Reading...
Play button
Play button
Use LEFT and RIGHT arrow keys to navigate between flashcards;
Use UP and DOWN arrow keys to flip the card;
H to show hint;
A reads text to speech;
49 Cards in this Set
- Front
- Back
|
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." |
