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LECOM Biostatistics 2022 > Important Definitions > Flashcards

Important Definitions Flashcards

1
Q

What makes a good hypothesis?

A

Valid data gathering and no fishing expeditions

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2
Q

What are the four steps of hypothesis testing?

A
  1. Make hypothesis
  2. Set criteria for decision
  3. Gather data and conduct statistical analysis
  4. Make decisions -> accept H0 or reject HA
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3
Q

What is the Null Hypothesis (H0)?

A

No difference
- All samples from same population
- Observed difference due to chance (random sampling)

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4
Q

What is the Alternative Hypothesis?

A

At least one sample from a different population
- Difference not due to chance

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5
Q

All statistics are based on…

A

Probability

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6
Q

What is our main goal in hypothesis testing?

A

Rejecting the null

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7
Q

If alpha < our result, then…

A

We accept the null

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8
Q

If alpha > our result, then…

A

We reject the null

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9
Q

What does mutually exclusive indicate?

A

Only one event can occur
Ex: heads or tails

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10
Q

What does independent variables indicate?

A

Either event could occur
Ex: roll a 6 on one die and a 6 on the other die

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11
Q

When do we use the additive rule?

A

With a mutually exclusive event

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12
Q

When do we use the multiplicative rule?

A

With an independent probability

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13
Q

What is sampling?

A

Selection of study subjects who will be
measured on some parameter to provide information about population

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14
Q

What are the representative sampling techniques?

A
  • Simple random sampling
  • Systematic sampling
  • Stratified sampling
  • Convenience sampling
  • Multistage sampling
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15
Q

Simple random sampling

A

Each individual has equal chance of being selected
Ex: drawn out of hat

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16
Q

Systematic sampling

A

Select at regular intervals through an ordered list
Ex: interview 3rd patient for one week

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17
Q

Stratified sampling

A

– Separate population into strata based on a Separate population into strata based on a characteristic
–– Randomly select proportion of participants from each stratum
Ex: picking only freshmen, and then select randomly within freshmen

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18
Q

Cluster sampling

A

Population already in strata
* Randomly select groups of strata

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19
Q

Convenience sampling

A

Cold call
* Does not typically represent population

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20
Q

Multistage sampling

A

Combines more than one sampling
Ex: randomly selected PCPs chosen for survey, when answering, second detailed survey sent out to randomly selected sample

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21
Q

What is a qualitative variable?

A

Catergorical: groups, cohorts
Ex: ethnicity, gender, disease status, age group, stage of cancer, pain rating

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22
Q

What is a quantiative variable?

A

Continious: measured quantities
Ex: height, weight, temperature, blood pressure, temperature

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23
Q

What is an independent variable?

A

Input, manipulative

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24
Q

What is an dependent variable?

