31:T422,html`

LRT Application:
Signal Detection from FDA Adverse Event Reporting System (FAERS)

This is a demo of querying data from FAERS database and identifying signals from adverse evenets by using Likelihood Ratio Test (LRT) method.

Part 1: Import Data and Construct Analytical Data Frame

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.

`32:Ta76,// async function generateLRTData() { // var url = 'https://api.fda.gov/drug/event.json?search=_exists_:(patient.drug.openfda.generic_name)+AND+patient.drug.openfda.generic_name:(%22'+drugName+'%22)+AND+receivedate:(['+dateFrom+'+TO+'+dateTo+'])&limit=1000&count=patient.reaction.reactionmeddrapt.exact&skip=0'; // var raw = await fetch(url).then(res => res.json()); // var 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:(['+dateFrom+'+TO+'+dateTo+'])&count=patient.reaction.reactionmeddrapt.exact&limit=1000&sort=patient.reaction.reactionmeddrapt.exact:desc'; // var raw2 = await fetch(url2).then(res => res.json()); // var dataTop = raw.results.sort((a,b) => b.count - a.count).filter((n,i) => i < maxNumberOfEvent).map(n => ({AE: n.term,ntrt: +n.count})); // var dataTopTerm = [...new Set(dataTop.map(n => n.AE))]; // var dataOther = raw.results.sort((a,b) => b.count - a.count).filter((n,i) => i >= maxNumberOfEvent).map(n => ({AE: n.term,ntrt: +n.count})); // var countDrugOtherTotal = dataOther.reduce((p,c)=>{p += c.ntrt; return p;}, 0); // var dataAll = raw2.results.sort((a,b) => b.count - a.count).map(n => ({AE: n.term,nidot: +n.count})); // var dataUnMatch = aq.from(dataTop).antijoin(aq.from(dataAll), ['AE', 'AE'], [aq.all(), aq.not('AE')]); // var dataUnMatchTerm = [...new Set(dataUnMatch.objects().map(n => n.AE))]; // for (let k of dataUnMatchTerm) { // var kStr = k.split(' ').join('%20'); // var 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:(['+dateFrom+'+TO+'+dateTo+'])&limit=1&skip=0'; // var dataK = await fetch(urlK).then(res => res.json()) // dataAll.push({'AE': k, 'nidot': dataK.meta.results.total}); // } // var countDrugAllFinalOther = dataAll.filter(n => dataTopTerm.indexOf(n.AE) == -1).reduce((p,c) => {p += c.nidot; return p;},0); // var 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; // }33:Tf76,async function generateLRTData() { // ========================= // Drug-specific AE counts // ========================= var url = `https://api.fda.gov/drug/event.json?api_key=${openFDA_api_key}` + `&search=` + `_exists_:patient.drug.openfda.generic_name` + `+AND+patient.drug.openfda.generic_name:"${drugName}"` + `+AND+receivedate:[${dateFrom}+TO+${dateTo}]` + `&limit=1000` + `&count=patient.reaction.reactionmeddrapt.exact` + `&skip=0`; var raw = await fetch(url).then(res => res.json()); if (!raw?.results) { console.warn("Drug-specific query failed:", raw); return []; } // ========================= // Background AE counts // ========================= var url2 = `https://api.fda.gov/drug/event.json?api_key=${openFDA_api_key}` + `&search=` + `_exists_:patient.drug.openfda.generic_name` + `+AND+_exists_:patient.reaction.reactionmeddrapt` + `+AND+receivedate:[${dateFrom}+TO+${dateTo}]` + `&count=patient.reaction.reactionmeddrapt.exact` + `&limit=1000`; var raw2 = await fetch(url2).then(res => res.json()); if (!raw2?.results) { console.warn("Background query failed:", raw2); return []; } // ========================= // Top drug-specific events // ========================= var dataTop = raw.results .slice() .sort((a, b) => b.count - a.count) .filter((n, i) => i < maxNumberOfEvent) .map(n => ({ AE: n.term, ntrt: +n.count })); var dataTopTerm = [...new Set(dataTop.map(n => n.AE))]; // ========================= // Other events // ========================= var dataOther = raw.results .slice() .sort((a, b) => b.count - a.count) .filter((n, i) => i >= maxNumberOfEvent) .map(n => ({ AE: n.term, ntrt: +n.count })); var countDrugOtherTotal = dataOther.reduce((p, c) => p + c.ntrt, 