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# EBP II

### Evidence Based Practice II- UWS Quarter 5 -Exam 1

P-value probability of false positives in the study result
Null Hypothesis will be 0 for the difference in groups; while the null will be 1 for OR, HR, RR
Mean the average, this will be the middle of the bell shaped curve
Median he value that divides a series of numbers in half when they are listed in order, this will be used for skewed data that does not conform to the bell shaped curve
Mode the most frequently occurring number in a series
Standard deviation found using the patient population, square root of the variance, (the average sum squared difference from the mean); measurement of participant variability, measurement in the variability of data, how spread out it is
Interquartile Range  the interquartile range (IQR), also called the mid-spread or middle fifty, is a measure of statistical dispersion, being equal to the difference between the third and first quartiles. IQR = Q3 − Q1
Skewed distribution non-normal bell curve
Normal distribution normal bell curve, symmetric around the mean with the mean as the peak of the curve going out toward but never reaching 0
Standard error of the mean found with the clinical data, the spread of the sampled means for the data gathered, the measure of the precision/variability of the measurement (SD)
Confidence Interval “the neighborhood of the truth” An estimated range of values around a point estimate.
Type I error stating there is a difference in groups when there isn’t one , incorrectly rejecting the null hypothesis AKA failure to accept the null, will result in false positives
Type II error stating that there is no difference in groups where there is one, incorrectly accepting the null hypothesis AKA failure to reject the null, will result in false negatives
Power the probability to correctly reject the null hypothesis when you should. Mathematically defined as 1-Type II error rate, depends on: sample size, difference between groups and type 1 error
Clinical significance Is the study result of practical interest? Do other findings matter?
Chi-square test comparison of categorical data for large sample chosen, may be used to compare groups…test whether observed frequencies are different from expected frequencies in a data table Fisher's exact test: categorical for small sample chosen (>5 subjects)
The t-test a statistical test used to detect the difference in two means, two groups, and factor in variability in data commonly used for continuous data, comparison of 2 different groups
The paired t-test a t-test for used when comparing two means that are within the same group Ex. The mean at the beginning of a study and the mean at the end of the study, comparison of dependent groups
Wilcoxon test a test for statistical significance of data that is not on a normal bell curve distribution (non-parametric), used for paired data–used for group comparisons, rank testing
Mann-Whitney U test a test for statistical significance of data that is not on a normal bell curve distribution (non-parametric), used for unpaired data –used for group comparisons, rank testing
ANOVA (analysis of variance) A way to analyze groups of means to see if they are equivalent or not; if the ANOVA model fits the data well, and if a statistically significant difference is detected then post-testing is conducted
Post-hoc testing compare the group pairs, done in the second stage of statistical analysis…three types: Tukey-used if the groups are unequal in size Bonferroni-for both equal and unequal groups Scheffé-very conservative to minimize type 1 error
Linear regression explains the differences in means. A calculation of the line of best fit passing through a set of data, which will allow for prediction about direction and amount variables change
Multiple linear regression explains the differences in means, in addition to explaining the differences in groups it can also be adjusted for age, gender, smoking, cancer etc…
Logistic regression allows for comparison of differences in odds between groups, results are an odds ratio which is a slight over estimate of relative risk
Multiple logistic regression in addition to explaining differences in OR between groups, they also adjust for age, gender, smoking, cancer etc…
Parametric tests (3) t-test, ANOVA, regression
Non-parametric tests for ranking (2)and Categorical Data (2) for ranking: Wilcoxon, Mann-Whitney; for categorical data: Chi-square, Fisher’s exact
Absolute Risk (AR Mainly used with RCT Probability of disease in the exposed group minus the probability of disease in the unexposed group Represents the excess risk due to exposure to the factor under investigation
Numbers Needed to Treat (NNT) the number of patients who would need to be treated in order to prevent one additional bad outcome
Numbers Needed to Harm (NNH) the number of patients who would need to be treated in order for one bad outcome to occur
Clinical questions correlate to what type of study design? Diagnosis Cross-Sectional analytic study
Clinical questions correlate to what type of study design? Harm Cohort study, population based case control
Clinical questions correlate to what type of study design? Prognosis Cohort study
Clinical questions correlate to what type of study design? Treatment RCT, Systematic review
Hills Postulates: Temporality Cause must come before effect
Hills Postulates: Repeatability The effects must be repeatable
Hills Postulates: Biological Gradient the does response effect—small dose and small response v. big dose and bigger response
Hills Postulates: Reversibility de-challenge v. re-challenge aka the interventional effect
Hills Postulates: Plausibility Does what’s happening makes sense according to biological knowledge at the time
Error Types (2) Systemic error: bias Random error: chance
Cross-Sectional Design assess health status and exposure level of subjects at a point in time
Case Control retrospective observational study comparing diseased and non-diseased groups
Cohort prospective observational study comparing diseased and non-diseased groups
RCT prospective experimental design where the sample is broken into 2+ groups who are then put into categories such as treatment, placebo, alternative treatment, double dosage etc.
Created by: 1390666327