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How to read a paper ?
The actual title is….
How to read, analyze, and criticize a
study.
Some Basic Concepts
Null hypothesis
The null hypothesis (H0) represents a theory that has been put forward, either
because it is believed to be true or because it is to be used as a basis for
argument, but has not been proved.
For example, in a clinical trial of a new drug, the null hypothesis might be that
the new drug is no better, on average, than the current drug.
We would write H0: there is no difference between the two drugs on average.
The alternative hypothesis (H1)is a statement of what a statistical hypothesis
test is set up to establish.
The final conclusion once the test has been carried out is always given in
terms of the null hypothesis. We either "Reject H0 in favor of H1" or "Do not
reject H0"; we never conclude "Reject H1", or even "Accept H1".
If we conclude "Do not reject H0", this does not necessarily mean that the null
hypothesis is true, it only suggests that there is not sufficient evidence against
H0 in favor of H1.
Rejecting the null hypothesis then, suggests that the alternative hypothesis
may be true.
Statistical Significance
Statistical significance is a statistical assessment of whether observations
reflect a pattern rather than just chance.
In statistical testing, a result is deemed statistically significant if it is unlikely
to have occurred by chance, and hence provides enough evidence to reject the
null hypothesis.
As used in statistics, significant does not mean important or meaningful, as it
does in everyday speech.
The amount of evidence required to accept that an event is unlikely to have
arisen by chance is known as the significance level or critical p-value.
The p-value is the probability of observing data at least as extreme as that
observed, given that the null hypothesis is true. If the obtained p-value is small
then it can be said that either the null hypothesis is false or an unusual event
has occurred.
Statistical power:
The power of a statistical test is the probability
that the test will reject the null hypothesis when
the null hypothesis is false.
Power analysis can be used to calculate the
minimum sample size required so that one can
detect an effect of a given size.
Power analysis can also be used to calculate the
minimum effect size that is likely to be detected in
a study using a given sample size.
p-value
Is the probability of obtaining a test statistic similar to the one that was
actually observed, if the null hypothesis was true.
One often "rejects the null hypothesis" when the p-value is less than the
significance level α (Greek alpha), which is often 0.05 or 0.01.
When the null hypothesis is rejected, the result is said to be statistically
significant.
The p-value is not the probability that the null hypothesis is true.
The p-value is not the probability that a finding is "merely by chance"
The p-value is not the probability of falsely rejecting the null hypothesis.
The p-value is not the probability that a replicating experiment would not
yield the same conclusion.
1 − (p-value) is not the probability of the alternative hypothesis being true
Odds Ratio
The odds ratio is the ratio of the odds of an event occurring in one group to the
odds of it occurring in another group.
An odds ratio of 1 indicates that the condition or event under study is equally
likely to occur in both groups.
An odds ratio greater than 1 indicates that the condition or event is more likely
to occur in the first group.
And an odds ratio less than 1 indicates that the condition or event is less likely
to occur in the first group.
Confidence Interval
A confidence interval gives an estimated range of values which is likely to
include an unknown population parameter.
The estimated range being calculated from a given set of sample data.
Confidence intervals are usually calculated so that this percentage is 95%, but
we can produce 90%, 99%, 99.9%
The width of the confidence interval gives us some idea about how uncertain
we are about the unknown parameter.
The difference between the two groups in FVL is 7.7%
With a range of 2 – 13.4%
How confident are we that the difference will always be within this range ?
95%
Step 1)
What kind of study are we
reading?
Meta-analysis
Contrasting and combining results from
different studies, in the hope of identifying
patterns among study results, sources of
disagreement among those results, or other
interesting relationships that may come to
light in the context of multiple studies.
Randomized controlled trial
(RCT)
A specific type of scientific experiment, and
the preferred design for a clinical trial. RCT
are often used to test the efficacy of various
types of intervention within a patient
population. RCT may also provide an
opportunity to gather useful information
about adverse effects, such as drug
reactions.
