OBSERVATIONAL STUDIES Instructor: Fabrizio D’Ascenzo [email protected] Role MD.
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Transcript of OBSERVATIONAL STUDIES Instructor: Fabrizio D’Ascenzo [email protected] Role MD.
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OBSERVATIONAL STUDIES
Instructor: Fabrizio D’[email protected]
www.emounito.orgwww.metcardio.org
Role MD
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CONFLICT OF INTEREST
None
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AIM OF THE COURSE
A critical appraisal
- Theorical- Practical
of observational studies
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TODAY’S PROGRAM: FIRST PART
1) Literature: clinical general concepts
2) Literature: clinical methodological concepts
3) Quick assessment of an observational study
4) Complete assessment of on observational study
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HOW TO READ and WRITE A STUDY
Two points of view:
- Clinical
- Methodological
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CLINICAL
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- Strenght of association- Temporality- Consistency
- Theorical Plausibility- Coherence
- Specificity in the cause- Dose-response
- Experimental evidence- Analogy
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STRENGHT OF ASSOCIATION
Size of the association as measured by appropriate statistical tests
Example Odds Ratio, Relative Risk
But
strength of association depends on the prevalence of other potential confounding
factors
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TEMPORALITY
Exposure should always precede the outcome
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CONSISTENCY
The association is consistent when results are replicated
in
studies in different settings using different methods.
If a relationship is causal, we would expect to find it
consistently in different studies and among different
populations.
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THEORICAL PLAUSIBILITY andCOHERENCE
The association agrees with currently accepted
understanding of pathological processes.
A causal association is increased if a biological gradient or
dose-response curve can be demonstrated.
The association should be compatible with existing theory
and knowledge.
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IS THIS ENOUGH?
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RELIABLE EVIDENCE?
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METHODOLOGICAL
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GRADING THE EVIDENCE
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WHY TO PERFORM AND READ NOT RANDOMIZED EVIDENCE?
• to save economical resources
• to create hypothesis, especially for non
randomizable patients
• to shed light on the generalizability of results
from existing randomized experiments
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HOW TO EVALAUTE NON RANDOMIZED EVIDENCE?
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QUICK ASSESSMENT OF AN OBSERVATIONAL STUDY
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3 CRUCIAL CONCEPTS
- DESIGN OF THE STUDY
- BIAS
- MULTIVARIATE ANALYSIS
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THREE DIFFERENT DESIGNS
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COHORT
Advantages: chances to appraise different
outcomes
Disvantages: if events/outcomes are unfrequent,
large number of patient is needed
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CASE-CONTROL
Advantages: studies for infrequent outcomes
Disvantages: controls patients need to be
selected from the whole population
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CROSS SECTIONAL
Advantages: easy to perform
Disvantages: limited function
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OR EASIER
• Retrospective>means testing an hypothesis on datasets
- already present- built for that hypothesis but not at the time of
patients’assessment
• Prospective>means testing an hypothesis on datasets built for it, to evaluate, study and insert data of the patients at the moment of their hospitalization/drug assumption/intervention
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REASON FOR ASSOCIATIONS
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REASON FOR ASSOCIATIONS
• Bias
• Confounding
• Chance
• Cause
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BIAS
Measure of association between exposure and
outcome is systematically wrong
Two directions:
- bias away from the null
- bias towards the null
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SELECTION BIAS
Unintended systematic difference between
the two or more groups, which is associated
with the exposure.
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FOR EXAMPLE
Inclusion of too selected patients:
> patients with more severe disease presentation are
often excluded
TO
obtain larger benefits
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If reported:
How many patients attain a complete follow up>
if a patient is lost at follow up, he/her may have dead (more probably) or alive
ATTRITION BIAS
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1192 consecutive patients undergoing PCI in our center
between January 2009 and January 2011
1116 patients with follow up data derived from Piedmont Region
dedicated registry (AURA)
37 not detectable
(30 not European….)
1155 at follow up of 787 days (median;474-1027)
Medical folders of each patient, and for re-hospitalizations were re-analyzed by a
physician
76 patients not recorded in Piedmont Region dedicated registry:
39 recovered through phone call
Figure 1.
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If reported:
who adjudicate the events:
- A blinded central committee
- Non blinded researchers
ADJUDICATION BIAS
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an error in measuring exposure or
outcome may cause information bias>lower
risk if the study is multicenter
ANALITICAL/INFORMATION BIAS
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IF REPORTED….
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CHANCE
The precision of an estimate of the association between
exposure and outcome is usually expressed as a confidence
interval
(usually a 95% confidence interval)
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The width of the confidence
interval is determined by the number of subjects with the outcome of interest,
which in turn is determined by the sample size.
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With 200 ptsVariables in the Equation
.069 .582 .014 1 .906 1.071 .342 3.351
.488 .567 .739 1 .390 1.629 .536 4.950
.769 .565 1.855 1 .173 2.158 .713 6.527
.010 .747 .000 1 .990 1.010 .233 4.368
2.111 .547 14.886 1 .000 8.256 2.825 24.126
DIABETE
PREGRESS
RICOVERO
V21
GSP_POSI
B SE Wald df Sig. Exp(B) Lower Upper
95.0% CI for Exp(B)
Variables in the Equation
.069 .238 .084 1 .773 1.071 .672 1.706
.488 .232 4.436 1 .035 1.629 1.034 2.564
.010 .305 .001 1 .975 1.010 .555 1.836
.769 .231 11.131 1 .001 2.158 1.373 3.390
2.111 .223 89.317 1 .000 8.256 5.329 12.791
DIABETE
PREGRESS
V21
RICOVERO
GSP_POSI
B SE Wald df Sig. Exp(B) Lower Upper
95.0% CI for Exp(B)
With 1000 pts
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CONFOUNDINGThe aim of an observational study is to examine
the effect of the exposure,
but
sometimes the apparent effect of the exposure
is
actually the effect of another characteristic
which is associated with the exposure and
with the outcome.
