How to estimate the association between change in a risk factor and a health outcome?

TL;DR

This paper reviews methods for estimating the effect of changes in risk factors on health outcomes using observational data, highlighting biases and providing recommendations for analysis strategies.

Contribution

It systematically reviews existing methodologies, identifies sources of bias, and offers guidance on choosing appropriate analysis plans for change-in-risk-factor studies.

Findings

Different methods have varying biases and limitations.

Proper timing of data collection affects bias.

Recommendations improve validity of observational change studies.

Abstract

Estimating the effect of a change in a particular risk factor and a chronic disease requires information on the risk factor from two time points; the enrolment and the first follow-up. When using observational data to study the effect of such an exposure (change in risk factor) extra complications arise, namely (i) when is time zero? and (ii) which information on confounders should we account for in this type of analysis? From enrolment or the 1st follow-up? Or from both?. The combination of these questions has proven to be very challenging. Researchers have applied different methodologies with mixed success, because the different choices made when answering these questions induce systematic bias. Here we review these methodologies and highlight the sources of bias in each type of analysis. We discuss the advantages and the limitations of each method ending by making our recommendations on the analysis plan.