Case-control study
Case-control studies are used to identify
factors that may contribute to a medical
condition by comparing subjects who have
that condition/disease (the 'cases') with
patients who do not have the
condition/disease but are otherwise similar
(the 'controls‘)
Cohort study
A cohort is a group of people who share a
common characteristic or experience within
a defined period (e.g., are born, are exposed
to a drug or vaccine or pollutant, or undergo
a certain medical procedure).
Can be retrospective, or prospective.
Cross-sectional study
Observation of all of a population (or a
representative sample), at one specific point in
time.
They differ from case-control studies in that they
aim to provide data on the entire population under
study, whereas case-control studies typically
include only individuals with a specific
characteristic.
Cross-sectional studies are descriptive studies.
What’s the point ?
Strength of evidence.
Level of strength of evidence, according to National Guideline
Clearing House.
Level Source
1++ High-quality meta-analyses, systematic reviews of randomised
controlled trials with a very low risk of bias.
1+ RCTs with a low risk of bias.
1- RCTs with a high risk of bias.
2++ High-quality case–control or cohort studies, with a very low risk of
bias.
2+ Well-conducted case–control or cohort studies with a low risk of bias.
2- Case–control or cohort studies with a high risk of bias.
3 Non-analytical studies (e.g., case reports, case series)
4 Expert opinion, formal consensus
Level of strength of evidence, according to National Guideline
Clearing House.
Level Source
1 Meta analysis.
2 RCT.
3 case–control or cohort studies.
4 Non-analytical studies (e.g., case reports, case series).
5 Expert opinion, formal consensus.
Step 2)
Study design
Study aims, primary, and secondary outcome
measures.
Study endpoints are formulated properly in the
initial study protocol.
Scientific hypotheses under investigation should
be pre-specified and explicitly mentioned.
Report blinding if possible (single, double, triple) .
Use and report randomization.
Report initial equality of baseline characteristics
and comparability of study groups.
Report any drop out of follow up samples.
Step 3)
Data: Types, Suitable
Statistical tests.
Data
Non
parametric
parametric
Nominal
Ordinal
Interval
Ratio
Variable
Discrete Continuous
Nominal data
Are given a particular name
Male /Female
Yes / No
Order is not important
Binominal, multinominal
Ordinal data
Similar to nominal data, but categories are
ordered logically.
equal to, less than, or greater than
Small, medium, large
Easy, intermediate, difficult
Interval data
Data has a meaningful order
Equal intervals between measurements
represent equal changes in the quantity of
whatever is being measured
Data have no natural zero
Example is Celsius scale of temperature. In
the Celsius scale, there is no natural zero, so
we cannot say that 70°C is double than
35°C.
Ratio data
Ratio data has all the qualities of interval
data (natural order, equal intervals) plus a
natural zero point.
height, weight, length
it can be said meaningfully that 10 m of
length is double than 5 m.
Any variable measured, in any
type of data can be either:
Discrete if there are only a finite number of
values possible (whole numbers)
Continuous data makes up the rest of
numerical data (can have fractions)
Make sure that the suitable
statistical test(s) was used for the
collected data.
Step 4)
Statistics
The only science that enables
different experts using the same
figures to draw different
conclusions.
Evan Esar (1899–1995) American writer.
There are three kinds of lies: lies,
damned lies, and statistics.
Benjamin Diserali
British Prime Minister (1804 – 1881)
If your experiment needs
statistics, you ought to have done
a better experiment.
Ernest Rutherford, (1871 – 1937) was a
British chemist and physicist who became
known as the father of nuclear physics.
I can prove anything by statistics
except the truth.
George Canning (1770 – 1827)
British statesman and politician.
Pitfalls of statistics…
1- Survey
Survey results in KSMC, is that 1 out of ten
employees is a smoker…
How many were surveyed?
Did they all respond?
Male to female ratio.
Occupation, age group, education.
What were the questions?
Did they understand the questions?
Who questioned them?
