Hello Sujit,
As always, this was an interesting read. Here are my suggestions:
1. Study design: prospective cohort study
2. Exposure: Sugar-sweetened, artificially sweetened and unsweetened coffee
Outcome: All-cause, cancer-related, and CVD-related mortality
Comparison groups: Non-coffee drinkers in UK Biobank
Data collection method: Exposures were self-reported and extracted from Biobank data
Measurement bias: It would have been interesting to know how the type of sweetener used in the coffee was ascertained by Biobank and eventually classified by study investigators. Also, the frequency at which exposure data was collected as people could change the exposure status from coffee drinker to non-coffee drinker or change type of sweetener from day to day, week to week and year to year. The above could all have introduced non-differential misclassification of exposure status potentially biasing effect measures towards the null. There may also have been a social desirability bias with people having a tendency to misreport exposure data based on what is considered socially desirable
3. Outcome: All-cause, cancer-related, and CVD-related mortality
Data collection method: Objective measure using death certificates
Measurement bias: Could have occurred depending on how cause of death is recorded for example if a study participant with cancer also had COVID-19 and died, would the cause of death be recorded as COVID or cancer? This type of errors could result in fewer outcomes and lower the power/precision of the study. It would also be great to know if those assessing the outcomes were blinded to exposure status of the participant as this could introduce observer bias leading to an over-estimation of the measure of effect.
4. Ethnicity was considered as a potential confounder of the association between consuming coffee and mortality. Some ethnic groups may be more likely to drink tea (so less coffee) and vice versa. Mortality rates may also differ for different ethnic groups. Ethnicity does not lie on the causal pathway between coffee drinking and death. It would be a problem if ethnicity data is missing depending on the proportion of the missing data and if these missing data are differential by exposure status as it could introduce bias due to missing data and reduce sample size included in final models if missing data are simply excluded from analyses.
5. Numerator: hazard rate of death in unsweetened coffee drinkers consuming 2.5 to 4.5 cups per day
Denominator: hazard rate of death in non-coffee drinkers
6. Even though a cohort study aids in establishing temporality, exposed and unexposed may be very different at baseline on both known and potentially unknown confounders. An RCT provides a higher level of evidence of causation because in addition to establishing temporality, randomization ensures that investigators have exposure groups which are comparable and exchangeable on expectation except for the intervention received and therefore balanced on both known and unknown confounders (provided we have a large enough sample size). In addition, an RCT might be considered ethical as there is equipoise on this question even though the harmful effects of sugar on the health have been clearly established and may raise ethical concerns. However, an RCT may not be feasible for logistical and other reasons.
I look forward to reading your thoughts on this crude first shot.
Kind regards,
Colette