Dan's Paper 3 Flashcards
What is the median?
Value in the middle when listing them all.
Great for non-normally distributed data.
What is the mean?
Average of all values.
Skewered by outliers.
What is a hazard ratio?
Treatment hazard rate divided by placebo hazard rate.
E.g. 10% in treatment die, 20% in control die -> hazard ratio = 0.5 ‘50% decrease in deaths’. E.g. Hazard ratio = 0.64 -> 36% decrease in deaths in treatment compared to control.
What is a confidence interval?
We can be 95% sure that the true number lies within that value.
What are the two types of significance?
Statistical and clinical.
What is statistical significance?
P value is less than 0.05.
Note a given hazard ratio has a 0.005 p value then there is a 0.5% chance of this hazard ratio occurring despite no clinical difference.
What is clinical significance?
Depends on the application and effect size.
A statistically significant hazard ratio of 0.98 is only a 2% decrease in death. In clinic this could or could not be significant depending on how many people are affected. Another example: 1 mmHg reduction in BP with antihypertensive (that might even have side effects) is probably not significant in clinic.
What are parametric tests?
Deal with normally distributed data.
More likely to get a statistically significant result, usually higher power.
What are non-parametric tests?
Deal with non-normal data.
Either do a t-test to show difference between two groups (that is a parametric test?) Or transform data e.g. log transform, other exponents to make it normally distributed and use parametric tests.
What is a technical replicate?
Taking the same reading multiple times on the same sample.
E.g. BP of one individual three times to make sure measurement is correct.
What is a biological replicate?
Same treatment to different samples.
E.g. take BP of six different people.
What are error bars?
Could be standard deviation, standard error, or 95% confidence interval.
Standard error is commonly used over the standard deviation as its SD divided by sample size and thus makes the error bars look smaller.
What is a test of multiple comparison, why is it important, and what is post-hoc testing?
Importance: p = 0.05 -> 1 in 20 tests will give a false positive.
Thus do a test for multiple comparisons such as ANOVA. Post-hoc testing e.g. Bonferroni is then done to see differences between individual samples and a control or between all possible combinations - some tests have higher false positives other false negatives.
What are the conditions to be able to run a t-test and what is the result?
Data normally distributed, equal variance in both groups of samples, data is continuous.
Result: one p-value - whether or not there is a statistical significance between the two groups.
What is the difference between one-way ANOVA and two-way ANOVA?
One way: multiple sample groups with only one variable e.g. effect of ramipril, amlodipine, both or placebo on BP.
Two way: multiple sample groups with multiple variables e.g. ramipril, amlodipine, placebo effect on BP in old and young people or in high vs low doses.
