Measurements

1
Measurements

• Measure of Variability, Scale Levels of
  Measurements, Descriptive Statistics, Measures of
  Central Tendency

2
Measurements need to

• Produce valid and reliable results
• be sensitive and specific
• be able to identify clinically important changes
• have outcome measures and endpoints defined
• be easy to interpret

3
Reasons for errors in measurement

• Improper function or calibration of equipment
• patients providing misleading or dishonest
  answers to verbal/written questions
• Improper recording/transcribing of data
• Investigators recording or making inaccurate
  measurements

4
Types of Errors

• Random error
• Random in occurrence, often balancing out over
  course of study
• mean or average of measurements still close to
  true value
• Large patient size reduces random error

5
Types of Errors

• Systematic error
• represents bias in measurements and does not tend
  to balance out over course of study.
• Bias can be knowingly or unknowingly
• Good study design minimizes systematic error.

6
Measurement Terms

• Validity- degree to which an instrument is
  measuring what it is intended to measure.
• Predictive, Criterion, Face
• Reliability- reproducibility of a test
• Sensitivity- ability to measure a small treatment
  effect
• Specificity- how well the test can differentiate
  between the effect resulting from treatment and
  random variation

7
Validity terms used in association with
measurements

• Predictive validity
• the extent to which a measurement or test
  actually reflects or predicts the true condition.
• Criterion (construct) validity
• the degree to which a measurement or test agrees
  with or obtains the same results as other proven
  tests designed to measure the same.
• Face validity
• the extent to which a measure appears reasonable
  or sensible for measuring a desired outcome

8
Reasons for False Positive Results

• Patient related
• patients werent as ill as originally believed,
  and drug was more effective in mildly ill pts.
• Patients were much more ill than originally
  believed, and drug was more effective in severely
  ill patients.
• A few patients had a very large response, which
  skewed the overall results.
• Patients gradually improved independent of drug
  treatment.

9
False Positive Results

• Patient related
• More medicine was absorbed than anticipated
• Patients took excess medication.
• Patients felt pressure to report a positive
  medicine effect
• Concomitant non drug therapy or other drug
  therapy improved results

10
False Positive Results

• Study Design and Drug Related
• Blinding was broken or ineffective
• open label study can sometimes produce a larger
  positive response
• no placebo control to help interpret
• error occurred in dosing patients- gave more drug
  than intended
• inadequate wash-out period, carry over effect
• inappropriate clinical endpoints, tests or
  parameters were used

11
False Positive Results

• Investigator related
• influenced response by great enthusiasm
• chose inappropriate tests to measure
• Results and Data Related
• systematic error- reporting large drug effect
• high percentage of non-responders dropped out
• not all data was analyzed

12
False Negative Results

• Patient Related
• were much more ill than realized
• responded less to the drug than anticipated
• study group had large number of non responders
• non-compliance-- took fewer doses
• concomitant medicines- interactions
• exposed to conditions that interfered with study

13
False Negative Results

• Drug Related
• not adequately absorbed
• kinetics were different in study group than in
  other patient groups
• Study Design Related
• Too few of patients
• inappropriate study design
• insufficient drug dose was tested

14
False Negative Results

• Study Design Related (cont.)
• Ineffective tests or parameters used
• Inadequate wash-out period in previous treatment
  period
• Concomitant non-drug therapy interfered
• Investigator Related
• influenced patients with skepticism displayed
• chose inappropriate tests to measure effects

15
False Negative Results

• Results and Data Related
• Patients who improved dropped out leaving higher
  number of non-responders
• systematic error resulted in reporting of an
  inappropriately small drug effect.

16
Outcome Measures

• Example A study is performed to compare the
  effects two antihypertensives, atenolol and
  propranolol in 2 groups of patients with mild
  high blood pressure. 2 types of outcomes
  measurements are selected for this study
  measures of efficacy and measures of safety
• Measures of efficacy BP, HR, symptom relief
• Measures of safety adverse effects, blood
  glucose, electrolytes, serum lipids

17
Criteria Used for Outcome Measures

• Presence or Absence criteria Is sign, symptom
  present or absent?
• Graded or Scaled Criteria the use of grading on
  a scale to measure clinical symptoms
• Relative change criteria- measured changes
• Global assessment criteria- Quality of Life
• Relative effect criteria- change in time to
  effect.

18
Measurement Endpoints

• Endpoints are measurable points used to
  statistically interpret the validity of a study.
• Valid studies have appropriate endpoints.
• Endpoints should be specified prior to start of
  study (should be included in study design)
• Quality studies have simple, few and objective
  endpoints.

