Final

Followed forward in time

Reviewed back in time

• Subject either receives the control or the experimental medication throughout the treatment
• Two or more groups receiving treatment at the same time

Each subject serves as his/her own control and receives both the study and control treatments

• Case report
• Case series

• quantitative research that describes naturally occuring events
• document and communicate experiences

Examine etiology, efficacy, or cause using a comparison strategy

Experimental

• Evaluate the efficacy of therapeutic or educational interventions
• Investigator controls allocation

• Case Control
• Cohort (follow up)
• Cross sectional

Disease

• Investigator does not control allocation
• Observes nature

• Retrospective
• Patients with the disease (cases) and without the disease (control) are compared to determine the exposure to the risk factor in question

• Quicker and less expensive than cohort or experimental
• Optimal for evaluating rare diseases
• Appropriate for evaluating disease with long latency periods

• 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

Follow up

Exposure

• Prospective or Retrospective
• Design may involve the evaluation of risk factors for disease development in a specified population

• 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

• 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

Slice in time

• 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

Cross sectional

• 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

"0"

• 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

Trial with the primary objective of showing that the response to the investigational product is not clinically inferior to a comparative agent

• 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)

• 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

study designed to determine the stability of drugs in various preparations

study that evaluates whether products are similar in rate and extent of absorption

research where responses to questions asked of subjects are analyzed to determine the incidence, distribution, and reltaionships of sociological and psychological variables

research focused on the impact of economic value of programs and services provided

evaluation of a patient's living situation based on the patient's environment, family life, financial situation, education, and health

study designed to examine drug use and frequency of side effects following FDA approval

study of economic impact of drug therapies or services

• Case report
• Case series
• Case-control
• Cohort
• RCT

• Process of systematically evaluating and combining the results of clinical trials that have been completed
• Systematically aggregates and quantifies results from multiple clinical trials

• Combining raw data to increase study power
• Strongest type of meta-analysis: combine all raw data from individual studies and start the analysis over

• Combining conclusions (i.e. odds ratio) of single trials to create an overall odds ratio
• Done most frequently

• Summating individual results (i.e. 7 of 10 trials support A is more effective than placebo)
• Not very strong
• Weakest type

• 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

• Measure precision against treatment effect
• Absense of publication biase is suggested by a symmetrical inverted funnel
• Open circles= unpublished studies; Closed circles= published studies

• 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.

• Designed for non-inferiority trials
• Actual P values for superiority are presented to allow independent assessment
• Analysis according to intention-to-treat principle

• 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

every individual in the universe with the specific characteristic(s) or disease state under study

group of individuals chosen as representatives from the population under study

characteristics that must be possessed by the patient in order to enroll in the study

Characterstics that prevent patient participation

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)

• Placebo
• Historical
• Cross-over
• Standard-treatment
• Within-patient comparison

Comparing current intervention to data collected in the past because it is no longer ethical to give the patient this historical treatment

• "head to head" study
• New ACE inhibitor in development would be compared to current available treatment, not a placebo

• Receive both treatments at one time
• Seen in dermatological studies

• 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

• Tossing a coin
• Inexpensive
• May wind up with mismatched groups in terms of numbers

• 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

• Not ideal but it is a possibility
• Used if you want to describe the patient sample

• 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

• May be difficult to blind a medication with a distinct tase, physiologic effect, or continuous titration
• Expensive and time consuming

• 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

• At least one party in the trial is blinded
• Investigators aware of the treatment assignment but the patient is unaware

• When bo the patient and the investigator are blinded
• Minimizes bias

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

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

Systematic error that enters a clinical trial and distorts the data obtained

Only going to select men to be in the study but it isn't representative of the entire population

• 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

Patients will remember everything if the experience isnegative but if there were no problems they may not remember much

When an interviewer is trying to collect information in a study from the patient and they do so in a way that is leading

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)

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)

• Minimize confounders
• Proper selection of patients
• Objective methods of selecting data
• Blinding
• Use of a control group
• Reliable data sources

• 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

• 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

• 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

• 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

• Generalizability
• Ability to extrapolate to the population of interest
• Can the results be applied to other groups, patient, or systems?

Reproducibility of measurements

Measuring what you want to test

• 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

• 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.