0); // ========================= // Background totals // ========================= var dataAll = raw2.results .slice() .sort((a, b) => b.count - a.count) .map(n => ({ AE: n.term, nidot: +n.count })); // ========================= // Find unmatched terms // ========================= var dataUnMatch = aq.from(dataTop) .antijoin( aq.from(dataAll), ['AE', 'AE'], [aq.all(), aq.not('AE')] ); var dataUnMatchTerm = [...new Set(dataUnMatch.objects().map(n => n.AE))]; // ========================= // Query unmatched terms // ========================= for (let k of dataUnMatchTerm) { var urlK = `https://api.fda.gov/drug/event.json?api_key=${openFDA_api_key}` + `&search=` + `_exists_:patient.drug.openfda.generic_name` + `+AND+patient.reaction.reactionmeddrapt.exact:"${k}"` + `+AND+receivedate:[${dateFrom}+TO+${dateTo}]` + `&limit=1` + `&skip=0`; var dataK = await fetch(urlK).then(res => res.json()); dataAll.push({ AE: k, nidot: dataK?.meta?.results?.total || 0 }); } // ========================= // Other background counts // ========================= var countDrugAllFinalOther = dataAll .filter(n => dataTopTerm.indexOf(n.AE) === -1) .reduce((p, c) => p + c.nidot, 0); // ========================= // Merge // ========================= var 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 // ========================= var totalNtrt = dataMatch.reduce((p, c) => p + c.ntrt, 0); var totalNidot = dataMatch.reduce((p, c) => p + c.nidot, 0); dataMatch.map(n => n.ndotj = totalNtrt); dataMatch.map(n => n.ndotdot = totalNidot); return dataMatch; }34:Tb9a,// async function generateLRTYearData() { // var LRTYearDataContainer = []; // for(var i of yearList){ // var url = 'https://api.fda.gov/drug/event.json?search=_exists_:(patient.drug.openfda.generic_name)+AND+patient.drug.openfda.generic_name:(%22'+drugName+'%22)+AND+receivedate:(['+i+'-01-01+TO+'+i+'-12-31])&limit=1000&count=patient.reaction.reactionmeddrapt.exact&skip=0'; // var raw = await fetch(url).then(res => res.json()); // var 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'; // var raw2 = await fetch(url2).then(res => res.json()); // var dataTop = raw.results.sort((a,b) => b.count - a.count).filter((n,i) => i < maxNumberOfEvent).map(n => ({Year: i.toString(), AE: n.term,ntrt: +n.count})); // var dataTopTerm = [...new Set(dataTop.map(n => n.AE))]; // var dataOther = raw.results.sort((a,b) => b.count - a.count).filter((n,i) => i >= maxNumberOfEvent).map(n => ({Year: i.toString(),AE: n.term,ntrt: +n.count})); // var countDrugOtherTotal = dataOther.reduce((p,c)=>{p += c.ntrt; return p;}, 0); // var dataAll = raw2.results.sort((a,b) => b.count - a.count).map(n => ({Year: i, AE: n.term, nidot: +n.count})); // var dataUnMatch = aq.from(dataTop).antijoin(aq.from(dataAll), ['AE', 'AE'], [aq.all(), aq.not('AE')]); // var dataUnMatchTerm = [...new Set(dataUnMatch.objects().map(n => n.AE))]; // for (var k of dataUnMatchTerm) { // var kStr = k.split(' ').join('%20'); // var 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'; // var dataK = await fetch(urlK).then(res => res.json()) // dataAll.push({'AE': k, 'nidot': dataK.meta.results.total}); // } // var countDrugAllFinalOther = dataAll.filter(n => dataTopTerm.indexOf(n.AE) == -1).reduce((p,c) => {p += c.nidot; return p;},0); // var 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; // }35:T1107,async function generateLRTYearData() { var LRTYearDataContainer = []; for (var i of yearList) { // ========================= // Drug-specific AE counts // ========================= var 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:"${drugName}"` + `+AND+receivedate:[${i}-01-01+TO+${i}-12-31]` + `&limit=1000` + `&count=patient.reaction.reactionmeddrapt.exact` + `&skip=0`; var raw = await fetch(url).then(res => res.json()); if (!raw?.results) { console.warn(`No results for year ${i}`, raw); continue; } // ========================= // All AE counts // ========================= var url2 = `https://api.fda.gov/drug/event.json?api_key=${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`; var raw2 = await fetch(url2).then(res => res.json()); if (!raw2?.results) { console.warn(`No background results for year ${i}`, raw2); continue; } // ========================= // Top events // ========================= var dataTop = raw.results .slice() .sort((a, b) => b.count - a.count) .filter((n, idx) => idx < maxNumberOfEvent) .map(n => ({ Year: i.toString(), AE: n.term, ntrt: +n.count })); var dataTopTerm = [...new Set(dataTop.map(n => n.AE))]; var dataOther = raw.results .slice() .sort((a, b) => b.count - a.count) .filter((n, idx) => idx >= maxNumberOfEvent) .map(n => ({ Year: i.toString(), AE: n.term, ntrt: +n.count })); var countDrugOtherTotal = dataOther.reduce((p, c) => p + c.ntrt, 0); // ========================= // Background data // ========================= var dataAll = raw2.results .slice() .sort((a, b) => b.count - a.count) .map(n => ({ Year: i.toString(), AE: n.term, nidot: +n.count })); // ========================= // Unmatched terms // ========================= var dataUnMatch = aq.from(dataTop) .antijoin( aq.from(dataAll), ['AE', 'AE'], [aq.all(), aq.not('AE')] ); var dataUnMatchTerm = [...new Set(dataUnMatch.objects().map(n => n.AE))]; // ========================= // Query unmatched terms // ========================= for (var k of dataUnMatchTerm) { var urlK = `https://api.fda.gov/drug/event.json?api_key=${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`; var dataK = await fetch(urlK).then(res => res.json()); dataAll.push({ AE: k, nidot: dataK?.meta?.results?.total || 0 }); } // ========================= // Final background totals // ========================= var countDrugAllFinalOther = dataAll .filter(n => dataTopTerm.indexOf(n.AE) === -1) .reduce((p, c) => p + c.nidot, 0); // ========================= // Merge // ========================= var 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 }); // ========================= // Totals // ========================= var totalNtrt = dataMatch.reduce((p, c) => p + c.ntrt, 0); var 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; }36:T669,// https://getbootstrap.com/docs/4.1/getting-started/introduction/#css html`css `37:T474,html ` pillCSS `38:T6ca,// 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["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"] // 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 mylist // fLRT`39:T45a,heatMapAE = addAnimation( addTooltips( Plot.plot({ padding: 0.01, marginLeft: 250, marks: [ Plot.cell( LRTResultYearJSONImport, // 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( LRTResultYearJSONImport, // 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 } )3a:[{"id":"ee1f5aabde17f1c2d5a82ccf5e9e6c21a492f515964499493f591855c750042c387d544b5fc9c81929275c9067cc1acd1fe2befea57a4c1a631c11f16ae1cfc6","href":"https://static.observableusercontent.com/files/ee1f5aabde17f1c2d5a82ccf5e9e6c21a492f515964499493f591855c750042c387d544b5fc9c81929275c9067cc1acd1fe2befea57a4c1a631c11f16ae1cfc6","name":"LRTResultJSON.json","type":"application/json","lastModified":1631192368786.487,"size":8861},{"id":"c4bb4a3e06c758e509e70676c9138bbb8427e87eeb0ca24e772b6cc4a9d075b6b9ccb26a9bdb98c0c1ee03e53339917266d53152e25988301c889a37a697af86","href":"https://static.observableusercontent.com/files/c4bb4a3e06c758e509e70676c9138bbb8427e87eeb0ca24e772b6cc4a9d075b6b9ccb26a9bdb98c0c1ee03e53339917266d53152e25988301c889a37a697af86","name":"LRTResultYearJSON.json","type":"application/json","lastModified":1631192652100.207,"size":60896}] 2f:{"title":"LRT Method Application: Part 1","cells":[{"id":0,"value":"# **LRT Method Application: Part 1**","pinned":false,"hidden":false,"mode":"md"},{"id":1032,"value":"pills = {\n const pillData = ['FARES-API', 'Arquero', 'Data Wrangling', 'Dynamic Inputs'];\n return html `\n

