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. 2021 Nov 30;40(27):6197-6208.
doi: 10.1002/sim.9179. Epub 2021 Sep 1.

Avoiding bias in self-controlled case series studies of coronavirus disease 2019

Osvaldo Fonseca-Rodríguez  1 , Anne-Marie Fors Connolly  1 , Ioannis Katsoularis  2 , Krister Lindmark  2 , Paddy Farrington  3
Affiliations

Avoiding bias in self-controlled case series studies of coronavirus disease 2019

Osvaldo Fonseca-Rodríguez et al. Stat Med. .

Abstract

Many studies, including self-controlled case series (SCCS) studies, are being undertaken to quantify the risks of complications following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19). One such SCCS study, based on all COVID-19 cases arising in Sweden over an 8-month period, has shown that SARS-CoV-2 infection increases the risks of AMI and ischemic stroke. Some features of SARS-CoV-2 infection and COVID-19, present in this study and likely in others, complicate the analysis and may introduce bias. In the present paper we describe these features, and explore the biases they may generate. Motivated by data-based simulations, we propose methods to reduce or remove these biases.

Keywords: COVID-19; bias; cardiovascular disease; epidemiological methods; mortality; self-controlled case series.

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Figures

FIGURE 1
FIGURE 1
Days from coronavirus disease 2019 to event (7‐day bins). Left: acute myocardial infarction; right: ischemic stroke
FIGURE 2
FIGURE 2
Days from event to death (7‐day bins). Left: acute myocardial infarction; right: ischemic stroke
FIGURE 3
FIGURE 3
Bias (top row) and mean squared error (bottom row) of the log relative incidence of Acute myocardial infarction (AMI) and ischemic stroke for the 1‐ to 28‐day post‐coronavirus disease 2019 risk period, by percentile of deaths attributed to the event. Circles: standard self‐controlled case series (SCCS) model. Full dots: extended SCCS model. Left: AMI; right: ischemic stroke
See this image and copyright information in PMC

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