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How to assess an abstract. Objectives Understand the principle differences between qualitative and quantitative research Understand the principle differences.

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Presentation on theme: "How to assess an abstract. Objectives Understand the principle differences between qualitative and quantitative research Understand the principle differences."— Presentation transcript:

1 How to assess an abstract

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

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

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

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

6 BIAS Selection bias Selection bias Observer bias Observer bias Participant bias Participant bias Withdrawal or drop out bias Withdrawal or drop out bias Recall bias Recall bias Measurement bias Measurement bias Publication bias Publication bias

7 Assessing quantitive research

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

9 P values & CI p value = the probability of the outcome being due to chance p value = the probability of the outcome being due to chance p = 1 in 20 (0.05). p = 1 in 20 (0.05). > 1 in 20 (0.051) = not significant > 1 in 20 (0.051) = not significant < 1 in 20 (0.049) = statistically 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

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

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

12 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.

13 NNT EXAMPLES

14 Screening tests – assessing their performance

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

16 Specificity The tests ability to correctly exclude those people without disease The tests ability to correctly exclude those people without disease If Specificity <100% then healthy people are told they may have disease If Specificity <100% then healthy people are told they may have disease = True Negatives = True Negatives True Negatives + False Positives True Negatives + False Positives i.e. all those who truly dont 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. If the test is positive, what is the chance of the person having the disease = positive predictive value. True Positives True Positives True positives + False Positives

18 Negative Predictive Value If the test is negative, what chance is there that the person doesnt have the disease = negative predictive value. If the test is negative, what chance is there that the person doesnt have the disease = negative predictive value. True negative True negative True negative + False negative True negative + False negative

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

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


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