Relative Risk & Odds Ratios
Summary
TLDRThis video introduces the concepts of relative risk and odds ratio, explaining how they are calculated and interpreted in epidemiological studies. It covers the differences between the two measures, their applications in cohort and case-control studies, and the importance of statistical significance. The video also discusses how to construct and use a 2x2 contingency table for these calculations and emphasizes the distinctions between odds and probability. Finally, it highlights the conditions under which each measure should be used and potential pitfalls in their interpretation.
Takeaways
- 📊 The video introduces the concepts of relative risk and odds ratio, explaining how they are used to compare the occurrence of events between two groups in studies.
- 🔍 An association is identified when the risk among the exposed is higher than among those not exposed, highlighting the relationship between exposure and outcome.
- 📈 The interpretation of relative risk or odds ratio involves two components: the point estimate (the actual number) and the statistical significance (p-value and/or confidence interval).
- 🚬 Relative risk is used for comparing the probability of an event occurring to all possible events, such as the risk of developing lung cancer in those exposed to secondhand smoke.
- 📝 A 2x2 contingency table is essential for calculating incidence rates and relative risk in cohort studies, providing frequency counts of events for both exposed and unexposed groups.
- 🔢 The formula for relative risk is the proportion of individuals with the event in the exposed group divided by the proportion in the unexposed group.
- 📉 A relative risk of one indicates no difference between groups, greater than one suggests a positive association, and less than one indicates a negative association or protective effect.
- ⚠️ The significance of relative risk is determined by the p-value and confidence interval; if the p-value is ≥ 0.05 or the interval includes 1, the risk is not statistically significant.
- 🚫 Case-control studies cannot calculate relative risk because they compare cases with the event to controls without the event, making them retrospective and not suitable for incidence or risk calculation.
- 🎰 Odds ratio is used when relative risk cannot be calculated, such as in case-control studies, and is based on the odds of an event occurring rather than the probability.
- 🔄 The formula for odds ratio is the cross product of a 2x2 table, dividing the odds of the exposed group by the odds of the unexposed group.
- 📚 Odds ratios can be calculated for both cohort and case-control studies and are comparable in magnitude to relative risk only when the outcome is rare.
- 📉 Odds ratios may overestimate risk when the outcome is common, so relative risk should be used if possible, and caution is advised when interpreting odds ratios.
- 📘 The video concludes by noting that odds ratios are common in medical literature for both study types and are often the result of logistic regression analysis.
Q & A
What are the two main statistics used to compare the occurrence of events between two groups in studies?
-The two main statistics used are relative risk and odds ratio.
What is the purpose of calculating relative risk or odds ratio in a study?
-The purpose is to determine whether an association exists between exposure and outcome and to assess how strong that association is.
What is the difference between the point estimate and statistical significance in the context of relative risk or odds ratio?
-The point estimate is the actual number representing the relative risk or odds ratio, while statistical significance is indicated by the p-value and/or confidence interval, showing the reliability of the point estimate.
When is the relative risk used in a study?
-Relative risk is used when comparing the probability of an event occurring to all possible events considered in a study, typically in cohort studies.
How is the relative risk calculated using a 2x2 contingency table?
-Relative risk is calculated by dividing the proportion of individuals who suffered the event in the exposed group by the proportion of individuals who suffered the event in the unexposed group.
What does a relative risk of 5.41 imply in the context of the secondhand smoke example?
-A relative risk of 5.41 implies that the risk of developing lung cancer in the exposed group is 5.41 times higher than in the unexposed group.
What are the three interpretations of relative risk values?
-A relative risk of one indicates no difference between groups, a value greater than one suggests a positive association or risk factor, and a value less than one indicates a negative association or protective effect.
Why can't relative risk be calculated from a case-control study?
-Relative risk cannot be calculated from a case-control study because it compares cases that have experienced the event with controls who have not, and it does not provide the necessary data to calculate incidence or risk.
What is the difference between odds and probability, and how do they relate to odds ratio?
-Odds are calculated as the probability of an event divided by the probability of the event not happening, while probability is the chance of an event occurring. The odds ratio compares the odds of an event in the exposed group to the odds in the unexposed group.
Why are odds ratios preferred over relative risk in case-control studies?
-Odds ratios are preferred in case-control studies because they can be calculated without needing incidence rates, which are not available in this study design.
How do you interpret an odds ratio of 1.48 in a case-control study?
-An odds ratio of 1.48 suggests that the odds of the exposed group experiencing the event (e.g., children with leukemia) are 1.48 times higher than the odds of the unexposed group.
When are relative risk and odds ratios comparable in magnitude?
-Relative risk and odds ratios are comparable in magnitude when the outcome under study is rare, as their formulas yield more similar results in such cases.
Why should caution be exercised when interpreting odds ratios?
-Caution is needed because odds ratios can overestimate risk when the outcome is more common, and they should not be assumed to represent the true risk ratio.
Why are odds ratios common in both case-control and cohort studies?
