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YR 3 - UGP > Clinical Stats > Flashcards

Clinical Stats Flashcards

1
Q

what are the 2 types of data?

A

qualitative (categorical) or quantitive (numerical)

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2
Q

describe the 3 types of qualitative data.

A

binary - gender
nominal - named categories e.g. blood type
ordinal - ordered categories e.g. stage of cancer

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3
Q

describe the 2 types of quantitive data

A

continuous - any number within range
discrete - whole numbers

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4
Q

what are descriptive statistics? give the 2 methods how they are used.

A

methods of organising, summarising and presenting data in a convenient and informative day

  1. graphical technique - visualise data
  2. numerical technique - using numeric and tabular form
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5
Q

when looking at collected data, what should be considered?

A
  • where is the centre?
  • what is the range?
  • any outliers/anomalies?
  • what’s the shape of distribution
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6
Q

how can you plot categorical data?

A

bar charts

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7
Q

how can you plot continuous data?

A

histograms
box plots

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8
Q

describe a box plot

A
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9
Q

what are the 3 distribution shapes?

A

left skewed
symmetric
right skewed

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10
Q

how can you describe numeric data? (10)

A
  • mean
  • median
  • mode
  • quartiles
  • interquartile range
  • range
  • variance
  • standard deviation
  • coefficient of variation
  • shape - skewness
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11
Q

what is the mean? is it affected by extreme values?

A

add all the values divide by the number to values

yes, extreme values affect it

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12
Q

what is the median? is it affected by extreme values?

A

the middle number

if there are too middle numbers, find the in-between valve of them

nope, its not affected by extreme values

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13
Q

what is the mode?

A

the most common number

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14
Q

is there relation between mean, median and mode (the central tendencies)?

A

no

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15
Q

4 ways to measure variation

A

range
IQ range
standard deviation
coefficient of variation

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16
Q

how do you measure the range?

A

highest value-lowest value

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17
Q

how is the IQ range measures?

A

75% value - 25% value of data

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18
Q

how is the variance of data measured?

A

bigger the variation = the bigger the disperse of the values

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19
Q

how is standard deviation measured? and what does it show?

A

shows variation about the mean

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20
Q

in taking standard deviations and sample variation, why are values squared?

A

to eliminate negative values

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21
Q

on SPSS, describe how the data is input.

A

each column = variable
each row = constant e.g. the patient

variable mode - can change the names of each column
- can change the value to create a code e.g. 1 = males and 2 = females

  • can change the decimals e.g. age doesnt need decimal
  • measure can be changed to scale, ordinal or nominal
    e.g. age = scale, sex = nominal, dfmt - ordinal

open excel sheet in SPSS, save the excel and open data

to use frequency stats
- go on analyse tab>descriptive stats>frequencies
- choose your variable
- click into the charts or statistics

to use descriptive stats
- go on analyse tab>descriptive stats>descriptive
- choose variables
- choose the descriptive stats

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22
Q

what is normal distribution, how does it appear on a graph? (3)

A

continuous data determined by the mean and the standard deviation

  • symmetrical
  • bell shaped
  • mean, median and mode = equal
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23
Q

what is the central limit theorem?

A

under appropriate conditions, the distribution of a mean converges to a standard normal distribution

  • happens over a long time
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24
Q

on a graph, what does the shape of the bell depend on?

A

the standard deviation

  • larger the S.D - the larger the bell
  • smaller the S.D - the smaller dispersion
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25
Q

from a bell on a graph, how do you find the mean?

A

tip of the bell, follow it down to find the value

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26
Q

describe the empirical rule.

A

if the area lies between the mean and +- standard deviation = covers 68% of the data

if the area lies between the mean and =- 2 standard deviation = covers 95% of the data

if the area lies between mean and =-3standard deviation = covers 99.7% of the data

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27
Q

how would you estimate the probability that a adult has an IQ between 70 and 115 on this graph?

A

overall area = 95%
minus the middle 68%

= 27% on either side of the middle
half it to get one side

= 13.5% the area from C-D

13.5+68 =81.5% probability

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28
Q

define population and parameter

A

a group of all items of interest

parameter - descriptive measure of a population

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29
Q

define sample and statistic

A

the set of data drawn from the population

statistic - a descriptive measure of a sample e.g. mean, mode, median, frequency, S.D

30
Q

what’s a draw back from descriptive statistics?

A

doesnt allow you to create conclusions from the data

31
Q

what is statistical inference ?

A

you can draw conclusions about populations based on sample data

32
Q

what is sampling variation

A

when statistics vary from sample to sample due to random chance

33
Q

how do you reduce sample variation

A

repeat sample multiple times
plot into histogram
will probably follow a normal distribution

= the centre would be the population mean

34
Q

how do you measure sampling variability

A

using standard error

  • it decreases with increased sampling size as variability decreases
35
Q

what is standard error

A

the standard deviation of a sample statistic

how different the population mean is likely to be from a sample mean

36
Q

what are confidence intervals and the formula?

A

how definite the data is

(sample statistic +- measure of confidence ) x standard error

37
Q

how do you find the confidence interval from SPSS?

A

you can interpret that there is 95% that the true mean lies between the upper bound and lower bound results

38
Q

how is a hypothesis test performed?

