# Biologic methods and stats Flashcards

1
Q

hormones for which measurement by standard assays are influenced by binding proteins

A

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

2
Q

what can interfere with assays

how to tell

A
• 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

3
Q

hook effect

A

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

4
Q

what are normal ranges in parameters

A

central 95% of unaffected population
2SD above and below mean
2.5% above and below are normal

5
Q

what is the null hypothesis

A

“ there is no difference in the frequency of ‘x’ between 2 groups”

6
Q

what is type 1 error

A

alpha

Incorrectly rejecting null hypothesis
ie when you find there is a difference but it’s just by chance

7
Q

what is type 2 error

A

beta

Stating there is no difference when you just didn’t have enough subjects to detect the difference

8
Q

what is power

A

1-beta

Probability the study could have detected a difference

Directly related to sample size and magnitude of the difference

9
Q

what increases your chances of having a type 1 error

A

Making multiple individual comparisons (≥20) can generate a p value ≤ 0.05 by chance alone

10
Q

how do you correct for multiple comparisons

A

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.

11
Q

what does p = 0.05 mean

A

5% chance that the difference you found was due to chance alone
Therefore if you make 20 comparison, you should find statistical significance somewhere

12
Q

what do you typically set beta at

A

.20 (power = 1 – ß) or a 20% chance that you will fail to find a difference that actually does exist.

13
Q

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?

A

t-test

ANOVA

14
Q

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?

A

Mann-Whitney U
Wilcoxon Rank Sum Test

Kruskal-Wallis

15
Q

what is odds ratio

A

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.

16
Q

if something has a probability of 80%, what’s the odds?

A

80:20 = 4

17
Q

relative risk

A

ratio of percent of those with a risk factor who have the disease compared to those without the risk factor who have the disease.

18
Q

when to use RR and when to use OR

A

RR
Useful in large prospective cohort studies

OR
case control
Retrospective studies

19
Q

Number Needed to Treat (NNT)

A

The NNT is the number of patients who need to be treated in order to prevent one additional “outcome”.

20
Q

how to calculate NNT

A

NNT = 1/ARR

21
Q

what is ARR
how to calculate

A

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.

22
Q

RRR

A

Relative Risk Reduction = ARR/placebo or non-intervention group rate

23
Q

95% confidence interval

A

A 95% confidence interval (95% CI) is the range of values which we can be 95% confident includes the population statistic from which the study sample was drawn

24
Q

how to interpret confidence intervals:
what is the null value for a mean?
what is the null value for OR/RR/Hazard Ratio?

A

Null value is 0
If 95% CI includes 0, not statistically significant

Null value is 1
If 95% CI includes 1, not statistically significant

25
Q

What is a bias

A

“any systematic error in an epidemiologic study that results in an incorrect estimate of the association between exposure and risk of disease”

26
Q

what is selection bias

A

Patient selection is not uniformly performed.
Patients selected are different than those not selected.

27
Q

Recall bias

A
• Differences in the accuracy of recalling past events/exposures between cases and controls
28
Q

Measurement bias

A

Systematic error in the measurement of data.

29
Q

Misclassification Bias

A

-Wrongly classifying a subject/mislabeling them

30
Q

what is a confounding variable

A

A confounding variable is associated with both the risk factor or ‘exposure’ and the disease being studied.
Can either inflate or deflate the true magnitude of the association
Should not be an intermediate link between exposure and disease.

31
Q

what is a correlation

A

Determine the strength of the linear relationship between two continuous variables.
Its value can range from -1 to +1.

closer to -1 to +1 indicate stronger
0 indicates no correlation

32
Q

what is regression
what are the types

A

Any statistical technique which focuses on the relationship between a dependent variable and one of more independent variables.

Linear regression – dependent variable is continuous or interval variable
Logistic regression – dependent variable is dichotomous (yes/no, dead/alive)

33
Q

Prevalence

A

Proportion (or fraction) of a group possessing a clinical condition at a given point of time.

34
Q

Incidence

A

Proportion (or fraction) of a group initially free of the condition that develop it over a period of time.

35
Q

sensitivity

A

a/(a+c) or TP/(TP + FN)

Proportion of people with the disease who have a positive test for the disease

if high, Very few false negatives

R/O disease

36
Q

Specificity

A

d/(b+d) or TN/(TN + FP)

Proportion of people without the disease who have a negative test

if high, Very few false positives

R/I disease

37
Q

Positive Predictive Value

A

a/(a+b) or TP/(TP + FP)

Probability of disease in a patient with a positive or abnormal test.