-Odds ratios are common because they are the result of logistic regression, a widely used statistical method in medical research for both types of study designs.
Outlines
📊 Introduction to Relative Risk and Odds Ratio
This paragraph introduces the concepts of relative risk and odds ratio, explaining their importance in comparing event occurrences between two groups. It discusses how these statistical measures help determine the strength of an association between exposure and outcome. The paragraph outlines the process of interpreting these numbers, emphasizing the point estimate and statistical significance indicated by the p-value and confidence interval. It also explains the calculation of relative risk using a 2x2 contingency table, providing an example with secondhand smoke exposure and lung cancer risk, resulting in a relative risk of 5.41. The interpretation of relative risk values is detailed, noting that values greater than, less than, or equal to one indicate positive association, negative association, or no difference, respectively. The paragraph concludes with a discussion on the statistical significance of relative risk, highlighting the importance of the p-value and confidence interval in determining the strength of the association.
🎲 Understanding Odds Ratio and Its Calculation
The second paragraph delves into the concept of the odds ratio, contrasting it with relative risk by explaining the difference between odds and probability. It clarifies that odds ratios are used when the study design is retrospective, such as in case-control studies, where relative risk cannot be calculated due to the nature of the data. The paragraph provides the formula for calculating the odds ratio using the cross product method from a 2x2 table and illustrates this with a real-life example of a case-control study on leukemia and parental smoking. It discusses the interpretation of odds ratios, which mirrors that of relative risk, and the conditions under which they are statistically significant. The paragraph also addresses the limitations of odds ratios, noting that they overestimate risk when the outcome is common, and emphasizes the importance of using relative risk when possible. It concludes by mentioning that odds ratios are common in medical literature and can result from logistic regression, a favored method in biomedical research.
Mindmap
Keywords
💡Relative Risk
💡Odds Ratio
💡Association
💡Point Estimate
💡Statistical Significance
💡Two-by-Two Contingency Table
💡Cohort Study
💡Case-Control Study
💡Odds
💡Confidence Interval
💡Logistic Regression
Highlights
Introduction to relative risk and odds ratio and their calculation.
Relative risk compares the probability of an event occurring between two groups.
An association exists when the risk among the exposed is higher than the risk among the unexposed.
Relative risk is calculated by dividing the incidence in the exposed group by the incidence in the unexposed group.
Example: Calculating relative risk for lung cancer in those exposed to secondhand smoke.
Interpretation of relative risk: a value of 1 indicates no difference, greater than 1 indicates a positive association, and less than 1 indicates a negative association.
Statistical significance of relative risk is shown by the p-value and/or confidence interval.
Relative risk cannot be calculated from case-control studies because they do not provide incidence data.
Odds ratio is used in case-control studies and compares the odds of exposure among cases and controls.
Calculation of odds ratio using a 2x2 table: a times d divided by b times c.
Example: Calculating odds ratio for leukemia in children with parental smoking history.
Interpretation of odds ratio: similar to relative risk, values indicate positive or negative association.
Odds ratios can be calculated for both cohort and case-control studies.
Relative risk and odds ratios are comparable only when the outcome is rare.
Caution is needed when interpreting odds ratios as they can overestimate risk in common outcomes.
Logistic regression commonly yields odds ratios in medical research.
Transcripts
hello this video will introduce relative
risk and odds ratio and how they are
calculated throughout the class we have
discussed comparing the occurrence of
events between two groups studies can
characterize these associations by using
one of two statistics relative risk or
odds ratio when evaluating the relative
risk or nadh's ratio we are really
trying to determine whether an
association exists and how strong it is
when we talk about an association think
of it as a relationship between exposure
and outcome as we discussed earlier this
semester there is an association when
the risk among the exposed is higher
than the risk among those who are not
exposed there are two components to your
interpretation of a number first is the
actual number which we refer to as the
point estimate and the second is the
statistical significance which is shown
by the p-value and/or the confidence
interval relative risk is used when
comparing the probability of an event
occurring to all possible events
considered in a study for example
consider the risk of developing lung
cancer in those who are exposed and
unexposed to secondhand smoke over
ten-year study period con study
conclusion the two-by-two contingency
table shown here is created containing
frequency counts of events for two
groups exposed and unexposed to the
secondhand smoke stimulus this table
provides all necessary data to calculate
the incidence of the event for both
exposed and unexposed individuals in a
cohort study relative risk is calculated
by dividing the proportion of
individuals who suffered the event in
the exposed group here it is a divided
by a plus B by the proportion of
individuals who suffered the event in
the unexposed group here it is C divided
by C plus D using our secondhand smoke
example let's input some numbers into
our 2x2 table and calculate relative
risk in our examples the risk of lung
cancer in the exposed group is 0.92 this
risk is divided by the risk in the
unexposed group 0.17 for a relative risk
of 5.41
relative risk provides a single number
ranging from zero to infinity
and there are three resulting
interpretations provided below when
interpreting the relative risk we
consider one to be null a relative risk
