The Association of Previous Vaccination with Live-Attenuated Varicella Zoster Vaccine and COVID-19 Positivity: An Israeli Population-Based Study

The Bacillus Calmette–Guérin (BCG) vaccine affords indirect protection against COVID-19, which is presumably due to priming of the innate immune system. It was hypothesized that the live attenuated Varicella Zoster (LAVZ) vaccine, recommended for the elderly population, would also protect against COVID-19 infection. A retrospective population-based cross-sectional study was conducted using the Leumit Health Services (LHS) database. LAVZ-vaccinated patients were matched with controls based on a propensity score model using 1:9 nearest-neighbor matching. Matching was based on age, gender, and the presence of some chronic disorders, which were selected according to their association with COVID-19 infection. Multivariate logistic regression analyses, adjusted for sex, age, smoking status, comorbidities, and chronic medications associated with COVID-19 risk, were used to estimate the association between LAVZ vaccination and COVID-19 RT-PCR results. Subjects (625) vaccinated with LAVZ and RT-PCR-tested for COVID-19 were identified. After 1:9 matching of subjects who received the LAVZ vaccine, 6250 subjects were included in the study. Multivariate logistic regression analysis demonstrated a significant and independent negative association between having received the LAVZ vaccine and the likelihood of COVID-19 infection (adjusted OR = 0.47 (95% CI 0.33–0.69, p < 0.001)). This association was further strengthened after separate analysis based on the time of LAVZ vaccination before COVID-19 RT-PCR testing. Individuals aged ≥50 years vaccinated with LAVZ had a decreased likelihood of being tested positive for COVID-19.

Impact of androgen deprivation therapy on mortality of prostate cancer patients with COVID-19: a propensity score-based analysis

Background Previous studies hypothesized that androgen deprivation therapy (ADT) may reduce severe acute respiratory syndrome coronavirus 2 (SARS-COV2) infectivity. However, it is unknown whether there is an association between ADT and a higher survival in prostate cancer patients with COVID-19. Methods We performed a retrospective analysis of prostate cancer (PC) patients hospitalized to treat COVID-19 in Brazil’s public health system. We compared patients with the active use of ADT versus those with non-active ADT, past use. We constructed propensity score models of patients in active versus non-active use of ADT. All variables were used to derive propensity score estimation in both models. In the first model we performed a pair-matched propensity score model between those under active and non-active use of ADT. To the second model we initially performed a multivariate backward elimination process to select variables to a final inverse-weight adjusted with double robust estimation model. Results We analyzed 199 PC patients with COVID-19 that received ADT. In total, 52.3% (95/199) of our patients were less than 75 years old, 78.4% (156/199) were on active ADT, and most were using a GnRH analog (80.1%; 125/156). Most of patients were in palliative treatment (89.9%; 179/199). Also, 63.3% of our cohort died from COVID-19. Forty-eight patients under active ADT were pair matched against 48 controls (non-active ADT). All patients (199) were analyzed in the double robust model. ADT active use were not protective factor in both inverse-weight based propensity score (OR 0.70, 95% CI 0.38–1.31, P = 0.263), and pair-matched propensity score (OR 0.67, 95% CI 0.27–1.63, P = 0.374) models. We noticed a significant imbalance in the propensity score of patients in active and those in non-active ADT, with important reductions in the differences after the adjustments. Conclusions The active use of ADT was not associated with a reduced risk of death in patients with COVID-19.

Evaluating the performance of propensity score matching based approaches in individual patient data meta-analysis

