Statistics an der University Of Pretoria | Karteikarten & Zusammenfassungen

Lernmaterialien für Statistics an der University of Pretoria

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Types of variables
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1.Continuous 
*there's meaning between the two spectrum of the variables.e.g. age/weight/height -2.5 years means something 
2.Categorical
*there's no meaning between the two.its either or
E.g.age GROUP,gender,citizenship 

Also called discrete. If there's only 2 categories its dichotomous variables(e.g. gender)

Continuous variables can be changed to categorical e.g. age to age groups

Changing a non dichotomous variable to a dichotomous one is called "to dichotomize"

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Types of commonly used statistical tests
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1.T test - compares mean value of two groups
2.Chi Square- determines whether two 'categorical' variables are related/associated to one another
3.Anova(analysis or variance analysis) - compares mean of >3 or more groups/across multiple domains
4.Correlation - determines whether two 'continous' variables are related 
5.Regression - establishing influence of one variable on an outcome variable.
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What is relative risk
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Its a comparison - the risk of outcome when you are exposed(absolute risk) vs the risk of outcome when you are not exposed 
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Define numbers needed to treat
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The number of patients who need to receive the intervention/exposure to prevent 1 outcome from happening

E.g.if outcome is death,the number of patients who need to be treated to prevent 1 death

Formula = 1/risk reduction(write in positive value even if result is negative)
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What is mean
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Addition of all the variables divided by total number of variable.
Works only in normally distributed data.

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What is median
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The middle number of all the data if it is organized in a numerical order.
Better used in non normally distributed data
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Correction for selection bias
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Randomization 
Restrictions 
Adjustments
Matching
Multiplication
Stratification 

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What is the P value
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P value calculates the number under the curve where you plot the frequency of a certain result(frequency at which certain results occur) when you run a tests multiple times and plot it.it is the number under the curve that tells you whether (or not) its probable that the null hypothesis is true.

The probability that you rejecting the null hypothesis was done incorrectly.

If p value is less than 0.05(alpha value=5%) you can confidently REJECT the null hypothesis.that means you can confidently say there is a relationship between two variables!"statistical significance"

E.g. p value of 0.02 means theres a 2% chance that you incorrectly rejected the null hypothesis.2% chance that the null hypothesis is right.
If the p value is low,the null hypothesis must go!

So you want a low p value.low is p value less than alpha value.low p value means you have found something(your results) that has a statistical significance and is not due to chance 

Affected by sample size.Larger samples give more correct P values.

Range = 0 - 1(0-100%)

Small P value suggests narrow CI (good)
Higher P value suggests wider CI
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PICO method of asking research questions 
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Population group
Intervention 
Control group
Outcome
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What is attributable risk
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How much of the outcome was due to the exposure/risk factor
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Types of bias
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1.Selection Bias
1.1.Healthy worker bias
1.2.Berkeson bias
1.3.Lost to follow up bias
1.4.non response bias

2.Information Bias - the means of taking info from subjects inadequate 
2.1.Misclassification bias (when the records are wrong,assigning disease where its not there).can be differential and non differential
2.2.Recall bias (participants dont remember everything correctly)
2.3.Interviewer bias (Interviewer behavior affects respondent response e.g.tone of question)

3.Response bias
Respondee wants to give you answer they think you want ,not because of anything interviewer does

4.Detection bias
*surveillance 
When you find something because you went looking for it.you are more likely to find it where you are looking for it.not that it wasnt there before e.g.seeing a high prevalence for a disease because we now have increased testing

5.Hawthorne/Rosenthal/Golem effect
**biases that results from the interaction of researchers with subjects.prevented by blinding in RCT studies
Hawthorne : people are likely to do better when watched.so more compliant patients are more likely to be chosen in trials(they adhere better because they are in a trial being watched)
Rosenthal : positive self fulfilling prophecy
Golem effect : negative self fulfilling prophecy 

***Confounding 
Can show a relationship where it doesn't exist or mask one that exists. Confounder creates illusion.
Confounders : age,gender,smoking.
Technically not bias.

Effect modification 
Changes the direction/nature of a relationship that's real

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TESTE DEIN WISSEN
Types of study designs
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Quantitative vs Qualitative 
1.Quantitative - descriptive: what,when,where,who

2.Qualitative  :
Descriptive or Analytical

2.1.Descriptive : where?what?Who?When?one variable.
  *cross sectional 

2.2.Analytical : 2 or more variables and how they relate to one another
  2.2.1.Observational
          *case control : look at patients eith certain outcome, create control group who dont have that outcome then see who had exposure.Retrospective study.opposite of cohort
          *cohort : follow exposed&unexposed pts forward to determine outcome.Prospective
          *cross sectional 
          ***ecological studies
  2.2.2.Experimental
         *RCT
         **quasi studies
         **natural experimental 



Case Series
-poor quality evidence.made of case reports.descriptive - description of individual patients,no control group.

Systematic Review
Summary of literature that uses specific methods to combine and appraise studies to answer a question.Quality depends on quality of the studies included in the review.

Meta-analysis 
Not same as above.Type of systematic review that takes conclusions from multiple studies and quantitatively summarize results.
         


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  • 9 Lernmaterialien

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Q:
Types of variables
A:
1.Continuous 
*there's meaning between the two spectrum of the variables.e.g. age/weight/height -2.5 years means something 
2.Categorical
*there's no meaning between the two.its either or
E.g.age GROUP,gender,citizenship 

Also called discrete. If there's only 2 categories its dichotomous variables(e.g. gender)

Continuous variables can be changed to categorical e.g. age to age groups

Changing a non dichotomous variable to a dichotomous one is called "to dichotomize"

Q:
Types of commonly used statistical tests
A:
1.T test - compares mean value of two groups
2.Chi Square- determines whether two 'categorical' variables are related/associated to one another
3.Anova(analysis or variance analysis) - compares mean of >3 or more groups/across multiple domains
4.Correlation - determines whether two 'continous' variables are related 
5.Regression - establishing influence of one variable on an outcome variable.
Q:
What is relative risk
A:
Its a comparison - the risk of outcome when you are exposed(absolute risk) vs the risk of outcome when you are not exposed 
Q:
Define numbers needed to treat
A:
The number of patients who need to receive the intervention/exposure to prevent 1 outcome from happening

E.g.if outcome is death,the number of patients who need to be treated to prevent 1 death

Formula = 1/risk reduction(write in positive value even if result is negative)
Q:
What is mean
A:
Addition of all the variables divided by total number of variable.
Works only in normally distributed data.

Mehr Karteikarten anzeigen
Q:
What is median
A:
The middle number of all the data if it is organized in a numerical order.
Better used in non normally distributed data
Q:
Correction for selection bias
A:
Randomization 
Restrictions 
Adjustments
Matching
Multiplication
Stratification 

Q:
What is the P value
A:
P value calculates the number under the curve where you plot the frequency of a certain result(frequency at which certain results occur) when you run a tests multiple times and plot it.it is the number under the curve that tells you whether (or not) its probable that the null hypothesis is true.

The probability that you rejecting the null hypothesis was done incorrectly.

If p value is less than 0.05(alpha value=5%) you can confidently REJECT the null hypothesis.that means you can confidently say there is a relationship between two variables!"statistical significance"

E.g. p value of 0.02 means theres a 2% chance that you incorrectly rejected the null hypothesis.2% chance that the null hypothesis is right.
If the p value is low,the null hypothesis must go!

So you want a low p value.low is p value less than alpha value.low p value means you have found something(your results) that has a statistical significance and is not due to chance 

Affected by sample size.Larger samples give more correct P values.

Range = 0 - 1(0-100%)

Small P value suggests narrow CI (good)
Higher P value suggests wider CI
Q:
PICO method of asking research questions 
A:
Population group
Intervention 
Control group
Outcome
Q:
What is attributable risk
A:
How much of the outcome was due to the exposure/risk factor
Q:
Types of bias
A:
1.Selection Bias
1.1.Healthy worker bias
1.2.Berkeson bias
1.3.Lost to follow up bias
1.4.non response bias

2.Information Bias - the means of taking info from subjects inadequate 
2.1.Misclassification bias (when the records are wrong,assigning disease where its not there).can be differential and non differential
2.2.Recall bias (participants dont remember everything correctly)
2.3.Interviewer bias (Interviewer behavior affects respondent response e.g.tone of question)

3.Response bias
Respondee wants to give you answer they think you want ,not because of anything interviewer does

4.Detection bias
*surveillance 
When you find something because you went looking for it.you are more likely to find it where you are looking for it.not that it wasnt there before e.g.seeing a high prevalence for a disease because we now have increased testing

5.Hawthorne/Rosenthal/Golem effect
**biases that results from the interaction of researchers with subjects.prevented by blinding in RCT studies
Hawthorne : people are likely to do better when watched.so more compliant patients are more likely to be chosen in trials(they adhere better because they are in a trial being watched)
Rosenthal : positive self fulfilling prophecy
Golem effect : negative self fulfilling prophecy 

***Confounding 
Can show a relationship where it doesn't exist or mask one that exists. Confounder creates illusion.
Confounders : age,gender,smoking.
Technically not bias.

Effect modification 
Changes the direction/nature of a relationship that's real

Q:
Types of study designs
A:
Quantitative vs Qualitative 
1.Quantitative - descriptive: what,when,where,who

2.Qualitative  :
Descriptive or Analytical

2.1.Descriptive : where?what?Who?When?one variable.
  *cross sectional 

2.2.Analytical : 2 or more variables and how they relate to one another
  2.2.1.Observational
          *case control : look at patients eith certain outcome, create control group who dont have that outcome then see who had exposure.Retrospective study.opposite of cohort
          *cohort : follow exposed&unexposed pts forward to determine outcome.Prospective
          *cross sectional 
          ***ecological studies
  2.2.2.Experimental
         *RCT
         **quasi studies
         **natural experimental 



Case Series
-poor quality evidence.made of case reports.descriptive - description of individual patients,no control group.

Systematic Review
Summary of literature that uses specific methods to combine and appraise studies to answer a question.Quality depends on quality of the studies included in the review.

Meta-analysis 
Not same as above.Type of systematic review that takes conclusions from multiple studies and quantitatively summarize results.
         


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