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When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi‐parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression Trees into a two‐parameter sensitivity analysis strategy that assesses sensitivity of posterior distributions of treatment effects to choices of sensitivity parameters. This results in an easily interpretable framework for testing for the impact of an unmeasured confounder that also limits the number of modeling assumptions. We evaluate our approach in a large‐scale simulation setting and with high blood pressure data taken from the Third National Health and Nutrition Examination Survey. The model is implemented as open‐source software, integrated into the treatSens package for the R statistical programming language. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

The aim of this study was to investigate the effect of the COVID-19 pandemic on regular clinic visits among people with diabetes and to elucidate the factors related to visit patterns among these patients during the pandemic. This was a longitudinal study using anonymized insurance claims data from the Joint Health Insurance Society in Tokyo from October 2017 to September 2020. First, we identified patients with diabetes who were fully enrolled in the health plan from fiscal year 2017 until September 2020 and who were regularly receiving glucose-lowering medications (every 1–3 months) from October 2017 to September 2018. We divided follow-up into the pre-pandemic period (October 2018 to March 2020) and the pandemic period (April 2020 to September 2020). A multilevel logistic regression model was used to determine the risks of delayed clinic visits/medication prescriptions (i.e., >3 months after a previous visit/prescription) during the pandemic period. We identified 1118 study participants. The number of delayed clinic visits/medication prescriptions during the pre-pandemic and pandemic periods was 188/3354 (5.6%) and 125/1118 (11.2%), respectively. There was a significant increase in delayed clinic visits during the pandemic (adjusted odds ratio 3.68 (95% confidence interval 2.24 to 6.04, P < .001), even after controlling for confounding factors. We also found a significant interaction between sex and delayed visits; women had significantly fewer clinic visits during the COVID-19 pandemic than men. We clarified the relationship of the COVID-19 pandemic with delays in regular clinic visits and medication prescriptions among people with diabetes. The response to the COVID-19 pandemic differed between men and women.

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