31:T756,html`

LRT Signal Analysis Application

The FDA Adverse Event Reporting System (FAERS) is a database that contains adverse event reports, medication error reports and product quality complaints resulting in adverse events that were submitted to FDA. The database is designed to support the FDA's post-marketing safety surveillance program for drug and therapeutic biologic products.

Several statistical methods that are available in the literature to analyze postmarket safety databases, such as the U.S. Federal Drug Administration’s (FDA) adverse event reporting system (FAERS), for identifying drug-event combinations with disproportionately high frequencies, are subject to high false discovery rates. Likelihood ratio test (LRT) based method, via an extensive simulation study, retains good power and sensitivity for identifying signals, as well as controls both the Type I error and false discovery rates.

Tutorials: LRT Method, Application Part 1 & Part 2

`32:T40c,heatMapAE = addAnimation( addTooltips( Plot.plot({ padding: 0.01, marginLeft: 250, marks: [ Plot.cell( JSON.parse(LRTResultYear), { x: (d) => d.year, y: (d) => d.AE, fill: (d) => d.pvalue, fillOpacity: 0.8, inset: 0.5, title: (d) => `${d.AE} \nYear: ${d.year} \nIs Signal: ${d.is_signal} \nLogLR: ${parseFloat(d.logLR).toFixed(2)} \nP-Value: ${d.pvalue} \nAlpha: ${d.alpha}` } ), Plot.text( JSON.parse(LRTResultYear), { x: "year", y: "AE", text: d => d.is_signal == true ? '❕' : '' } ) ], color: { type: "linear", domain: [0, 0.06, 1], range: ["#fc836a", "#3BAFDA", "lightblue"] }, x: { axis: "top", label: '' }, y: { label: '' }, width, height: 1500 }) ), { type: "rect", delay: 2 } )33:Tb60,// async function generateLRTData() { // let url = 'https://api.fda.gov/drug/event.json?search=_exists_:(patient.drug.openfda.generic_name)+AND+patient.drug.openfda.generic_name:(%22'+options.drugName+'%22)+AND+receivedate:(['+options.dateFrom.toISOString().slice(0, 10)+'+TO+'+options.dateTo.toISOString().slice(0, 10)+'])&limit=1000&count=patient.reaction.reactionmeddrapt.exact&skip=0'; // let raw = await fetch(url).then(res => res.json()); // let url2 = 'https://api.fda.gov/drug/event.json?search=_exists_:(patient.drug.openfda.generic_name)+AND+_exists_:(patient.reaction.reactionmeddrapt.exact)+AND+_exists_:(patient.drug.openfda.generic_name)+AND+receivedate:(['+options.dateFrom.toISOString().slice(0, 10)+'+TO+'+options.dateTo.toISOString().slice(0, 10)+'])&count=patient.reaction.reactionmeddrapt.exact&limit=1000&sort=patient.reaction.reactionmeddrapt.exact:desc'; // let raw2 = await fetch(url2).then(res => res.json()); // let dataTop = raw.results.sort((a,b) => b.count - a.count).filter((n,i) => i < options.maxNumberOfEvent).map(n => ({AE: n.term,ntrt: +n.count})); // let dataTopTerm = [...new Set(dataTop.map(n => n.AE))]; // let dataOther = raw.results.sort((a,b) => b.count - a.count).filter((n,i) => i >= options.maxNumberOfEvent).map(n => ({AE: n.term,ntrt: +n.count})); // let countDrugOtherTotal = dataOther.reduce((p,c)=>{p += c.ntrt; return p;}, 0); // let dataAll = raw2.results.sort((a,b) => b.count - a.count).map(n => ({AE: n.term,nidot: +n.count})); // let dataUnMatch = aq.from(dataTop).antijoin(aq.from(dataAll), ['AE', 'AE'], [aq.all(), aq.not('AE')]); // let dataUnMatchTerm = [...new Set(dataUnMatch.objects().map(n => n.AE))]; // for (let k of dataUnMatchTerm) { // let kStr = k.split(' ').join('%20'); // let urlK = 'https://api.fda.gov/drug/event.json?search=_exists_:(patient.drug.openfda.generic_name)+AND+_exists_:(patient.drug.openfda.generic_name)+AND+patient.reaction.reactionmeddrapt.exact:(%22'+kStr+'%22)+AND+receivedate:(['+options.dateFrom.toISOString().slice(0, 10)+'+TO+'+options.dateTo.toISOString().slice(0, 10)+'])&limit=1&skip=0'; // let dataK = await fetch(urlK).then(res => res.json()) // dataAll.push({'AE': k, 'nidot': dataK.meta.results.total}); // } // let countDrugAllFinalOther = dataAll.filter(n => dataTopTerm.indexOf(n.AE) == -1).reduce((p,c) => {p += c.nidot; return p;},0); // let dataMatch = aq.from(dataTop).join_left(aq.from(dataAll), ['AE', 'AE'], [aq.all(), aq.not('AE')]).objects() // .filter(n => n.nidot !== undefined); // dataMatch.push({ // 'AE': 'Other', // 'ntrt': countDrugOtherTotal, // 'nidot': countDrugAllFinalOther // }); // dataMatch.map(n => n.ndotj = dataMatch.reduce((p,c) => {p += c.ntrt; return p;}, 0)); // dataMatch.map(n => n.ndotdot = dataMatch.reduce((p,c) => {p += c.nidot; return p;}, 0)); // return dataMatch; // }34:T1068,async function generateLRTData() { // ========================= // Date formatting // ========================= const fromDate = options.dateFrom.toISOString().slice(0, 10); const toDate = options.dateTo.toISOString().slice(0, 10); // ========================= // Drug-specific AE counts // ========================= let url = `https://api.fda.gov/drug/event.json?api_key=${openFDA_api_key}` + `&search=` + `_exists_:patient.drug.openfda.generic_name` + `+AND+patient.drug.openfda.generic_name:"${options.drugName}"` + `+AND+receivedate:[${fromDate}+TO+${toDate}]` + `&limit=1000` + `&count=patient.reaction.reactionmeddrapt.exact` + `&skip=0`; let raw = await fetch(url).then(res => res.json()); if (!raw?.results) { console.warn("Drug-specific query failed:", raw); return []; } // ========================= // Background AE counts // ========================= let url2 = `https://api.fda.gov/drug/event.json?api_key=${openFDA_api_key}` + `&search=` + `_exists_:patient.drug.openfda.generic_name` + `+AND+_exists_:patient.reaction.reactionmeddrapt` + `+AND+receivedate:[${fromDate}+TO+${toDate}]` + `&count=patient.reaction.reactionmeddrapt.exact` + `&limit=1000`; let raw2 = await fetch(url2).then(res => res.json()); if (!raw2?.results) { console.warn("Background query failed:", raw2); return []; } // ========================= // Top drug-specific events // ========================= let dataTop = raw.results .slice() .sort((a, b) => b.count - a.count) .filter((n, i) => i < options.maxNumberOfEvent) .map(n => ({ AE: n.term, ntrt: +n.count })); let dataTopTerm = [...new Set(dataTop.map(n => n.AE))]; // ========================= // Other events // ========================= let dataOther = raw.results .slice() .sort((a, b) => b.count - a.count) .filter((n, i) => i >= options.maxNumberOfEvent) .map(n => ({ AE: n.term, ntrt: +n.count })); let countDrugOtherTotal = dataOther.reduce((p, c) => p + c.ntrt, 0); // ========================= // Background totals // ========================= let dataAll = raw2.results .slice() .sort((a, b) => b.count - a.count) .map(n => ({ AE: n.term, nidot: +n.count })); // ========================= // Find unmatched terms // ========================= let dataUnMatch = aq.from(dataTop) .antijoin( aq.from(dataAll), ['AE', 'AE'], [aq.all(), aq.not('AE')] ); let dataUnMatchTerm = [...new Set(dataUnMatch.objects().map(n => n.AE))]; // ========================= // Query unmatched terms // ========================= for (let k of dataUnMatchTerm) { let urlK = `https://api.fda.gov/drug/event.json?api_key=${openFDA_api_key}` + `&search=` + `_exists_:patient.drug.openfda.generic_name` + `+AND+patient.reaction.reactionmeddrapt.exact:"${k}"` + `+AND+receivedate:[${fromDate}+TO+${toDate}]` + `&limit=1` + `&skip=0`; let dataK = await fetch(urlK).then(res => res.json()); dataAll.push({ AE: k, nidot: dataK?.meta?.results?.total || 0 }); } // ========================= // Other background counts // ========================= let countDrugAllFinalOther = dataAll .filter(n => dataTopTerm.indexOf(n.AE) === -1) .reduce((p, c) => p + c.nidot, 0); // ========================= // Merge // ========================= let dataMatch = aq.from(dataTop) .join_left( aq.from(dataAll), ['AE', 'AE'], [aq.all(), aq.not('AE')] ) .objects() .filter(n => n.nidot !== undefined); dataMatch.push({ AE: 'Other', ntrt: countDrugOtherTotal, nidot: countDrugAllFinalOther }); // ========================= // Totals // ========================= let totalNtrt = dataMatch.reduce((p, c) => p + c.ntrt, 0); let totalNidot = dataMatch.reduce((p, c) => p + c.nidot, 0); dataMatch.map(n => n.ndotj = totalNtrt); dataMatch.map(n => n.ndotdot = totalNidot); return dataMatch; }35:Tbb2,// async function generateLRTYearData() { // let LRTYearDataContainer = []; // for(let i of yearList){ // let url = 'https://api.fda.gov/drug/event.json?search=_exists_:(patient.drug.openfda.generic_name)+AND+patient.drug.openfda.generic_name:(%22'+options.drugName+'%22)+AND+receivedate:(['+i+'-01-01+TO+'+i+'-12-31])&limit=1000&count=patient.reaction.reactionmeddrapt.exact&skip=0'; // let raw = await fetch(url).then(res => res.json()); // let url2 = 'https://api.fda.gov/drug/event.json?search=_exists_:(patient.drug.openfda.generic_name)+AND+_exists_:(patient.reaction.reactionmeddrapt.exact)+AND+_exists_:(patient.drug.openfda.generic_name)+AND+receivedate:(['+i+'-01-01+TO+'+i+'-12-31])&count=patient.reaction.reactionmeddrapt.exact&limit=1000&sort=patient.reaction.reactionmeddrapt.exact:desc'; // let raw2 = await fetch(url2).then(res => res.json()); // let dataTop = raw.results.sort((a,b) => b.count - a.count).filter((n,i) => i < options.maxNumberOfEvent).map(n => ({Year: i.toString(), AE: n.term,ntrt: +n.count})); // let dataTopTerm = [...new Set(dataTop.map(n => n.AE))]; // let dataOther = raw.results.sort((a,b) => b.count - a.count).filter((n,i) => i >= options.maxNumberOfEvent).map(n => ({Year: i.toString(),AE: n.term,ntrt: +n.count})); // let countDrugOtherTotal = dataOther.reduce((p,c)=>{p += c.ntrt; return p;}, 0); // let dataAll = raw2.results.sort((a,b) => b.count - a.count).map(n => ({Year: i, AE: n.term, nidot: +n.count})); // let dataUnMatch = aq.from(dataTop).antijoin(aq.from(dataAll), ['AE', 'AE'], [aq.all(), aq.not('AE')]); // let dataUnMatchTerm = [...new Set(dataUnMatch.objects().map(n => n.AE))]; // for (let k of dataUnMatchTerm) { // let kStr = k.split(' ').join('%20'); // let urlK = 