Bias
2- Risk Reduction
Event Rate
0
5
10
15
20
25
30
35
40
45
40%
Event Rate
0
5
10
15
20
25
30
35
40
45
Event Rate:
Absolute Reduction =
40%
30%
Event Rate
0
5
10
15
20
25
30
35
40
45
Event Rate:
Absolute Reduction = 10%
Relative Reduction = 25%
40%
30%
3- Average
SAP of fifteen subjects (mmHg):
90, 98, 110, 111, 113,
113, 121, 125, 127, 128,
130, 132, 136, 140, 165
The Average is ?
113
121.8
122.6
125
ALL ARE CORRECT !!
Average
Mean: summation of all results, divided by
their number.
Median: The middle value of arranged data.
Mode: most frequently recurring value.
Truncated mean: The same as mean, after
omitting the two extremes.
4- Multiple testing
In 1980, researchers at Duke randomized 1073 heart
disease patients into two groups, but treated the groups
equally.
Not surprisingly, there was no difference in survival.
Then they divided the patients into 18 subgroups based on
prognostic factors.
In a subgroup of 397patients (with three-vessel disease
and an abnormal leftventricular contraction) survival of
those in “group 1” was significantly different from
survival of those in “group 2” (p<.025).
How could this be since there was no treatment?
(Lee et al. “Clinical judgment and statistics: lessons from a simulated randomized trial in coronary artery disease,”
Circulation, 61: 508-515, 1980.)
The difference resulted from thecombined
effect of small imbalances in the subgroups.
A significance level of 0.05 means that your
false positive rate for one test is 5%.
If you run more than one test, your false
positive rate will be higher than 5%.
5- Reporting measurements with
unnecessary precision
Many numbers do not need to be reported
with full precision.
If a patient weighs 60 kg, reporting the
weight as 60.18 kg adds only confusion.
even if the measurement was that precise.
Thus, rounding numbers to two significant
digits improves communication.
6- Reporting only p-values for
results
For main results, report the absolute
difference between groups (relative or
percent differences can be misleading).
Report the 95% confidence interval for the
difference, instead of, or in addition to, p-
values.
“The effect of the drug on lowering diastolic
blood pressure was statistically significant (P<
0.05)”
Here, the size of the drop is not given, so its clinical
importance is not known.
Also, P could be 0.049; statistically significant (at the 0.05
level) but so close to 0.05 that it should probably be
interpreted similarly to a P value of 0.51, which is not
statistically significant.
The use of an arbitrary cut point, such as 0.05, to
distinguish between “significant” and “non significant”
results is one of the problems of interpreting P values.
The drug lowered diastolic blood pressure
by a mean of 18mmHg, from 110 to 92mmHg (95%
CI=2 to 34mmHg; P=0.02).”
7- Unnecessarily reporting baseline
statistical comparisons in
randomized trials
In a true randomized trial, each patient has an
equal probability of being assigned to either the
treatment or the control group.
Thus, any differences between groups at baseline
are, by definition, the result of chance.
Therefore, significant differences in baseline data
do not indicate bias (as they might in other
research designs).
8- Not defining “normal” or
“abnormal” when reporting
diagnostic test
results
The importance of either a positive or a
negative diagnostic test result depends on
how “normal” and “abnormal” are defined.
In fact, “normal” has at least six definitions
in medicine.
9- Confusing the “units of
observation” when reporting and
interpreting
results
The “unit of observation” is what is actually
being studied.
Problems occur when the unit is something
other than the patient
For example,
in a study of 50 eyes,
how many patients are involved?
What does a 50% success rate mean?
10- Percentage Vs Absolute
Sometimes statistics are given in absolute
terms in large number studies.
They are given in percentages in small
number studies.
Statistics better be reported in both ways.
We might hear that Blanko Corp. laid off 32
people or we might hear that they laid off
25% of their workforce.
