Samenvatting
Summary of Clinical Research in Practice Lectures, Workgroups, etc.
- Instelling
- Universiteit Leiden (UL)
Summary on lectures, workgroups, and assignments for Clinical Research in Practice (CRiP)
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Voorbeeld 3 van de 30 pagina's
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Voorbeeld van de inhoud
CLINICAL RESEARCH IN PRACTICEINDEX
Introduction to the course......................................................................................................................2
Cardiovascular risk..............................................................................................................................2
Research areas....................................................................................................................................2
CAREMA study....................................................................................................................................2
General info and administrative business...........................................................................................2
Epidemiology..........................................................................................................................................3
How to design a research question 24-09..........................................................................................3
Epidemiology: Revision of Basic Principles 24-09...............................................................................4
SSA 1 (Statistics and Epidemiology Computer Practicum 1)...............................................................5
Confounding and stratification 25-09.................................................................................................6
Effect modification and interaction 25-09..........................................................................................8
WG confounding and interaction 26-09.............................................................................................8
HC standardisation 02-10...................................................................................................................8
HC Directed Acyclic Graphs (DAGs) and confounding 2......................................................................9
WG DAGs............................................................................................................................................9
Molecular epidemiology.......................................................................................................................11
HC Molecular Epidemiology 1...........................................................................................................11
HC Molecular Epidemiology 2...........................................................................................................11
HC Molecular epidemiology 3...........................................................................................................11
WG Molecular epidemiology 2.........................................................................................................12
HC Molecular epidemiology 4...........................................................................................................12
Biostatistics...........................................................................................................................................13
Logistic regression 26-09..................................................................................................................13
SSA Logistic regression.....................................................................................................................13
CP 3 logistic regression.....................................................................................................................16
HC Survival analysis..........................................................................................................................18
SSA Survival analysis.........................................................................................................................19
HC Mixed models..............................................................................................................................23
SSa Mixed models.............................................................................................................................24
Data Management................................................................................................................................27
ADM college 2017.............................................................................................................................27
Practicals ADM..................................................................................................................................30
1
,INTRODUCTION TO THE COURSE
Broad introduction to methodology for applied research, applicable to clinical and biomedical
studies. Focus on study of myocardial risk.
CARDIOVASCULAR RISK
How does a myocardial infarct develop?
Coronary atherosclerosis, build-up of fat in the vessel walls, which can lead to an occlusion, or a
tear of the lining of the artery, which leads to a sudden block of the vessel and oxygen supply of the
heart is suddenly stopped.
What are the risk factors for a myocardial infarct?
Rationale: if you can predict it, you can prevent it.
Hypertension, smoking, diabetes mellitus, dyslipidemia, family history (longitudinal study from
1948-1958)
How can we risk-stratify a person before a heart attack happens?
Primary: prevent a heart attack before it has ever occurred.
Secondary: prevent a second heart attack in someone who has already had one.
Cardiac imaging for risk stratification.
What does the future hold?
Echocardiography, but also novel techniques such as molecular imaging.
RESEARCH AREAS
Epidemiology
Learn all stages of clinical research, such as choosing methods and managing data.
Clinical epidemiology: application of epidemiologic principles on questions that relate to etiology
(causes of disease), diagnosis, prognosis, and therapy.
Molecular epidemiology: application of epidemiologic principles on questions that can relate to
understanding (molecular){ mechanisms of disease in population.
Molecular epidemiology
Integrate clinical epidemiology, medical sciences and molecular biology to increase our
understanding of the mechanisms of disease in patient populations. Find correlations in molecular
data. We will focus on the metabolome and the genome.
Biostatistics
Prediction of an outcome using logistic regression (binary), cox regression (time-to-event) and mixed
model concepts (follow-up of a patient).
Data management
CAREMA STUDY
Investigate cardiovascular incidence, etiology and risk prediction.
Two kinds of data-sets: case-control design and prospective analysis.
GENERAL INFO AND ADMINISTRATIVE BUSINESS
Total of 100 points on the exam: 45 statistics, 30 epidemiology, 15 molecular epidemiology and 10
data management.
2
, EPIDEMIOLOGY
HOW TO DESIGN A RESEARCH QUESTION 24-09
The question sets out what you want to learn about the topic. What problem do you want to
solve?
The question can only be formulated after extensive research about the topic. do a review.
The REWARD Alliance: Reduce Research Waste and Reward Diligence.
Applied clinical research has 3 types:
Diagnosis: cross-sectional
Prognosis / Intervention: about the future, randomized trials and cohorts
Etiology: case-control studies (why did patients get the disease)
Research question: 4 components
1. Y: the outcome (prevalence, incidence)
2. X: determinants / exposure (characteristics or interventions)
3. Domain: to whom it may concern
4. Time dimension, from determinant to outcome.
Example 1: diagnostic research question
Q: Is there an added value of fecal calprotectin and hemoglobin in the diagnostic work-up for primary
care patients suspected of significant colorectal disease (SCD)?
1. Y: SCD
2. X: Fecal calprotectin and hemoglobin in the diagnostic work-up
3. Domain: patients suspected of significant colorectal disease (SCD)
4. Time dimension: none: in diagnostic studies there is no time dimension.
PICOT:
- Population = to whom it may concern
- Intervention
- Control
- Outcome (disease prevalence or incidence)
- Time dimension, period between start of intervention and when you expect the outcome to
occur.
Example 2: Research question on intervention
Risk of suicidality in clinical trials of antidepressants in adults: analysis of proprietary data submitted
to US Food and Drug administration
1. Incidence of suicidality
2. Antidepressants or no antidepressants
3. Adults with depression
4. During the first 2 months after the start of antidepressant treatment
Example 3: etiologic research question
Problem: the microcephaly epidemic in Brazil; the main hypothesis was that it is caused by congenital
Zika virus infection.
1. Microcephaly
2. Congenital Zika virus infection compared with no zika virus
3. Fetuses born in Brazil
4. 9 months; Zika infection during early state of pregnancy up to completion of development of
the brain.
3
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