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Long-term behavioral and health risk factors constitute a primary focus ofresearch on the etiology of chronic diseases. Yet, identifying criticaltime-windows during which risk factors have the strongest impact on diseaserisk is challenging. To assess the trajectory of association of an exposurehistory with an outcome, the weighted cumulative exposure index (WCIE) has beenproposed, with weights reflecting the relative importance of exposures atdifferent times. However, WCIE is restricted to a complete observed error-freeexposure whereas exposures are often measured with intermittent missingness anderror. Moreover, it rarely explores exposure history that is very distant fromthe outcome as usually sought in life-course epidemiology. We extend the WCIEmethodology to (i) exposures that are intermittently measured with error, and(ii) contexts where the exposure time-window precedes the outcome time-windowusing a landmark approach. First, the individual exposure history up to thelandmark time is estimated using a mixed model that handles missing data anderror in exposure measurement, and the predicted complete error-free exposurehistory is derived. Then the WCIE methodology is applied to assess thetrajectory of association between the predicted exposure history and the healthoutcome collected after the landmark time. In our context, the health outcomeis a longitudinal marker analyzed using a mixed model. A simulation study firstdemonstrates the correct inference obtained with this approach. Then, appliedto the Nurses' Health Study (19,415 women) to investigate the associationbetween BMI history (collected from midlife) and subsequent cognitive declineafter age 70. In conclusion, this approach, easy to implement, provides aflexible tool for studying complex dynamic relationships and identifyingcritical time windows while accounting for exposure measurement errors.

Time-varying exposure history and subsequent health outcomes: a two-stage approach to identify critical windows