Transcript 6. Examples – General Medicine
Evidence Based Medicine Examples
Edward G. Hamaty Jr., D.O. FACCP, FACOI
Systematic Reviews
Systematic Reviews
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Citation
Perel P, Roberts I. Colloids versus crystalloids for fluid resuscitation in critically ill patients.
Cochrane Database of Systematic Reviews
2007, Issue 4. Art. No.: CD000567. DOI: 10.1002/14651858.CD000567.pub3.
Systematic Reviews
• Although you have quickly found a recent review that proves that there is no advantage to colloid over crystalloids, your interest is piqued and you pull one of the review articles for further analysis.
Cochran Q (Chi-square) and I 2
• • • • • Heterogeneity can be assessed using the “eyeball” test or more formally with statistical tests such as the Cochran Q test.
If the Cochran chi-square (Cochran Q) is statistically significant there is
definite
heterogeneity. [P<0.1](The level of significance for Cochrane Q is often set at 0.1 due to the low power of the test to detect heterogeneity.
If the Cochran Q is not statistically significant, but the ratio of Cochran Q and the degrees of freedom (Q/df) is >1 there is
possible
heterogeneity.
If the Cochran Q is not statistically significant and Q/df is <1 then heterogeneity is
very unlikely
.
In the next example, Q/df is <1 (0.92/4 = 0.23) and the p value is not significant (0.92) indicating no heterogeneity.
Cochran Q (Chi-square) and I 2
• • • The Cochran Q test has low power to detect heterogeneity if there are few studies in the meta analysis and may, conversely, give a highly significant result if it comprises many large studies, even when the heterogeneity is unlikely to affect the conclusions.
An Index, I 2 , which does not depend upon the number of studies, the type of outcome data or the choice of treatment effect (e.g. relative risk) can be used to quantify the impact of heterogeneity and assess inconsistency.
I 2 = 100x(Q – df)/Q
Cochran Q (Chi-square) and I 2
• I 2 represents the percentage of the total variation across studies due to heterogeneity; it takes values from 0% to 100%, with the value of 0% indicating no observed heterogeneity. If there is evidence of statistical heterogeneity, we should proceed cautiously, investigate the reasons for its presence and modify our approach accordingly, perhaps by dividing the studies into subgroups of those with similar characteristics.
Systematic Reviews- Analysis
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http:// www.cebm.utoronto.ca/practise/ca/statscal/
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Systematic Reviews- Analysis 0.724
Using the tables will give you an idea of the magnitude of the effect. Calculations are more accurate.
Pooled difference in the risk of death with albumin was 6% (95% confidence interval 3% to 9%) with a fixed effects model. These data suggest that for every 17 critically ill patients treated with albumin there is one additional death.
ARR = 0.06, NNT = 1/0.06 = 16.66
= 17
Systematic Reviews- Analysis
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Diagnosis - Analysis
Diagnosis - Analysis
Diagnosis - Analysis
Are the Results Important?
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Sensitivity and specificity
Sensitivity is the proportion of people with disease who have a positive test. (True Positive) Specificity is the proportion of people free of a disease who have a negative test. (True Negative)
Are the Results Important?
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Using sensitivity and specificity: SpPin and SnNout
Sometimes it can be helpful just knowing the sensitivity and specificity of a test, if they are very high.
If a test has high specificity, i.e. if a high proportion of patients without the disorder actually test negative, it is unlikely to produce false positive results. Therefore, if the test is positive it makes the diagnosis very likely.
This can be remembered by the mnemonic SpPin: for a test with high specificity (Sp), if the test is positive, then it rules the diagnosis 'in'.
Similarly, with high sensitivity a test is unlikely to produce false negative results. This can be remembered by the mnemonic SnNout: for a test with high sensitivity (Sn), if the test is negative, then it rules 'out' the diagnosis.
Are the Results Important?
• • Positive Predictive Value = the proportion of people with a positive test who have disease.
True+/(True+ plus False+) • • Negative Predictive Value = the proportion of people with a negative test who are free of disease.
True-/(True- plus False-)
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Likelihood Ratios
What do all these numbers mean? The
Likelihood ratios
indicate by how much a given diagnostic test result will raise or lower the pretest probability of the target disorder. A likelihood ratio of 1 means that the posttest probability is exactly the same as the pretest probability. Likelihood ratios >1.0 increase the probability that the target disorder is
present, and the higher the likelihood ratio, the greater is this increase.
Conversely, likelihood ratios <1.0 decrease the probability of the target disorder, and the smaller the likelihood ratio, the greater is the decrease in probability and the smaller is its final value. How big is a "big" likelihood ratio, and how small is a "small" one? Using likelihood ratios in your day-to-day practice will lead to your own sense of their interpretation, but consider the following a rough guide: Likelihood ratios of >10 or < 0.1 generate large and often conclusive changes from pre- to posttest probability; Likelihood ratios of 5-10 and 0.1-0.2 generate moderate shifts in pre- to posttest probability; Likelihood ratios of 2-5 and 0.5-0.2 generate small (but sometimes important) changes in probability; and Likelihood ratios of 1-2 and 0.5-1 alter probability to a small (and rarely important) degree.
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Likelihood Ratios
Having determined the magnitude and significance of the likelihood ratios, how do we use them to go from pretest to posttest probability? We cannot combine likelihoods directly, the way we can combine probabilities or percentages; their formal use requires converting pretest probability to odds, multiplying the result by the Likelihood ratio, and converting the consequent posttest odds into a posttest probability. Although it is not too difficult, this calculation can be tedious and off-putting; fortunately, there is an easier way. A nomogram proposed by Fagan (Figure 1C-2) does all the conversions and allows an easy transition from pre- to posttest probability. The left-hand column of this nomogram represents the pretest probability, the middle column represents the likelihood ratio, and the right-hand column shows the posttest probability. You obtain the posttest probability by anchoring a ruler at the pretest probability and rotating it until it lines up with the likelihood ratio for the observed test result.
Pre-test Prob 70% x +LR 7.5 = Post-Test Prob = 95%
Diagnosis - Analysis
Therapy - Analysis
Therapy - Analysis
Therapy - Analysis
Therapy - Analysis
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Prognosis - Analysis
Prognosis - Analysis
Prognosis - Analysis
Prognosis - Analysis
Prognosis - Analysis