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MULTIVARIATE ANALYSIS
Multivariable analysis aims to explore the
relationship
between a dependent variable
and
two or more independent variables appraised
simultaneously.
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ARE ALL MULTIVARIATE ANALYSIS THE SAME?
• Logistic regression
• Cox Multivariate adjustement
• Propensity score
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HOW TO CHOOSE VARIABLESTo avoid:
- automatic algorithms with stepwise selection
To choose established association from:
- prior well conducted experimental or clinical studies
- strong associations (e.g.p<0.10 or p<0.05 at
univariate analysis)
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LOGISTIC REGRESSION: THE SIMPLEST ONE
The logit function transforms a dependent
variable ranging between 0 and 1 such as a
probability of an event
into a variable stemming from −∞ to +∞.
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Thus, event probabilities can be appraised as a
linear regression function
to
appraise the logit of the probability of an event
(dependent variable) given one or more
dependent variables
LOGISTIC REGRESSION: THE SIMPLEST ONE
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LOGISTIC REGRESSION: THE SIMPLEST ONE: LIMITS
Overfit model can be highly predictive in the
dataset in which the model was developed, but
not in one in which it is validated or tested.
Multicollinearity, whereby covariate present in
the model are unduly associated
Does not correct for time
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COX PROPORTIONAL HAZARD ANALYSIS: THE MOST USED ONE
• It addresses differences in follow-up duration and
censored data
• It is based on The hazard function, which forms
the basis of Cox analysis: the event rate at time t
conditional on survival until time t or late
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CENSORED DATA
Censored patients are exploited to compute
hazards and are assumed in the Cox model
to fail at the same rate as the non censored,
but are not supposed to survive to the next
time point.
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The term right censored implies that the event of
interest (i.e., the time-to-failure) is to the right of
our data point. In other words, if the units were to
keep on operating, the failure would occur at
some time after our data point (or to the right on
the time scale)
RIGHT CENSORED DATA
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INTERVAL CENSORED DATA
If we inspect a certain unit at 100 hours and find
it operating
and perform another inspection at 200 hours to
find that the unit is no longer operating,
then the only information we have is that the unit
failed at some point in the interval between
100 and 200 hours.
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A failure time is only known to be before a certain time.
LEFT CENSORED DATA
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PROPENSITY SCORES: THE NEW ONE
conditional probability of receiving an
exposure or treatment given a vector of
measured covariates
Courtesy of American Heart Association
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Propensity scores act as a proxy between
cases and covariates influencing exposure,
and thus can be used instead of such
covariates to simplify the analysis plan and
increase robustness
PROPENSITY SCORES: THE NEW ONE
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How to do it:
a logistic regression in a non-parsimonious fashion
results of this non-parsimonious logistic regression are
then exploited to build the propensity score
THEN
insert in multivariate adjustment to increase accuracy
matching
PROPENSITY SCORES: THE NEW ONE
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Different methods:
- calipers of width of 0.2 of the standard deviation of
the logit of the propensity score
- Mahalanobis metric
Matching
-greedy matching
MATCHING
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MATCHING
calipers of width of 0.2 of the standard deviation
of the logit
of the propensity score and the use of calipers of
width 0.02 and 0.03 tended to have superior
performance for estimating treatment effects
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Calibration
Whether the distances between the observed (treatment—yes or
no) and the predicted outcome from the model (propensity
score) are small and unsystematic. This is usually formally
appraised with the Hosmer–Lemeshow goodness of fit test.
PROPENSITY SCORES: THE NEW ONE
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Discrimination
How well the predicted probabilities derived from the
model classify patients into their actual treatment group.
This is usually quantified with c-statistic, receiver
operator characteristic, and area under the curve.
PROPENSITY SCORES: THE NEW ONE
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IS THIS THE SAME?
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It is important to keep in mind that even propensity
score methods can only adjust for observed
confounding covariates and not for unobserved
ones.
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IS EVERYTHING SO PERFECT?
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ACCURATE ASSESSMENT OF AN OBSERVATIONAL STUDY
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VARIABLES
Clearly define all outcomes, exposures, predictors,
potential confounders, and effect modifiers.
Give diagnostic criteria, if applicable
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DATA SOURCES/ MEASUREMENT
For each variable of interest, give sources of data and
details of
methods of assessment (measurement).
Describe comparability of
assessment methods if there is more than one group.
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STUDY SIZE
Explain how the study size was arrived at
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HOW TO DO IT?
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RESULTS
• Report numbers of individuals at each stage of
study—eg numbers potentially eligible, examined
for eligibility, confirmed eligible, included in the
study, completing follow-up, and analysed
• Give reasons for non-participation at each stage
• Consider use of a flow diagram
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DISCUSSION• Summarise key results with reference to study objectives
• Discuss limitations of the study, taking into account sources of
potential bias or imprecision. Discuss both direction and
magnitude of any potential bias
• Give a cautious overall interpretation of results considering
objectives, limitations, multiplicity of analyses, results from
similar studies, and other relevant evidence
• Discuss the generalisability (external validity) of the study
results
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FUNDING
Give the source of funding and the role of the
funders for the present study and, if
applicable, for the original study on which
the present article is based
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TAKE HOME MESSAGES
- Check for biological and methodological
Pitfalls
- Remember that multivariate analysis is multivariate analysis
- Remember that multivariate analysis is “only” multivariate analysis
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THANKS A LOT!!!!