measurements measurements measure of variability, scale levels of measurements, descriptive...

MeasurementsMeasurements

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

Tendency

Measurements need to:Measurements need to:

Produce valid and reliable resultsbe sensitive and specificbe able to identify clinically important

changeshave outcome measures and endpoints

definedbe easy to interpret

Reasons for errors in Reasons for errors in measurement:measurement:

Improper function or calibration of equipment

patients providing misleading or dishonest answers to verbal/written questions

Improper recording/transcribing of dataInvestigators recording or making

inaccurate measurements

Types of ErrorsTypes 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

Types of ErrorsTypes 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.

Measurement TermsMeasurement TermsValidity- degree to which an instrument is

measuring what it is intended to measure.– Predictive, Criterion, Face

Reliability- reproducibility of a testSensitivity- ability to measure a small

treatment effectSpecificity- how well the test can

differentiate between the effect resulting from treatment and random variation

Validity terms used in Validity terms used in association with association with measurements: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

Reasons for False Positive Reasons for False Positive ResultsResults

Patient related– patients weren’t 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.

False Positive Results 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

False Positive ResultsFalse Positive ResultsStudy 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

False Positive ResultsFalse 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

False Negative ResultsFalse 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


Drug Related– not adequately absorbed– kinetics were different in study group than in

other patient groupsStudy Design Related

– Too few of patients– inappropriate study design– insufficient drug dose was tested


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


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.

Outcome MeasuresOutcome 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 reliefMeasures of safety: adverse effects, blood

glucose, electrolytes, serum lipids

Criteria Used for Outcome Criteria Used for Outcome MeasuresMeasures

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 changesGlobal assessment criteria- Quality of LifeRelative effect criteria- change in time to

effect.

Measurement EndpointsMeasurement 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.

EndpointsEndpoints

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

Endpoints ExampleEndpoints 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?

Surrogate EndpointsSurrogate 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 AID’s.

– Measuring volume of acne instead of proportion of patient’s cleared of acne.

– Determining cardiovascular disease or atherosclerotic disease instead of measuring blood pressure in a study of antihypertensive drug treatment

Hawthorne EffectHawthorne Effect

Refers to the influence that a process of conducting a study may have on a subject’s behavior– Subject– Environment– Research design

Reasons for Clinical Reasons for Clinical Improvement in a Patient’s Improvement in a Patient’s

ConditionConditionNatural 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

Placebo EffectPlacebo 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 it’s psychological effect or to eliminate observe bias.

Placebo “response”= due to change in pt. Behavior following admin. of a placebo

Placebo “effect” = change in pt’s illness due to the symbolic importance of a treatment.

A placebo effect doesn’t require a placebo.

Why do we see a Placebo Why do we see a Placebo Effect?Effect?

Three different theories:1. The effect is produced by a decrease in

anxiety2. Expectations lead to a cognitive

readjustment of appropriate behavior.3. The effect is a classical conditioned

Pavlovian response.

Placebo EffectPlacebo Effect

Expectations lead to behavior change– Patient’s and providers expectations – Patient’s 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.

Appropriate Statistical TestsAppropriate Statistical TestsTo 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 variablesThe scales or levels of measurement used for the

dependent variables

Variables in a Study: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)

ExampleExample

Patients given Lovastatin to lower cholesterol.

Dependent variable- lowering of cholesterolIndependent variable- Lovastatin

There can be more than one independent and dependent variable in a study.

Dependent/Independent Dependent/Independent VariablesVariables

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?

Scales of Levels of Scales of Levels of MeasurementMeasurement

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

Scales of Levels of Scales of Levels of MeasurementMeasurement

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

Scale Levels of MeasurementScale Levels of Measurement

Continuous MeasurementInterval 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)

Continuous Level of Continuous Level of MeasurementMeasurement

Ratio level: – exact differences between measurements is

known and constant– true zero point (Centigrade temp scale)– can make ratio statements (2:1) that denote

relative size– Can be converted to an ordinal scale (but

ordinal scale can’t convert to interval)

Scale levels of MeasurementsScale 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?

Scale level of MeasurementsScale level of Measurements

Infectious Outcome Among 46 Patients

Infection No infection Total

Oxacillin 2 20 22

Placebo 0 24 24

Column total 2 44 46

What scale level of measurement?

Types of Interval/Ratio DataTypes of Interval/Ratio DataDiscrete 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)

Data DistributionsData 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

Data DistributionsData 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)

Bar Chart (Histogram)Bar Chart (Histogram)

0

10

20

30

40

50

60

70

80

90

1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

Flu cases

Continuous Level DataContinuous Level DataNormal DistributionNormal Distribution

Gaussian CurveGaussian Curve

Non-Normal DistributionsNon-Normal DistributionsBi-Modal CurveBi-Modal Curve

Weights of American Adults (women and men)

Non-symmetrical distributionsNon-symmetrical distributionsNon-normal distributionsNon-normal distributions

Continuous Distribution Continuous Distribution Examples:Examples:

The distribution of GPA’s of college students:

1.0 2.5 4.0

Continuous Distribution Continuous Distribution ExampleExample

Distribution of the ages of patients taking Digoxin

20 40 60 80

Descriptive StatisticsDescriptive Statistics

Measures of Central Tendency

Measures of Central Measures of Central TendencyTendency

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).


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.

How to Recognize “skewed” How to Recognize “skewed” datadata

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.


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.


Scale Level of Normal Non-Normal

Measurement Distribution Distribution

Nominal Mode Mode

Ordinal Median=mode Median /mode

Interval/Ratio Mean=med=mode Mean/med/mode

Descriptive StatisticsDescriptive Statistics

Measures of Variability

Measure of VariabilityMeasure 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.

