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2012-04-22 19:14:11

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  1. Prospective Study
    Followed forward in time
  2. Retrospective Study
    Reviewed back in time
  3. Parallel
    • Subject either receives the control or the experimental medication throughout the treatment
    • Two or more groups receiving treatment at the same time
  4. Crossover
    Each subject serves as his/her own control and receives both the study and control treatments
  5. Give two examples of a descriptive study
    • Case report
    • Case series
  6. Descriptive Study
    • quantitative research that describes naturally occuring events
    • document and communicate experiences
  7. Explanatory Study
    Examine etiology, efficacy, or cause using a comparison strategy
  8. What type of trial is a RCT or educational intervention study?
  9. Experimental Study
    • Evaluate the efficacy of therapeutic or educational interventions
    • Investigator controls allocation
  10. Name three types of observational explanatory studies
    • Case Control
    • Cohort (follow up)
    • Cross sectional
  11. When you think case- control think " ".
  12. Observational study
    • Investigator does not control allocation
    • Observes nature
  13. Case-control
    • Retrospective
    • Patients with the disease (cases) and without the disease (control) are compared to determine the exposure to the risk factor in question
  14. List some strengths of case-control
    • Quicker and less expensive than cohort or experimental
    • Optimal for evaluating rare diseases
    • Appropriate for evaluating disease with long latency periods
  15. List some limitations of case-control
    • Inefficient for evaluation of rare exposures
    • Prone to bias (i.e. recall bias)
    • Do not allow for direct calculation of incidence rates
    • Temporal sequence of exposure and outcome is not always known
  16. What is another name for a cohort study?
    Follow up
  17. When you think "cohort" think " "
  18. Cohort (Follow up)
    • Prospective or Retrospective
    • Design may involve the evaluation of risk factors for disease development in a specified population
  19. List some strengths of cohort
    • Appropriate for evaluating rare exposures
    • Temporal sequence of exposure and outcome is clear
    • Selection and recall bias is minimized
    • Allow for direct calculation of incidence rates
  20. List some limitations of cohort
    • Time and cost tend to be more than with case-control
    • Subjects are lost to follow up over course of the study
    • notefficient for evaluating rare diseases
  21. When you think cross sectional think " "
    Slice in time
  22. Cross sectional
    • Prevalence Study
    • Gather information about the prevalence of health-related states and conditions
    • Do not identify cause and effect relationships
    • Identify existence of health problems
  23. What type of study would an epidemiology study be categorized as?
    Cross sectional
  24. Superiority
    • Trial with the primary objective of showing that the response to the investigational product is better to a comparator
    • Typically done if we have a novel therapy and a placebo; superiority between ACE inhibitors
  25. What value cannot be crossed by the confidence interval in order to determine superiority?
  26. Equivalence
    • Trial with the primary objective of showing that the respose to 2 or more treatments differs by an amount that is clinically unimportant
    • Usually demonstrated by showing that the true treatment difference is likely to lie between a lower and an upper equivalence margin of clinically acceptable differences
    • AKA: if confidence intervals are between the margins then the differences are clinically unimportant but if a CI goes outside of the margin then there is a clinically important difference
  27. Non-inferiority
    Trial with the primary objective of showing that the response to the investigational product is not clinically inferior to a comparative agent
  28. Equivalence margin
    • pre-specified maximum allowable difference between the new and the standard treatment
    • (in a non-inferiority trial if the confidence interval crosses this line it shows inferiority)
  29. N-of-1
    • controlled study conducted in a single subject where periods of exposure to at treatment are compared to periods of exposure to placebo
    • i.e. one patient with a very rare disease with a new treatment
  30. Stability
    study designed to determine the stability of drugs in various preparations
  31. Bioequivalence
    study that evaluates whether products are similar in rate and extent of absorption
  32. Survey research
    research where responses to questions asked of subjects are analyzed to determine the incidence, distribution, and reltaionships of sociological and psychological variables
  33. Programmatic research