'https://api.fda.gov/drug/event.json?search=_exists_:(patient.drug.openfda.generic_name)+AND+_exists_:(patient.drug.openfda.generic_name)+AND+patient.reaction.reactionmeddrapt.exact:(%22'+kStr+'%22)+AND+receivedate:(['+i+'-01-01+TO+'+i+'-12-31])&limit=1&skip=0'; // let dataK = await fetch(urlK).then(res => res.json()) // dataAll.push({'AE': k, 'nidot': dataK.meta.results.total}); // } // let countDrugAllFinalOther = dataAll.filter(n => dataTopTerm.indexOf(n.AE) == -1).reduce((p,c) => {p += c.nidot; return p;},0); // let dataMatch = aq.from(dataTop).join_left(aq.from(dataAll), ['AE', 'AE'], [aq.all(), aq.not(['Year', 'AE'])]).objects() // .filter(n => n.nidot !== undefined); // dataMatch.push({ // 'Year': i.toString(), // 'AE': 'Other', // 'ntrt': countDrugOtherTotal, // 'nidot': countDrugAllFinalOther // }); // dataMatch.map(n => n.ndotj = dataMatch.reduce((p,c) => {p += c.ntrt; return p;}, 0)); // dataMatch.map(n => n.ndotdot = dataMatch.reduce((p,c) => {p += c.nidot; return p;}, 0)); // LRTYearDataContainer.push(...dataMatch); // } // return LRTYearDataContainer; // }36:Teea,async function generateLRTYearData() { let LRTYearDataContainer = []; for (let i of yearList) { // ========================= // Drug-specific AE counts // ========================= let url = `https://api.fda.gov/drug/event.json?api_key=${openFDA_api_key}` + `&search=` + `_exists_:patient.drug.openfda.generic_name` + `+AND+patient.drug.openfda.generic_name:"${options.drugName}"` + `+AND+receivedate:[${i}-01-01+TO+${i}-12-31]` + `&limit=1000` + `&count=patient.reaction.reactionmeddrapt.exact` + `&skip=0`; let raw = await fetch(url).then(res => res.json()); if (!raw?.results) { console.warn(`Drug-specific query failed for year ${i}:`, raw); continue; } // ========================= // Background AE counts // ========================= let url2 = `https://api.fda.gov/drug/event.json?api_key=${openFDA_api_key}` + `&search=` + `_exists_:patient.drug.openfda.generic_name` + `+AND+_exists_:patient.reaction.reactionmeddrapt` + `+AND+receivedate:[${i}-01-01+TO+${i}-12-31]` + `&count=patient.reaction.reactionmeddrapt.exact` + `&limit=1000`; let raw2 = await fetch(url2).then(res => res.json()); if (!raw2?.results) { console.warn(`Background query failed for year ${i}:`, raw2); continue; } let dataTop = raw.results .slice() .sort((a, b) => b.count - a.count) .filter((n, idx) => idx < options.maxNumberOfEvent) .map(n => ({ Year: i.toString(), AE: n.term, ntrt: +n.count })); let dataTopTerm = [...new Set(dataTop.map(n => n.AE))]; let dataOther = raw.results .slice() .sort((a, b) => b.count - a.count) .filter((n, idx) => idx >= options.maxNumberOfEvent) .map(n => ({ Year: i.toString(), AE: n.term, ntrt: +n.count })); let countDrugOtherTotal = dataOther.reduce((p, c) => p + c.ntrt, 0); let dataAll = raw2.results .slice() .sort((a, b) => b.count - a.count) .map(n => ({ Year: i.toString(), AE: n.term, nidot: +n.count })); let dataUnMatch = aq.from(dataTop) .antijoin( aq.from(dataAll), ["AE", "AE"], [aq.all(), aq.not("AE")] ); let dataUnMatchTerm = [...new Set(dataUnMatch.objects().map(n => n.AE))]; for (let k of dataUnMatchTerm) { let urlK = `https://api.fda.gov/drug/event.json?api_key=${openFDA_api_key}` + `&search=` + `_exists_:patient.drug.openfda.generic_name` + `+AND+patient.reaction.reactionmeddrapt.exact:"${k}"` + `+AND+receivedate:[${i}-01-01+TO+${i}-12-31]` + `&limit=1` + `&skip=0`; let dataK = await fetch(urlK).then(res => res.json()); dataAll.push({ Year: i.toString(), AE: k, nidot: dataK?.meta?.results?.total || 0 }); } let countDrugAllFinalOther = dataAll .filter(n => dataTopTerm.indexOf(n.AE) === -1) .reduce((p, c) => p + c.nidot, 0); let dataMatch = aq.from(dataTop) .join_left( aq.from(dataAll), ["AE", "AE"], [aq.all(), aq.not(["Year", "AE"])] ) .objects() .filter(n => n.nidot !== undefined); dataMatch.push({ Year: i.toString(), AE: "Other", ntrt: countDrugOtherTotal, nidot: countDrugAllFinalOther }); let totalNtrt = dataMatch.reduce((p, c) => p + c.ntrt, 0); let totalNidot = dataMatch.reduce((p, c) => p + c.nidot, 0); dataMatch.map(n => n.ndotj = totalNtrt); dataMatch.map(n => n.ndotdot = totalNidot); LRTYearDataContainer.push(...dataMatch); } return LRTYearDataContainer; }37:T8e7,funcLRT = py`import numpy as np import pandas as pd def flogLRv(x, fnidot, fndotj, fndotdot): logLRvr = x * (np.log(x) - np.log(fnidot)) logLRvr = logLRvr + (fndotj - x) * (np.log(fndotj - x) - np.log(fndotdot - fnidot)) logLRvr = logLRvr - fndotj * (np.log(fndotj) - np.log(fndotdot)); logLRvr.fillna(0) pj = x / fnidot qj = (fndotj - x) / (fndotdot - fnidot) logLRvr[ pj < qj ] = 0 return logLRvr def fLRT(qnum, number, alpha = 0.05, alphaSpending = 1): mylist = qnum mylist["ntrt"] = pd.to_numeric(mylist["ntrt"]) mylist["nidot"] = pd.to_numeric(mylist["nidot"]) mylist["ndotj"] = pd.to_numeric(mylist["ndotj"]) mylist["ndotdot"] = pd.to_numeric(mylist["ndotdot"]) mylist["RR"] = (mylist["ntrt"])*((mylist["ndotdot"]-mylist["nidot"])/(mylist["nidot"]*(mylist["ndotj"]-mylist["ntrt"]))) mylist["stdRR"] = 1/(mylist["ntrt"]) - 1/mylist["nidot"] + 1/(mylist["ndotj"]-mylist["ntrt"]) - 1/(mylist["ndotdot"]-mylist["nidot"]) mylist["stdRR"] = np.sqrt(mylist["stdRR"]) mylist["lowRR"] = np.log(mylist["RR"])- 1.96*(mylist["stdRR"]) mylist["lowRR"] = np.exp(mylist["lowRR"]) mylist["uppRR"] = np.log(mylist["RR"])+ 1.96*(mylist["stdRR"]) mylist["uppRR"] = np.exp(mylist["uppRR"]) mylist["logLR"] = flogLRv(mylist["ntrt"], mylist["nidot"], mylist["ndotj"][0], mylist["ndotdot"][0]) mylist = mylist.sort_values("logLR", ascending=False) cnstR = number np.random.seed(123) mylist["Pvector"] = mylist["nidot"] / mylist["ndotdot"] mylist["Pobs"] = mylist["ntrt"] / mylist["ndotj"] sim_cntdata = np.random.multinomial(mylist["ndotj"][0], mylist["Pvector"], size = cnstR) logLRsim = np.array([flogLRv(i, mylist["nidot"], mylist["ndotj"][0], mylist["ndotdot"][0]) for i in sim_cntdata]) maxlogLRsim = np.array([np.max(i) for i in logLRsim]) mylist["T_alpha"] = np.quantile(maxlogLRsim, 1 - alpha) mylist["pvalue"] = np.array([ np.mean(maxlogLRsim > i) for i in mylist["logLR"]]) if alphaSpending >= 1: mylist["is_signal"] = (mylist["logLR"] > mylist["T_alpha"]) else: mylist["alpha"] = alphaSpending mylist["is_signal"] = (mylist["pvalue"] < alphaSpending) mylist = mylist.sort_values("logLR", ascending=False) return {'LRT_Result': mylist, 'maxllr': maxlogLRsim, 'T_alpha': mylist["T_alpha"][0]} fLRT`38:T623,html`css `39:T474,html ` pillCSS `3a:[] 2f:{"title":"LRT Signal Analysis Application","cells":[{"id":0,"value":"# **LRT Signal Analysis Application**","pinned":false,"hidden":false,"mode":"md"},{"id":22,"value":"pills = {\n const pillData = ['Likelihood Ratio Test', 'Signal Detection', 'FDA Adverse Event Reporting System'];\n return html `\n

