2. OKU 9 Chapter 15: ORTHOPAEDIC RESEARCH Brian E. Walczak

8. KEY COMPONENTS FOR THE DEVELOPMENT OF THE CLINICIAN- SCIENTIST Significant scientific training – 1-2 years or more…. Protected Time – Minimum of 30% of the time Adequate funding – Sustained for a minimum of 5 years

9. INFERENCE …. The act of deriving logical conclusions from the existing knowledge regarding a condition Well designed study is to – Provide insight into the “TRUTH”

10. BIAS Nonrandom, systematic error in the design or conduct of a study that may result in mistaken inference about association or causation Types – Recall – Publication – Measurement – Selection

11. CONFOUNDING Occurs when a variable has an association with both the independent and dependent variable E.g. – Age – Gender – Socioeconomic status – Medial comorbidities

12. CHANCE The probability that two unrelated events will appear associated by random occurrence rather than through a causal assoication “Good” study need to control for chance Type I (alpha) error – Truth is no association (but you think there is) Type II (beta) error – Is an association (but you fail to prove it) Alpha = 0.05 (commonly accepted level) – could be anything – There is less than a 5% risk of chalking the association up to “chance”

15. Power Probability equal to 1-beta – Generally accepted as 0.8 – The more stringent the beta error, the narrower the confidence interval will be and the more certain one may be of the results in representing the truth

16. STUDY DESIGN AND LEVEL OF EVIDENCE Study – Observational No allocation of treatment groups Prospective or retrospective Descriptive Analytic Case reports, case series, cross-sectional study – Experimental (not suited for determining risk factors) Examines the efficacy of distinct treatment options GOLD STANDARD: – DOUBLE-BLIND PROSPECTIVE RANDOMIZED CLINICAL TRIAL

17. CASE SERIES Descriptive observational study Potential complications or successes of a cohort (group) LEVEL IV evidence

18. CROSS-SECTIONAL SURVEY Observational Descriptive “Snapshot”

19. CASE CONTROL Observational Patients with a given outcome are compared with patient without the outcome of interest RARE or UNCOMMON DZS Reported as “ODDS RATIO” Retrospective LEVEL III

20. COHORT Observational Relative Risk Prospective or retro LEVEL II or III (prospective or retrospective)

21. CLINICAL TRIALS Experimental Able to minimize “chance” LEVEL I or II

22. LITERATURE REVIEW SUMMARY OF EXPERT OPINION – LEVEL V EVIDENCE Meta-analysis – Well-organized systematic quantitative analysis of randomized clinical trials from which one may draw valid statistical inferences – LEVEL I EVIDENCE

23. META-ANALYSIS Quantitative analysis Similar study designs Must test for homogeneity Publication bias may be assessed using a funnel plot – Parametric (Egger’s linear regression model) – Non-parametric (Begg’s test) methods Should try to control for publication bias by including both PUBLISHED AND NONPUBLISHED STUDIES Summary estimate = Forest plot

25. DATA Continuous – Numerical info Any given value within a range of values Age BMI – Non-continuous variable is called “discrete” Ordinal – Ordered variable (this is why it is difference than a categorical) Fracture Classifications Socio-economic status Categorical (nominal) – Qualitative variables without ordering Gender Hair color

26. DATA DISTRIBUTION Continuous data – Parametric Explain the distribution by a SINGLE math equation Gaussian distribution – 69% of the values will fall within 1 SD of the mean – 95% of values with fall within 2 SD of the mean – 99% of the values will fall within 3 SD of the mean Mean, median, mode are all equal] – Mean = average – Median = “middle” value (50 th %) – Mode = the “most” – Nonparametric

27. NONPARAMETRIC Median, mode, mean are not the same Right skew (positive )= Mean > median > Mode Left skew (negative) = mean < median < mode Kurtosis = “FAT TAIL” RISK

29. “P” VALUE Probability of observation (compare this value to the alpha = level of significance (often < 0.05) Not practically significant – BUT, measures the strength of evidence in favor of the alternative hypothesis (vs. the “null” [Ho]) Type I error – Concluding an association exists when in fact it occurs by chance alone – E.g. falsely rejecting the null Concluding that a difference exists (potentially type I error) Type II error – Concluding an associating does not exist when it really does – E.g. falsely accepting the null Concluding that a difference does NOT exists (potentially type II error) IF no association reported, then power should be reported because this indicates the study’s ability to actually detect a difference

30. DIAGNOSTIC TESTING Sensitivity = TP/total (TP+FN) – 100% means that a test will ID ALL SICK PEOPLE A NEGATIVE RESULTS then would R/O the DZ Specificity = TN/total (TN+FP) – Probability that a person without the dz will be correctly ID PPV = Probability that a person who’s test is + actually has the dz – TP/TP+FP (all positives) NPV = Probability that person who’s test is – actually has no dz – TN/FN+TN

31. DIAGNOSTIC TESTS ODDS RATIO (retrospective) – CASE-CONTROL STUDY – ESTIMATES RELATIVE RISK – TP X TN/FP X FN RELATIVE RISK (prospective) – COHORT STUDY – Used to compare incidence rate in exposed and unexposed goups