Propensity Score-Based Methods in Comparative Effectiveness Research on Coronary Artery Disease

Tocilizumab effectiveness in mechanically ventilated COVID-19 patients (T-MVC-19 Study): a multicenter real-world evidence

BACKGROUND

This study aimed to evaluate the effectiveness of tocilizumab in mechanically ventilated patients with coronavirus disease 2019 (COVID-19).

RESEARCH DESIGN AND METHODS

This retrospective multicenter study included adults (≥18 years) diagnosed with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by real-time polymerase chain reaction (RT-PCR) from nasopharyngeal swab, and requiring invasive mechanical ventilation during admission. Survival analyses with inverse propensity score treatment weighting (IPTW) and propensity score matching (PSM) were conducted. To account for immortal bias, we used Cox proportional modeling with time-dependent covariance. Competing risk analysis was performed for the extubation endpoint.

RESULTS

A total of 556 (tocilizumab = 193, control = 363) patients were included. Males constituted the majority of the participants (69.2% in tocilizumab arm,74.1% in control arm). Tocilizumab was not associated with a reduction in mortality with hazard ratio [(HR) = 0.82,95% confidence interval (95%CI): 0.62-1.10] in the Inverse propensity score weighting (IPTW) analysis and (HR = 0.86,95% CI: 0.64-1.16) in the PSM analysis. However, tocilizumab was associated with an increased rate of extubation (33.6%) compared to the control arm (11.9%); subdistributional hazards (SHR) = 3.1, 95% CI: 1.86-5.16).

CONCLUSIONS

Although tocilizumab was not found to be effective in reducing mortality, extubation rate while on mechanical ventilation was higher among tocilizumab treated group.

Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data

(1) Background: Propensity score methods gained popularity in non-interventional clinical studies. As it may often occur in observational datasets, some values in baseline covariates are missing for some patients. The present study aims to compare the performances of popular statistical methods to deal with missing data in propensity score analysis. (2) Methods: Methods that account for missing data during the estimation process and methods based on the imputation of missing values, such as multiple imputations, were considered. The methods were applied on the dataset of an ongoing prospective registry for the treatment of unprotected left main coronary artery disease. The performances were assessed in terms of the overall balance of baseline covariates. (3) Results: Methods that explicitly deal with missing data were superior to classical complete case analysis. The best balance was observed when propensity scores were estimated with a method that accounts for missing data using a stochastic approximation of the expectation-maximization algorithm. (4) Conclusions: If missing at random mechanism is plausible, methods that use missing data to estimate propensity score or impute them should be preferred. Sensitivity analyses are encouraged to evaluate the implications methods used to handle missing data and estimate propensity score.

Assessing Heterogeneity of Treatment Effects in Observational Studies

Here we describe methods for assessing heterogeneity of treatment effects over prespecified subgroups in observational studies, using outcome-model-based (g-formula), inverse probability weighting, doubly robust, and matching estimators of subgroup-specific potential outcome means, conditional average treatment effects, and measures of heterogeneity of treatment effects. We compare the finite-sample performance of different estimators in simulation studies where we vary the total sample size, the relative frequency of each subgroup, the magnitude of treatment effect in each subgroup, and the distribution of baseline covariates, for both continuous and binary outcomes. We find that the estimators' bias and variance vary substantially in finite samples, even when there is no unobserved confounding and no model misspecification. As an illustration, we apply the methods to data from the Coronary Artery Surgery Study (August 1975-December 1996) to compare the effect of surgery plus medical therapy with that of medical therapy alone for chronic coronary artery disease in subgroups defined by previous myocardial infarction or left ventricular ejection fraction.

Evaluation of propensity score used in cardiovascular research: a cross-sectional survey and guidance document

BACKGROUND

Propensity score (PS) methods are frequently used in cardiovascular clinical research. Previous evaluations revealed poor reporting of PS methods, however a comprehensive and current evaluation of PS use and reporting is lacking. The objectives of the present survey were to (1) evaluate the quality of PS methods in cardiovascular publications, (2) summarise PS methods and (3) propose key reporting elements for PS publications.

METHODS

A PubMed search for cardiovascular PS articles published between 2010 and 2017 in high-impact general medical (top five by impact factor) and cardiovascular (top three by impact factor) journals was performed. Articles were evaluated for the reporting of PS techniques and methods. Data extraction elements were identified from the PS literature and extraction forms were pilot tested.

RESULTS

Of the 306 PS articles identified, most were published in Journal of the American College of Cardiology (29%; n=88), and Circulation (27%, n=81), followed by European Heart Journal (15%; n=47). PS matching was performed most often, followed by direct adjustment, inverse probability of treatment weighting and stratification. Most studies (77%; n=193) selected variables to include in the PS model a priori. A total of 38% (n=116) of studies did not report standardised mean differences, but instead relied on hypothesis testing. For matching, 92% (n=193) of articles presented the balance of covariates. Overall, interpretations of the effect estimates corresponded to the PS method conducted or described in 49% (n=150) of the reviewed articles.

DISCUSSION

Although PS methods are frequently used in high-impact medical journals, reporting of methodological details has been inconsistent. Improved reporting of PS results is warranted and these proposals should aid both researchers and consumers in the presentation and interpretation of PS methods.