    research focused on the impact of economic value of programs and services provided
  34. Quality of Life
    evaluation of a patient's living situation based on the patient's environment, family life, financial situation, education, and health
  35. Post-marketing surveillance
    study designed to examine drug use and frequency of side effects following FDA approval
  36. Pharmacoeconomic
    study of economic impact of drug therapies or services
  37. List the following in order of weak to increasing strength: case-control, case report, case series, RCT, Cohort
    • Case report
    • Case series
    • Case-control
    • Cohort
    • RCT
  38. Meta-analysis
    • Process of systematically evaluating and combining the results of clinical trials that have been completed
    • Systematically aggregates and quantifies results from multiple clinical trials
  39. Data pooling: Type 1
    • Combining raw data to increase study power
    • Strongest type of meta-analysis: combine all raw data from individual studies and start the analysis over
  40. Data Pooling: Type 2
    • Combining conclusions (i.e. odds ratio) of single trials to create an overall odds ratio
    • Done most frequently
  41. Data Pooling: Type 3
    • Summating individual results (i.e. 7 of 10 trials support A is more effective than placebo)
    • Not very strong
    • Weakest type
  42. List some limitations of meta-analysis
    • Heterogeneity of clinical trials: goal is for there to be no differences but in reality there will be small differences between the trials
    • Number of trials does not necessarily move you closer to the truth
    • Garbage in equals Garbage out
  43. Funnel Plot
    • Measure precision against treatment effect
    • Absense of publication biase is suggested by a symmetrical inverted funnel
    • Open circles= unpublished studies; Closed circles= published studies
  44. Blobbogram (Forest Plot)
    • Each line represents the confidence interval of a clinical trial
    • Diamond is the result of the meta analysis : middle is the estimate of effect and width is the confidence interval
    • If confidence interval crosses the line it means there was no effect. So if the diamond also crosses the line it shows no effect.
  45. Non-inferiority to Superiority
    • Designed for non-inferiority trials
    • Actual P values for superiority are presented to allow independent assessment
    • Analysis according to intention-to-treat principle
  46. Superiority to Non-inferiority
    • Protocol must contain a prospectively defined - delta for non-inferiority
    • Intention to treat and per-protocol analyses should show similar findings
    • Trial was properly designed and carried out with requirements of a non-inferiority trial
  47. Population
    every individual in the universe with the specific characteristic(s) or disease state under study
  48. Sample
    group of individuals chosen as representatives from the population under study
  49. Inclusion criteria
    characteristics that must be possessed by the patient in order to enroll in the study
  50. Exclusion characteristics
    Characterstics that prevent patient participation
  51. Patient demographics
    baseline characteristics of patients must be as similar as possible or controlled for in order to reduce confounding effect (i.e. age, disease states, medications)
  52. List the 5 types of controls
    • Placebo
    • Historical
    • Cross-over
    • Standard-treatment
    • Within-patient comparison
  53. Historical Control
    Comparing current intervention to data collected in the past because it is no longer ethical to give the patient this historical treatment
  54. Standard-treatment control
    • "head to head" study
    • New ACE inhibitor in development would be compared to current available treatment, not a placebo
  55. Within patient comparison control
    • Receive both treatments at one time
    • Seen in dermatological studies
  56. Randomization
    • Each subject has an equal and independent chance of being in any of the treatment arms
    • Ensures that all factors which can differ prognosis will be equally distributed throughout the groups
    • Examples: simple, block (cluster), non-randomization, stratification
  57. Simple Randomization
    • Tossing a coin
    • Inexpensive
    • May wind up with mismatched groups in terms of numbers
  58. Block Randomization
    • At any time if you stopped accepting patients your treatment groups will not be off by more than a block size
    • Block size of 4: 6 possible combinations of which 1 will be chosen. Once done with first block another combination is randomly chosen
  59. Non-Randomization
    • Not ideal but it is a possibility
    • Used if you want to describe the patient sample
  60. Stratification Randomization
    • Considered a type of randomization but actually occurs in combination with randomization
    • Strate= non-overlapping groups
    • Stratify patients by age, sex, heart failure stage and then randomly assign patients