19
Endpoints

• Objective- based on actual or measurable findings
  or events (heart rate, BP, Temp.)
• Subjective- based on thoughts, feelings, emotions
  (pain scale, mobility)
• Morbidity- quality or condition at the present--
  quality of life
• Mortality- causing death or a death rate

20
Endpoints Example

• In a study determining the effects of clonidine
  on quality of life, the researchers determine the
  number of days a patient misses work. Each
  patient is also asked to complete a rating scale
  to describe the degree of fatigue they
  experience.
• What type of endpoints are used?
• What type of criteria are used?

21
Surrogate Endpoints

• These reduce the quality and validity of the
  study.
• Surrogate or Substitute endpoint examples
• CD4/CD8 ratios instead of survival in studies
  for treatment of AIDs.
• Measuring volume of acne instead of proportion of
  patients cleared of acne.
• Determining cardiovascular disease or
  atherosclerotic disease instead of measuring
  blood pressure in a study of antihypertensive
  drug treatment

22
Hawthorne Effect

• Refers to the influence that a process of
  conducting a study may have on a subjects
  behavior
• Subject
• Environment
• Research design

23
Reasons for Clinical Improvement in a Patients
Condition

• Natural regression to the mean (most acute and
  some chronic conditions resolve on their own
• Specific effects of treatment (drug or
  intervention)
• Non-specific effects- attributable to factors
  other than specific drug/intervention effect.
• Called a Placebo Effect

24
Placebo Effect

• A placebo is an intervention designed to simulate
  medical therapy, but not believed to be a
  specific therapy for the target condition.
• A placebo is used either for its psychological
  effect or to eliminate observe bias.
• Placebo response due to change in pt. Behavior
  following admin. of a placebo
• Placebo effect change in pts illness due to
  the symbolic importance of a treatment.
• A placebo effect doesnt require a placebo.

25
Why do we see a Placebo Effect?

• Three different theories
• 1. The effect is produced by a decrease in
  anxiety
• 2. Expectations lead to a cognitive readjustment
  of appropriate behavior.
• 3. The effect is a classical conditioned
  Pavlovian response.

26
Placebo Effect

• Expectations lead to behavior change
• Patients and providers expectations
• Patients positive attitude toward provider and
  treatment
• Providers positive attitude toward therapy
• Provider interest in patient (sympathy, time,
  positive attitude)
• Compliant patients have better outcomes than
  noncompliant patients even with a placebo.
• The placebo response is stronger when stronger
  drugs are used.
• Crossover studies show a stronger placebo
  response when given in the 2nd period of study.

27
Appropriate Statistical Tests

• To determine whether appropriate statistical
  tests have been used, you must know 3 things
• 1. The specific research question or hypothesis
  being addressed.
• The number of independent and dependent variables
• The scales or levels of measurement used for the
  dependent variables

28
Variables in a Study

• Dependent variables
• those variables whose value depends upon or is
  influenced by another variable.
• It is the variable that is measured, and the one
  that changes as the result of a drug action.
• Independent variables
• Those variables which modify a dependent variable
  (drug treatment)

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Example

• Patients given Lovastatin to lower cholesterol.
• Dependent variable- lowering of cholesterol
• Independent variable- Lovastatin
• There can be more than one independent and
  dependent variable in a study.

30
Dependent/Independent Variables

• Example A single blind study of 30 patients with
  poison ivy dermatitis were randomized to receive
  either topical hydrocortisone 1 or 2 and apply
  QID. Severity of the dermatitis was evaluated
  daily using a 5 point scale, where 5- severe and
  0-none.
• What is the independent variable? Dependent
  variable?
• Example A study was conducted to compare the
  efficacy of procainamide and quinidine for
  reducing ventricular arrhythmias. The number of
  ventricular ectopic depolarizations was
  determined in patients both before and during
  therapy with either drug.
• What is the independent variable? Dependent
  variable?