ExampleExample

2 groups of men w/ mean SBP in each group is 120 mmHg. Are they similar?

First group: BP: 110,120,120,130Second group: BP: 80,90,150,160Both have mean= 120Spread of data or range of data is much

different

RangeRange

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

Interquartile RangeInterquartile 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 don’t have normal distributions

Standard Deviation (SD)Standard Deviation (SD)

Standardized measure of the spread of scores around the mean.

Useful for continuous (ratio/interval) dataWhen reported in a study: +/- 1SDNeeds normal distribution of dataThe mean +/- 1SD includes 68% of data

points (34% on each side of the mean)

Standard DeviationStandard 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)

Standard Deviation (SD)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)

VarianceVariance

Variance is estimate of the study data. Obtained by calculating the differences between each individual value and the overall mean

Needed for calculating the SDSD= varianceSD2= variance

Standard Error of the Mean Standard Error of the Mean (SEM)(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”

SD versus SEMSD 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 add’l samples of pts with same characteristics were studied, their mean values would fall between 11.5 and 15.7 68% of the time.

ReviewReview

Variance= differences between each individual value and the overall mean.

Variance used to calculate the SDSD= variance or SD2= varianceSEM or SE is derived from the SDSEM= SD/ sample size

Review…SEMReview…SEMSD= measure of the variability of individual

values about the sample meanSEM/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

ExampleExample

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?

Confidence Intervals (CI)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

Confidence Interval ExampleConfidence Interval Example

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

Confidence IntervalsConfidence Intervals

Can be calculated for nominal level data (proportions) and continuous level data

90% CI assoc. with narrower range of values (don’t need to be as confident)

99% CI assoc. with wider range of values (more confident the CI will contain true population value.

CI is influenced by: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)

Confidence Interval ExampleConfidence Interval ExampleTwo 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?

Confidence Intervals (CI)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.

Measures of VariabilityMeasures of Variability

Level of Measurement SD SEM CI

Nominal No No Yes

Ordinal No No No

Continuous Yes Yes Yes

Statistical vs. Clinical Statistical vs. Clinical SignificanceSignificance

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%.

Statistical vs. Clinical Statistical vs. Clinical SignificanceSignificance

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?

Ratios, Proportions and RatesRatios, Proportions and RatesRatio expresses the relationship between

two numbers. Men: Women (45:90)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)

Incidence and PrevalenceIncidence and PrevalenceIncidence 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

Descriptive StatisticsDescriptive StatisticsMeasures of Measures of

Risk/AssociationRisk/AssociationRelative RiskOdds RatioRelative Risk ReductionAbsolute Risk ReductionNumber Needed to TreatNumber Needed to Harm

Measures of RiskMeasures 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

Relative Risk Example: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 = 0.54=54% 125 120RR= 0.4/0.54 = 0.74 or 74%

Relative Risk (RR)Relative Risk (RR)

RR = 1 : When the risk in each group is the same

RR<1: When the risk in treatment group is smaller than the risk in the placebo group

RR>1: When the risk in the treatment group is greater than the risk in the placebo group

Relative Risk (RR)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.

Relative Risk ExampleRelative 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?

Relative Risk...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)

Odds Ratio (OR)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

Odds Ratio cont.Odds Ratio cont.

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

Odd’s Ratio (OR)Odd’s 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

Odds Ratio Example:Odds Ratio Example:

A case control study reported that 35 of 120 chronic renal failure patients took NSAID’s compared to only 20 of 110 similar patients without renal failure. What would be the odds ratio of developing renal failure if taking NSAID’s?

35 (taking NSAID’s w/ RF) A=35 B = 20

20 ( taking NSAID’s w/o RF) C = 85 D= 90

85 (not taking NSAID’s w/ RF)

90 (not taking NSAID’s w/o RF)

Renal Failure/NSAID’sRenal Failure/NSAID’sA/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

Odds RatioOdds 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.

OR<1: Odds of developing ADR in exposed group is less than odds in non-exposed.

OR>1: Odds of ADR in exposed group greater than the odds in non-exposed.

Odds Ratio (OR)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 OR=3:l.– The odds that Reyes Syndrome children had

taken ASA was approximately 3 times greater than for the children who did not develop Reyes Syndrome.

Interpreting the OR and RRInterpreting the OR and RR

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

Relative Risk Reduction Relative Risk Reduction (RRR)(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 groupRRR= 1-RR

Relative Risk Reduction Relative Risk Reduction (RRR)(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- doesn’t discriminate

between very large and very small actual incidence rates in the groups.

Relative Risk Reduction Relative Risk Reduction ExampleExample

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)

Drug H Example cont...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

Absolute Risk Reduction Absolute Risk Reduction (ARR)(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.

Number Needed to Treat Number Needed to Treat (NNT)(NNT)

NNT: number of individuals that need to be treated in order to prevent one adverse event or one outcome. NNT = 1

ARREx: study determine efficacy of drug preventing

cancer. Incidence of cancer in placebo 12%, in treatment group 7%

12%-7% = 5% 1/5% = 20=NNT (20 pts needed to treat to prevent 1 case of cancer

NNT= 1/ placebo - treatment group

Number Needed to Harm Number Needed to Harm (NNH)(NNH)

NNH= 1/ treatment- placebo groupEx: Headache occurred in 25% of placebo

patients and 75% of patients taking drug X.The NNH = 75%-25% = 50% 1/0.5 = 2Only 2 patients would need to be treated

with drug X in order to cause a headache occurrence.

ReviewReview

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

measurements measurements measure of variability, scale levels of measurements, descriptive...

Documents