  61. What are some limitations of blinding?
    • May be difficult to blind a medication with a distinct tase, physiologic effect, or continuous titration
    • Expensive and time consuming
  62. Open-label trial
    • Everyone, including the patient and the researcher, knows who is receiving what treatment
    • This is okay if you are measuring objective data (a lab value) and not subjective data
  63. Single-blind trial
    • At least one party in the trial is blinded
    • Investigators aware of the treatment assignment but the patient is unaware
  64. Double-blind trial
    • When bo the patient and the investigator are blinded
    • Minimizes bias
  65. Triple-blind trial example
    If a radiologist is involved in the study to perfrom the chest x-ray he would also not know which patient received what treatment in addition to the patient and investigator not knowing
  66. Double-dummy trial
    comparing 2 different dosage forms (inhaled vs. SQ insulin)--administer placebo inhaled/active SQ to one group and active inhaled/placebo SQ to the other group so neither knows whicho ne is active
  67. Bias
    Systematic error that enters a clinical trial and distorts the data obtained
  68. Selection Bias example
    Only going to select men to be in the study but it isn't representative of the entire population
  69. Observation Bias
    • direct observation or information bias that may enter usually because the study is not blinded
    • example: MD knows what the patient is receiving and his documentation about the effects are bias
  70. Recall Bias
    Patients will remember everything if the experience isnegative but if there were no problems they may not remember much
  71. Interviewer bias
    When an interviewer is trying to collect information in a study from the patient and they do so in a way that is leading
  72. Misclassification Bias
    A patient is classified one way when they should have been classified as another way (Class III HF when they should have been Class IV)
  73. Channeling Bias
    Allocation bias that happens when drugs with a similar therapeutic indication are given to patients with different prognostic indicators (i.e. sicker patients vs. not as sick patients)
  74. How can you control for bias?
    • Minimize confounders
    • Proper selection of patients
    • Objective methods of selecting data
    • Blinding
    • Use of a control group
    • Reliable data sources
  75. Intention-to-treat Analysis
    • Compares outcome based on the intended inital subjects' assignements
    • Determines the effect of treatment under usual conditions (i.e. how it will be in the real world)
    • Gives a conservative estimate of the treatment difference
    • No data should be eliminated
  76. As-treated Analysis
    • Analyzes subjects based on what intervention the subjects actually received
    • No data should be eliminated
    • Cleaner data regarding method effectiveness compared with intention-to-treat analysis
  77. Per-protocol Analysis
    • Analyzes those subjects who precisely followed the protocol
    • Gives superior information regarding method effectiveness compared with other analyses
    • Provides a more generous estimate of the differences between treatments
    • Problematic if compliance is related to prognosis
  78. Internal Validity
    • Within the confines of the study: the methods and analysis used stand up to scrutiny, the investigators' interpretation is supported, the results appear accurate
    • Affected by controls, randomization, blinding, confounders
    • Strong trial design= internal validity
  79. External Validity
    • Generalizability
    • Ability to extrapolate to the population of interest
    • Can the results be applied to other groups, patient, or systems?
  80. Reliability
    Reproducibility of measurements
  81. Validity
    Measuring what you want to test
  82. Is this reliable? Valid?
    • Reliable: Yes
    • Valid: No
    • Because it is clumped it is reliable because it is consistent (reproduce measurements) but it is not valid because it is not in the center so it didn't measure what we wanted to test
  83. Is this reliable? Valid?
    • Reliable: Yes
    • Valid: Yes
    • This is reliable because the dots are clumped meaning we were able to reproduce the measurement it is also valid because it is centered around the middle meaning we were able to measure what we wanted to.
  84. Attrition
    • People dropping out of the study
    • Expect 10-20% drop rate but not reasonable to be higher than 25%
  85. Interim Analysis
    Taking a look at the data before the actual end of the study to make sure that one group isn't at an increased risk vs. another group
  86. Early Stop Rules
    • Relate to interim analysis
    • Investigators define under what circumstances they would stop the study early
  87. Compliance Assessment
    • Direct observation therapy (RN observes pt taking medicine)
