The study is to explore the exposures (risk factors) that related to the effect. Effect itself defined as positive response to a treatment.
Exposures: some risk factors (demographic, family and past history of diseases, clinical conditions, intensity and hemodynamics response during event)
Main effects: cTn elevation
Treatment/intervention: event of long distance cycling
Population study: a sample group of participants join the event
Measurement:
observe risk factors before treatment
measure some marker before and after treatment
measure intensity and hemodynamics response during event
What is the suitable design for this research; observational study? (what kind of observational: cohort or cross sectional) Or One group pre test - post test design?
I think, if the purpose of research to do intervention, you can be selected pre-post design
If you mean observational study (i.e. cohort, case-control and cross-sectional study), then you may perform repeated measured ANOVA. From you explanation it is more clear that you are going to do a intervention study and measure two time points (before and after the treatment). So if your outcome is treatment (Yes or no), multiple logistic regression would be suitable to take all exposures into account for both before and after.
since you want to study the effect of multiple risk factors and you are measuring before and after cycling, the design is experimental - not randomized and not observational with One group of cyclists, since your comparison group is the same cyclists before event. For analysis if you can dichotomize cTn elevation and define what is considered illness or not, then a conditional logistic regression would be enough. If not a repeated measures analysis also known as longitudinal data analysis should be done.
Many thanks Mamunur Rashid, Frank Mo and Alice David for your comprehensive answer
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