Abstract
Case-crossover designs have become widespread in biomedical investigations of transient associations. However, the most popular reference-selection strategy - the time-stratified scheme - may not be an optimum solution to control systematic bias in case-crossover studies. To prove this, we conducted a time series decomposition for daily ozone records and examined the capability of the time-stratified scheme to control for yearly, monthly, and weekly time trends; and observed its failure on the control for the weekly time trend. To solve this issue, we proposed a new logistic regression approach in which we suggest the adjustment for the weekly time trend. We compared the performance of the proposed with that of the traditional method by simulation. We further conducted an empirical study to explore the performance of the new logistic regression approach in examining potential associations between ambient air pollutants and acute myocardial infarction hospitalizations. The time-stratified scheme provides effective control for yearly and monthly time trends but not of the weekly time trend. Uncontrolled weekly time trends could be the dominant source of systematic bias in time-stratified case-crossover studies. In contrast, the proposed logistic regression approach can effectively minimize systematic bias in a case-crossover study.
Highlights
Using the ozone time series decomposition, we concluded that the time-stratified reference-select scheme lacks capability on control of weekly time trend;
– The simulation study showed that control of weekly time trend can eliminate potential overlap bias as well as estimation bias;
Using decomposition of an ozone time series dataset, we scrutinized the capability of the time-stratified reference selection scheme to control for yearly, monthly, and weekly time trends when used in case-crossover studies
Summary
Since the initial work by Maclure (Maclure, 1991), the use of case-crossover designs has become widespread in epidemiological and medical investigations of transient associations between risk factors and adverse health events (Janes, Sheppard & Lumley, 2005a; Delaney & Suissa, 2009; Wang, Wang & Kindzierski, 2019), notably in the area of ambient air pollution research (Yusuf, Hawken, Ounpun, et al, 2004; Carracedo-Martınez, Taracido, Tobias, et al, 2010; Malig, Green, Basu, & Broadwin, 2013; Wang, Kindzieski & Kaul, 2015a & 2015b; Szyszkowicz & de Angelis, 2021). Like a cohort or a case-control study, confounding in a self-matching design arises when there is an unbalanced matching in determinants between hazard and reference periods, leading to various sources of systematic bias. These sources were previously reviewed by Mittleman and Mostofsky (Mittleman & Mostofsky, 2014)
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