2a:null 2b:["$","title",null,{"children":"Stat Notes | An Interactive Tool for Oncology Efficacy Analyses | Zhihao Yao | Observable"}] 30:T8b7,table2 = { const table = d3.create("table").attr('id', 'table2') const tbody = table.append("tbody"); tbody.append("tr") .data([['Month', ...d3.range(0,month+1)]]) .join("tr") .selectAll("td") .data(row => row) .join("td") .text(d => d) .style("text-align", "center") tbody.append("tr") .data([['ITT (Reference)', ...showAllNumbers(ADTTE)].filter((d,i) => i-1 <= month)]) .join("tr") .selectAll("td") .data(row => row) .join("td") .text(d => d) .style("text-align", "center") .style('color', '#aaa') tbody.append("tr") .data([['MARKFL1B=1 & MARKFL2=1', ...showAllNumbers(obj.data_11_21)].filter((d,i) => i-1 <= month)]) .join("tr") .selectAll("td") .data(row => row) .join("td") .text(d => d) .style("text-align", "center") .style('display', Flags.includes('MARKFL1B=1 & MARKFL2=1') ? true : 'none') .style('color', '#E14D2A') tbody.append("tr") .data([['MARKFL1B=0 & MARKFL2=0', ...showAllNumbers(obj.data_10_20)].filter((d,i) => i-1 <= month)]) .join("tr") .selectAll("td") .data(row => row) .join("td") .text(d => d) .style("text-align", "center") .style('display', Flags.includes('MARKFL1B=0 & MARKFL2=0') ? true : 'none') .style('color', '#FD841F') tbody.append("tr") .data([['MARKFL1B=1 & MARKFL2=0', ...showAllNumbers(obj.data_11_20)].filter((d,i) => i-1 <= month)]) .join("tr") .selectAll("td") .data(row => row) .join("td") .text(d => d) .style("text-align", "center") .style('display', Flags.includes('MARKFL1B=1 & MARKFL2=0') ? true : 'none') .style('color', '#3E6D9C') tbody.append("tr") .data([['MARKFL1B=0 & MARKFL2=1', ...showAllNumbers(obj.data_10_21)].filter((d,i) => i-1 <= month)]) .join("tr") .selectAll("td") .data(row => row) .join("td") .text(d => d) .style("text-align", "center") .style('display', Flags.includes('MARKFL1B=0 & MARKFL2=1') ? true : 'none') .style('color', '#379237') return table.node(); }31:T6e7,table4 = { const table = d3.create("table").attr('id', 'table4') const tbody = table.append("tbody"); tbody.append("tr") .data([['', 'ITT (Reference)', 'MARKFL1B=1 MARKFL2=1', 'MARKFL1B=0 MARKFL2=0', 'MARKFL1B=1 MARKFL2=0', 'MARKFL1B=0 MARKFL2=1', ]]) .join("tr") .selectAll("th") .data(row => row) .join("th") .text(d => d) .style("text-align", "center") .attr('class', (d,i) => 'hc'+i) .style('color', '#aaa') tbody.append("tr") .data([['Median', ...median_hr.median ]] ) .join("tr") .selectAll("td") .data(row => row) .join("td") .text(d => d) .style("text-align", "center") .attr('class', (d,i) => 'cc'+i) tbody.append("tr") .data([['Hazard Ratio', '-', ...median_hr.HR ]] ) .join("tr") .selectAll("td") .data(row => row) .join("td") .text(d => d) .style("text-align", "center") .attr('class', (d,i) => 'cc'+i) tbody.selectAll('.hc2') .style('display', Flags.includes('MARKFL1B=1 & MARKFL2=1') ? true : 'none') .style('color', '#E14D2A') tbody.selectAll('.hc3') .style('display', Flags.includes('MARKFL1B=0 & MARKFL2=0') ? true : 'none') .style('color', '#FD841F') tbody.selectAll('.hc4') .style('display', Flags.includes('MARKFL1B=1 & MARKFL2=0') ? true : 'none') .style('color', '#3E6D9C') tbody.selectAll('.hc5') .style('display', Flags.includes('MARKFL1B=0 & MARKFL2=1') ? true : 'none') .style('color', '#379237') return table.node(); }32:Td23,table3 = { const table = d3.create("table").attr('id', 'table3') const tbody = table.append("tbody"); tbody.append("tr") .data([['', 'Number of Events', 'Event Subjects', 'Number of Censored', 'Censored Subjects']]) .join("tr") .selectAll("th") .data(row => row) .join("th") .text(d => d) .style("text-align", "center") tbody.append("tr") .data([['ITT (Reference)', objNumbers.c0N, objNumbers.c0.map(d => d.USUBJID.replace('Subject', '')).join(', '), objNumbers.c1N, objNumbers.c1.map(d => d.USUBJID.replace('Subject', '')).join(', '), ]] ) .join("tr") .selectAll("td") .data(row => row) .join("td") .text(d => d) .style("text-align", "center") .style('color', '#aaa') tbody.append("tr") .data([['MARKFL1B=1 & MARKFL2=1', objNumbers.c0N_11_21, objNumbers.c0_11_21.map(d => d.USUBJID.replace('Subject', '')).join(', '), objNumbers.c1N_11_21, objNumbers.c1_11_21.map(d => d.USUBJID.replace('Subject', '')).join(', '), ]] ) .join("tr") .selectAll("td") .data(row => row) .join("td") .text(d => d) .style("text-align", "center") .style('display', Flags.includes('MARKFL1B=1 & MARKFL2=1') ? true : 'none') .style('color', '#E14D2A') tbody.append("tr") .data([['MARKFL1B=0 & MARKFL2=0', objNumbers.c0N_10_20, objNumbers.c0_10_20.map(d => d.USUBJID.replace('Subject', '')).join(', '), objNumbers.c1N_10_20, objNumbers.c1_10_20.map(d => d.USUBJID.replace('Subject', '')).join(', '), ]] ) .join("tr") .selectAll("td") .data(row => row) .join("td") .text(d => d) .style("text-align", "center") .style('display', Flags.includes('MARKFL1B=0 & MARKFL2=0') ? true : 'none') .style('color', '#FD841F') tbody.append("tr") .data([['MARKFL1B=1 & MARKFL2=0', objNumbers.c0N_11_20, objNumbers.c0_11_20.map(d => d.USUBJID.replace('Subject', '')).join(', '), objNumbers.c1N_11_20, objNumbers.c1_11_20.map(d => d.USUBJID.replace('Subject', '')).join(', '), ]] ) .join("tr") .selectAll("td") .data(row => row) .join("td") .text(d => d) .style("text-align", "center") .style('display', Flags.includes('MARKFL1B=1 & MARKFL2=0') ? true : 'none') .style('color', '#3E6D9C') tbody.append("tr") .data([['MARKFL1B=0 & MARKFL2=1', objNumbers.c0N_10_21, objNumbers.c0_10_21.map(d => d.USUBJID.replace('Subject', '')).join(', '), objNumbers.c1N_10_21, objNumbers.c1_10_21.map(d => d.USUBJID.replace('Subject', '')).join(', '), ]] ) .join("tr") .selectAll("td") .data(row => row) .join("td") .text(d => d) .style("text-align", "center") .style('display', Flags.includes('MARKFL1B=0 & MARKFL2=1') ? true : 