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49 Cards in this Set

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Measures of central tendency

Mean

Median

Mode
Mean = sum of observations / number of observations

Median = observation in the middle when all observations are ordered from smallest to largest; if even, mean of middle two.

Mode = observation that occurs most frequently
Measures of dispersion

Range
Standard deviation
Standard error of the mean
Percentile
Range = largest - smallest observation

Standard deviation
= variability of data around the mean
- 68% of sample pop will fall within on standard deviation
- 95% will fall within two
Standard error of the mean = how much variability can be expected when measuring the mean from several different samples
Percentile = % of a distribution below a specific value
Frequency of an event

Incidence

Prevalence
Incidence
- Number of new events that occur in a specific time interval / population at risk at the beginning of the time interval
- Represents the likelihood of developing an event in that time interval

Prevalence
- Number of individuals with a given disease at a given point in time / population at risk at that point in time
Magnitude of effect
Relative risk

Odds ratio

Absolute risk

Number needed to treat
Magnitude of effect

Relative risk
Relative risk or cohort studies
= Indicence in exposed / incidence in unexposed
Cohort study
- Group of patients who have variable exposure to a risk factor are followed over time for outcome
- Then those with and without the outcome are evaluated for some potentially causative variable
- Nurses' Health Study:colon ca:fiber intake
Magnitude of effect

Odds ratio
Odds ratio
- = Odds an individual with a specific condition has beene xposed to a risk factor / the odds that a control has been exposed
- Used in case-control studies
-- Patients with disease are identified
-- Compared with matched controls for exposure to a risk factor
-- Does not permit measurement of proportion exposed to the risk factor then developed disease -->
relative risk and incidence cannot be calculated
- Odds ratio approximates relative risk
- People with colon ca:compared with match controls:fiber intake compared
Magnitude of effect

Absolute risk

Attributable risk
Absolute risk

Attributable risk - additional incidence of disease related to an exposure taking into account the background rate of disease
= Incidence in exposed - incidence in unexposed
Magnitude of effect

Number needed to treat
Number needed to treat

=Reciprocal of absolute risk reduction

Placebo-controlled trial with 100 patients
30 died during the study period
- 10 receiving active drug = 20% mortality rate
- 20 receiving placebo = 40% mortality rate
40% - 20% = 20% = 0.2
1/0.2 = 5 = NNT
Magnitude of effect

Absolute risk

Population attributable risk
Population attributable risk

Contribution that an exposure has on incidence of a specific disease
= Attributable risk * prevalence of exposure to a risk factor
- Particularly important when considering public health measures and allocation of resources to reduce incidence of disease
Quality of measurements

Reliability

Validity
Reliability - Extent to which repeated measurements of a relatively stable phenomenon fall closely to each other.

Validity - Extent to which an observation reflects the "truth" of the phenomenon being measured.
Diagnostic test accuracy

Sensitivity

Specificity
Sensitivity
- Number of patients with a positive test / all patients who have the disease.
- Few false negatives
Specificity
- Number of patients with a negative test and do not have the disease / number of patients who do not have the disease
- Few false positives
Positive predictive value

Negative predictive value
Positive predictive value - Likelihood a patient with a positive test has the disease.

Negative predictive value - Likelihood a patient with a negative test does not have the disease.

Depend on prevalence of the disease within the population.
Likelihood ratio
Likelihood ratio
- Odds of having a disease relative to the prior probability of the disease.
- Independent of prevalence.
Positive likelihood ratio= Sensitivity / (1-specificity)
- Negative likelihood ratio=(1-sensitivity)/specificity
Accuracy
Number of true positive and true negatives / total number of observations
Confidence interval
- Gives values within which there is a high probability (95%) that the true population can be found
- Narrows as the number of observations increases (ie, its variance or disperson decreases)
Errors

Type I

Type II
Type I (Alpha)
- Probability of incorrectly concluding that there is a statistically significant difference
- The number about a p-value
- p<0.05 = less than 5% chance tha thte difference course have occured by chance
Type II (Beta)
- Probability of incorrectly concluding that there was no statistically significant difference.
- Reflects insufficient power of the study.
Power
= 1-Beta
- Ability of a study to detect a true difference
- The larger the difference, the fewer observations that will be required
Multivariate analysis

Multiple regression

Logistic regression
Multiple regression - Used when there's a continuous variable (eg, BP)
Logistic regression - Used when the outcome is dichotomous
Survival analysis

Kaplan-Meier analysis

Cox proportional hazards analysis
Kaplan-Meier analysis - Measures the ratio of surviving patients / total number of patients at risk
- Curve depicts probability of survival
- If curves are close together or cross, likely insignificant difference

