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Estimating the causal effects of multiple intermittent treatments with application to COVID-19.

ABSTRACT: To draw real-world evidence about the comparative effectiveness of multiple time-varying treatment regimens on patient survival, we develop a joint marginal structural proportional hazards model and novel weighting schemes in continuous time to account for time-varying confounding and censoring. Our methods formulate complex longitudinal treatments with multiple ``start/stop'' switches as the recurrent events with discontinuous intervals of treatment eligibility. We derive the weights in continuous time to handle a complex longitudinal dataset on its own terms, without the need to discretize or artificially align the measurement times. We further propose using machine learning models designed for censored survival data with time-varying covariates and the kernel function estimator of the baseline intensity to efficiently estimate the continuous-time weights. Our simulations demonstrate that the proposed methods provide better bias reduction and nominal coverage probability when analyzing observational longitudinal survival data with irregularly spaced time intervals, compared to conventional methods that require aligned measurement time points. We apply the proposed methods to a large-scale COVID-19 dataset to estimate the causal effects of several COVID-19 treatment strategies on in-hospital mortality or ICU admission, and provide new insights relative to findings from randomized trials.

SUBMITTER: Hu L

PROVIDER: S-EPMC8722604 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAs the number of COVID-19 deaths continues to rise worldwide, the identification of risk factors for the disease is an urgent issue, and it remains controversial whether atherogenic lipid-related traits including serum apolipoprotein B, low-density lipoprotein (LDL)-cholesterol, and triglyceride levels, are risk factors. The aim of this study was to estimate causal effects of lipid-related traits on COVID-19 risk in the European population using a two-sample Mendelian randomization (MR) approach.MethodsWe used summary statistics from a genome-wide association study (GWAS) that included 441,016 participants from the UK Biobank as the exposure dataset of lipid-related traits and from COVID-19 Host Genetics Initiative GWAS meta-analyses of European ancestry as the outcome dataset for COVID-19 susceptibility (32,494 cases and 1,316,207 controls), hospitalization (8316 cases and 1,549,095 controls), and severity (4792 cases and 1,054,664 controls). We performed two-sample MR analyses using the inverse variance weighted (IVW) method. As sensitivity analyses, the MR-Egger regression, weighted median, and weighted mode methods were conducted as were leave-one-out sensitivity analysis, the MR-PRESSO global test, PhenoScanner searches, and IVW multivariable MR analyses. A P value below 0.0055 with Bonferroni correction was considered statistically significant.ResultsThis MR study suggested that serum apolipoprotein B or LDL-cholesterol levels were not significantly associated with COVID-19 risk. On the other hand, we inferred that higher serum triglyceride levels were suggestively associated with higher risks of COVID-19 susceptibility (odds ratio [OR] per standard deviation increase in lifelong triglyceride levels, 1.065; 95% confidence interval [CI], 1.001-1.13; P = 0.045) and hospitalization (OR, 1.174; 95% CI, 1.04-1.33; P = 0.012), and were significantly associated with COVID-19 severity (OR, 1.274; 95% CI, 1.08-1.50; P = 0.004). Sensitivity and bidirectional MR analyses suggested that horizontal pleiotropy and reverse causation were unlikely.ConclusionsOur MR study indicates a causal effect of higher serum triglyceride levels on a greater risk of COVID-19 severity in the European population using the latest and largest GWAS datasets to date. However, as the underlying mechanisms remain unclear and our study might be still biased due to possible horizontal pleiotropy, further studies are warranted to validate our findings and investigate underlying mechanisms.

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Estimating the causal effects of multiple intermittent treatments with application to COVID-19.

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