'none') .style('color', '#379237') return table.node(); }33:Td26,{ return Plot.plot({ height : 300, width : 700, x: { axis: null, // domain: data.ages, domain: ['CR', 'PR', 'SD', 'PD', 'NE'], }, y: { grid: true, // tickFormat: "s" domain: [0, d3.max(dataLong, d=>d.numORR) + 2], label: '↑ Response', }, color: { // domain: dataLong.AVALC, domain: ['CR', 'PR', 'SD', 'PD', 'NE'], range: ['#59a14f', '#4e79a7', '#76b7b2', '#e15759', '#f28e2c'], // scheme: "tableau10", legend: true, }, fx: { domain: d3.groupSort(dataLong, v => d3.sum(v, d => -d.numGrp), d => d.GROUP), // domian: ['MARKFL1B=1 & MARKFL2=1', 'MARKFL1B=0 & MARKFL2=0', 'MARKFL1B=1 & MARKFL2=0', 'MARKFL1B=0 & MARKFL2=1'], label: null, // tickSize: 6 }, fy:{ }, facet: { data: dataLong, x: "GROUP", // color: 'GROUP', // x: ['MARKFL1B=1 & MARKFL2=1', 'MARKFL1B=0 & MARKFL2=0', 'MARKFL1B=1 & MARKFL2=0', 'MARKFL1B=0 & MARKFL2=1'], }, marks: [ Plot.barY(dataLong, {x: "AVALC", y: "numRS", fill: "AVALC", title: "AVALC", opacity: 0.8,}), Plot.text(dataLong, {x: "AVALC", y: "numRS", text: d=> d.numRS + '/' + d.numGrp, dy: -6, // fontWeight: 'bold', fill: '#333', }), Plot.ruleX(dataLong, {x: "AVALC", // y: d3.max(dataLong, d=>d.numRS) + 2, y: d => d.AVALC == 'PR' ? d.numORR : 0, dx: 14, stroke: '#999' }), Plot.ruleX(dataLong, {x: "AVALC", y: d => d.AVALC == 'CR' ? d.numORR : 0, dx: -15, stroke: '#999', }), Plot.text(dataLong, {x: d => d.AVALC == 'PR' ? d.AVALC : null, // y: d3.max(dataLong, d=>d.numRS) + 1.5, y: d => d.AVALC == 'PR' ? d.numORR + 1.5 : 0, dx: 10, fill: '#333', text: d => 'ORR = ' + d.rateORR, textAnchor: "end", }), Plot.text(dataLong, {x: d => d.AVALC == 'PR' ? d.AVALC : null, // y: d3.max(dataLong, d=>d.numRS) + 1, y: d => d.AVALC == 'PR' ? d.numORR + 0.9 : 0, dx: 2, fill: '#333', text: d => `(${d.numORR} / ${d.numGrp})`, textAnchor: "end", }), Plot.ruleY([0]), ] }) }34:Tbd6,obj = { const data_11_21 = joinedData.filter(d => d.MARKFL1B == 1 & d.MARKFL2 == 1) const data_10_20 = joinedData.filter(d => d.MARKFL1B == 0 & d.MARKFL2 == 0) const data_11_20 = joinedData.filter(d => d.MARKFL1B == 1 & d.MARKFL2 == 0) const data_10_21 = joinedData.filter(d => d.MARKFL1B == 0 & d.MARKFL2 == 1) const data_11_21Filtered = joinedDataFiltered.filter(d => d.MARKFL1B == 1 & d.MARKFL2 == 1) const data_10_20Filtered = joinedDataFiltered.filter(d => d.MARKFL1B == 0 & d.MARKFL2 == 0) const data_11_20Filtered = joinedDataFiltered.filter(d => d.MARKFL1B == 1 & d.MARKFL2 == 0) const data_10_21Filtered = joinedDataFiltered.filter(d => d.MARKFL1B == 0 & d.MARKFL2 == 1) const groupedData = funGroupedData(ADTTE, adtteFiltered) const groupedData_11_21 = funGroupedData(data_11_21, data_11_21Filtered) const groupedData_10_20 = funGroupedData(data_10_20, data_10_20Filtered) const groupedData_11_20 = funGroupedData(data_11_20, data_11_20Filtered) const groupedData_10_21 = funGroupedData(data_10_21, data_10_21Filtered) const plotDataForLine = funPlotDataForLine(groupedData) const plotDataForLine_11_21 = funPlotDataForLine(groupedData_11_21) const plotDataForLine_10_20 = funPlotDataForLine(groupedData_10_20) const plotDataForLine_11_20 = funPlotDataForLine(groupedData_11_20) const plotDataForLine_10_21 = funPlotDataForLine(groupedData_10_21) const plotDataForTicks = funPlotDataForTicks(groupedData, adtteFiltered) const plotDataForTicks_11_21 = funPlotDataForTicks(groupedData_11_21, data_11_21Filtered) const plotDataForTicks_10_20 = funPlotDataForTicks(groupedData_10_20, data_10_20Filtered) const plotDataForTicks_11_20 = funPlotDataForTicks(groupedData_11_20, data_11_20Filtered) const plotDataForTicks_10_21 = funPlotDataForTicks(groupedData_10_21, data_10_21Filtered) return { 'data_11_21': data_11_21, 'data_10_20': data_10_20, 'data_11_20': data_11_20, 'data_10_21': data_10_21, 'data_11_21Filtered': data_11_21Filtered, 'data_10_20Filtered': data_10_20Filtered, 'data_11_20Filtered': data_11_20Filtered, 'data_10_21Filtered': data_10_21Filtered, 'groupedData': groupedData, 'groupedData_11_21': groupedData_11_21, 'groupedData_10_20': groupedData_10_20, 'groupedData_11_20': groupedData_11_20, 'groupedData_10_21': groupedData_10_21, 'plotDataForLine': plotDataForLine, 'plotDataForLine_11_21': plotDataForLine_11_21, 'plotDataForLine_10_20': plotDataForLine_10_20, 'plotDataForLine_11_20': plotDataForLine_11_20, 'plotDataForLine_10_21': plotDataForLine_10_21, 'plotDataForTicks': plotDataForTicks, 'plotDataForTicks_11_21': plotDataForTicks_11_21, 'plotDataForTicks_10_20': plotDataForTicks_10_20, 'plotDataForTicks_11_20': plotDataForTicks_11_20, 'plotDataForTicks_10_21': plotDataForTicks_10_21, } }35:T5eb,dataLong = { const data = joinedData.map(d =>({ ...d, 'GROUP': d.MARKFL1B == 1 & d.MARKFL2 == 1 ? 'MARKFL1B=1 & MARKFL2=1' : d.MARKFL1B == 0 & d.MARKFL2 == 0 ? 'MARKFL1B=0 & MARKFL2=0' : d.MARKFL1B == 1 & d.MARKFL2 == 0 ? 'MARKFL1B=1 & MARKFL2=0' : d.MARKFL1B == 0 & d.MARKFL2 == 1 ? 'MARKFL1B=0 & MARKFL2=1' : 'Other' })) const data1 = aq.from(data) .groupby(['GROUP', 'AVALC']) .rollup({numRS: aq.op.count()}) .join( aq.from(data) .groupby(['GROUP']) .rollup({numGrp: aq.op.count()}) , ['GROUP', 'GROUP'] ) .orderby([aq.desc('GROUP'), 'AVALC']) .objects() const data2 = aq.from(data1.filter(d => ['CR', 'PR'].includes(d.AVALC))) .groupby(['GROUP']) .derive({ numORR: aq.op.sum('numRS'), }) .objects() .map(d => ({ ...d, 'rateORR' : (d.numORR / d.numGrp).toFixed(2) })) const data3 = aq.from(data1).join(aq.from(data2).select(['GROUP', 'numORR', 'rateORR']), ['GROUP', 'GROUP'] ).objects() return _.uniqWith(data3, _.isEqual); }36:T445,function funPlotDataForLine(groupedData) { /* Begin the line */ const plotData = [ { x : 0, y : 1, }, { x : groupedData[0]['t'], y : 1, } ]; for (let i = 0; i < groupedData.length - 1; i++){ plotData.push( { x : groupedData[i]['t'], y : groupedData[i]['prob'] }, { x : groupedData[i + 1]['t'], y : groupedData[i]['prob'] } ) } plotData.push( { x : groupedData[groupedData.length - 1]['t'], y : groupedData[groupedData.length - 1]['prob'] } ) const dataTimes = adtteFiltered.map(datum => datum.AVAL).sort(d3.ascending); const finalT = dataTimes[dataTimes.length - 1]; const timesFromGroupedData = plotData.map(datum => datum.x); const finalY = plotData[plotData.length - 1]['y']; if(!timesFromGroupedData.includes(finalT)){ plotData.push({x : finalT, y : finalY}) } // return plotData.slice().sort((a,b) => d3.ascending(a.x, b.x) || d3.ascending(b.y, a.y)); return plotData }37:Tc40,function showNumbers(ADTTE, data_11_21, data_10_20, data_11_20, data_10_21, month) { const c0 = ADTTE.slice().filter(d => d.AVAL <= month).filter(d => d.CNSR == 0) const c1 = ADTTE.slice().filter(d => d.AVAL <= month).filter(d => d.CNSR == 1) const c0N = c0.length const c1N = c1.length const sn = (enrollment - c0N -c1N) const st = ((enrollment - c0N -c1N) / (enrollment - c1N)).toFixed(3) const enrollment_11_21 = data_11_21.length const c0_11_21 = data_11_21.slice().filter(d => d.AVAL <= month).filter(d => d.CNSR == 0) const c1_11_21 = data_11_21.slice().filter(d => d.AVAL <= month).filter(d => d.CNSR == 1) const c0N_11_21 = c0_11_21.length const c1N_11_21 = c1_11_21.length const sn_11_21 = (enrollment_11_21 - c0N_11_21 -c1N_11_21) const st_11_21 = ((enrollment_11_21 - c0N_11_21 -c1N_11_21) / (enrollment_11_21 - c1N_11_21)).toFixed(3) const enrollment_10_20 = data_10_20.length const c0_10_20 = data_10_20.slice().filter(d => d.AVAL <= month).filter(d => d.CNSR == 0) const c1_10_20 = data_10_20.slice().filter(d => d.AVAL <= month).filter(d => d.CNSR == 1) const c0N_10_20 = c0_10_20.length const c1N_10_20 = c1_10_20.length const sn_10_20 = (enrollment_10_20 - c0N_10_20 -c1N_10_20) const st_10_20 = ((enrollment_10_20 - c0N_10_20 -c1N_10_20) / (enrollment_10_20 - c1N_10_20)).toFixed(3) const enrollment_11_20 = data_11_20.length const c0_11_20 = data_11_20.slice().filter(d => d.AVAL <= month).filter(d => d.CNSR == 0) const c1_11_20 = data_11_20.slice().filter(d => d.AVAL <= month).filter(d => d.CNSR == 1) const c0N_11_20 = c0_11_20.length const c1N_11_20 = c1_11_20.length const sn_11_20 = (enrollment_11_20 - c0N_11_20 -c1N_11_20) const st_11_20 = ((enrollment_11_20 - c0N_11_20 -c1N_11_20) / (enrollment_11_20 - c1N_11_20)).toFixed(3) const enrollment_10_21 = data_10_21.length const c0_10_21 = data_10_21.slice().filter(d => d.AVAL <= month).filter(d => d.CNSR == 0) const c1_10_21 = data_10_21.slice().filter(d => d.AVAL <= month).filter(d => d.CNSR == 1) const c0N_10_21 = c0_10_21.length const c1N_10_21 = c1_10_21.length const sn_10_21 = (enrollment_10_21 - c0N_10_21 -c1N_10_21) const st_10_21 = ((enrollment_10_21 - c0N_10_21 -c1N_10_21) / (enrollment_10_21 - c1N_10_21)).toFixed(3) return { 'c0' : c0, 'c1': c1, 'c0N' : c0N, 'c1N': c1N, 'st' : st, 'sn': sn, 'enrollment_11_21':enrollment_11_21, 'c0_11_21' : c0_11_21, 'c1_11_21': c1_11_21, 'c0N_11_21' : c0N_11_21, 'c1N_11_21': c1N_11_21, 'st_11_21' : st_11_21, 'sn_11_21' : sn_11_21, 'enrollment_10_20':enrollment_10_20, 'c0_10_20' : c0_10_20, 'c1_10_20': c1_10_20, 'c0N_10_20' : c0N_10_20, 'c1N_10_20': c1N_10_20, 'st_10_20' : st_10_20, 'sn_10_20' : sn_10_20, 'enrollment_11_20':enrollment_11_20, 'c0_11_20' : c0_11_20, 'c1_11_20': c1_11_20, 'c0N_11_20' : c0N_11_20, 'c1N_11_20': c1N_11_20, 'st_11_20' : st_11_20, 'sn_11_20' : sn_11_20, 'enrollment_10_21':enrollment_10_21, 'c0_10_21' : c0_10_21, 'c1_10_21': c1_10_21, 'c0N_10_21' : c0N_10_21, 'c1N_10_21': c1N_10_21, 'st_10_21' : st_10_21, 'sn_10_21' : sn_10_21, } }38:T12a3,plot1 = { const data = joinedData.map(d => ({ ...d, 'flag': d.MARKFL1B == 1 & d.MARKFL2 == 1 ? 'MARKFL1B=1 & MARKFL2=1' : d.MARKFL1B == 0 & d.MARKFL2 == 0 ? 'MARKFL1B=0 & MARKFL2=0' : d.MARKFL1B == 1 & d.MARKFL2 == 0 ? 'MARKFL1B=1 & MARKFL2=0' : d.MARKFL1B == 0 & d.MARKFL2 == 1 ? 'MARKFL1B=0 & MARKFL2=1' : 'ALL', }) ) const minAVALID = data.sort((a,b)=>a.AVAL - b.AVAL)[0].USUBJID return Plot.plot({ marginLeft: 10, marginRight: 10, height:data.length * 10, width:400, // color: { // legend:true, // }, style: { // background: '#fafafa', }, y: { label: null, tickSize: 0, margin: 10, axis: null, }, x:{ label: 'Month →', grid: true, domain: [0,24], ticks: 8, // axis: 'top', }, marks: [ Plot.ruleY(data, {x: "AVAL", y: "USUBJID", // stroke: d=>d.AVAL <= month ? 'orange' : 'black', stroke: d => Flags.includes('MARKFL1B=1 & MARKFL2=1') & d.MARKFL1B == 1 & d.MARKFL2 == 1 ? '#E14D2A' : Flags.includes('MARKFL1B=0 & MARKFL2=0') & d.MARKFL1B == 0 & d.MARKFL2 == 0 ? '#FD841F' : Flags.includes('MARKFL1B=1 & MARKFL2=0') & d.MARKFL1B == 1 & d.MARKFL2 == 0 ? '#3E6D9C' : Flags.includes('MARKFL1B=0 & MARKFL2=1') & d.MARKFL1B == 0 & d.MARKFL2 == 1 ? '#379237' : '#aaa', // stroke: Flags.includes('MARKFL1B=1 & MARKFL2=1') ? '#E14D2A' : 'black', strokeWidth: 3, strokeDasharray: d=>d.CNSR == 1 ? (3,3) : (0,0), sort: {y: 'x'}} ), Plot.dot(data.filter(d=>d.CNSR !== 1), { x: "AVAL", y: "USUBJID", // fill: d=> d.CNSR ? 'white': d.AVAL <= month ? 'orange' : 'black', // stroke: d=>d.AVAL <= month ? 'orange' : 'black', fill: d => Flags.includes('MARKFL1B=1 & MARKFL2=1') & d.MARKFL1B == 1 & d.MARKFL2 == 1 ? '#E14D2A' : Flags.includes('MARKFL1B=0 & MARKFL2=0') & d.MARKFL1B == 0 & d.MARKFL2 == 0 ? '#FD841F' : Flags.includes('MARKFL1B=1 & MARKFL2=0') & d.MARKFL1B == 1 & d.MARKFL2 == 0 ? '#3E6D9C' : Flags.includes('MARKFL1B=0 & MARKFL2=1') & d.MARKFL1B == 0 & d.MARKFL2 == 1 ? '#379237' : '#aaa', // fill: d => d.CNSR ? 'white' : null, // stroke: d => d.CNSR ? null : d.flag, r: 3} ), // Plot.dot(data.filter(d=>d.CNSR === 1), // { // x: "AVAL", y: "USUBJID", // // fill: d=> d.CNSR ? 'white': d.AVAL <= month ? 'orange' : 'black', // // stroke: d=>d.AVAL <= month ? 'orange' : 'black', // stroke: '#666', // // fill: d => d.CNSR ? 