OKU 9Chapter 15: ORTHOPAEDIC

RESEARCHBrian E. Walczak

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

INFERENCE

• …. The act of deriving logical conclusions from the existing knowledge regarding a condition

• Well designed study is to – Provide insight into the “TRUTH”

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

CONFOUNDING

• Occurs when a variable has an association with both the independent and dependent variable

• E.g.– Age– Gender– Socioeconomic status– Medial comorbidities

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”

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

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

CASE SERIES

• Descriptive observational study• Potential complications or successes of a

cohort (group)• LEVEL IV evidence

CROSS-SECTIONAL SURVEY

• Observational• Descriptive • “Snapshot”

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

COHORT

• Observational • Relative Risk• Prospective or retro• LEVEL II or III (prospective or retrospective)

CLINICAL TRIALS

• Experimental• Able to minimize “chance”• LEVEL I or II

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

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

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

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 (50th %)– Mode = the “most”

– Nonparametric

NONPARAMETRIC

• Median, mode, mean are not the same• Right skew (positive )= Mean > median >

Mode• Left skew (negative) = mean < median < mode• Kurtosis = “FAT TAIL” RISK

“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

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

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