• People dropping out of the study
• Expect 10-20% drop rate but not reasonable to be higher than 25%

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

• Relate to interim analysis
• Investigators define under what circumstances they would stop the study early

• Direct observation therapy (RN observes pt taking medicine)
• Pill Count
• Serum Concentration
• Ask the patient

Outcome of interest within the study

Intervention or what is being manipulated

Factor affecting the patients' conditions and associated statistically with the intervention being evaluated

• Applicable to a set of observations that contains ONE dependent variable and NO independent variables
• example: observational (cohort, case control)

• Applicable to a set of observations that contains ONE dependent variable and ONE independent variable
• example: clinical trial

• 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

• 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]

• Outcomes that patients care most about
• Example: Quality of life, morbidity, mortality/death

• Data with constant defined units of measure
• Mathematical relationship between the values
• Equal distance between increments
• ex. Interval Scale and Ratio scale

• Predetermined order with consistent level of magnitude with an arbitrary zero point assigned
• example: degrees Farenheit

• Interval scale with an absolute zero
• examples: BP, HR

Ordinal and Nominal

• 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

• 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

• 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)

• Determine the statistical importance of differences between groups and allow conclusion to be drawn from the data
• Can describe the whole population
• examples: SEM

• Arithmetic average of the data
• May be computed for CONTINUOUS data
• Extremely sensitive to outliers

• 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

• Most commonly obtained value in the distribution
• Useful to describe nominal, ordinal, and continuous data

• Difference between the largest and the smalles values in the distribution
• Highly sensitive to outliers

• 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

• Point on the distribution where a value is larger than x% of the other values in the group
• Not sensitive to outliers

• 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

• 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

• Bell-shaped curve
• Gaussian distribution
• Symmetrical scatter around the mean

• Not appropriate to use a mean
• Should use a median or mode when looking at bimodal data to describe the central value

Positive

• Negative
• Could measure with median or measure of variability can be the IQR

• 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

• 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

• 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

• 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

• 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

• 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

• Probability that the observed results in the study could be due to chance alone
• Detemined after the fact and calculated from the sample data

• 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

• 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

• 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

• 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

• 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

• Increased sample size
• increased alpha
• Large differences between population

• Poor choice of statistical test
• Poor study design

Includes the range of possible values for the true treatment difference that are statistically likely given the results of a specific trial

• Width of CI will narrow when sample size is increased
• Width of CI will widen when the sample size is decreased

• The width of the CI will narrow when power is increased
• The width of the CI will widen when power is decreased

If the CI includes "ONE" then significance is not achieved

If the CI includes "ZERO" then significance is not achieved

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

Clinical importance of the data generated in a study, regardless of statistical results

Only valid when used with continuous, normally distributed data

Applies to non-normal distributions or to data that do not meet the criteria for parametric tests

• Type of data collected (continuous, ordinal, nominal)
• Distribution of the data
• Number of groups being compared
• Study design
• Investigator or statistician preference

• One sided tests can detect a difference in one direction
• Two sided tests can detect a difference in two directions (better or worse)

• Used to compare the means from 2 independent samples
• i.e. parallel design

• Used when 2 groups contain the same people in the groups
• i.e. cross-over study design

• Analysis of variance
• Used in place of a t-test when comparing means of 3 or more groups
• i.e. Bonferroni, Tukey or Scheffe

• Analysis of covariance
• Method used for controlling for the effects of multiple confounding variables

• Compares percentages between 2 or more independent samples
• Most useful for nominal data
• Used to answer research questions about rates, proportions, or frequencies

• 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

• 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

Used to compare nominal data while controlling for the effect of a confounder

• 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

• Nonparametric equivalent of the paired t-test
• used for ordinal data
• Used when the data are matched or paired

• Non-parametric equivalent to ANOVA
• Used for ordinal data
• Used for 3 or more groups with independent samples

• Used for 3 or more groups where subjects participate in more than one group (paired sample)
• Used for ordinal data

Describes the strength of the relationship between variables

• 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

• Variables are continuous
• Variables follow a normal curve
• Observations are independent

• Heteroscedasticity (different spread or scatter from best-fit line)
• Non-linear relationship
• Outliers present

• 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
• r_s or p (greek rho)

Used to predict the value of one variable based on the knowledge of another

• One continuous dependent variable and one continuous independent variable
• (line cannot be extended)

• 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

• 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

Used when there is one continuous dependent variable and 2 or more continuous or discrete or independent variables/covariates

• 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

• 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

• Used when there is one discrete dependent variable and two or more continuous or discrete independent variables
• When the relationship is non-linear

• 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)]

• 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

• 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

• 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

• The number of patients who require treatment to prevent one event
• Assumes the baseline risk is equal for all patients

NNT= 1/ARR

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

• 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

• 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