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.
Currently, HIV prevalence in low-income countries is often estimated by the use of district health surveys. These surveys are nationally representative, conducted approximately every five years, and have large sample sizes (over 5000 respondents). The sample is usually based on a stratified two-stage cluster design first selecting a number of geographical areas and then using simple random sampling to select a number of households. This technique can lead to a non-representative sample because the participants are chosen in the geographic strata.
An example of a group that is frequently not captured adequately using DHS instruments, is nomadic pastoralists and mobile populations in general (migrants, homeless, etc.). Because these individuals and communities are not sedentary, they are not accurately captured (if captured at all) on the lists of households or individuals that can be used for simple random sampling. Therefore, even if the geographic unit in which they live is selected for the DHS, they may not be included in the sample.
Using respondent-driven sampling (RDS), as in the Firestone article, may be a good way to ensure that this population is captured. Identifying a number of “seeds” as initial respondents, who each identify additional respondents and so on through multiple waves may produce a useful cohort. However, given the close social and familial ties of the communities that travel together, even though RDS adjusts for small to moderate levels of network clustering using post-sampling weights, this approach may lead to significant sampling bias.