Title: LÄK BasV1 / Redogöra för vad som definierar en vetenskaplig frågeställning och kunna översiktligt beskriva hur grundläggande statistiska metoder används för att besvara vetenskapliga frågor (S3)

Q: Vad är Risk Ratio (=Relative Risk)?
A: - If RR=1, same risk exposed/unexposed
No association btn exposure & outcome
- If RR>1, +risk exposed than unexposed
An association btn exposure & outcome
- If RR<1, +risk unexposed than exposed
An inverse association btn exposure & outcome

Q: När inträffar Selection Bias?
A: Systematic Error
- Occurs when those who participate in the study are not representative for the target pop (=the whole group of ppl we wish to make claims abt)
- Can occur when the sample is taken from a special sub-group of the pop (e.g. males/females, or ppl at a certain hosp or in a certain med register)
- Can also occur when there is non-response or drop-out from the study —> BORTFALL
Ex: snus users who develop ulcer feel ashamed bc snus is believed to have harmful effects (right or wrong) —> don't participate in the study —> snus users in the study have less ulcers than the general pop of snus users

Q: Vilken konsekvens har Selection Bias?
A: - The consequence is that the observed risk ratio (=relative risk) is not equal to the true risk ratio (=risk ratio in the target pop)
- The observed risk ratio may be either larger or smaller than the true risk ratio

Q: Hur kan man undvika Selection Bias?
A: - Make sure that the sample is taken at random from the target pop; no special selection
—> Often easier said than done, we may only have data for men/women, or for ppl at a certain hosp or in a certain register + some ppl don't want to reply to questionnaire studies
—> Be careful when interpreting your results if you have reason to believe that your sample is not random

Q: När inträffar counfounding?
A: Systematic Error
- Confounding occurs when the exposure and the outcome have common causes (cf. hereunder: age=common cause)
- The common causes are called confounders
Ex:
Young ppl use snus more often than old ppl bc of age trend —> young ppl have a smaller risk of ulcer than old ppl (biological reasons) —> snus-users have less ulcers than the non-users

Q: Vad är konsekvensen av confounding?
A: - The risk ratio (=relative risk) cannot be interpreted as an effect of exposure
—> It depends on both:
      - the causal effect of the exposure on the outcome
      - the influence of the counfounders on the exposure and the             outcome
- The risk ratio (=relative risk) may be above or below 1, even in the absence of a causal effect (bc of counfounders).

Q: Hur kan man undvika counfounding?
A: Two, principally different ways of avoiding confounding:
- At the design stage, by randomising the exposure
—> RCTs
- At the analysis stage, by adjusting for measured confounders

Q: Vad är Randomised Controlled Trials (RCTs)?
A: - In a RCT, we make an intervention:
—> We assign exposure levels at random (≠observational study) to the study participants (e.g. by the flip of a coin, head for exposure, tail for no exposure)
—> We follow the participant over time, and measure the outcome during follow-up
—> We finally compare the outcome prevalence between those who were randomised to exposure and no exposure

Q: Vad är fördelen av RCTs?
A: - The random assignment breaks the influence of all confounders on the exposure/treatment:
—> In an ideal RCT, there is no confounding —> det är ren slump som påverkar om man är exponerad eller inte —> man bestämmer t ex att en grupp ska snusa helt slumpmässigt, snusning påverkas därmed inte längre av störfaktoren (counfounder) «åldern».

Q: Vad finns det för nackdelar med RCTs?
A: RCTs are often:
- unethical; in particular when we expect the exposure to be harmful (e.g. snus)
- impractical, since many exposures (e.g. BMI —> «you shall have that BMI») are very difficult to randomise
- expensive
—> And even if we manage to randomise the exposure, the study participant may not comply with the assignement
—> Most of our medical/scientific knowledge comes from observational (non-randomised) studies

Q: Hur kan man justera för confounders?
A: - At the analysis stage we may 'adjust' the observed risk ratio (=relative risk) for measured confounders
- Technically, many ways to do that (stratification, matching, regression modelling, propensity scores, etc.)
—> But we can only adjust for measured confounders
—> In observational studies, an observed association can always be explained by unmeasured confounders
—> Don't (over)interpret statistical associations in observational studies as causal effects