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

'+d+'

')}\n

\n`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":224,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":71,"value":"viewof drugName = text({\n title: \"Name of Drug\", \n value: 'Aspirin',\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":18,"value":"viewof dateFrom = date({\n title: 'From',\n value: \"2015-01-01\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":21,"value":"viewof dateTo = date({\n title: 'To',\n value: '2019-12-31'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":66,"value":"viewof maxNumberOfEvent = slider({\n min: 1, \n max: 500, \n step: 1,\n value: 50, \n title: \"Max Number of Event Terms\", \n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":246,"value":"

Our goal is to create a ${drugName} adverse events data frame for further LRT analysis after querying and wrangling the JSON files from FAERS. (You probably want to visit the basic LRT method introduction if the method is new for you.)

","pinned":false,"hidden":false,"mode":"md"},{"id":253,"value":"The dataset contains these 4 variables:\n\n| Variable Name | Definition | \n|:-----------:|:-------------:|\n| ntrt (${tex`n_{ij}`}) | number of events for the ${tex`ith`} AE and ${tex`jth`} drug |\n| nidot | total number of events for ${tex`ith`} AE (marginal total for ${tex`ith`} row). | \n|ndotj | total number of events for ${tex`jth`} drug (the marginal total for ${tex`jth`} column) | \n| ndotdot | grand total | ","pinned":false,"hidden":false,"mode":"md"},{"id":243,"value":"Inputs.table(dataMatch)","pinned":true,"hidden":false,"mode":"ojs"},{"id":658,"value":"