Typically a news source will try to make
the number sound as dramatic as it can, so if
Blanko is a huge company - say it has
200,000 employees - the source might find
it more impressive to say it laid off 20,000
people rather than 10% of the workforce.
If Blanko is small, say 100 employees, it
sounds more impressive to say they laid off
10% rather than just 10 people.
If improvement in a study was
10%
If the study included 2000 patients, it is
more impressive to report 200 improved
cases rather than 10 %
But if the study included only 50 patients, it
is more dramatic to report 10 %
improvement, rather than just 5 patients.
Step 5)
Graphs
Title
A B C D E
70
60
50
40
30
20
10
Source of
data
Can graphs be manipulated to
mislead you?
Title:
Great reduction of mortality rates
with drug (X), compared to (Z)
Comparison of mortality rates
between drugs (X) and (Z)
Y axis:
Y axis:
In a study on 3 antibiotics (A, B, C), the
rate of infection with a certain organism
was:
A : 972 cases
B: 984 cases
C: 955 cases
Misleading graph of NO
differenc…
A B C
Misleading graph of: Difference
What difference do you see
between the two graphs?
With line plot
3D graphs
Mortality
rates
Mortality rates
Data on X axis
This graph, comparing causes of death is
misleading, because it leaves out deaths
from heart disease, cancer, and stroke.
The omission tends to make smoking look
much more harmful by comparison.
Step 6)
Discussion and conclusion
Statistical significance.
Does not guarantee clinical significance.
Does not imply a cause-effect relationship.
Small differences between large groups can
be statistically significant but clinically
meaningless.
Failure to discuss sources of potential bias
and confounding factors.
Statistical Insignificance.
Lack of statistical significance is not proof of the absence
of an effect.
Results that are not statistically significant should not be
interpreted as "evidence of no effect,” but as “no evidence
of effect”
In studies with low statistical power, results that are not
statistically significant are not negative, they are
inconclusive.
Large differences between small groups can be clinically
important but not statistically significant.
Watch out for…
Drawing conclusions not supported by the
study data
Significance claimed without data analysis
or statistical test mentioned.
Missing discussion of the problem of
multiple significance testing if done.
My message…
When reading a study, read it from A to Z.
Don’t just read the title and conclusion.
Read it more than one time, then sit back,
and think about it.
How to read a paper

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How to read a paper

  • 1. How to read a paper ?
  • 2. The actual title is…. How to read, analyze, and criticize a study.
  • 3.
  • 5. Null hypothesis The null hypothesis (H0) represents a theory that has been put forward, either because it is believed to be true or because it is to be used as a basis for argument, but has not been proved. For example, in a clinical trial of a new drug, the null hypothesis might be that the new drug is no better, on average, than the current drug. We would write H0: there is no difference between the two drugs on average. The alternative hypothesis (H1)is a statement of what a statistical hypothesis test is set up to establish. The final conclusion once the test has been carried out is always given in terms of the null hypothesis. We either "Reject H0 in favor of H1" or "Do not reject H0"; we never conclude "Reject H1", or even "Accept H1". If we conclude "Do not reject H0", this does not necessarily mean that the null hypothesis is true, it only suggests that there is not sufficient evidence against H0 in favor of H1. Rejecting the null hypothesis then, suggests that the alternative hypothesis may be true.
  • 6. Statistical Significance Statistical significance is a statistical assessment of whether observations reflect a pattern rather than just chance. In statistical testing, a result is deemed statistically significant if it is unlikely to have occurred by chance, and hence provides enough evidence to reject the null hypothesis. As used in statistics, significant does not mean important or meaningful, as it does in everyday speech. The amount of evidence required to accept that an event is unlikely to have arisen by chance is known as the significance level or critical p-value. The p-value is the probability of observing data at least as extreme as that observed, given that the null hypothesis is true. If the obtained p-value is small then it can be said that either the null hypothesis is false or an unusual event has occurred.