Cox proportional hazards analysis - Accounts for many variables that are relevant for predicting a dichotomous outcome
- Permits time to be included (unlike logistic regression)
Power in a negative study
- Power is statistically probability of avoiding a type 2 error
- Probability that a study will not mistakenly accept the null hypothesis and conclude there was no effect when there really was one
- Assess the confidence interval to see if clinically important values exist within the range of the likely values represented by the confidence interval
Graphic relative risk
[A/(A+B)]/[C/(C+D)]
Graphic odds ratio
(A'/D')/(C'/B')
Graphic
Sensitivity
Specificity
PPV
NPV
Sensitivity = A / (A+C)
Specificity = D / (B+D)
PPV = A / (A+B)
NPV = D / (C+D)
Graphic accuracy
(A+D)/(A+B+C+D)
Statistics: Fisher's Exact Test
Statistics: Fisher's Exact Test

Used to test statistically whether there is any relation between two categorical variables where sample sizes are small
Statistics: ANOVA
Statistics: ANOVA

Observed variance is partitioned into components due to different explanatory variables when the number of groups is > two
Statistics: Student's t-test
Statistics: Student's t-test

Tells whether the variation between two groups is significant
Statistics: Sensitivity
Statistics: Sensitivity

The proportion of true positives that are correctly identified by the test.
Statistics: Specificity
Statistics: Specificity

The proportion of true negatives that are correctly identified by the test.
Statistics: Positive predictive value
Statistics: Positive predictive value

Proportion of patients with positive test results who are correctly diagnosed.
Statistics: Negative predictive value
Statistics: Negative predictive value

Proportion of patients with negative test results who are correctly diagnosed.
Statistics: Logrank test
Statistics: Logrank test

Used to compare the survival experience of two or more groups of individuals

Used to test the null hypothesis that there is no difference between the populations in the probably of an event (eg, death)
Statistics: Chi-square test
Statistics: Chi-square test

- For estimating how closely an observed distribution matches an expected distribution ("goodness of fit")
- Estimating whether two random variables are independent
Statistics: Validity vs reliability
Statistics: Validity vs reliability

Validity - The extent to which we are measuring what we hope to measure

Reliability - Measurement process yields consistent scores over repeat measurements
Statistics: One-sample t-test
Statistics: One-sample t-test

The level of outcome for a group is compared to a known standard

Useful for small numbers
Statistics: Two-sample t-test
Statistics: Two-sample t-test

Outcome levels of two groups are compared to each other

Useful for small numbers
Statistics: Wilcoxon Rank-Sum Test
Statistics: Wilcoxon Rank-Sum Test

Nonparametric alternative to the two-sample t-test which is based soley on the order in which the observations from the two samples fall by sorting the values as one group then ranking the ordinally and summing each groups ordinal scores
Statistics: Accuracy
Statistics: Accuracy

The degree of conformity of a measured or calculated quantity to its actual value

Degree of accuracy
Statistics: Precision
Statistics: Precision

Reproducibility or repeatability

The degree to which repeated measurements show the same or similar results

Degree of reproducibility
Statistics: Incidence
Statistics: Incidence

Annual number of people who have a case of the disease

The number of specified new events during a specified period in a specified population
Statistics: Prevalence
Statistics: Prevalence

The number of people who currently have the condition

The number of cases of a disease existing in a given population at a specific period of time or at a particular moment in time
Statistics: One-way ANOVA
Statistics: One-way ANOVA

A way to test the equality of three or more means at one time by using variances
Statistics: Two-way ANOVA
Statistics: Two-way ANOVA

Extension of one-way ANOVA but with two independent variables (factors)
Statistics: Kruskal-Wallis test
Statistics: Kruskal-Wallis test

Commonly used when there is one nominal variable and one measurement variable and the measurement variable does not meet the normality assumption of an ANOVA
Statistics: Phase I trial
Statistics: Phase I trial

- Initial introduction of drug into humans
- Determine the actions and side effects associated with increasing drugs
Statistics: Phase II trial
Statistics: Phase II trial
- Controlled clinical trials to evaluate the drug's effectiveness for a paticular condition
- Well controlled, closely monitored, relatively small number of patients
Statistics: Phase III trial
Statistics: Phase III trial
- Administration of new drug to larger number of patients to determine
1 Safety
2 Efficacy
3 Appropriate dosage
Statistics: Phase IV trial
Statistics: Phase IV trial
Postmarketing studies to delineate additional info
- Risks
- Benefits
- Optimal use
-- Different doses
-- Different schedules
-- Other populations
-- Other stages of disease
Statistics: Pilot
Statistics: Pilot

- Small-scale or trial run in preparation for a major study
- Pretesting of a particular research instrument
- "Do not take risk. Pilot test first."