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108 Cards in this Set

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Definition: Range
(Largest variable) - (Smallest variable)
Definition: Variance
The spread of the variables in the distribution
Definition: Standard Deviation
The square root of the variance.
Normal distribution: % of variables in the distribution one standard deviation above and below the mean.
68%
Normal distribution: % of variables in the distribution two standard deviations above and below the mean.
95%
Normal distribution: % of variables in the distribution three standard deviations above and below the mean.
99%
Definition: Z-score
The number of standard deviations above a mean where a variable lies.
Definition: Percentile
# of variables at or below that level.
Definition: Interquartile range
The range of values between the 25th and 75th percentile.
Definition: Left skewed distribution
Tail on left. Mode on right.
Defintion: Right skewed distribution
Tail on right. Mode on left.
Definition: T-distribution
Different distribution for each sample size
Central limit theorem
Given a sufficiently large sample size, a sample drawn from the population will be normally distributed regardless of the shape of the original population distribution.
Definitions: Standard Error of the Mean (descriptive and mathematical definitions)
Descriptive: Variability in a distribution of sample means
Which is larger: standard error the mean OR standard deviation
standard deviation
Definition: 95% confidence interval
the interval such that the true value has a 95% probability of lying within
Definition: P-value
Given the null hypothesis is true, the probability off getting a result as extreme or more extreme than the observed outcome by chance alone.
Definition: Alpha value
The value used to compare the p-value to to determine if it is significant.
P-value > Alpha value: Conclusion?
Results are likely due to chance alone and are not statistically significant.
P-value <= Alpha value
Results are unlikely due to chance alone and are statistically significant.
Definition: Null hypothesis
There is no difference between the groups being assessed.
Definition: Alternative hypothesis
There is a difference bbetween the two groups being assessed.
Definition: Two-tailed alternative hypothesis
The two groups' values are not equal. (no more specific than that)
Definition: One-tailed alternative hypothesis
One groups' values is less than those of the other group
Definition: Type 1 (Alpha) Error
Incorrectly rejecting the null hypothesis ( a false positive conclusion)
Definition: Type 2 (Beta) Error
Failure to reject the null hypothesis when the alternative hypothesis is correct (a false negative conclusion)
Definition: Power
The probability of finding a specified difference, or larger, when a true difference exists
The difference between statistical significance and clinical significance
Statistical significance only comments on chance, not benefit to patient.
Generally, minimum power for a clinically relevant difference
80% or more
Problem with performing multiple tests without adjusting error
Increases probability of a Type 1 error.
Bonferroni correction
Divide alpha by number of statistical tests to perform to yield a new alpha.
The name for the type of correction where alpha is divided by the number of statistical tests to perform.
Bonferroni Correction
Difference between parametric and non-parametric tests
Parametric: performed on data that are normally distributed
Chi-square test
Performed on discrete data
Fisher's exact test
Performed on discrete data
The other name for the Student t-test
two sample t-test
The other name for the two sample t-test
Student t-test
Student t-test/Two sample t-test
Performed on normally distributed data
One sample t-test
Performed on normally distributed data
What does ANOVA stand for?
ANalysis
ANOVA
Performed on normally distributed data
Paired t-test
Performed on normally distributed data
Correlation
Performed on normally distributed data
Pearson's Correlation coefficient
aka rho
Rho
aka Pearson's Correlation coefficient
Spearman Rank Correlation
Performed on non-normally distributed data
R-squared
aka Coefficient of determination
Coefficient of Determination
aka R-squared
Multiple regression analysis
Has two or more independent variables
Logistic regression analysis
Performed when the dependant variable is binary
Primary screening
Peformed to prevent a disease
Secondary screening
Peformed to reduce the impact of a disease
Sensitivity
Given disease is present, the probability of testing positive
Specificity
Given disease is absent, the probability of testing negative
Predictive value positive
Given the test is positive, the probability that the disease is present
Predictive value negative
Given the test is negative, the probability disease is absent
Types of descriptive studies
Case Report
Difference between a case report and a case series
One versus multiple interesting observations
Correlation study
Descriptive study done on large populations (the per capita meat consumption of a town and the prevalance of CAD there)
Prevalance study
aka Cross-Sectional study
Stratified Random Sample
Sample is first stratified into groups, and then subjects are tne randomly drawn from each group.
Types of analytic studies and how their subjects are selected
Case Control (Outcome, then exposure is assessed)
Randomized Controlled Trial: Pros
gold-standard
Randomized Controlled Trial: Cons
May take time = $ and loss-to-follow-up of subjects
Prospective Cohort Study: Pros
generates incidence data
Prospective Cohort Study: Cons
bad for rare outcomes
Retrospective Cohort Study: Pros
easy to do, so less $
Retrospective Cohort Study: Cons
bad for rare outcomes
Case Control: Pros
good for rare disease
Case Control: Cons
subject to several biases as recall bias, interview bias, selection bias
Meta-analysis: Pros
increases power by combining study results
Meta-analysis: Cons
Studies not done exactly the same way
Difference between intention-to-treat analysis and efficacy analysis, and which is prefered by the FDA
Intention to treat: All subjects analyzed by study arm, regardless of compliance
Phase One Study
Safety
Phase Two Study
Efficacy
Phase Three Study
Compares new treatment to standard therapy
Prevalance
The proportion of subjects in a group with a certain disease, includes new and old cases (a snapshot in time)
Incidence
New cases occurring over a defined period of time
Cumulative Incidence
aka Attack Rate
Attack Rate
aka Cumulative Incidence
Incidence Rate
aka Density
Density
aka Incidence Rate
Relationship of Prevalence to Incidence Rate
Prevalence = (Incidence Rate)(Average Duration of Disease)
Absolute Risk
The probability of going from a healthy state to an ill state (eg the probability that person x will develop condition y over the next z years when they don't have it already)
Relative Risk
The strength of association between an exposure and outcome.
(xy): x: exposure, y: disease
0=negative, 1=positive
Relative Risk (calculated from prevalances)
Relative Risk = Prevalence of disease in population A divided by Prevalence of disease in population B
Odds Ratio
An approximation of relative risk used in case control studies.
(xy): x: exposure, y: disease
0=negative, 1=positive
Attributable Risk
Excess disease in the exposed population that can be attributed to the exposure
(xy): x: exposure, y: disease
0=negative, 1=positive
Confounding
An erroneous study conclusion when a factor is associated with an exposure and is itself an independent risk factor for the outcome
Ways to control for confounding
Restriction (subjects with known risk factors are excluded)
Effect Modification
aka Interaction
Surveillance Bias
One cohort is followed more closely than another and is thus more likely to be diagnosed with the disease, leading to a difference in risk estimation between the two cohorts
Interaction
aka Effect Modification
Herd immunity
Disease protection of an unimmunized individual because the population is immunized
Epidemic curve
Plots an epidemic by:
Kaplan Meier survival cuves
Shows survival of different groups over time.
Bias
A systematic error with a study leading to an erroneous estimation of the association between the exposure and the outcome
4 major types of bias
-Recall bias (differential recall of exposure status by individuals based on their health status)
Criteria for cause-effect relationships
-Strength of association (how large or small is the relative risk/odds ratio
Definition: Epidemic
Greater than expected disease frequency in a defined population
Definition: Pandemic
Greater than expected disease frequency in a large defined area
Definition: Endemic
Constant presence of a disease in a defined population
Case Fatality Rate
Proportion of patients with a disease who die of that disease
Mortality Rate
Proportion of patients who die of a disease in a defined population
Key difference between Case Fatality rate and Mortality Rate
The denominator: