Subject either receives the control or the experimental medication throughout the treatment
Two or more groups receiving treatment at the same time
Crossover
Each subject serves as his/her own control and receives both the study and control treatments
Give two examples of a descriptive study
Case report
Case series
Descriptive Study
quantitative research that describes naturally occuring events
document and communicate experiences
Explanatory Study
Examine etiology, efficacy, or cause using a comparison strategy
What type of trial is a RCT or educational intervention study?
Experimental
Experimental Study
Evaluate the efficacy of therapeutic or educational interventions
Investigator controls allocation
Name three types of observational explanatory studies
Case Control
Cohort (follow up)
Cross sectional
When you think case- control think " ".
Disease
Observational study
Investigator does not control allocation
Observes nature
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
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
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
What is another name for a cohort study?
Follow up
When you think "cohort" think " "
Exposure
Cohort (Follow up)
Prospective or Retrospective
Design may involve the evaluation of risk factors for disease development in a specified population
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
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
When you think cross sectional think " "
Slice in time
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
What type of study would an epidemiology study be categorized as?
Cross sectional
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
What value cannot be crossed by the confidence interval in order to determine superiority?
"0"
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
Non-inferiority
Trial with the primary objective of showing that the response to the investigational product is not clinically inferior to a comparative agent
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)
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
Stability
study designed to determine the stability of drugs in various preparations
Bioequivalence
study that evaluates whether products are similar in rate and extent of absorption
Survey research
research where responses to questions asked of subjects are analyzed to determine the incidence, distribution, and reltaionships of sociological and psychological variables
Programmatic research
research focused on the impact of economic value of programs and services provided
Quality of Life
evaluation of a patient's living situation based on the patient's environment, family life, financial situation, education, and health
Post-marketing surveillance
study designed to examine drug use and frequency of side effects following FDA approval
Pharmacoeconomic
study of economic impact of drug therapies or services
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
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
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
Data Pooling: Type 2
Combining conclusions (i.e. odds ratio) of single trials to create an overall odds ratio
Done most frequently
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
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
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
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.
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
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
Population
every individual in the universe with the specific characteristic(s) or disease state under study
Sample
group of individuals chosen as representatives from the population under study
Inclusion criteria
characteristics that must be possessed by the patient in order to enroll in the study
Exclusion characteristics
Characterstics that prevent patient participation
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)
List the 5 types of controls
Placebo
Historical
Cross-over
Standard-treatment
Within-patient comparison
Historical Control
Comparing current intervention to data collected in the past because it is no longer ethical to give the patient this historical treatment
Standard-treatment control
"head to head" study
New ACE inhibitor in development would be compared to current available treatment, not a placebo
Within patient comparison control
Receive both treatments at one time
Seen in dermatological studies
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
May wind up with mismatched groups in terms of numbers
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
Non-Randomization
Not ideal but it is a possibility
Used if you want to describe the patient sample
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
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
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
Single-blind trial
At least one party in the trial is blinded
Investigators aware of the treatment assignment but the patient is unaware
Double-blind trial
When bo the patient and the investigator are blinded
Minimizes bias
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
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
Bias
Systematic error that enters a clinical trial and distorts the data obtained
Selection Bias example
Only going to select men to be in the study but it isn't representative of the entire population
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
Recall Bias
Patients will remember everything if the experience isnegative but if there were no problems they may not remember much
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
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)
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)
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
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
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
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
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
External Validity
Generalizability
Ability to extrapolate to the population of interest
Can the results be applied to other groups, patient, or systems?
Reliability
Reproducibility of measurements
Validity
Measuring what you want to test
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
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.