'white' : null, // // stroke: d => d.CNSR ? null : d.flag, // r: 3} // ), Plot.text(data, { x: "AVAL", y: "USUBJID", text: d=> d.CNSR === 1 ? "X" : '', fill: 'black', fontWeight: 'bold', fontSize: 10} ), Plot.text([[19, minAVALID]], { text: ['X Censoring'], fill: '#666', fontWeight: 'bold', textAnchor: 'start', fontSize: 12} ), Plot.text([[19, minAVALID]], { text: ['● Event'], dy: 10, fill: '#666', fontWeight: 'bold', textAnchor: "start", fontSize: 12} ), // Plot.ruleY([]), Plot.ruleX([0, 24]), Plot.ruleX([month], {stroke: '#333', strokeDasharray: (3,3)}), ] }) } 39:Tfbc,plot2 = { // const ref = getMedian(obj.groupedData) // let r = [ref] // Object.values(pick(dict, Flags)) // .map(d => { // r.push(getMedian(obj[d])) // }) let r1 = 'Median: ' + median_hr.median.join(' vs ') // let r2 = [] // r.slice(1) // .map(d => { // r2.push(ref === 'NA' | d === 'NA' ? 'NA' : (ref / d).toFixed(2)) // }) let r3 = 'Hazard ratio: ' + median_hr.HR.join(' | ') return Plot.plot({ marginLeft: 30, marginRight: 20, height:ADTTE.length * 10, width:700, style: { // background: '#fafafa', }, y: { label: '↑ Probability of event free beyond time T', // tickSize: 0, margin: 10, domain: [0,1], }, x:{ label: 'Month →', grid: true, domain: [0,24], ticks: 8, // axis: 'top', }, marks: [ // Plot.text([[1, 0.2]], // {text: [r1], // fontSize: 16, // textAnchor: "start", // fontWeight: 'bold', // fill: '#777' // }), // Plot.text([[1, 0.25]], // {text: [r3], // fontSize: 16, // textAnchor: "start", // fontWeight: 'bold', // fill: '#777' // }), Plot.ruleX([month], {stroke: '#333', strokeDasharray: (3,3)}), Plot.ruleY([0.5], {stroke: '#333', strokeDasharray: (3,3)}), Plot.line(obj.plotDataForLine, {x:'x', y:'y', stroke: '#aaa' }), // stroke: Flags.includes('ALL') ? '#333' : null }), Plot.text(obj.plotDataForTicks, {x:'x', y:'y', text: 'text', fontSize: 24, dy:-1, fill: 'blue',}), // fill: Flags.includes('ALL') ? 'blue' : null }), Plot.line(obj.plotDataForLine_11_21, {x:'x', y:'y', stroke: Flags.includes('MARKFL1B=1 & MARKFL2=1') ? '#E14D2A' : null }), Plot.text(obj.plotDataForTicks_11_21, {x:'x', y:'y', text: 'text', fontSize: 24, dy:-1, fill: Flags.includes('MARKFL1B=1 & MARKFL2=1') ? 'blue' : null }), Plot.line(obj.plotDataForLine_10_20, {x:'x', y:'y', stroke: Flags.includes('MARKFL1B=0 & MARKFL2=0') ? '#FD841F' : null }), Plot.text(obj.plotDataForTicks_10_20, {x:'x', y:'y', text: 'text', fontSize: 24, dy:-1, fill: Flags.includes('MARKFL1B=0 & MARKFL2=0') ? 'blue' : null }), Plot.line(obj.plotDataForLine_11_20, {x:'x', y:'y', stroke: Flags.includes('MARKFL1B=1 & MARKFL2=0') ? '#3E6D9C' : null }), Plot.text(obj.plotDataForTicks_11_20, {x:'x', y:'y', text: 'text', fontSize: 24, dy:-1, fill: Flags.includes('MARKFL1B=1 & MARKFL2=0') ? 'blue' : null }), Plot.line(obj.plotDataForLine_10_21, {x:'x', y:'y', stroke: Flags.includes('MARKFL1B=0 & MARKFL2=1') ? '#379237' : null }), Plot.text(obj.plotDataForTicks_10_21, {x:'x', y:'y', text: 'text', fontSize: 24, dy:-1, fill: Flags.includes('MARKFL1B=0 & MARKFL2=1') ? 'blue' : null }), ] }) } 3a:T116d,// JavaScript for sampling from a handful of discrete probability distributions // From: https://github.com/jacobmenick/sampling SJS = (function(){ // Utility functions function _sum(a, b) { return a + b; }; function _fillArrayWithNumber(size, num) { // thanks be to stackOverflow... this is a beautiful one-liner return Array.apply(null, Array(size)).map(Number.prototype.valueOf, num); }; function _rangeFunc(upper) { var i = 0, out = []; while (i < upper) out.push(i++); return out; }; // Prototype function function _samplerFunction(size) { if (!Number.isInteger(size) || size < 0) { throw new Error ("Number of samples must be a non-negative integer."); } if (!this.draw) { throw new Error ("Distribution must specify a draw function."); } var result = []; while (size--) { result.push(this.draw()); } return result; }; // Prototype for discrete distributions var _samplerPrototype = { sample: _samplerFunction }; function Bernoulli(p) { var result = Object.create(_samplerPrototype); result.draw = function() { return (Math.random() < p) ? 1 : 0; }; result.toString = function() { return "Bernoulli( " + p + " )"; }; return result; } function Binomial(n, p) { var result = Object.create(_samplerPrototype), bern = this.Bernoulli(p); result.draw = function() { return bern.sample(n).reduce(_sum, 0); // less space efficient than adding a bunch of draws, but cleaner :) } result.toString = function() { return "Binom( " + [n, p].join(", ") + " )"; } return result; } function Discrete(probs) { // probs should be an array of probabilities. (they get normalized automagically) // var result = Object.create(_samplerPrototype), k = probs.length; result.draw = function() { var i, p; for (i = 0; i < k; i++) { p = probs[i] / probs.slice(i).reduce(_sum, 0); // this is the (normalized) head of a slice of probs if (Bernoulli(p).draw()) return i; // using the truthiness of a Bernoulli draw } return k - 1; }; result.sampleNoReplace = function(size) { if (size>probs.length) { throw new Error("Sampling without replacement, and the sample size exceeds vector size.") } var disc, index, sum, samp = []; var currentProbs = probs; var live = _rangeFunc(probs.length); while (size--) { sum = currentProbs.reduce(_sum, 0); currentProbs = currentProbs.map(function(x) {return x/sum; }); disc = this.Discrete(currentProbs); index = disc.draw(); samp.push(live[index]); live.splice(index, 1); currentProbs.splice(index, 1); sum = currentProbs.reduce(_sum, 0); currentProbs = currentProbs.map(function(x) {return x/sum; }); } currentProbs = probs; live = _rangeFunc(probs.length); return samp; } result.toString = function() { return "Dicrete( [" + probs.join(", ") + "] )"; }; return result; } function Multinomial(n, probs) { var result = Object.create(_samplerPrototype), k = probs.length, disc = Discrete(probs); result.draw = function() { var draw_result = _fillArrayWithNumber(k, 0), i = n; while (i--) { draw_result[disc.draw()] += 1; } return draw_result; }; result.toString = function() { return "Multinom( " + n + ", [" + probs.join(", ") + "] )"; }; return result; } function NegBinomial(r, p) { var result = Object.create(_samplerPrototype); result.draw = function() { var draw_result = 0, failures = r; while (failures) { Bernoulli(p).draw() ? draw_result++ : failures--; } return draw_result; }; result.toString = function() { return "NegBinomial( " + r + ", " + p + " )"; }; return result; } function Poisson(lambda) { var result = Object.create(_samplerPrototype); result.draw = function() { var draw_result, L = Math.exp(- lambda), k = 0, p = 1; do { k++; p = p * Math.random() } while (p > L); return k-1; } result.toString = function() { return "Poisson( " + lambda + " )"; } return result; } return { _fillArrayWithNumber: _fillArrayWithNumber, // REMOVE EVENTUALLY - this is just so the Array.prototype mod can work _rangeFunc: _rangeFunc, 'Bernoulli': Bernoulli, Binomial: Binomial, Discrete: Discrete, Multinomial: Multinomial, NegBinomial: NegBinomial, Poisson: Poisson }; })();3b:Td9e,3c:T535,{ const r1 = tte.value.slice() for (const n of r1) { n.AVAL = +n.AVAL n.CNSR = +n.CNSR } const r = r1.sort((a,b) => a.AVAL - b.AVAL) let arr = [{'t': 0, 'd': 0, 'w': 0, 'n': enrollment, 'p': 1}] let int = {'t': 0, 'd': 0, 'w': 0, 'n': enrollment, 'p': 1} const timerange = d3.range(1, 25) timerange.map(m => { let t = m let d = r.filter(d => d.AVAL === m & d.CNSR !== 1).length let w = r.filter(d => d.AVAl === m & d.CNSR === 1).length let n = arr[arr.length-1].n - d - w let p = arr[arr.length-1].p * (1 - d / n) // console.log(arr[arr.length-1], arr.length,m) arr.push({'t': t, 'd': d, 'w': w, 'n': n, 'p': arr[arr.length-1].p}) arr.push({'t': t, 'd': d, 'w': w, 'n': n, 'p': p}) // console.log(arr[arr.length-1], arr.length,m) }) // let t = [] // const arr1 = timerange.reduce((acc, cur, i, array) => { // let t = cur // let d = r.filter(d => d.AVAL === cur & d.CNSR !== 1).length // let w = r.filter(d => d.AVAl === cur & d.CNSR === 1).length // let n = array[cur - 1].n - d - w // let p = array[cur - 1].p * (1 - d / n) // acc.push({'t': t, 'd': d, 'w': w, 'n': n, 'p': array[cur - 1].p}) // acc.push({'t': t, 'd': d, 'w': w, 'n': n, 'p': p}) // return acc // }, arr) return [r, arr,] }3d:[] 2e:{"id":"4c8664428dd24177","slug":"efficacy-analysis","title":"Stat Notes | An Interactive Tool for Oncology Efficacy Analyses","thumbnail":"5134cb726a69975331eb28876770e9e7247cfa1f902f8e1984d1a835c1ffc7cc","access_level":"public","create_time":"$D2022-10-28T01:48:26.097Z","update_time":"$D2026-05-21T11:57:51.561Z","view_time":null,"first_public_time":"$D2022-10-29T00:41:36.387Z","fork":false,"stars":2,"starred":false,"owner":{"type":"team","id":"610db6fe9b10a76c","login":"howardyao","name":"Zhihao Yao","avatar_url":"https://avatars-next.observableusercontent.com/0bac389abf8df9b4d7c6e09e8f96b464d2c01a64946e8b88cd7e143da5750c0e","tier":"starter_2024"},"version":2371,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"01b5d984f2428b1a","login":"howardyao","name":"Zhihao Yao","avatar_url":"https://avatars-next.observableusercontent.com/0bac389abf8df9b4d7c6e09e8f96b464d2c01a64946e8b88cd7e143da5750c0e","tier":null},{"type":"individual","id":"dd69269e711cf90a","login":"yuthmath","name":"Tinghui Yu","avatar_url":"https://avatars-next.observableusercontent.com/400c01255ebd1183659c58a9486b04fcbee7a47812e99bea6f7e1cce7ec72df1","tier":null}],"members":[{"type":"individual","id":"01b5d984f2428b1a","login":"howardyao","name":"Zhihao Yao","avatar_url":"https://avatars-next.observableusercontent.com/0bac389abf8df9b4d7c6e09e8f96b464d2c01a64946e8b88cd7e143da5750c0e","tier":null,"role":"editor"}],"comments":[{"id":"ac224fe69a90a78e","reply_to_id":null,"content":"The \"count by flag\" and slider for \"cut-off for Marker 1\" is a pretty interesting function. Suggest you consider putting them here. Add a line of text to explain: \n\nMarker1 is an underlying continuous measurement (e.g. percentage of cells in a tumor expressing PD-L1). A cut-off value is imposed on Marker1 to define a binary marker MarkFL1B for the targeted treatment. Try to change the cut-off of the companion diagnostic here:","create_time":"$D2022-10-31T19:57:30.553Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"dd69269e711cf90a","login":"yuthmath","name":"Tinghui Yu","avatar_url":"https://avatars-next.observableusercontent.com/400c01255ebd1183659c58a9486b04fcbee7a47812e99bea6f7e1cce7ec72df1","tier":null}}],"custom_thumbnail":true,"trashed":false,"subscribed":null,"forks":6,"collection":{"id":"0c98c590cb744ded","slug":"stat-notes","title":"Stat Notes","owner":{"id":"610db6fe9b10a76c","login":"howardyao","name":"Zhihao Yao"}},"type":"notebook","body":{"title":"Stat Notes | An Interactive Tool for Oncology Efficacy Analyses","cells":[{"id":0,"value":"# Stat Notes | An Interactive Tool for Oncology Efficacy Analyses\n\n> Efficacy analyses for new targeted oncology treatment in early phase usually require close collaboration between the biometric team and the clinical development team to identify delicate signals hidden in patient subgroups defined by biomarkers. The conventional paradigm of communication based on pre-planned TLF deliverables lack of efficiency.\n\n\n> A flexible presentation of PFS/OS/ORR/DoR data is very much desired to facilitate communication among a drug development team. Below is a demonstration of a new tool we developed to allow highly customizable statistical analyses for efficacy endpoints commonly seen in an oncology trial.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2271,"value":"