31
Scales of Levels of Measurement

• Nominal Level
• variables are grouped into mutually exclusive
  categories.
• Gender as female or male
• cured and not cured
• response and no response
• include histograms (bar graphs)
• weakest level of measurement
• referred to as dichotomous data

32
Scales of Levels of Measurement

• Ordinal level
• ranked or ordered categories
• 1-2-3-4
• severe, moderate, mild, none
• always, sometimes, never
• stronger level than nominal
• not measured quantitatively, but qualitatively
• distance between groups need not be equal

33
Scale Levels of Measurement

• Continuous Measurement
• Interval level exact difference between two
  measurements is known and constant
• has arbitrary zero point
• highest level of measurement
• quantitative data
• Examples BP (mm Hg) serum Theo levels (ug/ml),
  WBC (cells/cu mm)

34
Continuous Level of Measurement

• Ratio level
• exact differences between measurements is known
  and constant
• true zero point (Centigrade temp scale)
• can make ratio statements (21) that denote
  relative size
• Can be converted to an ordinal scale (but ordinal
  scale cant convert to interval)

35
Scale levels of Measurements
Baseline Pain Assessment 0 1 2 3
(absent) (mild) (mod) (severe) Placebo 0 2 18
14 PainawayR 0 4 12 16 (number of subjects in
each group with varying degrees of baseline pain
intensity. What scale level of measurement?
36
Scale level of Measurements
Infectious Outcome Among 46 Patients Infection N
o infection Total Oxacillin 2 20 22 Placebo 0 2
4 24 Column total 2 44 46 What scale level
of measurement?
37
Types of Interval/Ratio Data

• Discrete scale of data (non-continuous) when a
  measurement has the interval characteristics but
  can only be assigned integer values. (HR, number
  of patients admitted to hospital/day)
• Non discrete (continuous) scale of data each
  data point falls on a continuum with an infinite
  number of possible subdivisions (temp, BP, BG,
  weight)

38
Data Distributions

• Once data is collected, it can be organized into
  a distribution, or graph of frequency of
  occurrence, or chart of the number of times that
  each measurement value occurs.
• Bar Graphs
• Bar Chart (Histogram)
• Line Graphs

39
Data Distributions

• Nominal and Ordinal level data use histograms
  (Bar charts) because data classified into
  distinct categories
• Continuous level data are distributed in the form
  of curves and line graphs (normal distributions
  and non-symmetrical distributions)

40
Bar Chart (Histogram)
41
Continuous Level DataNormal DistributionGaussian
Curve
42
Non-Normal DistributionsBi-Modal Curve
Weights of American Adults (women and men)
43
Non-symmetrical distributionsNon-normal
distributions
44
Continuous Distribution Examples
The distribution of GPAs of college students
1.0 2.5
4.0
45
Continuous Distribution Example
Distribution of the ages of patients taking
Digoxin
20 40 60 80
46
Descriptive Statistics

• Measures of Central Tendency

47
Measures of Central Tendency

• Mean-
• mathematical average of a set of numbers.
• Affected by extreme data points (outliers)
• Useful for continuous level data
  (interval/ratio).
• Ex uric acid concentrations 8,6,5,4,3,2,2,2.
  Total number of samples 8. Sum of measurements
  32. 32/8 4 (mean).

48
Measures of Central Tendency

• Median
• Middle number of a group of numbers in which an
  equal number of responses above and below that
  point exist. (called 50th percentile)
• Not affected by outliers. Useful for ordinal,
  interval and ratio data and non-symmetrical.
• Ex Uric acid concentrations 8,6,5,4,3,2,2,2.
  Since even number, median lies between 4 and 3 or
  median 3.5.

49
How to Recognize skewed data

• If the magnitude of the difference between the
  mean and median is none or small, the data is
  approaching normal (symmetrical) distribution.
• If the difference between the mean and median is
  large, the data usually prove to be skewed.

50
Measures of Central Tendency

• Mode
• The most commonly or frequently occurring
  value(s) in a data distribution.
• Useful for nominal, ordinal, interval/ratio data.
• Only meaningful measure for nominal data.
• Can have more than one mode in set of data
• Ex Uric acid concentration 8,6,5,4,3,2,2,2. The
  mode 2.

51
Measures of Central Tendency
Scale Level of Normal Non-Normal Measurement D
istribution Distribution Nominal Mode Mode Ord
inal Medianmode Median /mode Interval/Ratio Me
anmedmode Mean/med/mode
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Descriptive Statistics

• Measures of Variability

53
Measure of Variability

• Two distributions can have the same mean, median
  and/or mode and yet be very different.
• Variability refers to how spread out (or close
  together) the data are.

