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CEM analysesCEM analyses
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What are we trying to What are we trying to measure?measure?
First, the risks associated with each First, the risks associated with each ARV regimenARV regimen Each regimen will be treated like a Each regimen will be treated like a
single drugsingle drug
Second, the risks associated with Second, the risks associated with individual drugsindividual drugs
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ApproachApproach
Keep it simpleKeep it simple most of the important results can be most of the important results can be
revealed by simple measuresrevealed by simple measures
Keep it accurateKeep it accurate clean, quality dataclean, quality data controlled at inputcontrolled at input Example: variation in spellingExample: variation in spelling
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Small things 1Small things 1
Rates Rates What to do with drop-outs?What to do with drop-outs? Choosing the denominatorChoosing the denominator
total cohort includes drop-outstotal cohort includes drop-outs demographicsdemographics
total follow-up questionnaires returnedtotal follow-up questionnaires returned general rates eg rates of events; reactions & general rates eg rates of events; reactions &
incidentsincidents Sites / regionsSites / regions
Total per question answeredTotal per question answered Rate per data element eg traditional meds, TBRate per data element eg traditional meds, TB
Small things 2Small things 2Examples of rates Overall reporting rate (response rate)
Total cohort enrolled = 8432 Total follow-up forms received = 6998 Reporting rate = 6998/8432 = 83%
Rates of events Total follow-up forms received = 6998 Total patients with oral candidiasis = 843 Rate of oral candidiasis = 843/6998 = 12%
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Small things 3Small things 3
Examples of ratesExamples of rates Rates per total question answered for Rates per total question answered for
use of traditional medicinesuse of traditional medicines Total follow-up forms received = 6998Total follow-up forms received = 6998 Total TM questions answered = 5432Total TM questions answered = 5432 Total answered ‘Yes’ = 3954Total answered ‘Yes’ = 3954 Rate of use of traditional medicines = Rate of use of traditional medicines =
3954/5432 = 73%3954/5432 = 73%
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Small things 4Small things 4Calculation of riskCalculation of risk The rate of occurrence of an event in The rate of occurrence of an event in
the exposed cohort is a measure of the exposed cohort is a measure of absolute riskabsolute risk
Attributable riskAttributable risk: this is a measure of : this is a measure of the increased risk associated with the the increased risk associated with the medicine. It is calculated as follows:medicine. It is calculated as follows: rate in the exposed cohort minus rate in the exposed cohort minus rate before exposure (in the control rate before exposure (in the control
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Small things 4Small things 4
DosesDoses Analyses are done on the total daily doseAnalyses are done on the total daily dose Total daily dose x weekly e.g.Total daily dose x weekly e.g.
300mg 3 x weekly300mg 3 x weekly Or 100mg 7 x weeklyOr 100mg 7 x weekly
DurationDuration Duration in daysDuration in days
1 m = 30 days1 m = 30 days 1 y = 365 days1 y = 365 days Between x & y –take mid-pointBetween x & y –take mid-point
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Data manipulationData manipulationCollationCollation
summary of reporting rates for summary of reporting rates for males, females and totals;males, females and totals;
age/sex profiles of the cohort; age/sex profiles of the cohort; patient numbers by region or site;patient numbers by region or site; event profiles by clinical category;event profiles by clinical category;
within a clinical categorywithin a clinical category reactions & incidentsreactions & incidents
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Profile of Ages at First Prescription
213543
1477
3325
5827 5969
6552
3601
503468
945
1939
3426
4704
4254 4297
2289
280
0
5
10
15
20
25
< 20 20-30 30-39 40-49 50-59 60-69 70-79 80-89 90 plus
% o
f tot
al k
now
n ag
es
Celecoxib Rofecoxib
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IMMP example –COX-2IMMP example –COX-2Age & sexAge & sex CelecoxibCelecoxib RofecoxibRofecoxib
MeanMean 6363 5858
ModeMode 5959 5353
<40 years<40 years 6.9%6.9% 12.6%12.6%Highly significantHighly significant
70+ years70+ years 32.7%32.7% 25.7%25.7%Highly significantHighly significant
WomenWomen 61.6%61.6% 60.5%60.5%
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Celecoxib dose mg/no./%Celecoxib dose mg/no./%
100 6,622 8.1 200 65,591 80.5 300 274 0.3 400 8,927 11.0 600 46 800 30
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Event profilesEvent profiles
CelecoxibCelecoxib RofecoxibRofecoxib Odds RatioOdds Ratio
CirculatoryCirculatory 315 (17%)315 (17%) 214 (20%)214 (20%) NSNS
AlimentaryAlimentary 302 (17%)302 (17%) 218 (20%)218 (20%) 0.8 (0.66,0.97)0.8 (0.66,0.97)
DiedDied 293 (16%)293 (16%) 179 (16%)179 (16%)
SkinSkin 160 (9%)160 (9%) 59 (5%)59 (5%) 1.7 (1.24-2.30)1.7 (1.24-2.30)
RespiratorRespiratoryy
108 (6%)108 (6%) 54 (5%)54 (5%)
PsychiatricPsychiatric 88 (5%)88 (5%) 49 (5%)49 (5%)
RenalRenal 72 (4%)72 (4%) 46 (4%)46 (4%)
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Events collationEvents collation
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Event collationsEvent collationsLook Carefully
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Understanding the Understanding the dictionarydictionary
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ExamplesExamples
Annex 8 events collationAnnex 8 events collation Annex 12 eye events with signalAnnex 12 eye events with signal Annex 11 deathsAnnex 11 deaths Annex 13 concomitant medicinesAnnex 13 concomitant medicines
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RiskRisk
Risk calculationRisk calculation Relative risk (rate ratio)Relative risk (rate ratio)
Rate of A / rate of B = relative riskRate of A / rate of B = relative risk Confidence intervalsConfidence intervals
Risk factors eg ageRisk factors eg age RRRR Multiple logistic regressionMultiple logistic regression
Risks of individual drugsRisks of individual drugs Compare events before and after drug Compare events before and after drug
substitutions with the patients acting as substitutions with the patients acting as their own controls eg their own controls eg d4t(30)-3TC-NVP to d4t(30)-3TC-EFVd4t(30)-3TC-NVP to d4t(30)-3TC-EFV
Compare events between regimens where Compare events between regimens where substitutions have taken placesubstitutions have taken place
Use multiple logistic regression to test each Use multiple logistic regression to test each drug as a risk factor for events of interest drug as a risk factor for events of interest
Large database needed for these analyses Large database needed for these analyses (pooled international data)(pooled international data)
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Life table (survival) analysisLife table (survival) analysis
Helps to characterise a reactionHelps to characterise a reaction time to onsettime to onset spread of onset timesspread of onset times
Testing a possible signalTesting a possible signal
Statistical programmesStatistical programmes
CemFlowCemFlow automated outputsautomated outputs
MedCalcMedCalc free for 25 sessionsfree for 25 sessions
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Don’t be afraid!
of statistics