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Critical appraisal of the medical literature

Critical appraisal of the medical literature. Partini Pudjiastuti, Sudigdo Sastroasmoro Child Health Department Faculty of Medicine University of Indonesia. Population & Sample. Sudigdo Sastroasmoro sudigdo_s@yahoo.com.

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Critical appraisal of the medical literature

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  1. Critical appraisal of the medical literature Partini Pudjiastuti, Sudigdo Sastroasmoro Child Health Department Faculty of Medicine University of Indonesia

  2. Population & Sample Sudigdo Sastroasmoro sudigdo_s@yahoo.com

  3. Populationis a large group of study subjects (human, animals, tissues, blood specimens, medical records, etc) with defined characteristics [“Population is a group of study subjects defined by the researcher as population”] • Sampleis a subset of population which will be directly investigated. Sample should be (or assumed to be) representative to the population; otherwise all statistical analyses will be invalid • All investigations arealwaysperformed in the sample, and the results will be applied to the population

  4. Avoid using ambiguous terms • Sample population • Sampled population • Populasi sampel

  5. Target population= domain = population in which the results of the study will be applied. Usually character-ized by demographic & clinicalcharacteristics; e.g. normal infants, teens with epilepsy, post-menopausal women with osteoporosis. • Accessible population = subset of target population which can be accessed by the investigator. Frame: time& place. Example: teens with epilepsy in RSCM, 2000-2005; women with osteoporosis, 2002 RSGS • Intended sample = subjects who meet eligibility criteria and selected to be included in the study • Actual study subjects = subjects who actually completed the participation in the study

  6. Usually based on practical purposes Accessible population (+ time, place) Appropriate sampling technique Actual study subjects Intended Sample Subjects completed the study [Subjects selected for study] [Non-response, drop outs, withdrawals, loss to follow-up] Target population = DOMAIN (demographic, clinical)

  7. External validity II: Does AP represent TP? Target population Accessible population [External validity I: Does IS represent AP?} Actual study subjects Intended Sample [Internal validity: does ASS represent IS?]

  8. Sampling methods A. Probability sampling • Simple random sampling (r. table, computer) • Stratified random sampling • Systematic sampling • Cluster sampling • Others: two stage cluster sampling, etc B. Non-probability sampling • Consecutive sampling • Convenience sampling • Judgmental sampling

  9. Note • All statistical analyses (inferences) are based on random sampling • Whether or not a sample is representative to the population depends on whether or not it resembles the results if it were done by random sampling

  10. IMPORTANT!!! Statistical significancevs.clinical importance • Negligible clinical difference may be statistically very significant if the number of subjects >>>. e.g., difference in reduction of cholesterol level of 3 mg/dl, n1=n2 = 10,000; p = 0.00002 • Large clinical difference may be statistically non-significant if the no of subjects <<<, e.g. 30% difference in cure rate, if n1 = n2 = 10, p = 0.74

  11. x = 220 x = 217 Clinical importance vs. statistical significance Cholesterol level, mg/dl Standard treatment x = 300 mg/dl R n=10000 Clinical n=10000 New treatment x = 300 mg/dl t = df = 9998 p = 0.00002 Statistical

  12. Clinical Statistical Clinical importance vs. statistical significance Cured Died Standard Rx 0 10 (100%) New Rx 3 7 (70%) Absolute risk reduction = 30% Fischer exact test: p = 0.211

  13. Correlation between abdominal circumference and total cholesterol level in middle-aged men • N = 200 • R = 0.22, p = 0.031 • Conclusion: There was a significant correlation between abdominal circumference and total cholesterol level in the subjects studied. Measuring abdominal circumference may predict the cholesterol level in middle-aged healthy men.

  14. How important is important? • Two percent mortality reduction is probably not important in your clinic • In a community prevention, a simple measure that reduce 2% severe morbidity is probably important. • Low dose aspirin reduces 2% cardiac events in 5 years (without aspirin 400 cardiac events per 10,000, with aspirin 200 cardiac events) • Requires judgment

  15. How to infer? • Can the results of the study (in sample) be applied in the accessible or target population? • Hypothesis testing & confidence interval

  16. Statistic and Parameter • An observed value drawn from the sample is called a statistic(cf.statistics, the science) • The corresponding value in population is called a parameter • We measure, analyze, etc statistics and translate them as parameters

  17. Proportion Percentage Mean Median Mode Difference in proportion/mean OR RR Sensitivity Specificity Kappa LR NNT Examples of statistics:

  18. There are 2 ways in inferring statistic into parameter: • Hypothesis testing p value • Estimation:confidence interval (CI) P Value & CI tell the same concept in different ways

  19. P value • Determines the probability that the observed results are caused solely by chance (probability to obtain the observed results if Ho were true)

  20. Group Success Failure Total C 30 (60%) 20 (40%) 50 E 40 (80%) 10 (20%) 50 X2= ; df = 1; p = 0.0432

  21. Group Success Failure Total C 30 (60%) 20 (40%) 50 E 40 (40%) 10 (20%) 50 X2= ; df = 1; p = 0.0432 If drugs E and C were equally effective, we still can have the above result (difference of success rate of 20%) but the probability is small (4.32%) If drugs E and C were equally effective, the probability that the result is merely caused by chance is 4.32% If we define in advance that p<0.05 is significant, than the result is called statistically significant