54
Example

• 2 groups of men w/ mean SBP in each group is 120
  mmHg. Are they similar?
• First group BP 110,120,120,130
• Second group BP 80,90,150,160
• Both have mean 120
• Spread of data or range of data is much different

55
Range

• Range the interval between the lowest and
  highest values within a data group.
• Can be significantly influenced by outlying data
  (extreme values)
• Used for ordinal, interval or ratio data

56
Interquartile Range

• Measure of variability directly related to the
  median. The median represents the 50th
  percentile.
• The interquartile range is that range described
  by the interval between the 25th and 75th
  percentile values.
• Used for ordinal, interval/ratio data that dont
  have normal distributions

57
Standard Deviation (SD)

• Standardized measure of the spread of scores
  around the mean.
• Useful for continuous (ratio/interval) data
• When reported in a study /- 1SD
• Needs normal distribution of data
• The mean /- 1SD includes 68 of data points (34
  on each side of the mean)

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Standard Deviation

• Mean /- 2 SD include about 95 of data points
  (47.7 of the values on each side of the mean)
• Mean /- 3 SD include about 97.7 of the data
  points (49.8 of values on each side of the mean)
• DBP 100 mmHg /- 5 mmHg includes data points from
  95-105 mmHg(assume 1 SD unless tells you
  differently)

59
Standard Deviation (SD)

• The larger the SD, the further the data points
  deviate from the mean (more variable data).
• The smaller the SD, the closer the data points
  are to the mean (less variable data).
• Ex 0.9 /- 0.2mg and 1.1 /- 0.6mg
• Which has more widely scattered data?
• Answer 1.1 /- 0.6 mg (larger SD)

60
Variance

• Variance is estimate of the study data. Obtained
  by calculating the differences between each
  individual value and the overall mean
• Needed for calculating the SD
• SD variance
• SD2 variance

61
Standard Error of the Mean (SEM)

• SEM is a way of estimating the variability of an
  individual sample mean relative to the population
  as a whole.
• SEM SD/ variance or SD/ sample size
• SEM is used to calculate the Confidence Intervals
  (CI)
• Improperly used in place of SD because it is a
  smaller number and looks better

62
SD versus SEM

• Mean Serum Theophylline concentrations of a group
  of patients was 13.6 /- 2.1 (1SD).
• Conclude about 68 of patients had conc.
  Somewhere in the range of 11.5-15.7.
• Serum Theo conc. of a group of patients was 13.6
  /- 2.1 (SEM)
• could assume that if several addl samples of pts
  with same characteristics were studied, their
  mean values would fall between 11.5 and 15.7 68
  of the time.

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Review

• Variance differences between each individual
  value and the overall mean.
• Variance used to calculate the SD
• SD variance or SD2 variance
• SEM or SE is derived from the SD
• SEM SD/ sample size

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ReviewSEM

• SD measure of the variability of individual
  values about the sample mean
• SEM/SE measure or indication of the variability
  of individual sample means about the true but
  unknown population mean.
• SEM is used to estimate the reliability
  (precision) of a study sample in terms of how
  likely it is that the sample mean represents the
  true population mean.
• SEM is used to calculate the CI

65
Example

• In a study of the effectiveness of Drug X on the
  Blood Sugar concentrations in 15 patients, the
  authors report the mean BS values in the patients
  as 150 /- 2.3mg.
• Would this represent the SEM or SD?

66
Confidence Intervals (CI)

• Represents a range that has a high probability of
  containing the true population value.
• The likelihood that a study samples value
  reflects the true value of the population.
• Calculated for a desired level of probability
  (95). A 95 CI means there is a 95 probability
  that the true population value falls within the
  CI range

67
Confidence Interval Example

• The mean difference in healing rates between
  placebo and penicillin was reported to be 59 (CI
  24-72).

68
Confidence Intervals

• Can be calculated for nominal level data
  (proportions) and continuous level data
• 90 CI assoc. with narrower range of values
  (dont need to be as confident)
• 99 CI assoc. with wider range of values (more
  confident the CI will contain true population
  value.

69
CI is influenced by

• 1. Level of confidence selected
• 2. SEM (larger SEM, wider the CI)
• 3. Standard Deviation (SD) of the study sample.
  (larger SD, then larger SEM, then wider the CI)
• 4. Size of the study group. (The larger the
  sample size, the smaller the SEM, and narrower
  the CI)

70
Confidence Interval Example
Two similar studies are published about efficacy
of Pravastatin for reducing cholesterol. Both
sets of patients are comparable. Study 1 enrolled
200 patients. Study 2 enrolled 50 patients. The
mean /- SD treatment reduction in cholesterol
concentrations in the Study 1 patients was 15.2
mg /- 2.0 mg. The corresponding values in the
Study 2 patients was 17.1 mg /- 2.0 mg Which
mean values (15.2 mg or 17.1 mg) would most
likely have a wider 95 CI associated with it?
71
Confidence Intervals (CI)

• CI applies to continuous data, proportions
  (nominal data), medians, regression slopes,
  relative risk data, response rates, survival
  rates, median survival duration, hazard ratios,
  non-random selection or assignment between
  groups.