A
  1. create null hypothesis
  2. predict the sampling variability assuming null hypothesis is true
  3. do the experiment
  4. compare observed difference vs expected difference
  5. reject or accept the null hypothesis
39
Q

what is p value

A

the probability of the data under the null hypothesis

40
Q

when do you accept or reject the null hypothesis

A

reject the null hypothesis if the p value is less than 0.05 and accept the conclusion that the study has results

41
Q

what are the types of error, describe them, where can they be found on a graph?

A

a-error - if the null hypothesis is true but we rejected it
- type I error
- false positive e.g. you’re not preggers but u are

b-error - if the null hypothesis is false but we accepted it
- type II error
- false negative

42
Q

what is power?

A

correct rejection of null hypothesis

if the null hypothesis is false and we reject is

43
Q

what is the null hypothesis

A

there are no differences in the study e.g. no difference between weather in leeds vs spain

44
Q

what is the t test

A

ratio between observed difference vs expected difference

45
Q

if the null hypothesis is rejected, is the study statistically significant

A

yes

46
Q

when concluding the hypothesis, which 3 things must you consider?

A
  • it is statistically significant? - if the null hypothesis is rejected, then yes
  • is it clinically significant? - needs to be checked with clinician
  • is it a causal association
47
Q

describe the results from this t test

A
  • always look at two sided p value as it shows the two possible values on each side
  • only consider one sided p value if you have prior context
  • use first row if equal variances are assumed

..

  • the significance of the Levenes Test is 0.30 which is > 0.05 p value so we reject that the groups have similar variance
  • SO, we use the results from the second row
  • the p value is .852 so we accept the null hypothesis that the two groups have the same mean
48
Q

where the p value on a chi squared test?

A

under asymptomatic significance

49
Q

what 3 tests can be used to measure continuous outcomes - comparing means?

A
  1. independent two sample t test - independent groups
    e.g. men vs women, smoker vs non-smoker
  2. paired t test - correlating groups
    e.g. twins, measurement before and after tx
  3. ANOVA - 2+ independent groups
50
Q

what 2 tests are used to measure categorical outcome? - comparing proportions

A

chi-squared - independent obeserviations

McNemar’s chi-square test - correlated observations

51
Q

what is to be assumed for continuous data?

A

that the outcome is normally distributed

52
Q

how do you know if the observations are correlated?

A

if you’ve got the same test subject at the beginning and at the end

e.g. testing if Rajan is 6” at the start of the day and if he is still 6” at the end

53
Q

what if the assumption is NOT normally distributed or the sample size is too small, what test is used then?

A

non-parametric test
the Wilcoxon sign-rank test or the Mann- Whitney U test

  • don’t calculate the means as it may be misleading
  • use the median score
54
Q

what does the ANOVA test mean?

A

Analysis of Variance test

55
Q

why is the ANOVA test difficult to interpret?

A

it tells you that two groups differ, but not which ones, it requires more analysis

56
Q

with ANOVA, why cant you just do multiple paired t tests? e.g. 3 paired test

A

more chance of making a type 1 error

1-(0.95)^3 = 14% chance

57
Q

what test can be used to check multiple tests are significant?

A

Bonferroni Correction

  • do paired tests
    the new p value
    = 0.05/number of tests
  • use the new p value to see which groups are significant
58
Q

what is the alternative to ANOVA for a non-parametric test?

A

Kruskal-Walls test

59
Q

what is the alternative to Chi-Squared if there are sparse cells?

A

Fisher’s Exact Test

60
Q

what do you do when concluding a confidence interval?

A

report the upper and lower bound

  • if it covers 0, then it is not statistically significant, accept null hypothesis
  • if it doesn’t cover 0, then it is statistically significant, reject null hypothesis
61
Q

what is correlation coefficient?

A

when you want to assess relationship between 2 variables

result will be from -1 to 1
then 0 = no correlation
- positive number = positive correlation
- negative = negative correlation

62
Q

what is linear regression?

A

when the two variables are treated as equals

  • one variable = independent predictor
  • other = dependent outcome
63
Q

define y=mx+c

A

m = gradient
c = where it intercepts on the y axis

64
Q

describe absolute risk difference, relative risk ratio and odds ratio with vitamins 12/33 and placebo 22/35

A

absolute risk difference
- find the difference between the results in percentage
e.g. 62.9%-36.4% = 26.5%

risk ratio
- lower risk/higher risk
36.4%/62.9% = 57.8%

= 42% decrease in relative risk

odds ratio
- the ratio of a event occurring:not occurring
36.4% / 1-36.4
//////
62.9 / 1- 62.9%

= 0.34
= 66% decrease in relative odds

65
Q

quick tip how to interpret odds ratio if RR>1 or RR<1

A

if RR>1 then odds ratio will always be bigger

if RR<1 then odds ratio will always be smaller;;er

66
Q

describe kaplian-meier

A

estimates survival functions for each group

  • describes study populations
67
Q

what is survival analysis?

A

statistical method for analysing longitudinal data on the occurrence of events

68
Q

what is a time-to-event?

A

the time from entry into a study until the study has a particular outcome

69
Q

what is censoring in survival analysis

A

when subjects are lost to follow up/dropped out/study ends before they die

70
Q

what are all the tests

cant flipping make tik-toks and bake kupcakes with marshmallow

A

CFMTTABKWM

YR 3 - UGP (5 decks)

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