Clinical Sciences: Statistics, studies and other shizz Flashcards
Mean
The average of a series of observed values
Median
The middle value if series of observed values are placed in order
Mode
The value that occurs most frequently within a dataset
Range
The difference between the largest and smallest observed value
Randomised controlled trial
Participants randomly allocated to intervention or control group (e.g. standard treatment or placebo)
Cohort study
Observational and prospective. Two (or more) are selected according to their exposure to a particular agent (e.g. medicine, toxin) and followed up to see how many develop a disease or other outcome.
Cohort study outcome measure
RELATIVE RISK
Case-control study
Observational and retrospective. Patients with a particular condition (cases) are identified and matched with controls. Data is then collected on past exposure to a possible causal agent for the condition.
Case-control study outcome measure
The usual outcome measure is the odds ratio.
Cross-sectional survey
Provide a ‘snapshot’, sometimes called prevalence studies
Specificity formula
TN / (TN + FP)
Sensitivity formula
TP / (TP + FN )
Positive predictive value formula
TP / (TP + FP)
Negative predictive value formula
TN / (TN + FN)
Likelihood ratio for a positive test result
sensitivity / (1 - specificity)
Likelihood ratio for a negative test result
(1 - sensitivity) / specificity
Sensitivity definition
Proportion of patients with the condition that have a positive test result
Specificity definition
Proportion of patients without the condition who have a negative test result
Positive predictive value
The chance that the patient has the condition if the diagnostic test is positive
Negative predictive value
The chance that the patient does not have the condition if the diagnostic test is negative
Likelihood ratio for a positive test result
How much the odds of the disease increase when a test is positive
Likelihood ratio for a negative test result
How much the odds of the disease decrease when a test is negative
Confidence interval
a range of values within which the true effect of intervention is likely to lie
Standard error of the mean
The standard error of the mean (SEM) is a measure of the spread expected for the mean of the observations - i.e. how ‘accurate’ the calculated sample mean is from the true population mean
How to calculate SEM
SD / square root (n)
Standard deviation
measure of how much dispersion exists from the mean
Clinical trial Phase 1
Determines pharmacokinetics and pharmacodynamics and side-effects prior to larger studies
Clinical trial phase 2
Assess efficacy + dosage
Clinical trial phase 3
Assess effectiveness
Clinical trial phase 4
Postmarketing surveillance
What does parametric mean?
something which can be measured, usually normally distributed
Name 2 parametric tests
Student’s t-test - paired or unpaired*
Pearson’s product-moment coefficient - correlation
Name 4 Non-parametric tests
Mann-Witney U test
Wilcoxon signed rank test
Chi-squared test
Spearman rank, Kendall Rank
Mann-Whitney U test - what does it do?
compares ordinal, interval, or ratio scales of unpaired data
Wilcoxon signed-rank test - what does it do?
compares two sets of observations on a single sample, e.g. a ‘before’ and ‘after’ test on the same population following an intervention
chi-squared test - what does it do?
used to compare proportions or percentages e.g. compares the percentage of patients who improved following two different interventions
Spearman / Kendall test- what does it do?
Correlation
p value
the probability of obtaining a result by chance at least as extreme as the one that was actually observed, assuming that the null hypothesis is true
type I error
the null hypothesis is rejected when it is true (false positive)
type II error
he null hypothesis is accepted when it is false (false negative)
The power of a study
The power of a study is the probability of (correctly) rejecting the null hypothesis when it is false, i.e. the probability of detecting a statistically significant difference
nominal data
Observed values can be put into set categories which have no particular order or hierarchy. You can count but not order or measure nominal data (for example birthplace)
Ordinal data
Observed values can be put into set categories which themselves can be ordered (for example NYHA classification of heart failure symptoms)
Discrete data
Observed values are confined to a certain values, usually a finite number of whole numbers (for example the number of asthma exacerbations in a year)
Continuous data
Data can take any value with certain range (for example weight)
Binomial data
Data may take one of two values (for example gender)
Interval data
A measurement where the difference between two values is meaningful, such that equal differences between values correspond to real differences between the quantities that the scale measures (for example temperature)
