Biostatistics Flashcards
1
Q
What is Biostatistics defined as?
A
-the application of statistical theory in medicine, public health, or biology
2
Q
Population
A
- large group of all subjects interest in a study.
- very large almost impossible to collect data from whole population
- have to select a subset of subjects to get samples from a population, then generalize findings from samples to the target population
3
Q
conceptual population
A
-population of persons people/ identity
4
Q
population in clinical setting?
A
-usually talk about it as population of measurements such as weight, height, and blood pressure
5
Q
Total population
A
- target population
- all of the subjects of interest in a study, about which the study wants to generalize the conclusion
- ex: all 12 year olds in US
6
Q
Defined population
A
- a subpopulation confined by certain characteristic(s) such as demographics and geographical areas in the total population
- ex: 12 year olds in RUSD
7
Q
Study population
A
The group of individuals in a study
- In a clinical trial, all participants who followed criteria of inclusions and exclusions make up the study population
- ex 12 year olds in certain school in RUSD
8
Q
Sample
A
- a procedure to select samples from the study population, they are representative of the total population
- validity based on how random and well rounded this sample group is
9
Q
How do we estimate the required sample size?
A
1) pre-determined power (80/90%)
2) specific significance level
3) mean & variance of the primary outcome; can be approximated
4) the design of the study
10
Q
what is a statistic?
A
- calculated from a sample for a specific characteristic of the sample
- will be used to estimate the corresponding parameter of the study population & further generalized to the target population to find mean & standard deviation
11
Q
Why do we sampling to get samples?
A
Money, Time, Practicality, and Accuracy
12
Q
Probability sampling
A
- each subject has a known probability of being selected
1) simple random sampling
2) stratified random sampling
3) Systematic random sampling
4) Clustered random sampling
13
Q
simple random sampling?
A
based on probability to take the samples
-each member of the subset has an equal probability of being chosen
14
Q
stratified random sampling?
A
- stratify the study population into subgroups, then take random samples from such subgroups
- ex: names of 25 employees being chosen out of a hat from a company of 250
15
Q
Systematic random sampling
A
- type of probability sampling
- members from a larger population are selected according to a random starting point and a fixed, periodic interval
- interval, is calculated by dividing the population size by the desired sample size
16
Q
Clustered random sampling
A
- the researcher divides population into separate groups (clusters)
- a random sample of clusters is selected from the pop
- researcher conducts his analysis on data from the sampled clusters
17
Q
non-probability sampling
A
- each subject doesn’t know probability of being selected, –stimations are biased
1) voluntary samples
2) convenience samples
18
Q
voluntary samples
A
-whoever is self selected into the samples
19
Q
convenience samples
A
-whoever is convenient to be selected and/or investigated. -ex: staff members in med school recruited for some trials
20
Q
Sampling errors
A
- random errors
- are unavoidable
- the differences between the sample & population, due to sampling randomness
21
Q
sampling errors effect on the data?
A
- Random error does not have consistent effects across the entire sample
- the sum would be zero if the sample size is large enough.
22
Q
what does sampling error add? what does it not affect?
A
-random error adds variability to the data but doesn’t affect average performance of the samples
23
Q
Non-sampling errors
A
- more serious due to mistakes made in the acquisition of data, inappropriate sample selection, or response biases
- can bias estimation
24
Q
Random Samples vs. Randomization
A
Both involve the use of the probability sampling method.
25
Random sampling
- determines who should be included in the samples;
- related to sampling procedure;
- effect external validity (generalizability)
26
Randomization or random assignment
- determines if sampled subjects should be in treatment or control group
- related to design operation
- effects internal validity
27
how we can conduct correct population inferences from the sample data we have?
- mean and variance are the cornerstones for statistical inference
- used to estimate the corresponding values of population.
28
1) letters used for population?
| 2) letters used for sample estimation?
1) greek letters
| 2) sample estimation
29
How use an unbiased estimation of population variance?
- use n-1 instead of n in sample variance equation
| - N (capital N) used for the population variance
30
Standard deviation (SD)?
- measure of the variability of a measurement among the subjects in a population or in a sample
- can be estimated for both population & sample
- often unknown for pop. but can be estimated from SD of its samples
31
Standard Error (SE)?
- measure of the precision of the sample mean
- decreases w/ increasing sample size
- estimated only for sample.
32
In general what is reported when summarizing the sampling variation?
- SD (standard deviation)
| - SE is given for any statistical inference of mean
33
What is Outcome?
