What is EBP?
integration of best research evidence with our clinical expertise and our patient's unique values and circumstances
What are the sources of evidence?
- patient's unique values and circumstances
- clinical expertise
- best research evidence
Name the steps of the scientific method:
- 1. observe an event
- 2. develop a hypothesis that makes a prediction
- 3. test the hypothesis
- 4. observe the result
- 5. revise the hypothesis
- 6. repeat as needed
What are background questions?
ask for information about a condition
What are the two essential components of a background question?
- a question root (who, what, where, when, how, why) with a verb
- a condition or an aspect of a condition
What is a foreground question?
ask for specific information about managing patients with a condition
What are the 3 essential components of a foreground question?
- patient and/or problem
- exposure (intervention) or comparision intervention
- clinical outcomes
What are the major sources of knowledge/information?
- trial and error
- logical reasoning
- the scientific method (research)
List the hierarchy of study types:
- evidence-based clinical guidelines
- systematic reviews and meta-analyses of randomized controlled trials
- randomized controlled trials
- non-randomized intervention studies
- observational studies
- qualitative studies
- case series, case reports
What is the 3rd step for EBP?
critically appraise the quality of the research evidence
Critical Appraisal involes understanding:
- the question(s) being asked
- the methods used to examin the question(s)
- -data collection
- the statistical analysis of the data
- -knowing basic terms and how they contribute to interpreting the data
- the conclusions made by the researchers and deciding if you think they are accurate, appropriate, and/or meaningful to you and the question(s) you have
In research studies what population sample should be used to collect data?
What are the scales/levels of data measurement?
- the mathematical precision with which the values of a variable can be expressed is the levle of measurement
- nominal-qualitative, categorical
- ordinal, interval, ratio-quantitative, progressively more precise mathematically
- a qualitative (or categorical) level of measurement; has no mathematical interpretation
- variables whose values vary in kind or quality but not in amount
- in terms of the variable "occupation", you can say that a lawyer is not equal to a therapist, but you can't say that the lawyer is more occupational or less occupational than the therapist
- specify only order of cases in "greater than" and "less than" distinctions
- patient/client satisfaction is an ordinal measure
- a rehab-specific example is manual muscle testing grades
- specific increment but no absolute 0
- numbers represent fixed measurement units but have no absolute zero point
- ex: temperature w/ Fahrenheit scale
- fixed measuring units with an absolute zero point. Zero means absolutely no amount of whatever the varible indicates
- ratio numbers can be added and subtracted, and because the numbers begin at an absolute zero point, they can be multiplied and divided
- ex: goniometric measures of ROM
What does reliability of measures mean?
extent to which a measure produces the same result uner different conditions
will the measure produce the same results when given on two different occasions? Typically expressed as a correlation coefficient (r)
the extent to which two or more individuals agree
the degree of agreement among multiple repetitions of a diagnostic test performed by the same individual
Reliability is a property of a:
measurement instrument...not of an experiment/study.
Validity of Measures:
extendt to which the measure indicates what it is supposed to measure
is the measure appropriate at face value? Does the measure look like it is going to measure what it is supposed to measure?
does the measure cover the full range of the concept's meaning?
- can scores obtained with one measure be accurately compared to those obtained using another (more established) measure?
- two types: concurrent and predictive
- a measure should fit well with other measures of similar theoretical concepts
- Ex: Scores on a marital satisfaction scale should be negatively related to spouse abuse
are the methods used in the study correct and are the results accurate?
- Are the findings applicable beyond that particular study?
- When you have a particular patient/clinical question in mind, you have to ask yourself this question when appraising the literature
KEY POINT: Questions to ask in Critical Apprasial Regarding External Validity:
- Is the study purpose relevant to your clinical question?
- Are the study's inclusion and exclusion criteria clearly defined and would the patient in your clinical question qualify for the study?
- Are the intervention and comparison/control groups receiving an intervention related to your clinical question?
- Are the outcome measures used in the study relevant to your clinical question and are they conducted in a clinically realistic manner?
- Is the study population sufficiently similar to the patient in you clinical question to justify expectation that the patient would respond similarly to the population?