    • Pill Count
    • Serum Concentration
    • Ask the patient
  88. Dependent Variable
    Outcome of interest within the study
  89. Independent Variable
    Intervention or what is being manipulated
  90. Confounding Variable
    Factor affecting the patients' conditions and associated statistically with the intervention being evaluated
  91. Univariable Analysis
    • Applicable to a set of observations that contains ONE dependent variable and NO independent variables
    • example: observational (cohort, case control)
  92. Bivariable Analysis
    • Applicable to a set of observations that contains ONE dependent variable and ONE independent variable
    • example: clinical trial
  93. Multivariable Analysis
    • Applicable to a set of observations that contains ONE dependent variable and MORE THAN ONE independent
    • variables
    • example: parallel study with more than one intervention
  94. Disease-oriented Evidence (DOE)
    • Involves the measurement of an intermediate outcome in a study such as surrogate marker or end point
    • Aimed at increasing understanding of a disease and its incidence, prevalence, diagnosis and treatment, and prognosis
    • examples: intermediate outcomes, surrogate markers, disease markers [BP, A1c]
  95. Patient-oriented evidence (POE)
    • Outcomes that patients care most about
    • Example: Quality of life, morbidity, mortality/death
  96. Continuous Data
    • Data with constant defined units of measure
    • Mathematical relationship between the values
    • Equal distance between increments
    • ex. Interval Scale and Ratio scale
  97. Interval Scale
    • Predetermined order with consistent level of magnitude with an arbitrary zero point assigned
    • example: degrees Farenheit
  98. Ratio Scale
    • Interval scale with an absolute zero
    • examples: BP, HR
  99. Discreet Data
    Ordinal and Nominal
  100. Ordinal Data
    • Limited number of categories that DO have implied rank or order
    • Order of numbers is meaninful but not mathematical
    • Ordinal scale can be ranked in a specific order, be it low to high or high to low
    • Examples: liker-scale questionnaires, pain intensity score
  101. Nominal Data
    • Names categories that hae no implied rank or order
    • No mathematical relationship between classifications
    • Subjects are categorized based on some mutual characterstics, which the entire group possess
    • Examples: presence or absence of disease, gender, hair color
  102. Descriptive Statistics
    • Describe data set
    • Summarizes data into a useful form
    • examples: Measures of central tendency (mean, median, mode), measures of variability (range, IQR, percentile, and SD)
  103. Inferential Statistics
    • Determine the statistical importance of differences between groups and allow conclusion to be drawn from the data
    • Can describe the whole population
    • examples: SEM
  104. Mean
    • Arithmetic average of the data
    • May be computed for CONTINUOUS data
    • Extremely sensitive to outliers
  105. Median
    • The 50th percentile
    • Value above which or below which half of the data points lie
    • Not sensitive to outliers
    • Useful for continuous or ordinal data
  106. Mode
    • Most commonly obtained value in the distribution
    • Useful to describe nominal, ordinal, and continuous data
  107. Range
    • Difference between the largest and the smalles values in the distribution
    • Highly sensitive to outliers
  108. Interquartile Range
    • Measure of variability directly related to the median
    • Describes the range between the 25th and 75th percentile values
    • Clearly defines where the middle 50% of measures occurs and indicates the spread of data
    • Used to descrive the variability for ordinal data
  109. Percentile
    • Point on the distribution where a value is larger than x% of the other values in the group
    • Not sensitive to outliers
  110. Standard Deviation
    • Describes the variability of data about the sample mean
    • Meaningful only when it is calculated for normally distributed continuous data
    • 68% of data will fall within + 1 SD and about 95% of data will fall within + 2 SD
    • Not sensitive to outliers
  111. Standard Error of the Mean
    • Measure of the precision with which a sample mean estimates the population mean
    • SEM= SD/sqrt (n)
    • Used to calculate confidence intervals
    • The true but unmeasured mean of the larger population of interest will lie within two standard errors of the sample mean about 95% of the time
    • SEM decreases with increase in sample size
  112. Normal Distribution
    • Bell-shaped curve
    • Gaussian distribution
    • Symmetrical scatter around the mean
  113. Bimodal Distribution
    • Not appropriate to use a mean
    • Should use a median or mode when looking at bimodal data to describe the central value
