How to assess an abstract

Objectives Understand the principle differences between qualitative and quantitative research Understand the basic statistics employed in research Be able to assess a piece a research with confidence!

Qualitative research Which type of questions does it answer? What methodologies are employed? Improving their validity

Assessing a qualitative paper Is the qualitative approach appropriate? Methodology Data analysis Results and conclusion

Quantitative Types of quantitative research RCT – design features, advantages & disadvantages Cohort Studies Case control studies Cross section surveys

BIAS Selection bias Observer bias Participant bias Withdrawal or drop out bias Recall bias Measurement bias Publication bias Selection bias – select sicker patients to get the active or new Rx and fitter patients to get placebo or older Rx Observer bias – if we know the patient has active treatment can subconsciously record health status as being better Participant 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 calibrated Publication bias – positive studies get published much more often than equivocal or negative studies

Assessing quantitive research

Commonly used statistics P values Relative Risk Reduction Absolute Risk Reduction Numbers Need to Treat Sensitivity Specificity Positive Predictive Value Negative Predictive Value

P values & CI p value = the probability of the outcome being due to chance p = 1 in 20 (0.05). > 1 in 20 (0.051) = not significant < 1 in 20 (0.049) = statistically significant CONFIDENCE INTERVALS This 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 population Straightforward, surely. If not see Simple Statistics by Frances Clegg, Cambridge Press.

RR, AR, ARR & RRR What are they? How do you calculate them?

Warfarin & AF study The annual rate of stroke was 4.5% for the control group Absolute Risk (Control group) = 0.045 1.4% for the warfarin group Absolute Risk (experimental group) = 0.014 Absolute Risk Reduction = 0.045 – 0.014 = 0.031 NNT = 32 Relative Risk = 0.014/0.045 = 0.31 = 31% Relative Risk Reduction = 0.045 – 0.014/0.045 = 0.68 = 68%

1/ARR = Number Needed to Treat. NNT How many people you need to treat with the study intervention to stop the study event from happening once. 1/ARR = Number Needed to Treat.

NNT EXAMPLES But these are all in different patient groups with interventions with very different costs so tables of NNTS are illustrative but no answer.

Screening tests – assessing their performance

Sensitivity The test’s ability to correctly identify those people with disease. If Sensitivity is <100% Disease is missed. So = True Positives True Positives + False negatives i.e. all those who truly Have the disease

Specificity The test’s ability to correctly exclude those people without disease If Specificity <100% then healthy people are told they may have disease = True Negatives True Negatives + False Positives i.e. all those who truly don’t have the disease

Positive predictive value If the test is positive, what is the chance of the person having the disease = positive predictive value. True Positives True positives + False Positives

Negative Predictive Value If the test is negative, what chance is there that the person doesn’t have the disease = negative predictive value. True negative True negative + False negative

Accuracy True positive + True negative True negative +true positive+ false negative + false positive

Urine dipstick to screen for Diabetes Example- urine dip test vs GTT (the gold standard) Diabetes +ve Diabetes –ve Result of urine test (n=27) (n=973) Glucose present (13) True +ve 6 False +ve 7 Glucose absent (987) False –ve 21 True -ve 966