Biologic methods and stats Flashcards
hormones for which measurement by standard assays are influenced by binding proteins
1) Free T4 and T3: thyroid binding globulin can alter measurement of total T4 and T3
2) Testosterone: binds to albumin and SHBG. Free testosterone can be measured or calculated
3) Estrogen: binds to albumin and SHBG
4) Cortisol: binds to corticosteroid binding globulin and albumin. 90% of cortisol is bound to CBG. Free cortisol can be measured with urine sample for 24 hours
5) Vitamin D (1,25 and 25): binds to vitamin D binding protein (DBP) and albumin.
6) Growth hormone: growth hormone binding protein
what can interfere with assays
how to tell
- autoantibodies - will bind to the ligand and prevent it from binding to assay
- heterophiles antibodies - bind to capture Ab/assay and block the hormone receptor sites
both of these make it appear as if there are less substrate/hormone present in the sample
if you dilute the sample it will not dilute in a linear fashion
hook effect
solid state assay
when large amounts of substrate are present
direct binding of the reporter Ab preventing the reported Ab from binding to the surface
ex: macroPRL
serial dilution
what are normal ranges in parameters
central 95% of unaffected population
2SD above and below mean
2.5% above and below are normal
what is the null hypothesis
“ there is no difference in the frequency of ‘x’ between 2 groups”
what is type 1 error
alpha
Incorrectly rejecting null hypothesis
ie when you find there is a difference but it’s just by chance
what is type 2 error
beta
Stating there is no difference when you just didn’t have enough subjects to detect the difference
what is power
1-beta
Probability the study could have detected a difference
Directly related to sample size and magnitude of the difference
what increases your chances of having a type 1 error
Making multiple individual comparisons (≥20) can generate a p value ≤ 0.05 by chance alone
how do you correct for multiple comparisons
Divide the desired type I error rate by the number of comparisons to be made.
For example, with 3 comparisons:
.05/3 = .017
The new significant p value for comparisons is 0.017 and not 0.05.
what does p = 0.05 mean
5% chance that the difference you found was due to chance alone
Therefore if you make 20 comparison, you should find statistical significance somewhere
what do you typically set beta at
.20 (power = 1 – ß) or a 20% chance that you will fail to find a difference that actually does exist.
if you have 2 independent samples with normal distribution, what kind of test should you use to analyze?
what if it is more than 2?
t-test
ANOVA
if you have 2 independent samples with NOT normal distribution, what kind of test should you use to analyze?
what if it is more than 2?
Mann-Whitney U
Wilcoxon Rank Sum Test
Kruskal-Wallis
what is odds ratio
Odds ALWAYS implies a ratio of two probabilities.
Probability of event happening over probability of event not happening
OR is a ratio of two ratios.
if something has a probability of 80%, what’s the odds?
80:20 = 4
relative risk
ratio of percent of those with a risk factor who have the disease compared to those without the risk factor who have the disease.
when to use RR and when to use OR
RR
Useful in large prospective cohort studies
OR
case control
Retrospective studies
Number Needed to Treat (NNT)
The NNT is the number of patients who need to be treated in order to prevent one additional “outcome”.
how to calculate NNT
NNT = 1/ARR
what is ARR
how to calculate
Attributable (Absolute) Risk Reduction
ARR = risk of outcome in non-intervention group – risk of outcome in intervention group
The difference between the control
group’s event rate and the experimental group’s event rate.
RRR
Relative Risk Reduction = ARR/placebo or non-intervention group rate