","pinned":false,"hidden":false,"mode":"html"},{"id":687,"value":"### **Construct overall LRT data**","pinned":false,"hidden":false,"mode":"md"},{"id":300,"value":"**Data query: Adverse events of ${drugName} as \"${tex`n_{ij}`}\"**","pinned":false,"hidden":false,"mode":"md"},{"id":28,"value":"// url = 'https://api.fda.gov/drug/event.json?search=_exists_:(patient.drug.openfda.generic_name)+AND+patient.drug.openfda.generic_name:(%22'+drugName+'%22)+AND+receivedate:(['+dateFrom+'+TO+'+dateTo+'])&limit=1000&count=patient.reaction.reactionmeddrapt.exact&skip=0'\n\nurl = `https://api.fda.gov/drug/event.json?api_key=${openFDA_api_key}` +\n `&search=` +\n `_exists_:patient.drug.openfda.generic_name` +\n `+AND+patient.drug.openfda.generic_name:\"${drugName}\"` +\n `+AND+receivedate:[${dateFrom}+TO+${dateTo}]` +\n `&limit=1000` +\n `&count=patient.reaction.reactionmeddrapt.exact` +\n `&skip=0`","pinned":false,"hidden":false,"mode":"ojs"},{"id":37,"value":"raw = await fetch(url).then(res => res.json())","pinned":true,"hidden":false,"mode":"ojs"},{"id":296,"value":"rawTable = Inputs.table(raw.results)","pinned":true,"hidden":false,"mode":"ojs"},{"id":310,"value":"**Data query: Adverse events of all drugs as \"_nidot_\"**","pinned":false,"hidden":false,"mode":"md"},{"id":90,"value":"// 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:(['+dateFrom+'+TO+'+dateTo+'])&count=patient.reaction.reactionmeddrapt.exact&limit=1000&sort=patient.reaction.reactionmeddrapt.exact:desc'\n\n\nurl2 = {\n const search = [\n `_exists_:patient.drug.openfda.generic_name`,\n `_exists_:patient.reaction.reactionmeddrapt`,\n `receivedate:[${dateFrom}+TO+${dateTo}]`\n ].join(\"+AND+\");\n\n return `https://api.fda.gov/drug/event.json?api_key=${openFDA_api_key}&search=${search}&count=patient.reaction.reactionmeddrapt.exact&limit=1000`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":93,"value":"raw2 = await fetch(url2).then(res => res.json())","pinned":true,"hidden":false,"mode":"ojs"},{"id":350,"value":"raw2Table = Inputs.table(raw2.results)","pinned":true,"hidden":false,"mode":"ojs"},{"id":358,"value":"topNText = md`**Dynamically select top N (_current = ${maxNumberOfEvent}_) adverse events
Combine all others into one category called _\"Other\"_**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":368,"value":"Top N dataset","pinned":false,"hidden":false,"mode":"md"},{"id":76,"value":"dataTop = raw.results.sort((a,b) => b.count - a.count)\n .filter((n,i) => i < maxNumberOfEvent)\n .map(n => ({\n AE: n.term,\n ntrt: +n.count\n }));","pinned":true,"hidden":false,"mode":"ojs"},{"id":85,"value":"dataTopTerm = [...new Set(dataTop.map(n => n.AE))]","pinned":true,"hidden":false,"mode":"ojs"},{"id":371,"value":"\"Other\" dataset","pinned":false,"hidden":false,"mode":"md"},{"id":80,"value":"dataOther = raw.results.sort((a,b) => b.count - a.count)\n .filter((n,i) => i >= maxNumberOfEvent)\n .map(n => ({\n AE: n.term,\n ntrt: +n.count\n }));","pinned":true,"hidden":false,"mode":"ojs"},{"id":379,"value":"Sum up the count number of all others as ${tex`n_{ij}`} of \"Others\"","pinned":false,"hidden":false,"mode":"md"},{"id":87,"value":"countDrugOtherTotal = dataOther.reduce((p,c)=>{p += c.ntrt; return p;}, 0);","pinned":true,"hidden":false,"mode":"ojs"},{"id":383,"value":"**Since the database only allows us to query 1000 items, some adverse events of ${drugName} may not be in the list of adverse events of all drugs**","pinned":false,"hidden":false,"mode":"md"},{"id":96,"value":"dataAll = raw2.results.sort((a,b) => b.count - a.count)\n .map(n => ({\n AE: n.term,\n nidot: +n.count\n }));","pinned":true,"hidden":false,"mode":"ojs"},{"id":388,"value":"**Use \"_antijoin_\" function (from Arquero library)
