy", "topics": [ "computePower" ] }, { "page": "plotPowerCont", "title": "Plotting of Power Curve versus Correlate of Risk Effect Size for Continuous Biomarkers", "topics": [ "plotPowerCont" ] }, { "page": "plotPowerTri", "title": "Plotting of Power versus Correlate of Risk Effect Size for Dichotomous and Trichotomous Biomarkers", "topics": [ "plotPowerTri" ] }, { "page": "plotROCcurveTri", "title": "Plotting of ROC Curves for Trichotomous Biomarkers", "topics": [ "plotROCcurveTri" ] }, { "page": "plotRRgradVE", "title": "Plotting of the Ratio of Relative Risks for Higher/Lower Latent Subgroups against Correlate of Risk Effect Size for Trichotomous Biomarkers", "topics": [ "plotRRgradVE" ] }, { "page": "plotVElatCont", "title": "Plotting Treatment (Vaccine) Efficacy Curves for Different Correlate of Risk Relative Risks for Continuous Biomarkers", "topics": [ "plotVElatCont" ] } ], "_readme": "https://github.com/mjuraska/corpower/raw/HEAD/README.md", "_rundeps": [ "lattice", "Matrix", "osDesign", "survival" ], "_vignettes": [ { "source": "placeboAndBIPdataSimulationAlgorithms.Rmd", "filename": "placeboAndBIPdataSimulationAlgorithms.html", "title": "CoRpower's Algorithms for Simulating Placebo Group and Baseline Immunogenicity Predictor Data", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Algorithms for Simulating Placebo Group Data", "Trichotomous (, X) and (, S(1)) Using Approach 1", "Trichotomous (, X) and (, S(1)) Using Approach 2", "Continuous (, X^) and (, S^(1))", "Algorithms for Simulating a Baseline Immunogenicity Predictor (BIP)", "Trichotomous (, X, S(1),) and (, BIP) Using Approach 1", "Trichotomous (, X, S(1),) and (, BIP) Using Approach 2", "Continuous (, X^, S^(1),) and (, BIP^*)" ], "created": "2019-04-16 18:43:06", "modified": "2019-04-17 23:08:39", "commits": 3 }, { "source": "CoRpowerIntroduction.Rmd", "filename": "CoRpowerIntroduction.html", "title": "Introduction to R Package CoRpower", "engine": "knitr::rmarkdown", "headings": [ "Set-up and notation | Without replacement sampling", "Algorithm for trichotomous biomarker $S(1)$ | Without replacement sampling", "Illustration: hypothetical randomized placebo-controlled VE trial", "Trial design", "Illustration: calculation of input parameters with computeN()", "Assumptions", "Number of vaccine recipients observed to be at risk at $\\tau$", "Number of observed cases in vaccine recipients between $\\tau$ and $\\tau_{\\mathrm{max}}$", "Number of observed controls in vaccine recipients between $\\tau$ and $\\tau_{\\mathrm{max}}$", "Number of observed cases (controls) in vaccine recipients between $\\tau$ and $\\tau_{\\mathrm{max}}$ with measured $S(1)$", "Compute $N_1, n_{cases,1}, n_{controls,1},$ and $n^S_{cases,1}$ with computeN()", "Illustration: CoRpower for trichotomous (, S(1)) | Without replacement sampling", "Scenario 1: vary control:case ratio (Approach 1) | Trichotomous $S(1)$, without replacement sampling", "Run simulations and compute power with computePower()", "Plot power curves with plotPowerTri()", "Scenario 2: vary (, Sens) and (, Spec) (Approach 1) | Trichotomous (, S(1)), without replacement sampling", "Scenario 3: vary (, P^{lat}_0, P^{lat}_2, P_0, P_2) (Approach 1) | Trichotomous (, S(1)), without replacement sampling", "Scenario 4: vary (, \\rho ) (Approach 2) | Trichotomous (, S(1)), without replacement sampling", "Plot $RR_t$ vs. $RR_2^{lat}/RR_0^{lat}$ with plotRRgradVE()", "Plot ROC curves with plotROCcurveTri()", "Scenario 5: vary (, P^{lat}_0, P_0, P^{lat}_2, P_2 ) (Approach 2) | Trichotomous (, S(1)), without replacement sampling", "Scenario 6: vary (, n_{cases,1}) (Approach 2) | Trichotomous (, S(1)), without replacement sampling", "Illustration: CoRpower for binary (, S(1)) | Without replacement sampling", "Scenario 7: vary (, n_{cases,1}) (Approach 2) | Binary (, S(1)), without replacement sampling", "Algorithm for continuous biomarker (, S^{\\ast}(1)) | Without replacement sampling", "Illustration: CoRpower for continuous (, S^{\\ast}(1)) | Without replacement sampling", "Scenario 8: vary (, \\rho ) | Continuous (, S^{\\ast}(1)), without replacement sampling", "Plot power curves with plotPowerCont()", "Plot $VE^{lat}_{x^{\\ast}}$ curves with plotVElatCont()", "Scenario 9: vary (, P_{lowestVE}^{lat} ) | Continuous (, S^{\\ast}(1)), without replacement sampling", "Bernoulli / case-cohort sampling of (, S(1)) (or (, S^{\\ast}(1)))", "Illustration: CoRpower for trichotomous (, S(1)) and continuous (, S^{\\ast}(1)) | Bernoulli sampling", "Scenario 10: vary (, p ) (Approach 1) | Trichotomous (, S(1) ), Bernoulli sampling", "Scenario 11: vary (, p ) | Continuous (, S^{\\ast}(1)), Bernoulli sampling" ], "created": "2019-04-16 18:43:06", "modified": "2019-09-27 21:25:09", "commits": 4 } ], "_score": 4.176091259055681, 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