  • 7. Statistical power: The power of a statistical test is the probability that the test will reject the null hypothesis when the null hypothesis is false. Power analysis can be used to calculate the minimum sample size required so that one can detect an effect of a given size. Power analysis can also be used to calculate the minimum effect size that is likely to be detected in a study using a given sample size.
  • 8. p-value Is the probability of obtaining a test statistic similar to the one that was actually observed, if the null hypothesis was true. One often "rejects the null hypothesis" when the p-value is less than the significance level α (Greek alpha), which is often 0.05 or 0.01. When the null hypothesis is rejected, the result is said to be statistically significant. The p-value is not the probability that the null hypothesis is true. The p-value is not the probability that a finding is "merely by chance" The p-value is not the probability of falsely rejecting the null hypothesis. The p-value is not the probability that a replicating experiment would not yield the same conclusion. 1 − (p-value) is not the probability of the alternative hypothesis being true
  • 9. Odds Ratio The odds ratio is the ratio of the odds of an event occurring in one group to the odds of it occurring in another group. An odds ratio of 1 indicates that the condition or event under study is equally likely to occur in both groups. An odds ratio greater than 1 indicates that the condition or event is more likely to occur in the first group. And an odds ratio less than 1 indicates that the condition or event is less likely to occur in the first group.
  • 10. Confidence Interval A confidence interval gives an estimated range of values which is likely to include an unknown population parameter. The estimated range being calculated from a given set of sample data. Confidence intervals are usually calculated so that this percentage is 95%, but we can produce 90%, 99%, 99.9% The width of the confidence interval gives us some idea about how uncertain we are about the unknown parameter.
  • 11. The difference between the two groups in FVL is 7.7% With a range of 2 – 13.4% How confident are we that the difference will always be within this range ? 95%
  • 12. Step 1) What kind of study are we reading?
  • 13. Meta-analysis Contrasting and combining results from different studies, in the hope of identifying patterns among study results, sources of disagreement among those results, or other interesting relationships that may come to light in the context of multiple studies.
  • 14. Randomized controlled trial (RCT) A specific type of scientific experiment, and the preferred design for a clinical trial. RCT are often used to test the efficacy of various types of intervention within a patient population. RCT may also provide an opportunity to gather useful information about adverse effects, such as drug reactions.
  • 15. Case-control study Case-control studies are used to identify factors that may contribute to a medical condition by comparing subjects who have that condition/disease (the 'cases') with patients who do not have the condition/disease but are otherwise similar (the 'controls‘)
  • 16. Cohort study A cohort is a group of people who share a common characteristic or experience within a defined period (e.g., are born, are exposed to a drug or vaccine or pollutant, or undergo a certain medical procedure). Can be retrospective, or prospective.
  • 17. Cross-sectional study Observation of all of a population (or a representative sample), at one specific point in time. They differ from case-control studies in that they aim to provide data on the entire population under study, whereas case-control studies typically include only individuals with a specific characteristic. Cross-sectional studies are descriptive studies.
  • 18. What’s the point ? Strength of evidence.
  • 19. Level of strength of evidence, according to National Guideline Clearing House. Level Source 1++ High-quality meta-analyses, systematic reviews of randomised controlled trials with a very low risk of bias. 1+ RCTs with a low risk of bias. 1- RCTs with a high risk of bias. 2++ High-quality case–control or cohort studies, with a very low risk of bias. 2+ Well-conducted case–control or cohort studies with a low risk of bias. 2- Case–control or cohort studies with a high risk of bias. 3 Non-analytical studies (e.g., case reports, case series) 4 Expert opinion, formal consensus
  • 20. Level of strength of evidence, according to National Guideline Clearing House. Level Source 1 Meta analysis. 2 RCT. 3 case–control or cohort studies. 4 Non-analytical studies (e.g., case reports, case series). 5 Expert opinion, formal consensus.
  • 22. Study aims, primary, and secondary outcome measures. Study endpoints are formulated properly in the initial study protocol. Scientific hypotheses under investigation should be pre-specified and explicitly mentioned. Report blinding if possible (single, double, triple) . Use and report randomization. Report initial equality of baseline characteristics and comparability of study groups. Report any drop out of follow up samples.
