Common mistakes in comparative cross-sectional studies

Comparative cross-sectional studies are the cross-sectional versions of cohort studies. They are used when we want to investigate the “effect” of a supposed exposure on an outcome but when, instead of following up “exposed” and “non-exposed” groups overtime to see for the development of the outcome, both the “exposure” status and the outcome are ascertained at the same point in time. In essence, analysis of comparative cross-sectional data focuses on answering whether or not the supposed “exposure” does have an “effect” on (or is significantly associated with) the outcome of interest adjusting for confounding variables.

However, there are two common mistakes observed in comparative cross-sectional studies. These are:

1. Considering comparative cross-sectional studies as “two in one”; i.e., considering them as two independent studies and in effect, analyzing the data and reporting the results independently for the “exposed” and “non-exposed” groups.
2. Forgetting the hypothesis. The null hypothesis in comparative cross-sectional studies is that the supposed “exposure” does not have and “effect” on the outcome. However, this hypothesis is often forgotten and researchers go searching for any independent variables associated with the outcome. That is, they plug in as many independent variables as possible into a multivariable model and they are taken away by whatever comes out significant forgetting the hypothesis.

To conclude, researchers using the comparative cross-sectional study should focus on answering whether or not the (“exposure”) variable used to define the comparison groups does have an “effect” on (association with) the outcome variable controlling confounding variables. The malpractice of considering a comparative cross-sectional study as a set of two independent cross-sectional studies and hence analyzing the data and reporting the results separately for the two comparison groups falls short of answering its objective and must be avoided. Besides, straying away from the main hypothesis and talking principally about other covariates that happen to be significant in the process of confounder control shall also be avoided.