\n
Biostatisticians and SAS programmer, now you are relieved from the endless subgroup analyses! Medical researchers, you have a ton of analyses readily available in your hand!
\n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":1998,"value":"> Starting from a set of validated ADaM data (simulated here), what kind of analyses would you like to see? Have fun playing with all the interesting toys we provide below!\n\n> 😊 Note to the development team: if you see any exciting signals, please hold your horse. Ask your statistician for proper interpretation of the data and a fully validated report before investing into a new trial. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2081,"value":"{\n // const el = html``;\n const el = htl.html`

`\n el.onclick = () => {\n const triggerElement = document.getElementById(\"trigger\");\n // Inside a notebook you would ordinarily avoid selecting elements like this and just refer to the `trigger` cell directly,\n // but since you want to do something outside the notebook context you'll have to do something like this.\n triggerElement.dispatchEvent(new Event(\"input\"));\n };\n return el;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1760,"value":"---\n## Kaplan-Meier Curves (for PFS/OS/DoR)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2070,"value":"viewof enrollment = Inputs.range([40, 80], {label: \"Sample size (ITT) to be simulated\", step: 5, value: 60})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2065,"value":"

Two biomarkers are simulated, MARKFL2 is a binary marker. Marker1 is an underlying continuous measurement (e.g. percentage of cells in a tumor expressing PD-L1). A cut-off value is imposed on Marker1 to define a binary marker MarkFL1B for the targeted treatment. Try to change the cut-off of the companion diagnostic here:

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2078,"value":"viewof cutoff = Inputs.range([1, 100], {label: \"MARKFL1 Cutoff\", step: 1, value: 50})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2074,"value":"

Count by Flags

\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

	MARKFL1B=0	MARKFL1B=1
MARKFL2=0	${showAllNumbers(obj.data_10_20)[0]}	${showAllNumbers(obj.data_11_20)[0]}
MARKFL2=1	${showAllNumbers(obj.data_10_21)[0]}	${showAllNumbers(obj.data_11_21)[0]}

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":2291,"value":"viewof Flags = Inputs.select(\n [\n 'MARKFL1B=1 & MARKFL2=1',\n 'MARKFL1B=1 & MARKFL2=0',\n 'MARKFL1B=0 & MARKFL2=1',\n 'MARKFL1B=0 & MARKFL2=0',\n ], \n {multiple: true, label: htl.html`Which subgroup are you interested in?

(CTRL + Click for Multiple Selection)`,value: ['MARKFL1B=1 & MARKFL2=1']}\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":925,"value":"Plot.legend({\n color:{\n type: 'ordinal',\n domain: ['ITT (Reference)', \n 'MARKFL1B=1 & MARKFL2=1',\n 'MARKFL1B=1 & MARKFL2=0',\n 'MARKFL1B=0 & MARKFL2=1',\n 'MARKFL1B=0 & MARKFL2=0',\n ], \n range: ['#aaa', '#E14D2A', '#3E6D9C', '#379237', '#FD841F',]\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1935,"value":"md`##### Follow each patient for a maximum of ${month} months`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":929,"value":"viewof month = Inputs.range([0, 24], {label: \"\", step: 1, value: 18})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":933,"value":"plot_comb = html`\n

\n ${[plot2, plot1]}\n

\n\n

ADTTE

USUBJID: dynamic ITT with default sample size = 60
AVAL: integer samples from a uniform distribution ${tex`\\small1 \\le x \\le 24`}
CNSR: samples from a binomial distribution (n = 1, p = 0.2)
PARAMCD: flag for endpoint

\n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":28,"value":"// tte = table(ADTTE)\ntte = Inputs.table(ADTTE)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":978,"value":"

ADSL

USUBJID: dynamic ITT with default sample size = 60
MARKFL1: integer samples from a uniform distribution ${tex`\\small1 \\le x \\le 100`}
MARKFL2: samples from a binomial distribution (n = 1, p = 0.6)
MARKFL1B: derived from MARKFL1 based on a dynamic cutoff

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":58,"value":"// table(ADSL)\nInputs.table(ADSL)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1749,"value":"

ADRS

USUBJID: dynamic ITT with default sample size = 60
AVALC: 'SD', 'CR', 'PR', 'PD', 'NE' simulated by using multinomial distribution with prob = [0.2, 0.4, 0.2, 0.1, 0.1]
PARAMCD: flag for response criteria