Q: Vad är Reverse Causation (omvänd kausalitet)?
A: Systematic Error
- Reverse causation (omvänd kausalitet) occurs when the outcome affects the exposure, and not the other way around
Ex: Doctors believe that snus causes + aggravates ulcer —> advise snus users to quit if they have ulcer —> CCL: snus users have less ulcers than the non-users

Q: Vad är konsekvensen av Reverse Causation (omvänd kausalitet)?
A: —> The risk ratio (=relative risk) cannot be interpreted as an exposure effect
—> It reflects the effect of the outcome on the exposure, not the effect of the exposure on the outcome

Q: Hur kan man undvika Reverse Causation (omvänd kausalitet)?
A: - Difficult to deal with at the analysis stage
- By design, reverse causation is avoided in RCTs bc randomisation breaks the influence of the outcome on the exposure
Hur, om t ex alla läkare råder snusare att sluta? —> Vi bestämmer vem ska snusa och inte helt slumpmässigt, så vi kastar läkarna åt sidan här (de som vi bestämmer ska snusa ska alltså inte sluta)

Q: Vad finns det för systematiska fel?
A: —> Selection Bias
The sample is not representative of the target pop
—> Confounding
The exposure and the outcome have common causes
—> Reverse Causation
The outcome causes the exposure, not the other way around

Q: Most likely, [...] RR will be different from [...] RR due to [...], in the hypothetical and unrealistic case where no systematic errors have occured.
A: the observed; the true; random errors

Q: Vad är Confidence Interval (CI)?
A: Range of plausible values for the true RR.
—> The narrower the CI, the less uncertainty about the true RR = much better.
—> The width of the CI depends on the sample size; the larger the sample, the narrower the CI.

Q: Vad är p-value?
A: Often, we primarily want to know whether the true RR = 1 (e.g. no association btn snus & ulcer)
—> the hypothesis that the true RR = 1 is called the 'null hypothesis,' abbreviated H_0
—> the p-value (p) is an objective measure of the strength of the evidence in the observed data against H_0
—> 0 ≤ p ≤ 1
—> the smaller the p-value, the stronger the evidence against H_0
cf. normalfördelning av H_0 om H_0 antas vara sant —> p = sannolikheten att den RR vi fått kan stämma med H_0 om det antas vara sant.

Q: Vad beror p-värdet på?
A: The p-value depends on:
- the sample size: the larger the sample, the smaller the p (cf. CI)
- the strength of the observed RR: the further away from 1, the smaller the p
(cf. sannolikhetsfördelning av H_0 om det antas vara sant —> (1) = högre sannolikhet kring H_0=1; (2) = extrema värden är mindra sannolika)

Q: Hur kan man göra ett beslut kring evidensens styrka?
A: The p-value is an objective measure of the strength of the evidence against H_0 (the smaller the p-value, the stronger evidence against H_0).
—> Sometimes, we want to make a formal decision as to whether H_0 should be rejected or not (e.g. should we publicly declare snus as being preventive against ulcer?)
—> This decision is called hypothesis testing
—> We reject H_0 when the evidence against H_0 is 'strong enough.'
But what is 'strong enough?' —> when the p-value is 'small enough!'
—> The rejection threshold for the p-value is called the significance level (e.g. a 5% significance level means that we have decided to reject H_0 if p < 0.05)
—> Using a low significance level means that we require strong evidence against H_0 for rejection
—> Using a high significance level means that we are satisfied with weak evidence against H_0 for rejection.
(cf. court analogy)

Q: Vad är Type I and Type II Errors?
A: —> Always a trade-off between the risk for type I and type II errors:
- low significance level (difficult to reject H_0) => small risk for type I errors, but large risk for type II errors
- high significance level (easy to reject H_0) => small risk for type II errors, but large risk for type I errors
—> Convention: 5% significance level (reject H_0 if p<0.05).
—> The risk for type I errors = the significance level
e.g. suppose that:
- 100 research groups are studying snus and ulcer, and
- all research groups use the conventional 5% significance level, and
- H_0 is true, i.e. there is no association between snus and ulcer.
—> then 5 of these groups will wrongly reject H_0 and report that snus and ulcer are associated. (cf. normalfördelning, probs(0.01)=1%, probs(>0.90)=90% osv. – om vi förutsätter att bara slumpen inverkar och att det inte finns några systematiska fel —> då får 5 utav 100 grupper ett p<0.05)