of one means there is no difference
between the two groups and the incidence
and risk in the exposed is the same as
the risk and the incidence in the non
exposed there is no increased risk and
no association if the relative risk is
greater than one the incidence of needs
posed is greater than the incidence in
the non exposed there is a positive
association or detrimental effect also
known as the risk factor of being
exposed to the stimulus if the relative
risk is less than 1 the incidence in the
exposed is less than the incidence in
the non expose there is a negative
association or protective effect of
being exposed to the stimulus remember
the further the relative risk is from 1
the stronger the Association when you
interpret a relative risk remember to
take into account whether the
Association is significant this applies
the odds ratio as well the relative risk
will be reported alongside a p-value
and/or a 95% confidence interval if the
p-value is not less than 0.05 or if the
confidence interval includes 1 the
relative risk is not statistically
significant this is true no matter how
large or small the relative risk is you
may have noticed that I said you have
all the information you need to
calculate a relative risk when we do a
cohort study this is not true for case
control studies you cannot calculate
incidence or risk in a case control
study thus you cannot calculate relative
risk from a case control study why not a
case control study compares cases that
have experienced the event and controls
who have not and then assesses whether
each individual was exposed to a
stimulus or not in the example here the
researcher compared 300 people of cancer
to 300 people without cancer the disease
rate is 50% just because of the way the
study was designed not because 50% of
people under 15 years of age of cancer
thus the case control study is
retrospective when relative risk cannot
be calculated like in case control
studies researchers will often present
an odds ratio before I show you the
formula for calculating an odds ratio
here's a reminder about the difference
between odds and probability relative
risk uses probability of getting disease
AHS ratio uses odds which is calculated
as probability divided by one minus
probability so if there's a 60%
probability that I will win this race
the odds are 1.5 that I will win so
thinking in terms of odds and
probability the relative risk is the
probability that an exposed person gets
disease divided by a probability that an
unexposed person gets the disease the
odds ratio is odds that a case or person
with disease was exposed divided by the
odds a control or person without disease
was exposed
remember that even though you can
mathematically convert between odds and
probability it is never okay to
calculate a relative risk from case
control study data odds are calculated
by dividing the proportion of people
experiencing events by the proportion of
people not experiencing event thus an
odds ratio is a ratio of two odds one
for individuals exposed to the stimulus
and the other for those not exposed to
the stimulus here is the calculation for
the odds ratio it is the same as the
cross product using the 2x2 table that
we designed earlier the calculation is a
times B divided by B times C let's look
at a real-life example this is a case
control study of children with leukemia
compared to children without leukemia
which look to see whether history of
parental smoking was associated with
cancer here is a 2x2 table created just
as was done in the earlier example to
get the odds ratio we divide the number
of kids with parental smoking by the
number of kids without parental smoking
in the cancer group for an odds of 0.43
this is divided by the odds of parental
smoking in the non cancer group 0.294 an
odds ratio of one point four eight
this is different from the relative risk
equation because we don't use total
exposed or total unexposed anywhere in
the equation odds ratios can range from
zero to infinity
they have three interpretations
identical to those presented above for
relative risk the rule for determining
whether an odds ratio statistically
significant is also the same as with
relative risk odds ratios can be
calculated for both cohort and
case-control designs odds ratios are
used when comparing events to non-event
with this calculation depending on the
study design for example consider
comparing a group of individuals who
develops measles to those who did not
and then determining whether they
received all the recommended
vaccinations in a cohort study the odds
ratio is calculated by dividing the odds
of experiencing the event in the exposed
group a divided by B by the odds the
unexposed group experiences the effect C
divided by B in a case control study the
odds ratio is calculated by dividing the
odds that cases works both to the risk a
divided by C by the odds that the
controls were exposed B divided by D
relative risk can only appear in cohort
studies or possibly at times and
randomized control studies relative risk
and odds ratios are comparable in
magnitude only when the outcome under
study is rare for instance some cancers
this is because the results of the
relative risk formula and odds ratio
formula become more similar as the
denominator gets larger and as the
number of disease cases gets smaller it
is important to consider that odds
ratios consistently overestimate risk
when the outcome is more common for
instance in hyperlipidemia as a result
eliten risk should be used if possible
and caution should be exhibited when
interpreting odds ratios additionally
don't assume that a case is a case
control study just because you see an
odds ratio and the results ratios are
very common in the medical literature
for both case control and cohort studies
they are the result of logistic
regression which is every bi
medical researchers favorite mystical
method this concludes the video
تصفح المزيد من مقاطع الفيديو ذات الصلة
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Summary Measures Used in Systematic Reviews
Study Design Part 3 - Cross Sectional Studies
Regression and R-Squared (2.2)
Why the p-Value fell from Grace: A Deep Dive into Statistical Significance
Study Designs (Cross-sectional, Case-control, Cohort) | Statistics Tutorial | MarinStatsLectures
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