72
Measures of Variability
Level of Measurement SD SEM
CI Nominal No No Yes Ordinal No No
No Continuous Yes Yes Yes
73
Statistical vs. Clinical Significance

• Example A new antihypertensive drug is studied
  to determine whether it decreases the rate of
  myocardial infarction. The results indicate that
  the drug decreases MI by 11 with a 95 CI
  -2-25.

74
Statistical vs. Clinical Significance

• The 95 CI for the relative risk of headache
  development with a new diabetes drug is reported
  as 1.20 (CI 0.95-1.50) and for a placebo drug as
  1.25 (CI 0.88-1.76).
• Are these showing statistical significance?

75
Ratios, Proportions and Rates

• Ratio expresses the relationship between two
  numbers. Men Women (4590)
• Proportion specific type of ratio expressed as a
  percentage. 12 experienced cough when using
  this drug) (12 of total study population)
• Rate form of proportion that includes a specific
  time frame. (18 died from influenza in the US
  last year)

76
Incidence and Prevalence

• Incidence Rate
• Number of new cases of a disease per
  time
• Total population at risk
• Prevalence Rate
• Number of existing cases of a disease per
  time
• Total population at risk

77
Descriptive StatisticsMeasures of
Risk/Association

• Relative Risk
• Odds Ratio
• Relative Risk Reduction
• Absolute Risk Reduction
• Number Needed to Treat
• Number Needed to Harm

78
Measures of Risk

• Relative Risk (RR)
• the risk or incidence of an adverse event
  occurring or of a disease developing during
  treatment in a particular group.
• RR pts in treatment group w/ ADR
• Total of pts in treatment group
• pts in placebo group w/ ADR
• Total pts in placebo group

79
Relative Risk Example

• A new drug is being compared to placebo to
  prevent development of diabetic retinopathy (DR).
• Treatment DR No DR Total
• New drug 50 75 125
• Placebo 65 55 120
• What is the risk of DR developing during
  treatment in patients taking the new drug?
• 50__ 0.4 40 Risk in placebo? 65
  0.5454
• 125 120
• RR 0.4/0.54 0.74 or 74

80
Relative Risk (RR)

• RR 1 When the risk in each group is the same
• RRlt1 When the risk in treatment group is smaller
  than the risk in the placebo group
• RRgt1 When the risk in the treatment group is
  greater than the risk in the placebo group

81
Relative Risk (RR)

• Example The risk of an adverse event developing
  during therapy with an eye medication compared to
  the placebo group was listed as 1.5. What does
  this mean?
• Answer That the eye med is 1.5 times more likely
  to cause an adverse event than the placebo being
  used.

82
Relative Risk Example

• 92 men and women who were recovering from heart
  attacks were followed and surveyed a year later.
  14 of the 92 patients had died. When death rates
  were calculated according to pet ownership, only
  3 of the 53 pet owners (5.6) were no longer
  living, compared to 11 of 39 (28) patients who
  were without animals.
• Relative risk 0.056/0.28 0.2
• What does this mean?

83
Relative Risk...

• Relative Risk does NOT tell us the magnitude of
  the absolute risk.
• Example A RR of 33 could mean that the
  treatment reduces the risk of an adverse event
  from 3 down to 1 or from 60 down to 20. These
  may or may not be significant depending on the
  population and adverse event (minor or major
  adversity)

84
Odds Ratio (OR)

• Commonly reported measure in case control
  designs. Case control starts with outcomes.
  (looks back for risk factors)
• OR pts taking drug w/ ADR
• pts taking drug w/o ADR__
• pts not taking drug w ADR
• pts not taking drug w/o ADR

85
Odds Ratio cont.

• The Odds Ratio could also be expressed as
• Treatment A Deaths
• Treatment A Survival_____
• Treatment B Deaths
• Treatment B Survival

