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. 

Cross-sectional studies: Longer duration of participant recruitment does not make them longitudinal

Sometimes researchers think that they are doing a longitudinal study simply because their study requires a longer duration to recruit the needed number of study participants. However, it is not the length of time it takes to enroll the needed number of study participants that defines cross-sectional studies. It is the temporal relationship of the ascertainment of exposure and outcome that matters.

In cross-sectional studies exposure and outcome are ascertained at the same time. That is both are measured at the same point in time. One doesn’t precede the other. Measurement per study participant is done only once. No repeat/follow up measurement at some later time.

Conversely, the distinguishing feature of longitudinal studies is repeated measurement. Two or more measurements are done per study participant at given time intervals. The temporal relationship between exposure and outcome is such that the outcome comes later than the exposure. 

To conclude, a cross-sectional study may take a year to accomplish whereas a longitudinal study may be accomplished in few weeks or months. They can simply be distinguished by the fact that the former involves a single measurement per study participant (no repeat/follow up measurement at a later time) and ascertainment of exposure and outcome is done at the same time while the latter involves at least one baseline measurement and at least one other follow up measurement and the presumed exposure is ascertained before the outcome occurs.