  114. Is this a postive or negative skew?
  115. Is this a postive or negative skew?
    • Negative
    • Could measure with median or measure of variability can be the IQR
  116. Steps in hypothesis testing
    • Define the research question to be studied and restate it in terms of the H0 and H1
    • Collect data on the sample subjects
    • Calculate the test statistic
    • Evaluate the evidence against H0
    • State the conclusion
  117. Null Hypothesis
    • Statistical hypothesis denoted by H0
    • The hypothesis investigators test when they conduct a study
    • Goal is to disprove this hypothesis
    • Hypothesis of no difference: Group A=Group B
  118. Research Hypothesis
    • AKA alternative hypothesis denoted by H1
    • Difference exists between groups Group A does not equal Group B
    • Usually the purpose or objective of the study
  119. Test statistic
    • The test statistic is a number, computed from the data, that is compared with the associated probabilty distribution to determine the P value
    • Associated with a statistical test that uses it
  120. Alpha
    • False-positive rate the investigators are willing to tolerate (type I error)
    • Whe achieved, allows H0 to be rejected and H1 to be accepted
    • Decided upon before the study
    • Generally < 0.05
  121. How do you interpret alpha
    • Probability of making a type I error is < 0.05
    • 1 out of 20 times a type I error is made
    • 5% chance of a false positive
  122. P-value
    • Probability that the observed results in the study could be due to chance alone
    • Detemined after the fact and calculated from the sample data
  123. Interpreting P values
    • P < alpha: statistically significant (not zero) and not due to chance
    • P > alpha: may be due to chance
    • P-value means that if the H0 is true, then the statistical test should discredit H0 5% of the time or less
  124. Beta
    • The false-negative rate the investigators are willing to accept
    • The probability of making a type II error
    • < 0.2 acceptable but < 0.1 ideal
  125. Type I error
    • alpha-type error
    • Reject the null hypothesis when it is true (false positive)
    • Concluding that there is a difference in fact there is not a difference
  126. Type II error
    • Beta type error
    • Accept null hypothesis when it is false
    • Concluding that there is no difference when in fact there is a difference (false negative)
    • many be due to chance or small sample size
  127. Fill in the chart with the type of error:
  128. Power
    • Defined by beta (Power= 1- beta)
    • Ability to detect a difference if a difference exists--minimizes type II error
    • If power is not achieved, but difference is found between the groups then the power is irrelevant
  129. What factors will increase power?
    • Increased sample size
    • increased alpha
    • Large differences between population
  130. What factors will decrease power?
    • Poor choice of statistical test
    • Poor study design
  131. Confidence Interval
    Includes the range of possible values for the true treatment difference that are statistically likely given the results of a specific trial
  132. Relationship of confidence interval with sample size
    • Width of CI will narrow when sample size is increased
    • Width of CI will widen when the sample size is decreased
  133. Relationship of confidence interval with power
    • The width of the CI will narrow when power is increased
    • The width of the CI will widen when power is decreased
  134. Interpreting the CI of a ratio
    If the CI includes "ONE" then significance is not achieved
  135. Interpreting the CI of a non-ratio variable
    If the CI includes "ZERO" then significance is not achieved
  136. Statistical Significance
    The observed difference is associated with a P value that is below the established alpha level, indicating that chance isnot a likely explanation for the difference observed
  137. Clinical Significance
    Clinical importance of the data generated in a study, regardless of statistical results
  138. Parametric Test
    Only valid when used with continuous, normally distributed data
  139. Non-parametric
    Applies to non-normal distributions or to data that do not meet the criteria for parametric tests
  140. What factors should be considered when chosing a statistical test?
    • Type of data collected (continuous, ordinal, nominal)
    • Distribution of the data
    • Number of groups being compared
    • Study design
    • Investigator or statistician preference
  141. One-sided vs. Two-sided Parametric Test
    • One sided tests can detect a difference in one direction
    • Two sided tests can detect a difference in two directions (better or worse)
  142. T-test
    • Used to compare the means from 2 independent samples
    • i.e. parallel design
  143. Paired T-test
    • Used when 2 groups contain the same people in the groups
    • i.e. cross-over study design
  144. ANOVA
    • Analysis of variance
    • Used in place of a t-test when comparing means of 3 or more groups
    • i.e. Bonferroni, Tukey or Scheffe
  145. ANCOVA
    • Analysis of covariance
    • Method used for controlling for the effects of multiple confounding variables
  146. Chi-Square
    • Compares percentages between 2 or more independent samples
    • Most useful for nominal data
    • Used to answer research questions about rates, proportions, or frequencies
  147. Fisher's Exact Test
    • Used instead of Chi-square when: The matrix or any cell will be less than 5 or the entire sample size is less than 40
    • Used with 2 independent sample groups
  148. McNemar's Test
    • Used to compare nominal data for paired samples (i.e. cross-over)
    • May be used in a case-control design in which data are obtained from related patients
  149. Mantel-Haenszel
    Used to compare nominal data while controlling for the effect of a confounder
  150. Mann-Whitney U test
    • Non-parametric equivalence to the t-test
    • Used most often when data are measured on an ordinal scale
    • Used for independent samples
    • May also be used for data that are not normally distributed or when the variable is discrete
    • Mann-Whiteney U= Wilcoxon Rank Sum
  151. Wilcoxon Signed Rank Test
    • Nonparametric equivalent of the paired t-test
    • used for ordinal data
    • Used when the data are matched or paired
  152. Kruskal-Wallis
    • Non-parametric equivalent to ANOVA
    • Used for ordinal data
    • Used for 3 or more groups with independent samples
  153. Friedman
    • Used for 3 or more groups where subjects participate in more than one group (paired sample)