\n ${pillData.map(d => '

'+d+'

')}\n

\n`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":214,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":318,"value":"viewof options = formWithSumbit(html``)","pinned":false,"hidden":false,"mode":"ojs"},{"id":409,"value":"md`⬆ Click \"Submit\" to run the app`","pinned":false,"hidden":false,"mode":"ojs"},{"id":132,"value":"

\n### **Overall LRT Analysis**","pinned":false,"hidden":false,"mode":"md"},{"id":154,"value":"LRTResultTable = Inputs.table(\n JSON.parse(LRTResult),\n {columns: [\"AE\", \"ntrt\", \"nidot\", \"ndotj\", \"ndotdot\", \"logLR\", \"T_alpha\", \"pvalue\", \"is_signal\"],\n width: {AE: 250},\n // height: 550,\n align: {\n ntrt: \"center\",\n nidot: \"center\",\n ndotj: \"center\",\n ndotdot: \"center\",\n logLR: \"center\",\n T_alpha: \"center\",\n pvalue: \"center\",\n is_signal: \"center\"\n }\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":293,"value":"#### **Simulation Result for Event based LRT**","pinned":false,"hidden":false,"mode":"md"},{"id":271,"value":"LRTResultPlot = Plot.plot({\n height: 250,\n y: {\n grid: true\n },\n x: {\n label: \"Likelihood Ratio →\"\n },\n marks: [\n Plot.rectY(JSON.parse(maxLLR), \n Plot.binX({y: \"count\"}, \n {x: \"maxllr\", \n thresholds: 30,\n fill: \"steelblue\"\n })),\n Plot.ruleY([0]),\n Plot.ruleX([JSON.parse(T_alpha)[0]], {stroke: \"red\"}),\n Plot.text([[JSON.parse(T_alpha)[0], 50]], {text: ['Rejection Region, LLR>'+JSON.parse(T_alpha)[0].toFixed(3)], dx: 19, fill:'red'}),\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":135,"value":"### **LRT Trend Analysis by Year**","pinned":false,"hidden":false,"mode":"md"},{"id":160,"value":"LRTResultByYearTable = Inputs.table(\n JSON.parse(LRTResultYear),\n {columns: [\"AE\", \"year\", \"ntrt\", \"nidot\", \"ndotj\", \"ndotdot\", \"logLR\", \"T_alpha\", \"pvalue\", \"alpha\", \"is_signal\"],\n width: {AE: 250},\n // height: 550,\n align: {\n ntrt: \"center\",\n nidot: \"center\",\n ndotj: \"center\",\n ndotdot: \"center\",\n logLR: \"center\",\n T_alpha: \"center\",\n pvalue: \"center\",\n is_signal: \"center\"\n }\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":167,"value":"#### **Trend Analysis Visualization**","pinned":false,"hidden":false,"mode":"md"},{"id":192,"value":"heatmapLegend = legend({\n color: d3.scaleLinear([0, 0.06, 1], [\"#fc836a\", \"#3BAFDA\", \"lightblue\"]),\n title: \" P-Value\",\n tickFormat: d => [0,1][d],\n ticks: 1\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":180,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":203,"value":"### **Reference**\n\n

