Quantitative Bias Analysis for a Misclassified Confounder: A Comparison Between Marginal Structural Models and Conditional Models for Point Treatments

Association of COVID-19 vaccination with risks of hospitalization due to cardiovascular and other diseases: A study using data from the UK Biobank

OBJECTIVES

To explore whether COVID-19 vaccination protects against hospital admission by preventing infections and severe disease.

METHODS

We leveraged the UK Biobank and studied associations of COVID-19 vaccination (BioNTech-BNT162b2 or Oxford-AstraZeneca-ChAdOx1) with hospitalizations from cardiovascular and other selected diseases (N = 393,544; median follow-up = 54 days among vaccinated individuals). Multivariable Cox, Poisson regression, propensity score matching, and inverse probability treatment weighting analyses were performed. We also performed adjustment using prescription-time distribution matching, and prior event rate ratio.

RESULTS

We observed that COVID-19 vaccination (at least one dose), compared with no vaccination, was associated with reduced short-term risks of hospitalizations from stroke (hazard ratio [HR] = 0.178, 95% confidence interval [CI]: 0.127-0.250, P = 1.50e-23), venous thromboembolism (HR = 0.426, CI: 0.270-0.673, P = 2.51e-4), dementia (HR = 0.114, CI: 0.060-0.216; P = 2.24e-11), non-COVID-19 pneumonia (HR = 0.108, CI: 0.080-0.145; P = 2.20e-49), coronary artery disease (HR = 0.563, CI: 0.416-0.762; P = 2.05e-4), chronic obstructive pulmonary disease (HR = 0.212, CI: 0.126-0.357; P = 4.92e-9), type 2 diabetes (HR = 0.216, CI: 0.096-0.486, P = 2.12e-4), heart failure (HR = 0.174, CI: 0.118-0.256, P = 1.34e-18), and renal failure (HR = 0.415, CI: 0.255-0.677, P = 4.19e-4), based on standard Cox regression models. Among the previously mentioned results, reduced hospitalizations for stroke, heart failure, non-COVID-19 pneumonia, and dementia were consistently observed across regression, propensity score matching/inverse probability treatment weighting, prescription-time distribution matching, and prior event rate ratio. The results for two-dose vaccination were similar.

CONCLUSIONS

Taken together, this study provides further support to the safety and benefits of COVID-19 vaccination, and such benefits may extend beyond reduction of infection risk or severity per se. However, causal relationship cannot be concluded and further studies are required.

Ten things to remember about propensity scores

Propensity score methods are popular to control for confounding in observational biomedical studies of risk factors or medical treatments. This paper focused on aspects of propensity score methods that often remain undiscussed, including unmeasured confounding, missing data, variable selection, statistical efficiency, estimands, the positivity assumption, and predictive performance of the propensity score model.

Recombinant hepatitis B vaccine uptake and multiple sclerosis risk: A marginal structural modeling approach

BACKGROUND

There has been an ongoing debate regarding the association between uptake of recombinant vaccine against hepatitis B virus (HBV) and multiple sclerosis (MS) risk. This case-control study tested the hypothesis whether recombinant HBV vaccination status is causally associated with MS risk using targeted maximum likelihood estimation (TMLE) technique.

METHODS

Confirmed 110 MS cases and age (± 5 years), sex and nativity matched (1:1) 110 controls were enrolled. Data were collected on sociodemographics, environmental factors, history of vaccinations and past morbidities through face-to-face interview both from cases and controls. To estimate the causal parameters including marginal odds ratio (OR), causal relative risk (RR), causal risk difference (RD) and their 95% confidence intervals (CIs), we implemented case-control-weighted TMLE for a matched design that uses data-adaptive flexible stacked ensemble-based machine learning system namely Super Learner. Additionally, population preventable fraction (PPF) of MS risk was computed.

RESULTS

This study demonstrated a significant nonspecific protective effect of HBV vaccination against MS risk (marginal OR 0.44; 95% CI: 0.19-0.68; p = 0.006; causal RR 0.64, 95% CI: 0.46-0.89; p = 0.004). The significant causal RD showed that among the vaccinated 19% fewer MS cases occurred owing to their HBV vaccination (causal RD -0.19; 95% CI: -0.32 - -0.06; p = 0.014). In the source population, vaccination against HBV led to 17.4% reduced MS risk (PPF = 17.4%; 95% CI: 3.8%, 36.3%).

CONCLUSION

The results suggest a significant nonspecific protective effect of recombinant HBV vaccine against MS risk. Future studies may contemplate to confirm these results.

Mecor: An R package for measurement error correction in linear regression models with a continuous outcome

Measurement error in a covariate or the outcome of regression models is common, but is often ignored, even though measurement error can lead to substantial bias in the estimated covariate-outcome association. While several texts on measurement error correction methods are available, these methods remain seldomly applied. To improve the use of measurement error correction methodology, we developed mecor, an R package that implements measurement error correction methods for regression models with a continuous outcome. Measurement error correction requires information about the measurement error model and its parameters. This information can be obtained from four types of studies, used to estimate the parameters of the measurement error model: an internal validation study, a replicates study, a calibration study and an external validation study. In the package mecor, regression calibration methods and a maximum likelihood method are implemented to correct for measurement error in a continuous covariate in regression analyses. Additionally, methods of moments methods are implemented to correct for measurement error in the continuous outcome in regression analyses. Variance estimation of the corrected estimators is provided in closed form and using the bootstrap.