Attrition
People dropping out of the study
Expect 10-20% drop rate but not reasonable to be higher than 25%
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
Early Stop Rules
Relate to interim analysis
Investigators define under what circumstances they would stop the study early
Compliance Assessment
Direct observation therapy (RN observes pt taking medicine)
Pill Count
Serum Concentration
Ask the patient
Dependent Variable
Outcome of interest within the study
Independent Variable
Intervention or what is being manipulated
Confounding Variable
Factor affecting the patients' conditions and associated statistically with the intervention being evaluated
Univariable Analysis
Applicable to a set of observations that contains ONE dependent variable and NO independent variables
example: observational (cohort, case control)
Bivariable Analysis
Applicable to a set of observations that contains ONE dependent variable and ONE independent variable
example: clinical trial
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
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
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
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)
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
Mean
Arithmetic average of the data
May be computed for CONTINUOUS data
Extremely sensitive to outliers
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
Mode
Most commonly obtained value in the distribution
Useful to describe nominal, ordinal, and continuous data
Range
Difference between the largest and the smalles values in the distribution
Highly sensitive to outliers
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
Percentile
Point on the distribution where a value is larger than x% of the other values in the group
Not sensitive to outliers
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
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
Normal Distribution
Bell-shaped curve
Gaussian distribution
Symmetrical scatter around the mean
Bimodal Distribution
Not appropriate to use a mean
Should use a median or mode when looking at bimodal data to describe the central value
Is this a postive or negative skew?
Positive
Is this a postive or negative skew?
Negative
Could measure with median or measure of variability can be the IQR
Steps in hypothesis testing
Define the research question to be studied and restate it in terms of the H_{0} and H_{1}
Collect data on the sample subjects
Calculate the test statistic
Evaluate the evidence against H_{0}
State the conclusion
Null Hypothesis
Statistical hypothesis denoted by H_{0}
The hypothesis investigators test when they conduct a study
Goal is to disprove this hypothesis
Hypothesis of no difference: Group A=Group B
Research Hypothesis
AKA alternative hypothesis denoted by H_{1}
Difference exists between groups Group A does not equal Group B
Usually the purpose or objective of the study
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
Alpha
False-positive rate the investigators are willing to tolerate (type I error)
Whe achieved, allows H_{0} to be rejected and H_{1} to be accepted
Decided upon before the study
Generally < 0.05
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
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
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 H_{0} is true, then the statistical test should discredit H_{0} 5% of the time or less
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
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
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
Fill in the chart with the type of error:
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
What factors will increase power?
Increased sample size
increased alpha
Large differences between population
What factors will decrease power?
Poor choice of statistical test
Poor study design
Confidence Interval
Includes the range of possible values for the true treatment difference that are statistically likely given the results of a specific trial
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
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
Interpreting the CI of a ratio
If the CI includes "ONE" then significance is not achieved
Interpreting the CI of a non-ratio variable
If the CI includes "ZERO" then significance is not achieved
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
Clinical Significance
Clinical importance of the data generated in a study, regardless of statistical results
Parametric Test
Only valid when used with continuous, normally distributed data
Non-parametric
Applies to non-normal distributions or to data that do not meet the criteria for parametric tests
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
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)
T-test
Used to compare the means from 2 independent samples
i.e. parallel design
Paired T-test
Used when 2 groups contain the same people in the groups
i.e. cross-over study design
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
ANCOVA
Analysis of covariance
Method used for controlling for the effects of multiple confounding variables
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
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
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
Mantel-Haenszel
Used to compare nominal data while controlling for the effect of a confounder
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
Wilcoxon Signed Rank Test
Nonparametric equivalent of the paired t-test
used for ordinal data
Used when the data are matched or paired
Kruskal-Wallis
Non-parametric equivalent to ANOVA
Used for ordinal data
Used for 3 or more groups with independent samples
Friedman
Used for 3 or more groups where subjects participate in more than one group (paired sample)
Used for ordinal data
Fill in the blank with the appropriate test.
What is correlation?
Describes the strength of the relationship between variables
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
Characterstics of Pearson's r
Variables are continuous
Variables follow a normal curve
Observations are independent
When is correlation not defined well by Pearson's r?
Heteroscedasticity (different spread or scatter from best-fit line)
Non-linear relationship
Outliers present
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
r_{s} or p (greek rho)
What is regression?
Used to predict the value of one variable based on the knowledge of another
Simple linear regression
One continuous dependent variable and one continuous independent variable
(line cannot be extended)
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
What is the coefficient of determination?
Represents how much of a dependent variable is explained by an independent variable
r^{2}= simple regression analysis
R^{2}= multiple regression analysis
Higher value indicates a stronger relationship
Multiple Linear Regression
Used when there is one continuous dependent variable and 2 or more continuous or discrete or independent variables/covariates
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
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
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
Fill in the the chart below:
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