Sampling Assignment:
It is not unusual to encounter epidemiological or medical studies that use a non-probabilistic sampling scheme. For example, many randomized controlled trials use convenience sampling. Identify a research question of interest to you, the population of interest, and a study design you might use to examine the question. Explain how you might incorporate a sampling strategy into the study design (you might, for example, use respondent-driven sampling to initiate a cohort study). Briefly discuss possible logistical/practical advantages and disadvantages to this plan. Finally, discuss whether you think incorporating the sampling strategy might help (1) reduce bias in the estimation of univariate quantities (such as disease prevalence) and (2) reduce bias in the estimation of causal effects.
I am interested in studying the real-life effect of a metformin intervention on reducing risk of developing type 2 diabetes among high risk low-income Mexican adults living in Mexico City using a clustered randomized control trial design. We would randomly select a number of clinics who serve the low-income population and randomize half for the intervention and half for the control. Within clinics, we would offer the intervention to all eligible participants using a pre-determined definition (age, pre-diabetes, BMI). From the eligible participants, we would randomly select a number of them and invite them to enroll in our study. Controls, would receive standard of care (weight loss counseling, which those on the intervention would also receive).
Some of the logistical issues with this is that it will take much longer to recruit a sufficient sample size since we first need to identify a sufficiently large eligible pool from which to randomly draw. Since the population of study is also of low SES, it may be particularly challenging to be able to successfully enroll all those randomly selected to be in the study, and thus potentially introducing some selection bias.
Depending on how successful we are on enrolling all randomized participants, we may be able to draw valid inferences on the estimation of univariate quantities (such as disease prevalence among those who receive medical care at Seguro Popular), but if there ends up being a large self-selection then we would not be able to present valid estimates. Regardless of the final sample, we would be able to estimate causal effects of Metformin for the prevention of Type 2 diabetes among high-risk low-income Mexican adults.