  22. Similar interpretation applies to ALL hypothesis testing: t-test, Anova, non-parametric tests, Pearson correlation, multivariate tests, etc: If null-hypothesis null were true, the probability of obtaining the result was ……. (example 0,02 or 2%, etc)

  23. Confidence Intervals • Estimate the range of values (parameter) in the population using a statistic in the sample (as point estimate)

  24. If the observed result in the sample is X, what is the figure in the population? X X P X S CI A statistic (point estimate)

  25. Most commonly used CI: • CI 90% corresponds to p 0.10 • CI 95% corresponds to p 0.05 • CI 99% corresponds to p 0.01 Note: • p value  only for analytical studies • CI  for descriptive and analytical studies

  26. How to calculate CI General Formula: CI = p  Z x SE • p = point of estimate, a value drawn from sample (a statistic) • Z = standard normal deviate for , if  = 0.05  Z = 1.96 (~ 95% CI)

  27. Example 1 • 100 FKUI students  60 females (p=0.6) • What is the proportion of females in Indonesian FK students? (assuming FKUI represents FK in Indonesia)

  28. Example pq = SE(p) n . . 0 6 x 0 4 = ± % . . 95 CI 0 6 1 96 100 = ± . . 0 6 1 96 X0.5/10 = ± . . = . ; 0 . 0 6 0 1 0 5 7

  29. Example 2: CI of the mean • 100 newborn babies, mean BW = 3000 (SD = 400) grams, what is 95% CI? 95% CI = x  1.96 x SEM

  30. Examples 3: CI of difference between proportions (p1-p2) • 50 patients with drug A, 30 cured (p1=0.6) • 50 patients with drug B, 40 cured (p2=0.8)

  31. Example 4: CI for difference between 2 means Mean systolic BP: 50 smokers = 146.4 (SD 18.5) mmHg 50 non-smokers = 140.4 (SD 16.8) mmHg x1-x2 = 6.0 mmHg 95% CI(x1-x2) = (x1-x2)  1.96 x SE (x1-x2) SE(x1-x2) = S x V(1/n1 + 1/n2)

  32. Example 4: CI for difference between 2 means

  33. Other commonly supplied CI • Relative risk (RR) • Odds ratio (OR) • Sensitivity, specificity (Se, Sp) • Likelihood ratio (LR) • Relative risk reduction (RRR) • Number needed to treat (NNT)

  34. Suggested CI presentation: • 95%CI: 1.5 to 4.5 • 95%CI: -2.5 to 4.3 • 95%CI: -12 to -6 • Not recommended: 3 +1.5 • Not recommended: -9+ -3

  35. In contrast to CI for proportion, mean, diff. between proportions/means, where the values of CI are symmetrical around point estimate, CI’s for RR, OR, LR, NNT are asymmetrical because the calculations involve logarithm

  36. Examples • RR = 5.6 (95% CI 1.2 ; 23.7) • OR = 12.8 (95% CI 3.6 ; 44,2) • NNT = 12 (95% CI 9 ; 26)

  37. If p value <0.05, then 95% CI: exclude 0 (for difference), because if A=B then A-B = 0  p>0.05 • exclude 1 (for ratio), because if A=B then A/B = 1,  p>0.05 • For small number of subjects, computer calculated CI may not meet this rule due to correction for continuity automatically done by the computer

  38. Concluding remarks • In every study sample should (assumed to) be representative to the population. Otherwise all statistical calculations are not valid • p values (hypothesis testing) gives you the probability that the result in the sample is merely caused by chance, it does not give the magnitude and direction of the difference • Confidence interval (estimation) indicates estimate of value in the population given one result in the sample, it gives the magnitude and direction of the difference

  39. Concluding remarks • p value alone tends to equate statistical significance and clinical importance • CI avoids this confusion because it provides estimate of clinical values and exclude statistical significance • whenever applicable, supply CI especially for the main results of study • in critical appraisal of study results, focus should be on CI rather than on p value.

  40. 1 • The ultimate goal of clinical research is the use of evidence in source population

  41. 2 • The best non-probabiity sampling is consecuitve sampling

  42. 3 • P value refers to the probability of getting the observed result when the Ho were false

  43. 4 • The mean difference of 2 measurements is 20 mmHg, with 95% CI 15 to 25 mmHg. The p value should be “statistically significant”

  44. 5 • Confidence intervals give more information than p value

  45. 6 • It is possible to have a study with good internal validity but poor external validity

  46. 7 • If the odds ratio is 5, then the 95% CI may have values from 3 to 11

  47. 8 • It is possible to have a significant difference even when the clinical difference is not important, but clinically important difference always statistically significant

  48. 9 • Appropriate sampling method is mandatory to ensure generalization

  49. 10 • Clinical epidemiology may include animal studies

  50. 11 • The more wide the confidence interval, the more precise the result of any study

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