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The flashcards below were created by user
Rx2013
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power is a calculation that
indicates whether or not a study can accurately detect statistically significant differences between samples when a difference exists
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simply stated, power =
1 - beta
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want power to be at least
80%
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a priori calculation of power
number of subjects needed to meet power is figured out before the study is performed
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post hoc calculation of power
- number of subjects needed to meet power is calculated after the study has been performed
- potential for bias
- could be manipulation just to get published
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If a statistically significant difference between groups is detected
power is less of an issue b/c power is the chance that we will find a difference
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study did not enter enough pt to meet power but difference was detected?
info we can use
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study had enough participants to meet power and showed a difference between groups?
info we can use
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study had enough pt to meet power and showed no difference between groups
info we canuse
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study did not have enough to meet power and results did not show difference between groups
- may experience type II error
- study did not meet power
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power is mentioned but without details, no % set or number of pt required to meet power
power not met
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power is claimed to be met but not reported
power not met
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Three types of analysis
- per protocol
- intent to treat
- modified intent to treat
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per protocol
number of subjects completing the trial
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intent to treat (ITT)
number of subjects randomized into each group
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modified intent to treat
number of subjects randomized and met pre-specified criteria (ex. 8 weeks of tx)
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descriptive statistics are calculated to describe characteristics of
- a group
- ex. patient demographics & frequency
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measures of central tendency
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mean
- used on interval or ratio data
- may be misleading on ordinal data
- affected by outliers
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Median
- used on ordinal
- non-parametric test
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mode
- variable that occurs most frequently
- most useful with nominal data
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measures of variability
- useful in measuring how close data is to the measure of central tendancy
- range
- interquartile range
- variance
- standard deviation
- standard deviation of the mean
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Range
- degree of spread
- influenced by outliers
- difference between largest and smallest observed
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interquartile range
- used for ordinal, interval and ratio data
- range of values remaining when the largest and smallest 25% are removed
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Variance
- gives more info of the data set's variability
- measures avg squared distance from the mean
- often more useful to use standard deviation to express variability
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Standard deviation
- square root of variance
- interval and ratio data
- only useful with normally distributed data
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Standard error of the mean
- derived from SD
- SEM is always smaller than SD
- greater N = smaller SEM
- used to calculate confidence intervals
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Confidence interval
- indication of the outcome within the population
- range of values in which the true value is included
- ex. 95% sure that the range of values contains the true value
- descriptive
- help interpret clinical significance of data
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the width of CI measures
- reliability of sample data
- wide interval = less reliable
- small interval = more reliable
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if the CI crosses zero with ordinal and interval data
- possibility that there was not difference between treatment groups
- should not be reported as statistically significant
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if the CI crosses 1with ratio data
possibility that there is no difference between treatment groups
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advantages of CI
- may expose manipulation
- help in determining clinical significance
- compliments P-value
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if trial shows there is no statistical significance between two groups, pay attention to
upper end of CI
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if trial shows that there is a statistical difference between two groups, pay attention to
the lower end of CI
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