86
Odds Ratio (OR)
Odds of developing a disease or ADR if exposed
(to drug) Odds of developing a disease or ADR if
not exposed (to drug)
OR Disease Present
Absent Exposed factor A
B Not exposed to factor C
D OR A/C A X D OR A/B A
X D B/D B X C
C/D B X C
87
Odds Ratio Example
A case control study reported that 35 of 120
chronic renal failure patients took NSAIDs
compared to only 20 of 110 similar patients
without renal failure. What would be the odds
ratio of developing renal failure if taking
NSAIDs? 35 (taking NSAIDs w/ RF) A35
B 20 20 ( taking NSAIDs w/o RF) C 85
D 90 85 (not taking NSAIDs w/ RF) 90 (not
taking NSAIDs w/o RF)
88
Renal Failure/NSAIDs
A/C A/B B/D C/D 35/ 85 0.41 or
35/20 1.75 20/ 90 0.22
85/90 0.94 0.41 1.86 1.75
1.86 0.22
0.94
89
Odds Ratio

• OR l The odds of developing an adverse event
  or disease in the exposed (treatment) group is
  the same as the odds in the non-exposed
  (non-treatment) group.
• ORlt1 Odds of developing ADR in exposed group is
  less than odds in non-exposed.
• ORgt1 Odds of ADR in exposed group greater than
  the odds in non-exposed.

90
Odds Ratio (OR)

• Example The odds that ASA was taken by children
  who developed Reyes Syndrome vs. the odds that
  ASA was taken by similar children who did not
  develop Reyes Syndrome was reported as OR3l.
• The odds that Reyes Syndrome children had taken
  ASA was approximately 3 times greater than for
  the children who did not develop Reyes Syndrome.

91
Interpreting the OR and RR

• 1. Degree of validity of the study design.
• 2. The confidence interval (CI)
• 3. Relative Risk Reduction (RRR)

92
Relative Risk Reduction (RRR)

• Ex If a new drug is shown to reduce the risk of
  cancer, what is the exact percentage of this
  reduction?
• RRR measure of the reduction in the relative
  risk in the exposed group.
• RRR Rate in control group-rate in tx group
• Rate in control group
• RRR 1-RR

93
Relative Risk Reduction (RRR)

• Incidence of cancer was 7 in treatment group and
  12 in placebo( control) group.
• RRR 12-7 0.42 42
• 12
• Disadvantage of RRR- doesnt discriminate between
  very large and very small actual incidence rates
  in the groups.

94
Relative Risk Reduction Example

• A study is performed to determine the efficacy of
  a new LMWH, Drug H in preventing PE from post
  surgical patients. 299 post surgical patients are
  randomized to receive Drug H and 355 receive
  placebo. 43 patients developed PE in the placebo
  group, and 21 developed PE in the treatment
  group. What is the relative risk reduction by
  Drug H (reducing the risk of PE)

95
Drug H Example cont...
Incidence of PE in placebo group 43/355 0.12
12 Incidence of PE in Drug H group 21/299
0.07 7 RRR Rate in control group - rate in
treatment group Rate
in control group RRR 12-7 / 12 0.12- 0.07/
0.12 0.42 42 OR another way to calculate is
RRR 1-RR 1- 21/299 / 43/355 1- 7/12
12/12-7/12 5/12 0.42 42 or 1- 0.07/0.12
1-0.58 0.42
96
Absolute Risk Reduction (ARR)

• ARR Incidence rate in control group - incidence
  rate in treatment group.
• Ex cancer treatment 7, placebo 12
• ARR 12-7 5
• For serious conditions though, a small ARR can
  still be very clinically relevant.

97
Number Needed to Treat (NNT)

• NNT number of individuals that need to be
  treated in order to prevent one adverse event or
  one outcome. NNT 1
• ARR
• Ex study determine efficacy of drug preventing
  cancer. Incidence of cancer in placebo 12, in
  treatment group 7
• 12-7 5 1/5 20NNT (20 pts needed to
  treat to prevent 1 case of cancer
• NNT 1/ placebo - treatment group

98
Number Needed to Harm (NNH)

• NNH 1/ treatment- placebo group
• Ex Headache occurred in 25 of placebo patients
  and 75 of patients taking drug X.
• The NNH 75-25 50 1/0.5 2
• Only 2 patients would need to be treated with
  drug X in order to cause a headache occurrence.

99
Review

• In a diabetes study, 4 of Glucotrol users and
  18 of placebo pts. Developed CHF within 10
  years.
• RRR 18-4 14 0.77 77 RR
• 18 18
  
• ARR 18-4 14
• NNT 1/0.14 7 pts
• In Glucotrol group 26 had HA vs. 3 in placebo.
• NNH 26-3 23 1/0.23 4