A

Outcome, response, predicted

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25
What are measures of central tendency?
Mean, median, mode
26
What are measures of dispersion?
Range, interquartile range, percentiles Standard deviations (SEM), max/min, kurtosis, skewness
27
What does sample size mean?
Number of samples in each group Larger sample sizes tend to have more statistical power
28
How can we reduce variance?
Increase the sample size Better define our sample populations Censor outliers
29
Unimodal distributions
Normal and skewed distribution
30
What is the bell-shaped curve?
Gaussian Distribution
31
What is Gaussian Distribution used for?
Continuous variables
32
What is described of in Gaussian Distribution?
Mean, and standard deviation
33
In a skewed distribution...
Mean not equal to median
34
Positive skew
Mean > median > mode
35
Negative skew
Mean < median < mode
36
What is kurtosis?
Peakedness or flatness of frequency distribution
37
Higher kurtosis means...
Variance from infrequent extreme deviations
38
What is platykurtic?
Negative kurtosis
39
What is leptokurtic?
Positive kurtosis
40
What is a type I (alpha) error?
Probability of rejecting null when it is true
41
What is type II (beta) error?
Probability of accepting null when it is false
42
What are the rules of categorical data?
Data are arranged into catergories Data are an independent, random sample from the population Must be positive integer, > 0
43
What do we assume in categorical data?
Random sample of the population
44
What are the type of categorical data studies?
1. Cross-sectional: recruit study group and define (at least) two categories. 2. Prospective: recruit groups with some difference (risk factors) and follow them. 3. Retrospective: start with condition and look backwards at potential causes. 4. Experimental: recruit group, manipulate variables 5. Diagnostic Test: positive + negative results
45
What are the requirements of the student's t-test?
1. Data is continuous (not categories) 2. Data is a random sample from the population
46
What are the assumptions of the student's t-test?
1. Each of the 2 populations being compared is **normally distributed** 2. Each of the 2 populations should have the same variance
47
One sample t-test
Compare a group's mean to a "benchmark" group
48
Independent t-test
Compare two independent groups
49
Dependent t-test
Compare 2 groups that are somehow related
50
Two-tailed t-test?
LECOM students will score significantly different than group X
51
One-tailed t-test?
LECOM students will score significantly higher than group X
52
ANOVA stands for...
ANalysis Of VAriance
53
What is ANOVA used for?
Comparing means of 3 or more groups
54
What are the assumptions of ANOVA?
 Independent observations  Normally distributed data  Homogeneity of variance [Levene’s Test]
55
What is a post hoc test identify?
Specific groups that are different
56
When do you use Tukey post hoc?
Compare each group to every other group
57
When do you use Dunnett's post hoc?
Compare all groups to a control group
58
When can you develop a correlation?
Both dependent and independent variables are continious
59
What does correlation not imply?
Causation
60
What are the detailed criteria for assessing evidence?
– Strength –– Consistency –– Specificity –– Temporality –– Biological gradient –– Plausibility –– Coherence –– Experiment –– Analogy
61
Strength in Bradford-Hill Criteria is defined as...
Larger association is more likely causal effect
62
Consistency in Bradford-Hill Criteria is defined as...
Stronger association when same findings observed by different people and different samples
63
Specificity in Bradford-Hill Criteria is defined as...
Specific population (or risk factor) with specific disease and no other likely explanation
64
Temporality in Bradford-Hill Criteria is defined as...
Effect happens after the cause
65
Biological gradient in Bradford-Hill Criteria is defined as...
More exposure = greater incidence
66
Plausibility in Bradford-Hill Criteria is defined as...
Plausible mechanism between cause and effect helpful
67
Coherence in Bradford-Hill Criteria is defined as...
Link between epidemiological and laboratory findings increases likelihood
68
Experiment in Bradford-Hill Criteria is defined as...
"Occasionally it is possible to appeal to experimental evidence"
69
Analogy in Bradford-Hill Criteria is defined as...
Effect of similar factors may be considered
70
Degree of correlation measured by...
r
71
...is coefficient of determination
Rsquared
72
What is Spearman's Rank?
Nonparametric test for relationship between 2 variables
73
When do we use survival analysis?
Used to analyze data in which time until the event is of interest
74
Types of bias
* Selection bias (sampling) ** Measurement bias –– Hawthorne effect ** Experimenter bias –– Pygmalion effect ** Lead-time bias ** Recall bias ** Late-look bias ** Confounding bias ** Design bias
75
What is selection bias?
Sample is not representative of population
76
What is measurement bias?
Leading question
77
What is the Hawthorne effect?
Participant behavior altered by knowing they are being studied
78
Experimenter bias (Pygmalion effect)
Expected results accidently communicated to participants
79
Lead-time bias
False estimate of survival rates
80
Recall bias
Participants do not accurately recall events
81
Late-look bias
Participants with severe disease less likely to be identified because they die first
82
Confounding bias
A factor in a study is related to co-factor being examined
83
Design bias
Choosing inappropriate study design to answer question
84
What is incidence?
Measure new cases of disease or injury in of disease or injury in population over specified time period
85
What is prevalence?
Measure of total number of cases of disease or injury in population over specified time period
86
What is validitiy?
Test detects what it was designed to test
87
What is test sensitivity?
Proportion of pts with disease where screening test is positive Higher sensitivity, better test in finding diseased pts
88
What is test specificity?
Proportion of pts without disease where screening test is negative Higher specificity, healthy pts not diagnosed as diseased
89
Case-control study
Find cases and look in the past
90
Cohort study
Choose a group and follow
91
What are the advantages/disadvantages?
* AdvantagesAdvantages –– Can estimate incidences –– Can calculate risks –– Exposure precedes disease ** DisadvantagesDisadvantages –– Rare disease –– Slow time from exposure to disease development –– Loss to follow-up –– More expensive – longitudinal in time
92
What is phase I in clinical trials
Small number of healthy volunteers
93
What is phase II in clinical trials?
Small # of pts with disease
94
What is phase III in clinical trials?
Randomized Control Trial (next)
95
What is phase IV in clinical trials?
Surveillance after approval and use
96
What are key elements to RCT?
Treatments, outcomes, benefits
97
What is meta-analysis?
A quantitative, formal, epidemiological study design used to systematically assess previous research studies to derive conclusions about that body of research
98
What are the advantages of meta-analysis?
Improve precision, answer questions not posed by individuals studies, settle controversies arising from apparently conflicting studies
99
What are the principles of meta-analysis?
* It is important to clearly state your hypotheses and search strategies to decrease bias and heterogeneity * Clearly identify patient population, inclusion and exclusion criteria, desired outcomes and intervention to be studied * Make sure you are comparing apples to apples!

LECOM Biostatistics 2022 (4 decks)

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