VISN3211 Lecture Notes

.docx
School
University of New South Wales **We aren't endorsed by this school
Course
VISN 3211
Subject
Statistics
Date
Aug 7, 2023
Pages
17
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EBP AND REVIEW OF STATISTICAL MODELS Evidence based practice - combination of the best available research, the patient's preferences/circumstances, clinical environment and practitioner's expertise Five steps in EBP: 1. Asking clinical questions - translation of uncertainty to an answerable question 2. Acquiring information - systematic retrieval of best evidence available 3. Appraising information - critical appraisal of evidence for validity, clinical relevance and applicability 4. Applying information - application of results in practice 5. Auditing practice - evaluation of performance Appraisal - evaluating the relevant research evidence to find the highest quality evidence available relevant to your question Critical appraisal - process of assessing and interpreting evidence by systematically considering its validity and its relevance to the question Internal validity - extent to which the research is reliable External validity - indication of the generalizability of the findings Considerations and codes for research ethics 1. Human subject protection - minimizing harm, respect human dignity and privacy 2. Honesty - reporting findings, interpretation and publication status, deception and misrepresenting data, fabrication, falsification or plagiarism 3. Objectivity - avoiding bias in experimental design, commercial interests, data interpretation 4. Openness - share data and results, open to criticism, resources 5. Confidentiality - protect identification of subjects and records How to ensure standards University level Organisations/committees Publishing bodies Funding bodies - Australian Research Council Common law Correlation - way of measuring the extent to which two variables are related Measures the pattern of responses across variables Observing what naturally goes on in the world without directly interfering with it Varies between -1 and +1, where 0 = no relationship Effect size: 0.1 = small effect, 0.3 = medium effect, 0.5 = large effect Page 1 of 17
Coefficient of determination, r 2 by squaring the value of r you get the proportion of variance in one variable shared by the other What to consider The significance of r The magnitude of r The +/- sign of r For example There was no significant relationship between the number of adverts watched and the number of packets of toffee purchased, r = 0.87, p 0.54 r = 0.87 is a large effect the sign of r is positive - as one variable increases so does too the other but this does not imply causation Describing a straight line Y i = b 0 + b 1 X i + ε i b 1 - regression coefficient for the predictor, gradient of the regression line, direction/strength of relationship b o - intercept, point at which the regression line crosses the Y-axis Summary of linear regression Way of predicting one variable from another by fitting a statistical model to the data in the form of a straight line which best summarises the pattern of data We have to assess how well the line fits the data using: o R squares - tells us how much variance is explained by the model compared to how much variance there is to explain in the first place, proportion of variance in the outcome variable that is shared by the predictor variable Page 2 of 17
o F which tells us how much variability the model can explain relative to how much it can't explain i.e. ratio of hwo good the model is compared to how bad the model is o B-value - tells us the gradient of the regression line and the strength of the relationship between a predictor and the outcome variable, if its significant (sig. < 0.05) then the predictor variable significantly predicts the outcome variable t-test Independent t-test Compares two means based on independent data e.g. data from different groups of people Dependent t-test Compares two means based on related data E.g. data from the same people measured at different times Data from matched samples observed difference betweensample means expected difference between populationmeansif nullhypothesis istrue ¿ estimateof the standard error of thedifference betweentwo samplemeans t = ¿ i.e. we compare the difference between the sample means that we collected to the difference between the sample means that we would expect to obtain if there was no effect Use the standard error as a gauge of the variability between sample means If the difference between the samples we have collected is larger than what we would expect based on the standard error then we can assume one of the two: o There is no effect and sample means in our population fluctuate a lot and we have, by chance, collected two samples that are atypical of the population from which they came o The two samples come from different populations but are typical of their respective parent population. In this scenario, the difference between samples represents a genuine difference between the samples (so the null hypothesis is incorrect) Type I error - occurs when we believe that there is a genuine effect in our population when in fact there isn't, probability is at -level (usually 0.05) Type II error - occurs when we believe that there is no effect in the population when, in reality, there is, probability is the -level (often 0.2) Non-Parametric tests Lecture Outline Page 3 of 17
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