    • Used for ordinal data
  154. Fill in the blank with the appropriate test.
  155. What is correlation?
    Describes the strength of the relationship between variables
  156. What is pearson's correlation?
    • Describes the strength of the linear association between two continuous, normally distributed variables
    • "r"
    • r = +1 : strong, perfectly positive linear association
    • r = 0 : indicates no association
    • r = -1 : indicates a strong, perfectly negative linear association
  157. Characterstics of Pearson's r
    • Variables are continuous
    • Variables follow a normal curve
    • Observations are independent
  158. When is correlation not defined well by Pearson's r?
    • Heteroscedasticity (different spread or scatter from best-fit line)
    • Non-linear relationship
    • Outliers present
  159. Spearman's correlation (Spearman's rho)
    • Non-parametric equivalent to Perason's r
    • Useful when variables are not normally distributed, are on an ordinal scale, or when the relationship is non-linear
    • Useful when outliers are present or when there is a smalls ample size
    • rs or p (greek rho)
  160. What is regression?
    Used to predict the value of one variable based on the knowledge of another
  161. Simple linear regression
    • One continuous dependent variable and one continuous independent variable
    • (line cannot be extended)
  162. Characteristics and assumptions for simple linear regression
    • The dep. variable is continuous
    • Relationship is linear
    • Variables follow a normal distribution
    • Observations are independent
    • Data have the same spread or scatter
    • No outliers
  163. What is the coefficient of determination?
    • Represents how much of a dependent variable is explained by an independent variable
    • r2= simple regression analysis
    • R2= multiple regression analysis
    • Higher value indicates a stronger relationship
  164. Multiple Linear Regression
    Used when there is one continuous dependent variable and 2 or more continuous or discrete or independent variables/covariates
  165. Characteristics and assumptions of multiple linear regression
    • Dependent variable is on a continuous scale
    • Relationship is linear
    • Variables follow a normal distribution
    • Observations are independent
    • Data have the same spread or scatter
    • No outliers
  166. Simple Logistic Regression
    • Used when there is one discrete dependent variable and one categorical or continuous independent variable
    • Used when the relationship between variables is non-linear
    • Often used in survival analyzes
  167. Multiple Logistic Regression
    • Used when there is one discrete dependent variable and two or more continuous or discrete independent variables
    • When the relationship is non-linear
  168. Fill in the the chart below:
  169. Relative Risk/Risk Ratio
    • Used for prospective studies
    • "Ratio of risk of an event occuring in the experimental group compared with the control group"
    • Probability that a condition will occur
    • Indicates the risk of the event after the experimental treatment as a percentage of the orginal risk
    • < 1= therapy decreased risk, 0= no difference, > 1= therapy increased hte risk
    • RR= [A/(A+B)]/[C/(C+D)]
  170. Odds Ratio
    • Used for retrospective studies or case-controls
    • Ratio of odds of an event occuring in the experimental group compared with the control group
    • Does not take into account the total number of participants
    • <1 = therapy decreased the risk, 0= no difference, > 1= therapy increased the risk
    • OR= AxD/ CxB
  171. Relative Risk Reduction
    • Estimates the percentage of baseline risk that is removed as a result of therapy
    • Percent reduction in the experimental group event rate compared with the control group rate
    • 0= there was no effect of the treatment compared with the control
    • RRR= 1-RR x 100
    • RRR= [% placebo- % treatment]/% placebo
  172. Absolute Risk Reduction
    • The difference in the event rate between a control group and an experimental group
    • Provides the percentage of patients spared the adverse event as the result of the treatment

    ARR= % placebo- % treatment
  173. Number needed to treat
    • The number of patients who require treatment to prevent one event
    • Assumes the baseline risk is equal for all patients

    NNT= 1/ARR
  174. Number Needed to Harm
    The number of people that need to be at risk for a define period of time for a harm to come to one person

    NNH= 1/ARR
  175. Survival Analysis
    • Evaluating time to event
    • Analyze the time between a given starting point and a given event
    • Adjusts for the fact that patients are followed for different lengths of time and that the event may not have occured in some subjects
  176. Survival Curve Comparisons
    • Log-rank test: determine whether there is a statistically significant difference between the two curves
    • A hazard ratio can summarize a survival analysis and takes all time points into consideration
    • Interpet hazard ratio the same as relative risk