17 Research and Statistics Flashcards
Hierarchy of evidence
1 Systematic reviewed and meta-analysis 2 RCTs 3 Cohort studies 4 Case-control studies 5 Cross-sectional surveys 6 Case reports 7 Expert opinion 8 Anecdotal
Nominal measurement
Labels, mutually exclusive, exhaustive
Male and female
Ordinal measurement
Rank ordering, distance between ratings not equal
1st, 2nd, 3rd place
Interval measurement
Equal intervals between ratings, no true zero
Temperature
Ratio measurement
Equal intervals, true zero
10-m walk time
Reliability
Consistency, dependability
Random and systemic errors limit reliability
Data must be reliable before considered valid
Standard error of measurement
How repeated measures on same instrument tend to be distributed around true score
Large SEM = low reliability
Construct validity
How well test measures the abstract construct it’s supposed to measure, like pain, intelligence, QOL
Content validity
How well content of test matches a content domain associated with the construct
Usually refers to surveys or questionnaires
Face validity
Under content validity
Test appears to test what it’s supposed to
Criterion-related validity
Compares test with other measures already validated (gold standard)
Concurrent- measured at same time as other
Predictive- compare to future measure
Floor effect
A measure’s lowest score is unable to assess a patient’s level of ability
Ceiling effect
A measure’s highest score is unable to assess a patient’s level of ability
Normative data
Represents scores pulled from literature to provide normal values for specific variables within a population
Provides approximate guidelines
Minimal detectable change
Minimum amount of change in a patient’s score that ensures the change is not the result of measurement error
Minimal clinically important difference
Smallest amount of change in an outcome that might be considered important by patient or clinician
Sensitivity
True positive rate
Likelihood that someone with condition will be positive on test
High = few false negatives
Specificity
True negative rate
Likelihood that someone without condition will be negative on diagnostic test
Low = more false positives
Positive predictive value
Percentage of people who are positive on the diagnostic test who have the condition
Negative predictive value
Percentage of people who are negative on the diagnostic test who do not have the condition
Positive likelihood ratio
Indicates how many times more or less likely a positive test result will occur in someone with condition than without
True positive compared to false positive rate
Large >10
Moderate 5-10
Small 2-5
Neutral 1
Negative likelihood ratio
Indicates how many times more or less likely a negative test result will occur in someone without condition than in someone with True negative compared to false negative Large <0.1 Moderate 0.1-0.2 Small 0.2-0.5 Neutral 1
Measures of central tendency
Mean
Median
Mode
Best measures of central tendency for data
Nominal = mode
Ordinal = median
Interval/ratio (not skewed) = mean
Interval/ratio (skewed) = median
Percentiles/quartiles
25th percentile = Q1
50th = Q2
75th = Q3
Standard deviation on normal distribution/bell curve
- 2% within 1 SD of mean
- 4% within 2
- 7% within 3
Intraclass correlation coefficient
Measure of reliability of ratings
Describes how strongly units in the same group resemble each other
Ranges 0-1 (low to high agreement)
ICC >0.75 is good reliability
Low if poor agreement between raters or if there is not much variability between subjects
Inferential statistics
Allow us to use samples to make generalizations about the populations the samples represent
Null vs experimental hypothesis
Null = No relationship between X and Y Experimental = Relationship between X and Y
p-values
Probability of observing your sample results given that the null hypothesis is true
p = 0.05 means 5% chance of finding a difference as large as or larger than the one in your study given that null is true
p<0.05 means reject null, X and Y are different
Parametric stats
Interval or ratio scales
Assumes normal distribution
Assumes variance in data for the samples compared are roughly equal
Nonparametric stats
Nominal or ordinal scales
Does not rely on assumptions about population
Significance testing for 2 independent groups
Parametric = unpaired t-test Nonparametric = Mann-Whitney U test
Significance testing for 2 related scores
Parametric = paired t-test Nonparametric = Wilcoxon signed-rank t-test
Significance testing for 3 or more independent groups
Parametric = One-way analysis of variance (ANOVA) Nonparametric = Kruskal-Wallis analysis of variance by ranks
Significance testing for 3 or more related scores
Parametric = One-way repeated measures ANOVA Nonparametric = Friedman two-way analysis of variance by ranks
r-squared
Measures percentage of variation in the values of the dependent variable that can be explained by the variation in the independent variable
Ranges 0-1
r-squared = 0.84 means 84% of variance in Y can be explained by changes in X; remaining variance is random variability
Confidence interval
Range of values likely to encompass the true value
Confidence level is probability that CI encompasses true value
Higher CL is wider CI
Type 1 error
False positive
Fire alarm goes off without fire
Type 2 error
False negative
Fire alarm does not sound with fire
Statistical power
Probability of type 1 error is associated with level of significance (alpha)
Probability of type 2 error is beta
Power = 1-beta
Power means x% chance that treatment effect will be detected
How to increase statistical power
Increasing alpha (p from 0.05 to 0.01)
Increasing sample size
Large effect size (can have smaller sample if expect large effects)
