# 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