2 ObjectivesUnderstand the principle differences between qualitative and quantitative researchUnderstand the basic statistics employed in researchBe able to assess a piece a research with confidence!
3 Qualitative research Which type of questions does it answer? What methodologies are employed?Improving their validity
4 Assessing a qualitative paper Is the qualitative approach appropriate?MethodologyData analysisResults and conclusion
5 Quantitative Types of quantitative research RCT – design features, advantages & disadvantagesCohort StudiesCase control studiesCross section surveys
6 BIAS Selection bias Observer bias Participant bias Withdrawal or drop out biasRecall biasMeasurement biasPublication biasSelection bias – select sicker patients to get the active or new Rx and fitter patients to get placebo or older RxObserver bias – if we know the patient has active treatment can subconsciously record health status as being betterParticipant bias – e.g. in study looking at Gi bleeds in NSAID v non-NSAID users, the people who are not prescribed NSAIDs buy them OTC.Withdrawal / drop out – if lose people from the study those left at thend may not be representative of those originally included, and their numbers may be very much smaller so affecting the validity and generalisability of event rates.Recall – mothers of kids with leukaemia remember living near high voltage cables. Mothers of kids without leukaeimia won’t remember living near cables coz to them it’s a trivial fact.Measurement bias – e.g. measuring BP in trials with sphygs that are not calibratedPublication bias – positive studies get published much more often than equivocal or negative studies
8 Commonly used statistics P valuesRelative Risk ReductionAbsolute Risk ReductionNumbers Need to TreatSensitivitySpecificityPositive Predictive ValueNegative Predictive Value
9 P values & CIp value = the probability of the outcome being due to chancep = 1 in 20 (0.05).> 1 in 20 (0.051) = not significant< 1 in 20 (0.049) = statistically significantCONFIDENCE INTERVALSThis defines the range of values between which we could be 95% certain that this result would lie if this intervention was applied to the general populationStraightforward, surely. If not see Simple Statistics by Frances Clegg, Cambridge Press.
10 RR, AR, ARR & RRRWhat are they?How do you calculate them?
11 Warfarin & AF studyThe annual rate of stroke was 4.5% for the control groupAbsolute Risk (Control group) = 0.0451.4% for the warfarin groupAbsolute Risk (experimental group) = 0.014Absolute Risk Reduction = – = 0.031NNT = 32Relative Risk = 0.014/0.045 = 0.31 = 31%Relative Risk Reduction = – 0.014/0.045 = 0.68 = 68%
12 1/ARR = Number Needed to Treat. NNTHow many people you need to treat with the study intervention to stop the study event from happening once.1/ARR = Number Needed to Treat.
13 NNT EXAMPLESBut these are all in different patient groups with interventions with very different costs so tables of NNTS are illustrative but no answer.
15 SensitivityThe test’s ability to correctly identify those people with disease.If Sensitivity is <100% Disease is missed.So = True PositivesTrue Positives + False negativesi.e. all those who truly Have the disease
16 SpecificityThe test’s ability to correctly exclude those people without diseaseIf Specificity <100% then healthy people are told they may have disease= True NegativesTrue Negatives + False Positivesi.e. all those who truly don’t have the disease
17 Positive predictive value If the test is positive, what is the chance of the person having the disease = positive predictive value.True PositivesTrue positives + False Positives
18 Negative Predictive Value If the test is negative, what chance is there that the person doesn’t have the disease = negative predictive value.True negativeTrue negative + False negative