Lan Huang, Jyoti Zalkikar, and Ram Tiwari. Likelihood ratio test based method for signal detection in drug classes using FDA’s AERS database. Journal of Biopharmaceutical Statistics, Special Issue on Drug Safety, Vol23(1), 2013

Jianjin Xu, Lan Huang, Zhihao Yao, et al. Statistical methods for clinical study site selection for inspection, Therapeutic Innovation & Regulatory Science (TIRS), 2018

Zhiheng Xu, Jianjin Xu, Zhihao Yao, Lan Huang, Mary Jung, Ram Tiwari (2020) Evaluating Medical Device Safety Signals Using a Likelihood Ratio Test Method, JBS, 28;1-10, DOI: 10.1080/10543406.2020.1783284

","pinned":false,"hidden":false,"mode":"md"},{"id":6,"value":"

\n### Appendix","pinned":false,"hidden":false,"mode":"md"},{"id":42,"value":"#### Data Loading and Wrangling","pinned":false,"hidden":false,"mode":"md"},{"id":84,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":447,"value":"$34","pinned":true,"hidden":false,"mode":"ojs"},{"id":102,"value":"yearList = {\n var yearContainer = []\n var dateFromYear = +options.dateFrom.toISOString().slice(0, 10).split('-')[0],\n dateFromMonth = +options.dateFrom.toISOString().slice(0, 10).split('-')[1],\n dateFromDay = +options.dateFrom.toISOString().slice(0, 10).split('-')[2],\n dateToYear = +options.dateTo.toISOString().slice(0, 10).split('-')[0],\n dateToMonth = +options.dateTo.toISOString().slice(0, 10).split('-')[1],\n dateToDay = +options.dateTo.toISOString().slice(0, 10).split('-')[2];\n\n for(var i = dateFromYear; i<= dateToYear; i++){\n yearContainer.push(i);\n }\n\n return yearContainer;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":100,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":450,"value":"$36","pinned":true,"hidden":false,"mode":"ojs"},{"id":121,"value":"LRTData = generateLRTData()","pinned":false,"hidden":false,"mode":"ojs"},{"id":124,"value":"LRTYearData = generateLRTYearData();","pinned":false,"hidden":false,"mode":"ojs"},{"id":107,"value":"#### Signal Detection and Visualization","pinned":false,"hidden":false,"mode":"md"},{"id":111,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":248,"value":"maxLLR = py`import pandas as pd\npd.DataFrame(${funcLRT}(pd.read_json(${JSON.stringify(LRTData)}),${options.numberOfSimulation})['maxllr'], columns=['maxllr']).to_json(orient=\"records\")`","pinned":false,"hidden":false,"mode":"ojs"},{"id":277,"value":"T_alpha = py`import pandas as pd\npd.Series(${funcLRT}(pd.read_json(${JSON.stringify(LRTData)}),${options.numberOfSimulation})['T_alpha']).to_json(orient=\"records\")`","pinned":false,"hidden":false,"mode":"ojs"},{"id":116,"value":"LRTResult = py`import pandas as pd\n${funcLRT}(pd.read_json(${JSON.stringify(LRTData)}),${options.numberOfSimulation})['LRT_Result'].to_json(orient=\"records\")`","pinned":false,"hidden":false,"mode":"ojs"},{"id":143,"value":"LRTResultYear = py`import pandas as pd\nimport numpy as np\nLRT_result_Year = pd.DataFrame()\n\nfor k in range(len(${yearList})):\n LRTYearData = pd.read_json(${JSON.stringify(LRTYearData)})\n countAeListYeark = LRTYearData[LRTYearData[\"Year\"] <= ${yearList}[k]].groupby(\"AE\").agg({\"ntrt\": \"sum\"})\n countAeListTotalYeark = LRTYearData[LRTYearData[\"Year\"] <= ${yearList}[k]].groupby(\"AE\").agg({\"nidot\": \"sum\"})\n combine_data_Yeark = pd.concat([countAeListYeark, countAeListTotalYeark], axis=1, join=\"inner\")\n combine_data_Yeark[\"ndotj\"] = np.sum(combine_data_Yeark[\"ntrt\"])\n combine_data_Yeark[\"ndotdot\"] = np.sum(combine_data_Yeark[\"nidot\"])\n alphaSpending = 0.05 / (2**k)\n LRT_result_Yeark = ${funcLRT}(combine_data_Yeark, ${options.numberOfSimulation}, alphaSpending = alphaSpending)['LRT_Result']\n LRT_result_Yeark[\"year\"] = str(${yearList}[k])\n LRT_result_Year = LRT_result_Year.append(LRT_result_Yeark)\nLRT_result_Year['AE'] = LRT_result_Year.index\n\nLRT_result_Year.to_json(orient=\"records\")`","pinned":false,"hidden":false,"mode":"ojs"},{"id":171,"value":"height = 1900","pinned":false,"hidden":false,"mode":"ojs"},{"id":174,"value":"width = 800","pinned":false,"hidden":false,"mode":"ojs"},{"id":177,"value":"margin = ({top: 30, right: 30, bottom: 50, left: 240})","pinned":false,"hidden":false,"mode":"ojs"},{"id":183,"value":"import {legend, swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":186,"value":"import {addAnimation} from \"@mkfreeman/plot-animation\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":189,"value":"import {addTooltips} from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":196,"value":"#### Library","pinned":false,"hidden":false,"mode":"md"},{"id":216,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":327,"value":"function form(form) {\n const container = html`

