This document defines important statistical terms used in medical research: sensitivity measures the probability a test is positive when the disease is present, while specificity measures the probability a test is negative when the disease is absent. It also defines precision, accuracy, confidence intervals, risk ratios, odds ratios, incidence, prevalence, and provides examples of how these terms are used and interpreted.

1. Important definitions in statistics
ABOUBAKR ELNASHAR
Benha University Hospital, Egypt
ABOUBAKR ELNASHAR

2. Sensitivity:
Probability of test to be positive when the disease
is present
True positive test
Specificity
Probability of the test to be negative when the
disease is absent
True negative test
Systematic review
is qualitative reports
Meta-analysis
Qualitative analysis of systematic review
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3. Precision
a description of a level of measurement that
yields consistent results when repeated. It is
associated with the concept of "random error", a
form of observational error that leads to
measurable values being inconsistent when
repeated.
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4. Precision or positive predictive value
the proportion of the true positives against all the
positive results (both true positives and false
positives)
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5. Accuracy
two definitions:
a level of measurement with no inherent
limitation (i.e. free of systematic error, another
form of observational error).
ISO definition
a level of measurement that yields true (no
systematic errors) and consistent (no random
errors) results.
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6. Accuracy
used as a statistical measure of how well a binary
classification test correctly identifies or excludes a
condition.
Accuracy
is the proportion of true results (both true
positives and true negatives) among the total
number of cases examined.
To make the context clear by the semantics, it is
often referred to as the "Rand accuracy" or "Rand
index".It is a parameter of the test.
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7. Accuracy may be determined from sensitivity and specificity,
provided prevalence is known, using the equation:
The accuracy paradox for predictive analytics states that
predictive models with a given level of accuracy may have
greater predictive power than models with higher accuracy. It
may be better to avoid the accuracy metric in favor of other
metrics such as precision and recall.
In situations where the minority class is more important, F-
measuremay be more appropriate, especially in situations
with very skewed class imbalance.
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8. Another useful performance measure is the balanced
accuracy
which avoids inflated performance estimates on imbalanced
datasets.
It is defined as the arithmetic mean of sensitivity and
specificity, or the average accuracy obtained on either class:
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9. Confidence interval
A way of expressing certainty about the findings
from a study or group of studies, using statistical
techniques.
A confidence interval describes a range of
possible effects (of a treatment or intervention)
that is consistent with the results of a study or
group of studies.
I am confident 95% that the range is between so
and so
If the range cross 1 , it is insignificant
95% CI (1.05-1.15)= I am 95% confident that the
risk between 1.05 and 1.15
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10. A wide confidence interval indicates a lack of
certainty or precision about the true size of the
clinical effect and is seen in studies with too few
patients.
Where confidence intervals are narrow they
indicate more precise estimates of effects and a
larger sample of patients studied.
It is usual to interpret a ‘95%’ confidence interval
as the range of effects within which we are 95%
confident that the true effect lies
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11. In case control study
It is better to have more controls than cases
In clinical studies
It is better for cases and control to be the same
For numbers: t test
For %: chi square
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12. RR
If 1: no association
<1: negative association
>1: positive association
RR= 2 i.e. risk is doubled
= 5 i.e. risk is 5 times
= 0.5 i.e. negative association ad the risk is
halfed
OR
Is like RR and interpreted as it
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13. Very common 1/1-1/10 A person in family
Common 1/10-1/100 A person in street
Uncommon 1/100-1/1000 A person in village
Rare 1/1000-1/10,000 A person in small town
Very rare <1/10,000 A person in large town
Royal College of Obstetricians and
Gynaecologists
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14. Incidence
The rate of new (or newly diagnosed) cases of
the disease.
It is generally reported as the number of new
cases occurring within a period of time (e.g.,
per month, per year).
It is more meaningful when the incidence rate
is reported as a fraction of the population at risk
of developing the disease (e.g., per 100,000 or
per million population).
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15. The accuracy of incidence data depends upon
the accuracy of diagnosis and reporting of the
disease.
In some cases (including ESRD) it may be
more appropriate to report the rate of treatment
of new cases since these are known, whereas
the actual incidence of untreated cases is not.
Incidence rates can be further categorized
according to different subsets of the population
– e.g., by gender, by racial origin, by age group
or by diagnostic category.
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16. Prevalence
The actual number of cases alive, with the
disease either during a period of time (period
prevalence) or at a particular date in time (point
prevalence).
Period prevalence provides the better measure
of the disease load since it includes all new cases
and all deaths between two dates
Point prevalence only counts those alive on a
particular date.
Prevalence is also most meaningfully reported as
the number of cases as a fraction of the total
population at risk and can be further categorized
according to different subsets of the population.
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