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L8 - Biostatistics Continued
Describe the types of variables and…
L8 - Biostatistics Continued
- Describe the types of variables and samples in research,including independent, related, categorical and continuous.
- Describe, apply and interpret key hypotheses tests including independent t test, paired t test, Mann-Whitney U; Wilcoxon
signed-rank; Chi Square and Fisher’s exact
- Describe, apply and interpret tests of association with a focus on the three main types of correlation.
- Describe, apply and interpret tests of prediction with a focus on simple linear regression
- Describe, apply and interpret survival analysis.
- To describe and recognise common problems in research including: intervention effect; restricted ranges; violating the independence of observations; mistaking correlation for causation; unequal groups; intra-group dependency, and external validity issues.*
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Simple Regression
Simple Linear Regression involves estimating an equation which descrives the relationship between two variables, a dependant variable, and an explanaotry/independent variable
Confers Predictive Value
It is more likely that data will not fit on this line the that they do
It measures the "Goodness of Fit"=> line of best fit
R^2 = measures the degree to which the model explains the relationship (generally other confounders/cvarates that are difficult to measure)
Using R^2 to determine whether or not the gradient is Statistically Significant
R^2 = the degree to which the model explains the relationship
Associations
Conceptually… If X and Y are correlated then it is easy to think of them as being related, i.e. moving in the same direction (positively correlated) or opposite direction (negatively
correlated)
Correlation
Correlation, quantifies the degree to which two variables are related/associated.
BUT it doesn’t say anything about the relative rates at which the variables change
Kendall, and Spearman's Correlation
- Both rank correlations (i.e. is the highest ranking X variable correlated with the highest ranking Y variable)
- Does not assume that X and Y are linearly related
- Often used for ordinal variables
- Both non-parametric correlations
- Spearman's is typically more common
Pearson's Correlation
- Likely the most widely known Correlation
- Measure if X and Y are linearly related
Assumes
- Both variables are continuous
- Both variables are normally distributed
- Errors are normally distributed about the regression line
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Variable Types
Dependent variables:
Variables whose value is altered as the independent variable is manipulated (a rating scale score)
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Example
Do people have higher blood pressure after they complete 15 minutes of aerobic exercise compared to their resting blood pressure rate?
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Any covariate (confounders) variables? These are characteristics of the participants that may impact on the outcome and you haven’t controlled for in your study design,
e.g. are some participants smokers, overweight, children, hungry etc?
Independent or Related Samples
Independent samples yield results which are not influenced by results from other samples involved in the same experiment. Date derived during the experiment is not related to one another
The data does not depend on, or relate to, each other.
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Survival Analysis
The Kaplan-Meier method is used to estimate the probability of survival past
given time points.
The survival distributions of two or more groups can be compared for equality.
Example
In a study on the effect of drug dose on cancer survival in rats, you could use the Kaplan-Meier method to understand the survival distribution (based on time until death) for rats receiving one of four different drug doses and then compare the survival distributions (experiences) between the four doses to determine if they are equal.
Outcome being measured doesn't have to be literal "survival" => the time until failure of knee replacement
Slide 50 - 52
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