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":1753,"value":"// table(ADRS)\nInputs.table(ADRS)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":756,"value":"---\n## Descriptive Statistics","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":769,"value":"> #### ADTTE","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":752,"value":"adtteSummary = SummaryTable(ADTTE, {label: \"\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":772,"value":"> #### ADSL","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":759,"value":"adslSummary = SummaryTable(ADSL, {label: \"\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1746,"value":" #### ADRS","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1743,"value":"adrsSummary = SummaryTable(ADRS, {label: \"\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":432,"value":"---\n## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":583,"value":"#### Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":951,"value":"ADTTE = {\n trigger\n return aq.table({\n USUBJID: d3.range(1,enrollment+1).map(d => 'Subject' + d),\n AVAL: Array.from({length: enrollment}, d3.randomInt(1, 24)),\n CNSR: Array.from({length: enrollment}, d3.randomBinomial(1, 0.2)),\n PARAMCD: new Array(enrollment).fill('PFS'),\n }).objects()\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":955,"value":"ADSL = {\n trigger\n return aq.table({\n USUBJID: d3.range(1, enrollment + 1).map(d => 'Subject' + d),\n MARKFL1: Array.from({length: enrollment}, d3.randomInt(1, 100)),\n MARKFL2: Array.from({length: enrollment}, d3.randomBinomial(1, 0.6)),\n })\n .objects()\n .map(d => ({...d, 'MARKFL1B': d.MARKFL1 > cutoff ? 1 : 0}))\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1735,"value":"ADRS = {\n trigger\n \n const probs = [0.2, 0.4, 0.2, 0.1, 0.1]\n const mult = SJS.Multinomial(enrollment, probs).draw()\n const rs = ['CR', 'PR', 'SD', 'PD', 'NE']\n let r = []\n\n mult.map((d,i) => {\n r.push(...new Array(d).fill(rs[i]))\n })\n \n const rsArray = _.shuffle(r)\n \n \n return aq.table({\n USUBJID: d3.range(1, enrollment + 1).map(d => 'Subject' + d),\n AVALC: rsArray,\n PARAMCD: new Array(enrollment).fill('BESTRESP'),\n })\n .objects()\n \n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":588,"value":"> ##### ADTTE filtered by month","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":586,"value":"adtteFiltered = ADTTE.filter(d => d.AVAL <= month)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":596,"value":"> ##### ADTTE joined ADSL","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":593,"value":"joinedData = aq.from(ADTTE)\n .join(aq.from(ADSL), ['USUBJID', 'USUBJID'])\n .join(aq.from(ADRS), ['USUBJID', 'USUBJID'])\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":599,"value":"> ##### ADTTE joined ADSL, filtered by month","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":602,"value":"joinedDataFiltered = joinedData.filter(d => d.AVAL <= month)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":627,"value":"> ##### Object contains datasets for plots","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":624,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":646,"value":"> ##### Object contains datasets for notes","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":643,"value":"objNumbers = showNumbers(ADTTE, obj.data_11_21, obj.data_10_20, obj.data_11_20, obj.data_10_21, month)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1639,"value":"> ##### Median & HR","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1642,"value":"median_hr_old = {\n const ref = getMedian(obj.groupedData)\n\n let r = [ref]\n\n\n Object.values(pick(dict, Flags))\n .map(d => {\n r.push(getMedian(obj[d]))\n })\n\n\n let r1 = 'Median: ' + r.join(' vs ')\n\n let r2 = []\n r.slice(1) \n .map(d => {\n r2.push(ref === 'NA' | d === 'NA' ? 'NA' : (ref / d).toFixed(2))\n })\n\n\n let r3 = 'Hazard ratio: ' + r2.join(' | ')\n\n return {'median': r, 'HR': r2}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2241,"value":"median_hr = {\n const ref = getMedian(obj.groupedData).median\n const refhr = getMedian(obj.groupedData).hrMedian\n let r = [ref]\n let rhr = [refhr]\n\n Object.values(pick(dict, Flags))\n .map(d => {\n r.push(getMedian(obj[d]).median)\n })\n\n Object.values(pick(dict, Flags))\n .map(d => {\n rhr.push(getMedian(obj[d]).hrMedian)\n })\n\n\n let r1 = 'Median: ' + r.join(' vs ')\n\n let r2 = []\n r.slice(1) \n .map(d => {\n r2.push(ref === 'NA' | d === 'NA' ? 'NA' : (ref / d).toFixed(2))\n })\n\n let r2hr = []\n rhr.slice(1) \n .map(d => {\n r2hr.push( (refhr / d).toFixed(2))\n })\n\n\n let r3 = 'Hazard ratio: ' + r2.join(' | ')\n\n return {'median': r, 'HR': r2hr }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2061,"value":"> ##### ORR","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2058,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":580,"value":"#### Function","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":282,"value":"function funGroupedData(ADTTE, adtteFiltered) {\n const eventTimes = [... new Set(\n ADTTE\n .slice().sort((a,b) => d3.ascending(a.AVAL, b.AVAL))\n .filter(d => d['CNSR'] === 0)\n .map(d => d.AVAL)\n )]\n\n function d(t){\n // the number of events that happened at time t \n return adtteFiltered.filter(d => d.AVAL === t).length\n }\n\n function n(t){\n // the number of individuals known to have survived up to time t\n // (not dead and not censored)\n return ADTTE.filter(d => d.AVAL >= t).length\n }\n\n function S(t){\n // Survival function at time t\n let product = 1;\n for(const time of eventTimes.filter(time => time <= t)){\n product = product * (1 - d(time) / n(time));\n }\n return product;\n }\n \n const res = eventTimes.map(time => new Object({\"t\" : time, \"prob\" : S(time)})) \n\n return res.length === 0 ? [{'t':0, 'prob': 1}] : res\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":236,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":243,"value":"function funPlotDataForTicks(groupedData, adtteFiltered) {\n const censoredEventTimes = adtteFiltered.slice().sort((a,b) => d3.ascending(a.AVAL, b.AVAL))\n .filter(d => d.CNSR === 1).map(d => d.AVAL)\n const plotData = [];\n\n for (const censoredEventTime of censoredEventTimes){\n // let y = groupedData[0]['prob'];\n let y = 1\n \n // for (let i = 1; i < groupedData.length; i ++){\n for (let i = 0; i < groupedData.length; i ++){\n if(groupedData[i]['t'] < censoredEventTime){\n y = groupedData[i]['prob']\n } \n }\n\n plotData.push({\"x\" : censoredEventTime, \"y\": y, \"text\": \"+\"}) \n }\n \n return plotData;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":636,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1277,"value":"function showAllNumbers(ADTTE) {\n let res = [ADTTE.length]\n d3.range(1, 25).map(month => {\n const c0 = ADTTE.slice().filter(d => d.AVAL <= month).filter(d => d.CNSR == 0)\n const c1 = ADTTE.slice().filter(d => d.AVAL <= month).filter(d => d.CNSR == 1)\n const c0N = c0.length\n const c1N = c1.length\n const sn = (ADTTE.length - c0N -c1N) \n res.push(sn)\n })\n\n return res\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1473,"value":"function getMedian_old(data) {\n const diff = data\n .filter(d => d.t <= month)\n .slice()\n .sort((a,b) => {\n return Math.abs(a.prob - 0.5) - Math.abs(b.prob - 0.5);\n })\n let median = 0\n let hrMedian = 0\n\n if(diff.filter(d => d.prob < 0.5).length !== 0 & diff.length !== 0) {\n median = diff[0].t\n // diff.length == 0 ? 1 : hrMedian = diff[0].t\n }else{\n // hrMedian = diff.length == 0 ? 1 : diff[0].t\n median = 'NA'\n }\n // return {'median': median, 'hrMedian': hrMedian}\n return median\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2245,"value":"function getMedian(data) {\n const dataFiltered = data\n .slice()\n .filter(d => d.t <= month)\n\n const belowMedian = dataFiltered.filter(d => d.prob < 0.5)\n\n let median = 0\n let hrMedian = 0\n\n median = belowMedian.length === 0 ? 'NA' : belowMedian.sort((a,b)=>a.t-b.t)[0].t\n\n hrMedian = belowMedian.length === 0 ? dataFiltered.length === 0 ? 1 : d3.max(dataFiltered, d=>d.t) : belowMedian.sort((a,b)=>a.t-b.t)[0].t\n \n return {'median': median, 'hrMedian': hrMedian}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1476,"value":"function pick(obj, keys) {\n return Object.keys(obj).filter(k => keys.includes(k)).reduce((res, k) => Object.assign(res, {[k]: obj[k]}), {});\n} ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1481,"value":"dict = {\n const flagOptions = [\n 'MARKFL1B=1 & MARKFL2=1',\n 'MARKFL1B=0 & MARKFL2=0',\n 'MARKFL1B=1 & MARKFL2=0',\n 'MARKFL1B=0 & MARKFL2=1',\n ]\n\n const b = [\n 'groupedData_11_21',\n 'groupedData_10_20',\n 'groupedData_11_20',\n 'groupedData_10_21',\n ]\n\n let r ={}\n const c = flagOptions.map((d,i) => {\n r[d] = b[i]\n })\n\n return r\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":605,"value":"#### Plot","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":95,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":128,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":649,"value":"#### Other","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1689,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1721,"value":"// JavaScript for sampling from a handful of discrete probability distributions\n// From: https://github.com/jacobmenick/sampling\n\nfunction sample_from_array(array, numSamples, withReplacement) {\n var n = numSamples || 1,\n result = [],\n copy,\n disc,\n index;\n\n if (!withReplacement && numSamples > array.length) {\n\tthrow new Error(\"Sampling without replacement, and the sample size exceeds vector size.\")\n }\n\n if (withReplacement) {\n\twhile(numSamples--) {\n\t disc = SJS.Discrete(SJS._fillArrayWithNumber(array.length, 1));\n\t result.push(array[disc.draw()]);\n\t}\n } else {\n\t// instead of splicing, consider sampling from an array of possible indices? meh?\n\tcopy = array.slice(0);\n\twhile (numSamples--) {\n\t disc = SJS.Discrete(SJS._fillArrayWithNumber(copy.length, 1));\n\t index = disc.draw();\n\t result.push(copy[index]);\n\t copy.splice(index, 1);\n\t console.log(\"array: \"+copy);\n\t}\t\n }\n return result;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":725,"value":"// trigger to re-run ADTTE ADSL \nviewof trigger = {\n const element = html`