to find out the unmatched adverse events**","pinned":false,"hidden":false,"mode":"md"},{"id":395,"value":"dataUnMatch.view()","pinned":true,"hidden":false,"mode":"ojs"},{"id":98,"value":"dataUnMatch = aq.from(dataTop).antijoin(aq.from(dataAll), ['AE', 'AE'], [aq.all(), aq.not('AE')]);","pinned":true,"hidden":false,"mode":"ojs"},{"id":103,"value":"dataUnMatchTerm = [...new Set(dataUnMatch.objects().map(n => n.AE))];","pinned":true,"hidden":false,"mode":"ojs"},{"id":410,"value":"**Iterate all the unmatched adverse events to query their \"nidot\" data from FAERS\nand append the new data to the existing data**","pinned":false,"hidden":false,"mode":"md"},{"id":105,"value":"drugAllFinal = {\n for (let k of dataUnMatchTerm) {\n var kStr = k.split(' ').join('%20');\n var urlK = 'https://api.fda.gov/drug/event.json?search=_exists_:(patient.drug.openfda.generic_name)+AND+_exists_:(patient.drug.openfda.generic_name)+AND+patient.reaction.reactionmeddrapt.exact:(%22'+kStr+'%22)+AND+receivedate:(['+dateFrom+'+TO+'+dateTo+'])&limit=1&skip=0';\n var dataK = await fetch(urlK).then(res => res.json())\n dataAll.push({'AE': k, 'nidot': dataK.meta.results.total});\n }\n return dataAll;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":110,"value":"countDrugAllFinalOther = drugAllFinal.filter(n => dataTopTerm.indexOf(n.AE) == -1).reduce((p,c) => {p += c.nidot; return p;},0);","pinned":true,"hidden":false,"mode":"ojs"},{"id":418,"value":"dataFrameText = md`**Finally, we get our desired data frame by combining
[AE, ntrt(${tex`n_{ij}`}), nidot] of top ${maxNumberOfEvent} and \"Other\" together.
\nndotj = sum(ntrt)
\nndotdot = sum(nidot).**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":157,"value":"dataMatch = {\n var dataMatch = aq.from(dataTop).join_left(aq.from(drugAllFinal), ['AE', 'AE'], [aq.all(), aq.not('AE')]).objects()\n .filter(n => n.nidot !== undefined);\n \n dataMatch.push({\n 'AE': 'Other',\n 'ntrt': countDrugOtherTotal,\n 'nidot': countDrugAllFinalOther\n });\n\n dataMatch.map(n => n.ndotj = dataMatch.reduce((p,c) => {p += c.ntrt; return p;}, 0));\n dataMatch.map(n => n.ndotdot = dataMatch.reduce((p,c) => {p += c.nidot; return p;}, 0)); \n \n return dataMatch;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":456,"value":"**Let's wrap them in one function called \"generateLRTData\"**","pinned":false,"hidden":false,"mode":"md"},{"id":460,"value":"LRTDataTable = Inputs.table(LRTData)","pinned":true,"hidden":false,"mode":"ojs"},{"id":462,"value":"$32","pinned":true,"hidden":false,"mode":"ojs"},{"id":1066,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":538,"value":"LRTData = generateLRTData()","pinned":false,"hidden":false,"mode":"ojs"},{"id":643,"value":"### **Construct LRT data by year**","pinned":false,"hidden":false,"mode":"md"},{"id":752,"value":"dataByYearText = md`**Iterate ${drugName}'s adverse events data query and generation by year.
Then, concaternate them together.**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":464,"value":"yearList = {\n var yearContainer = []\n var dateFromYear = +dateFrom.split('-')[0],\n dateFromMonth = +dateFrom.split('-')[1],\n dateFromDay = +dateFrom.split('-')[2],\n dateToYear = +dateTo.split('-')[0],\n dateToMonth = +dateTo.split('-')[1],\n dateToDay = +dateTo.split('-')[2];\n\n for(var i = dateFromYear; i<= dateToYear; i++){\n yearContainer.push(i);\n }\n\n return yearContainer;\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":631,"value":"LRTDataYearTable = Inputs.table(LRTYearData)","pinned":false,"hidden":false,"mode":"ojs"},{"id":518,"value":"$34","pinned":true,"hidden":false,"mode":"ojs"},{"id":1063,"value":"$35","pinned":true,"hidden":false,"mode":"ojs"},{"id":530,"value":"LRTYearData = generateLRTYearData();","pinned":false,"hidden":false,"mode":"ojs"},{"id":673,"value":"**Now, our two datasets are ready for further analysis ✨**
\n**One for the overall signal detection, the other for the trend analysis by year.**