${form}`;\n form.addEventListener(\"submit\", event => event.preventDefault());\n form.addEventListener(\"change\", () => container.dispatchEvent(new CustomEvent(\"input\")));\n form.addEventListener(\"input\", () => container.value = formValue(form));\n container.value = formValue(form);\n return container\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":329,"value":"function formValue(form) {\n const object = {};\n for (const input of form.elements) {\n if (input.disabled || !input.hasAttribute(\"name\")) continue;\n let value = input.value;\n switch (input.type) {\n case \"range\":\n case \"number\": {\n value = input.valueAsNumber;\n break;\n }\n case \"date\": {\n value = input.valueAsDate;\n break;\n }\n case \"radio\": {\n if (!input.checked) continue;\n break;\n }\n case \"checkbox\": {\n if (input.checked) value = true;\n else if (input.name in object) continue;\n else value = false;\n break;\n }\n case \"file\": {\n value = input.multiple ? input.files : input.files[0];\n break;\n }\n case \"select-multiple\": {\n value = Array.from(input.selectedOptions, option => option.value);\n break;\n }\n }\n object[input.name] = value;\n }\n return object;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":333,"value":"function formWithSumbit(form) {\n const container = html`

${form}`;\n form.addEventListener(\"submit\", event => {\n event.preventDefault();\n container.value = formValue(form);\n container.dispatchEvent(new CustomEvent(\"input\"));\n });\n form.addEventListener(\"input\", event => {\n // event.preventDefault();\n // Need this, because otherwise the viewof Generator catches bubbling input events\n event.stopPropagation();\n });\n return container;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":113,"value":"import { py, pyodide } from '@gnestor/pyodide'","pinned":false,"hidden":false,"mode":"ojs"},{"id":16,"value":"import {date, slider, text} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":13,"value":"import { aq, op } from '@uwdata/arquero'","pinned":false,"hidden":false,"mode":"ojs"},{"id":10,"value":"import {Plot, d3} from \"@observablehq/plot\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":426,"value":"import {openFDA_api_key} from \"@howardyao/lrt-method-application-part-1\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W3a","stdlib":"1","titling":"auto"}