- statistical procedures used to summarize, organize, and simplify data
- patterns can be seen from the organized data
- they summarize data but don't test for differences or associations
All research reports include descriptive statistics, such as:
- participant characteristics
- scores of participants on outcome measure(s)
- these often are in the participants section of a research report or at the beginning of the results section.
We can describe our data by using a Frequency Distribution. This is usually presented as a table or graph and always presents:
- the set of sub-categories that made up the original category
- the frequency of each score/category
What are three important characteristics of frequency distribution?
- central tendency
When can mode be used?
with any type of data
When can median be used?
interval and ratio data; frequently ordinal data; never nominal data
When can mean be used?
interval and ratio data; sometimes ordinal data; never nominal data
- Describes in an exact quantitative measure, how spread out/clustered together the measures are
- Variablility is usually defined in terms of distance
- inter-quartile range
- standard deviation
- simplest and most obvious way of describing spread/variablility
- Range= highest-lowest
- the range only takes into account the two extreme scores and ignores any values in between
use with medians
Standard deviation is used with?
- most frequent value
- doesn't take into account exact scores
- unaffected by extreme scores
- not useful when there are several values that occur equally often in a set
- the values that fall exactly in the midpoint of a ranked distribution
- does not take into account exact scores
- unaffected by extreme scores
- in a small set it can be unrepresentative
Mean (arithmetic average)
- takes into account all values
- easily distored by extreme values
- the mean is the preferred measure of central tendency
When is the mean not the preferred measure of central tendency?
- when there are extreme scores or skewed distribution
- non interval data
- discrete variables
- a more sophisticated measure of variablility is one that shows how scores cluster around the mean
- distance of a score from the mean
a number that measures how far away each number in a set of data is from their mean
If standard deviation is large,
it means the numbers are spread out from their mean
If standard deviation is small,
it means the numbers are close to their mean
Frequency Distribution: The normal distribution:
- symmetrical around the mid point, where the greatest frequency of scores occur
- in a normal distribution, the mean, median and mode are the same value
KEY POINT: The Beauty of Normal Distribution
no matter what the mena and standard deviation are for you data set, the area within one standard deviation is about 68% of your data; the area w/in 2 standard deviations is about 95%; and the area w/in 3 standard deviations is aobut 99.7%
all individuals of interest to the study
the particular group of participants you are testing: Selected from population
Statistical inference about populations:
A treatment that worked for a sample of patients will work for other patients with similar characteristics
Inferential statistics let us estimate population characteristics from:
mathematical characteristics of populations (m)
mathematical characteristics of samples (x=mean)
Statistics are used to estimate:
Samples must be:
representative of the population
sample has same characteristics as the population
How can we ensure samples are representative?
samples drawn according to the rule of Equal Probability of Selection Method: every case in the population has the same chance of being selected for the sample
Central Limit Theorem:
for any trait or variable, even those that are not normally distributed in the population, as sample size grows larger, the sampling distribution of sample means will become normal in shape
What is a variable?
- something that varies
- represent persons or objects that can be manipulated, controlled, or merely measured for the sake of research
- ones that are more or less controlled
- researchers manipulate these variables as they see fit
- they still vary, but variation is relatively known or taken into account
- often there are many in a given study
- not controlled or manipulated in any way, but are simply measured
- vary in relation to independent variables, and while results can be predicted, the data is always measured
- there can be any number of dependent variables, but usually there is one of interest to isolate and study
- intentionally manipulated
- vary at known rate
- intentionally left alone
- vary at unknown rate
Graphing dependent vs. independent variables:
the dependent variable is placed on the y-axis, while the independent is on the x
What is the goal of research?
to determine if the independent variable of interest to us has a statistically significant effect on the dependent variable. That means, an effect that is unlikely to be due to chance variations or sampling error
Overview of the Hypothesis Testing Process:
- 1. State the null hypothesis
- 2. Look at the data and decide on an appropriate statistical test
- 3. Compute the statistical test, look at p-value
- 4. if p-value is less than alpha, reject null hypothesis; if p-value is greater than alpha, fail to reject null hypothesis
- researchers make initial assumption that manipulation of independent variable will have NO EFFECT on the dependent variable (will be null)
- if any observed difference b/w the experimental and control groups is assumed to be due to chance unless proven otherwise
- no difference b/w groups, unless by chance
The alternative hypothesis:
the two means really are differnet, and it's not just chance