  • 23. Step 3) Data: Types, Suitable Statistical tests.
  • 25. Nominal data Are given a particular name Male /Female Yes / No Order is not important Binominal, multinominal
  • 26. Ordinal data Similar to nominal data, but categories are ordered logically. equal to, less than, or greater than Small, medium, large Easy, intermediate, difficult
  • 27. Interval data Data has a meaningful order Equal intervals between measurements represent equal changes in the quantity of whatever is being measured Data have no natural zero Example is Celsius scale of temperature. In the Celsius scale, there is no natural zero, so we cannot say that 70°C is double than 35°C.
  • 28. Ratio data Ratio data has all the qualities of interval data (natural order, equal intervals) plus a natural zero point. height, weight, length it can be said meaningfully that 10 m of length is double than 5 m.
  • 29. Any variable measured, in any type of data can be either: Discrete if there are only a finite number of values possible (whole numbers) Continuous data makes up the rest of numerical data (can have fractions)
  • 30. Make sure that the suitable statistical test(s) was used for the collected data.
  • 31.
  • 32.
  • 33.
  • 34.
  • 36. The only science that enables different experts using the same figures to draw different conclusions. Evan Esar (1899–1995) American writer.
  • 37. There are three kinds of lies: lies, damned lies, and statistics. Benjamin Diserali British Prime Minister (1804 – 1881)
  • 38. If your experiment needs statistics, you ought to have done a better experiment. Ernest Rutherford, (1871 – 1937) was a British chemist and physicist who became known as the father of nuclear physics.
  • 39. I can prove anything by statistics except the truth. George Canning (1770 – 1827) British statesman and politician.
  • 41. 1- Survey Survey results in KSMC, is that 1 out of ten employees is a smoker…
  • 42. How many were surveyed? Did they all respond? Male to female ratio. Occupation, age group, education. What were the questions? Did they understand the questions? Who questioned them? Bias
  • 44.
  • 47. Event Rate 0 5 10 15 20 25 30 35 40 45 Event Rate: Absolute Reduction = 10% Relative Reduction = 25% 40% 30%
  • 49. SAP of fifteen subjects (mmHg): 90, 98, 110, 111, 113, 113, 121, 125, 127, 128, 130, 132, 136, 140, 165 The Average is ? 113 121.8 122.6 125
  • 51. Average Mean: summation of all results, divided by their number. Median: The middle value of arranged data. Mode: most frequently recurring value. Truncated mean: The same as mean, after omitting the two extremes.
  • 52. 4- Multiple testing In 1980, researchers at Duke randomized 1073 heart disease patients into two groups, but treated the groups equally. Not surprisingly, there was no difference in survival. Then they divided the patients into 18 subgroups based on prognostic factors. In a subgroup of 397patients (with three-vessel disease and an abnormal leftventricular contraction) survival of those in “group 1” was significantly different from survival of those in “group 2” (p<.025). How could this be since there was no treatment? (Lee et al. “Clinical judgment and statistics: lessons from a simulated randomized trial in coronary artery disease,” Circulation, 61: 508-515, 1980.)
  • 53. The difference resulted from thecombined effect of small imbalances in the subgroups. A significance level of 0.05 means that your false positive rate for one test is 5%. If you run more than one test, your false positive rate will be higher than 5%.
  • 54. 5- Reporting measurements with unnecessary precision Many numbers do not need to be reported with full precision. If a patient weighs 60 kg, reporting the weight as 60.18 kg adds only confusion. even if the measurement was that precise. Thus, rounding numbers to two significant digits improves communication.
  • 55. 6- Reporting only p-values for results For main results, report the absolute difference between groups (relative or percent differences can be misleading). Report the 95% confidence interval for the difference, instead of, or in addition to, p- values.
