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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."