\n**Please check the algorithm and visualization in PART 2**","pinned":false,"hidden":false,"mode":"md"},{"id":662,"value":"

","pinned":false,"hidden":false,"mode":"html"},{"id":692,"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":2,"value":"

\n### **Appendix**","pinned":false,"hidden":false,"mode":"md"},{"id":12,"value":"import {date, slider, text} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":190,"value":"import {form} from \"@mbostock/form-input\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":34,"value":"import { aq, op } from '@uwdata/arquero'","pinned":true,"hidden":false,"mode":"ojs"},{"id":201,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":1027,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":766,"value":"// import { py, pyodide } from '@gnestor/pyodide'","pinned":false,"hidden":false,"mode":"ojs"},{"id":904,"value":"import {Plot, d3} from \"@observablehq/plot\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":758,"value":"### **Part 2 Components**","pinned":false,"hidden":false,"mode":"md"},{"id":764,"value":"$38","pinned":true,"hidden":false,"mode":"ojs"},{"id":769,"value":"// LRTResult = py`import pandas as pd\n// ${funcLRT}(pd.read_json(${JSON.stringify(LRTData)}),100).to_json(orient=\"records\")`","pinned":true,"hidden":false,"mode":"ojs"},{"id":994,"value":"// LRTResultJSON = JSON.parse(LRTResult)","pinned":true,"hidden":false,"mode":"ojs"},{"id":997,"value":"LRTResultJSONImport = await FileAttachment(\"LRTResultJSON.json\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":772,"value":"LRTResultTable = Inputs.table(\n LRTResultJSONImport,\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":true,"hidden":false,"mode":"ojs"},{"id":800,"value":"LRTAanlysisByYearText = md`The LRT method was also applied to longitudinal data from ${d3.min(yearList)} to ${d3.max(yearList)} where a monotone **alpha-spending** function was used to ensure the control of type I error at each look and overall for trend analysis.\n\n ${tex`\\large \\alpha_{(k)} = \\frac{0.05}{2^k}, \\small k = 0, 1, 2, \\cdots `}
\n\n\nThat is, alpha over 2 to power k. Here k=0 (means the 1st time period), 1 (the 2nd time peroid), and so on.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":804,"value":"// LRTResultYear = py`import pandas as pd\n// import numpy as np\n// LRT_result_Year = pd.DataFrame()\n\n// for k in range(len(${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 = ${funcLRT}(combine_data_Yeark, 100, alphaSpending = alphaSpending)\n// LRT_result_Yeark[\"year\"] = str(${yearList}[k])\n// LRT_result_Year = LRT_result_Year.append(LRT_result_Yeark)\n// LRT_result_Year['AE'] = LRT_result_Year.index\n\n// LRT_result_Year.to_json(orient=\"records\")`","pinned":true,"hidden":false,"mode":"ojs"},{"id":1008,"value":"// LRTResultYearJSON = JSON.parse(LRTResultYear)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1011,"value":"LRTResultYearJSONImport = await FileAttachment(\"LRTResultYearJSON.json\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":808,"value":"LRTResultByYearTable = Inputs.table(\n LRTResultYearJSONImport,\n // JSON.parse(LRTResultYear),\n {columns: [\"AE\", \"year\", \"ntrt\", \"nidot\", \"ndotj\", \"ndotdot\", \"logLR\", \"T_alpha\", \"pvalue\", \"alpha\", \"is_signal\"],\n width: {AE: 160},\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":true,"hidden":false,"mode":"ojs"},{"id":822,"value":"trendVizText = md`**Trend Analysis Visualization**\n\nThe heatmap consists of all adverse events and different years. The color code indicates the p-value of one adverse reaction in a certain year. The darker the red, the smaller the p-value; The lighter the blue, the greater the p-value. An exclamation mark (❕) represents that the adverse event is identified as a signal in a certain year (hover over on each cell to see the details). `","pinned":false,"hidden":false,"mode":"ojs"},{"id":827,"value":"height = 1900","pinned":false,"hidden":false,"mode":"ojs"},{"id":866,"value":"width = 800","pinned":false,"hidden":false,"mode":"ojs"},{"id":830,"value":"margin = ({top: 30, right: 30, bottom: 50, left: 240})","pinned":true,"hidden":false,"mode":"ojs"},{"id":973,"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":908,"value":"$39","pinned":true,"hidden":false,"mode":"ojs"},{"id":969,"value":"import {legend, swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":898,"value":"import {addAnimation} from \"@mkfreeman/plot-animation\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":901,"value":"import {addTooltips} from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1055,"value":"openFDA_api_key = \"cFk7IpEcaSziKffAUeDBcHQCLlaRUW4tlqc4itGW\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W3a","stdlib":"1","titling":"auto"}

LRT Application: Signal Detection from FDA Adverse Event Reporting System (FAERS)

Part 1: Import Data and Construct Analytical Data Frame

LRT Application:
Signal Detection from FDA Adverse Event Reporting System (FAERS)