- a specific characteristic of interest being studied
- could be more than one
- also called endpoints
- can be described or quantified by different measurement scales
34
Measurement scales (4)?
1) Nominal
2) Ordinal
3) Interval
4) Ratio
35
Nominal
- Categorizing subjects by characteristics such as gender and ethnicity
- the categories have neither order or ranking
- neither logic nor mathematical operation
36
Ordinal
- ranking subjects into different orders
- precise differences between ranks do not exist, and one rank is not better than other
- pain severity
37
Interval
- quantitative measurement with equal units, every one unit difference is meaningful and constant,
- doesn't have absolute zero point
- ex: temperature
38
Ratio
- uses zero to present the absence of value (absolute zero point)
- ex: height, age, weight
39
Qualitative data
- describe subjects in qualities, cannot measure subjects in precise quantities
- nominal scale and ordinal scale.
40
Quantitative data
- quantify the quantities of the subjects, can be measured by scales or counted by numbers.
- can be discrete or continuous
41
Discrete
- Reflect a number during the counting process, no decimal
- Zero is the minimum
- ex: number of children
42
Continuous
Reflect a measurement with decimal places, often depends on the precision of the measuring device
43
What does probability provide?
- a quantitative description of the chances (of successful treatment outcomes) or likelihoods (of disease) associated with various outcomes
- provides a bridge between sample statistics and population parameters
44
what three components do you need to estimate probability?
1) outcome
2) sample space
3) an event
45
outcome?
- a possible result from an experiment
| - ex: toss coin get heads or tails
46
Sample Space?
- the set of all possible outcomes of an experiment
| ex: flip coin can be head or tails
47
An Event?
- a subset of outcomes that are equally likely to take place, can be defined by one or more members of the sample space.
ex: toss coin can be H/T; but if toss coin more than once can be HHT or TTH etc
48
Empirical Probability ?
- estimated as the proportion of: how many times the event of interest occurred / the total number of all the potential events that might be observed
- epidemiology, is defined by empirical probability
49
Marginal probability
the probability of an event occurring
50
Conditional probability
- measure of the probability of an event occurring given that another event has occurred.
- important in medicine since disease is based on many factors
51
Prevalence
- expressed & reported as a percentage, per 1000, or per million
- measure of disease burden in the population but not a measure of risk
- is a snapshot in time, but can use different time scales
52
time scales used in prevalence calculations?
1) point prevalence (at a certain time point, most common)`
2) period prevalence (during a certain time period)
3) cumulative incidence (before a time point)
53
what does incidence rate measure?
1) new cases in a population over a given period
2) risk of developing a disease within a given period
3) how quickly new cases develop in the population
- per 100; 100 million, or 1000
- also called absolute risk
54
how are prevalence and incidence rate related?
-Prevalence= Incidence × Disease Duration
55
how does a new treatment for lung cancer patients that prolongs survival effect the prevalence, incidence & disease duration?
1) prevalence increased
2) incidence unchanged
3) duration increased
56
how does an effective AIDS vaccine created and approved
| effect the prevalence, incidence & disease duration?
1) prevalence decreased
2) incidence decrease
3) duration unchanged
57
how does a sensitive & early detection test is developed to diagnose cancer at earlier stages to make cancer relatively easy to control
effect the prevalence, incidence & disease duration?
1) prevalence increase
2) incidence increase
3) duration increase
58
Mortality Rate
(𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑒𝑎𝑡ℎ𝑠 𝑑𝑢𝑟𝑖𝑛𝑔 𝑎 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 𝑝𝑒𝑟𝑖𝑜𝑑)/(𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑒𝑟𝑠𝑜𝑛𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑑𝑢𝑟𝑖𝑛𝑔 𝑡ℎ𝑒 𝑝𝑒𝑟𝑖𝑜𝑑
59
Case fatality rate
- is a %
| - (𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑒𝑎𝑡ℎ𝑠 𝑎𝑓𝑡𝑒𝑟 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 𝑜𝑛𝑠𝑒𝑡 𝑜𝑟 𝑑𝑖𝑎𝑔𝑛𝑜𝑠𝑖𝑠)/(𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡ℎ𝑒 𝑝𝑒𝑟𝑠𝑜𝑛𝑠 𝑑𝑖𝑎𝑔𝑛𝑜𝑠𝑒𝑑 𝑤𝑖𝑡ℎ 𝑡ℎ𝑒 𝑑𝑖𝑠𝑒𝑎𝑠𝑒) x 100