  • 56. “The effect of the drug on lowering diastolic blood pressure was statistically significant (P< 0.05)” Here, the size of the drop is not given, so its clinical importance is not known. Also, P could be 0.049; statistically significant (at the 0.05 level) but so close to 0.05 that it should probably be interpreted similarly to a P value of 0.51, which is not statistically significant. The use of an arbitrary cut point, such as 0.05, to distinguish between “significant” and “non significant” results is one of the problems of interpreting P values.
  • 57. The drug lowered diastolic blood pressure by a mean of 18mmHg, from 110 to 92mmHg (95% CI=2 to 34mmHg; P=0.02).”
  • 58. 7- Unnecessarily reporting baseline statistical comparisons in randomized trials In a true randomized trial, each patient has an equal probability of being assigned to either the treatment or the control group. Thus, any differences between groups at baseline are, by definition, the result of chance. Therefore, significant differences in baseline data do not indicate bias (as they might in other research designs).
  • 59. 8- Not defining “normal” or “abnormal” when reporting diagnostic test results The importance of either a positive or a negative diagnostic test result depends on how “normal” and “abnormal” are defined. In fact, “normal” has at least six definitions in medicine.
  • 60. 9- Confusing the “units of observation” when reporting and interpreting results The “unit of observation” is what is actually being studied. Problems occur when the unit is something other than the patient
  • 61. For example, in a study of 50 eyes, how many patients are involved? What does a 50% success rate mean?
  • 62. 10- Percentage Vs Absolute Sometimes statistics are given in absolute terms in large number studies. They are given in percentages in small number studies. Statistics better be reported in both ways.
  • 63. We might hear that Blanko Corp. laid off 32 people or we might hear that they laid off 25% of their workforce. Typically a news source will try to make the number sound as dramatic as it can, so if Blanko is a huge company - say it has 200,000 employees - the source might find it more impressive to say it laid off 20,000 people rather than 10% of the workforce. If Blanko is small, say 100 employees, it sounds more impressive to say they laid off 10% rather than just 10 people.
  • 64. If improvement in a study was 10% If the study included 2000 patients, it is more impressive to report 200 improved cases rather than 10 % But if the study included only 50 patients, it is more dramatic to report 10 % improvement, rather than just 5 patients.
  • 66. Title A B C D E 70 60 50 40 30 20 10 Source of data
  • 67. Can graphs be manipulated to mislead you?
  • 68. Title: Great reduction of mortality rates with drug (X), compared to (Z) Comparison of mortality rates between drugs (X) and (Z)
  • 70. Y axis: In a study on 3 antibiotics (A, B, C), the rate of infection with a certain organism was: A : 972 cases B: 984 cases C: 955 cases
  • 71. Misleading graph of NO differenc… A B C
  • 72. Misleading graph of: Difference
  • 73. What difference do you see between the two graphs?
  • 77. Data on X axis
  • 78. This graph, comparing causes of death is misleading, because it leaves out deaths from heart disease, cancer, and stroke. The omission tends to make smoking look much more harmful by comparison.
  • 80. Statistical significance. Does not guarantee clinical significance. Does not imply a cause-effect relationship. Small differences between large groups can be statistically significant but clinically meaningless. Failure to discuss sources of potential bias and confounding factors.
  • 81. Statistical Insignificance. Lack of statistical significance is not proof of the absence of an effect. Results that are not statistically significant should not be interpreted as "evidence of no effect,” but as “no evidence of effect” In studies with low statistical power, results that are not statistically significant are not negative, they are inconclusive. Large differences between small groups can be clinically important but not statistically significant.
  • 82. Watch out for… Drawing conclusions not supported by the study data Significance claimed without data analysis or statistical test mentioned. Missing discussion of the problem of multiple significance testing if done.
  • 83. My message… When reading a study, read it from A to Z. Don’t just read the title and conclusion. Read it more than one time, then sit back, and think about it.

Editor's Notes

  1. The probability of getting the same results of the treat group in the non-treat group.