38
Q

Negative Predictive Value

A

d/(c+d) or TN/(FN + TN)

Probability of not having the disease when the test result is normal or negative

39
Q

Positive Likelihood ratio

A

Probability of test result in the presence of disease
OVER
Probability of test result in people without disease

Likelihood Ratio = Sensitivity OVER
1-Specificity

Divides the probability that a patient with the disease will test positive by the probability that a patient without the disease will test positive

40
Q

what do LR mean

A

> 10 suggests large and conclusive change in pretest to posttest probability
5 - 10 suggests moderate change
2 - 5 suggests small, although occasionally important changes in probability
1 -2 suggests small, rarely important changes in probability
< 1 decrease the probability of disease

41
Q

what are on the axes

A

Sensitivity on the y-axis
1- specificity on the x-axis
Plotting the true positive rate against the false positive rate

Describes accuracy of test over a range of cut-off points

Overall accuracy of test described by the area under the curve (the larger the area the better the test)

42
Q

Case Reports

A

Presentations of single case or handful of cases

Important way for unusual diseases or unusual presentations of disease are brought to attention

43
Q

Case series

A

Prevalence survey of a group of individuals with a particular disease at one point in time

Describes clinical manifestations of disease including both purported causes and effects

44
Q

Cross-Sectional or Prevalence Studies

A

Prevalence: fraction or proportion of the group who are diseased

All people examined, including cases and noncases
Single point in time

45
Q

Cohort Studies

A

Group of people (cohort) is assembled none of whom has experienced the outcome of interest

People are classified according to characteristics that might be related to outcome

People are observed over time

Relate initial characteristics to subsequent outcome events

46
Q

A

Establishes incidence
Follows logic, if people are exposed, will they get the disease?
Exposure elicited without bias since outcome is not known
Can asses relationship between exposure and many diseases

Inefficient as many more subject must be enrolled than will experience the outcome of interest
Expensive
Results not available for a long time
Can only assess relationship between disease and exposure to relatively few factors recorded at the outset of study

47
Q

Case control study

A

Patients with the disease and a group of otherwise similar people who do not have the disease are selected

Researchers look backward to determine the frequency of exposure in the two groups

Estimate the relative risk of disease related to exposure

48
Q

A

Cases can be identified unconstrained by the natural frequency of disease
Good for rare disease
Look at many exposure at the same time
Do not need to wait a long time for the answer
Able to address important questions rapidly and efficiently

Can only estimate relative risk
Incidence rates not measured
Fraught with bias
Selection of controls – controls and cases must have an equal chance of being exposed to risk factor
Measuring exposure affected by presence of disease

49
Q

A

Minimize selection bias
Equal distribution of known and unknown risk factors for disease (confounders).
Often both provider and patient are blinded to study.

Expensive
Time consuming
Often, patients and/or providers figure out which arm they are in (placebo pill tastes different)

50
Q

Systemic Review versus Meta-Analysis

A

Systemic review answers a defined research question by collecting and summarizing all empirical evidence that fits pre-specified eligibility criteria.
Meta-analysis is the use of statistical methods to summarize the results of these studies.

51
Q

An article describing a test - what are criteria for it to be significant

A

i. P value
ii. 95% CI
iii. Sens & Spec
iv. PPV & NPV
v. Power: ability of test to detect a true difference

52
Q

what is What is the statistical term to describe the precision of the relative risk

A

confidence interval

53
Q

A

Lead time is the interval between the diagnosis of a disease at screening and when it would have been detected due to development of symptoms.
= Represents the amount of time by which the diagnosis has been advanced asa result of screening.

Lead time bias occurs when a screened population appears to have longer survival compared to an unscreened population because the diagnosis was simply made earlier because of screening (vs. actually prolonging disease survival).

54
Q

what is length time bias

A

Overestimation of survival duration due to the relative excess of cases detected that are slowly progressing

♣ Screening is more likely to detect cases of diseases in individuals with a longer
preclinical phase, and therefore whose disease is progressing more slowly.
These people are likely to have a better prognosis
♣ This means that individuals detected by screening are likely to have longer survival because their disease is likely to progress more slowly vs. those who
go to MD with symptomatic disease
♣ Results in apparent increase in survival among people with disease detected by screening… overestimation of the benefit of screening.

55
Q

Negative likelihood ratio

A

= (1–sensitivity)/specificity

i. Divides the probability that a patient with the disease will test negative by the probability that a patient without the disease will test negative