Background Individual-patient data meta-analysis (IPD-MA) is an increasingly popular approach because of its analytical benefits. IPD-MA of observational studies must overcome the problem of confounding, otherwise biased estimates of treatment effect may be obtained. One approach to reducing confounding bias could be the use of propensity score matching (PSM). IPD-MA can be considered as two-stage clustered data (patients within studies) and propensity score matching can be implemented within studies, across studies, and combining both. Methods This article focuses on implementation of four PSM-based approaches for the analysis of data structure that exploit IPD-MA in two ways: (i) estimation of propensity score model using single-level or random-effects logistic regression; and (ii) matching of propensity scores (PS) across studies, within studies or preferential-within studies. We investigated the performance of these approaches through a simulation study, which considers an IPD-MA that examined the success of different treatments for multidrug-resistant tuberculosis (MDR-TB). The simulation parameters were varied according to three treatment prevalences (according to studies, 50% and 30%), three levels of heterogeneity between studies (low, moderate and high) and three levels of pooled odds ratio (1, 1.5, 3). Results All approaches showed greater biases at the higher levels of heterogeneity regardless of the choices of treatment prevalences. However, matching of propensity scores using within-study and preferential-within study reported better performance compared to matching across studies when treatment prevalence varied across-studies. For fixed prevalences, a random-effect propensity score model to estimate propensity scores followed by matching of propensity scores across-studies achieved lower biases compared to other PSM-based approaches. Conclusions Propensity score matching has wide application in health research while only limited literature is available on the implementation of PSM methods in IPD-MA, and until now methodological performance of PSM methods have not been examined. We believe, this work offers an intuition to the applied researcher for the choice of the PSM-based approaches.

The Relationship between Timing of Pretransplant Kidney Biopsy, Graft Loss, and Survival in Kidney Transplantation: An Italian Cohort Study

<b><i>Introduction:</i></b> Kidney biopsy is performed to assess if an extended criteria graft can be used for transplantation. It may be performed before or after cross-clamping during organ procurement. This study aims to evaluate whether the timing of biopsy may modify cold ischemia times (CIT) and/or graft outcomes. <b><i>Methods:</i></b> Kidney transplants performed in our center from January 2007 to December 2017 were analyzed. Grafts with preimplantation kidney biopsy were included. Biopsies were performed during surgical back table (ex situ kidney biopsy [ESKB]) until 2012 and since then before the aortic cross-clamping (in situ kidney biopsy [ISKB]). To overcome biases owing to different distributions, a propensity score model was developed. The study population consists in 322 patients, 115 ESKB, and 207 ISKB. <b><i>Results:</i></b> CIT was significantly lower for ISKB (730 min ISKB vs. 840 min ESKB, <i>p</i> value = 0.001). In both crude (OR 0.27; 95% confidence interval, 95% CI 0.12–0.60; <i>p</i> value = 0.002) and adjusted analyses (OR 0.37; 95% CI 0.14–0.94; <i>p</i> value = 0.039), ISKB was associated with a reduced odd of graft loss when compared to ESKB. <b><i>Discussion/Conclusion:</i></b> Performing preimplantation kidney biopsy during the recovery, prior to the aortic cross-clamping, may be a strategy to reduce CIT and improve transplant outcomes.

311Effective prospective study sampling from an existing large-scale national survey using propensity score methods

Focus of Presentation Cohort study recruitment can be complex, often requiring extensive pre-screening to recruit sufficient numbers of exposed and unexposed participants. We discuss a prospective study of the impact of racism on adult health in New Zealand (NZ), with emphasis on propensity-score based sampling and recruitment methods accessing participants from a national survey (NZ Health Survey 2017/18, n = 12,530 eligible adults). Discussion will cover sampling design, response rate, and confounder balance in the final sample. Key empirical results will be summarised. Findings The NZHS provided a sampling frame with complete baseline exposure and covariate data, giving n = 2,099 exposed individuals (reported racial discrimination on NZHS). A propensity-score model (stratified by ethnicity) allowed invitation of unexposed individuals balanced on key sociodemographic confounders. Recruitment used postal invitations with telephone follow-up: individuals could respond by paper survey, online questionnaire or telephone interview. Response rate was 54%, with comparable rates in exposed/unexposed individuals, with key sociodemographic factors well-balanced by exposure status. Conclusions/Implications Racism is an important determinant of health inequity, with limited prospective research in New Zealand. Our approach enabled appropriate recruitment from a sampling frame with baseline exposure status (NZHS), including allowance for exposure variability by ethnicity. Propensity-score matching on baseline covariates allowed for balance on key confounders at invitation, with balance maintained in the final sample. Key messages Secondary sampling from large national surveys can provide efficient recruitment for prospective studies. We achieved a highly satisfactory response rate, and propensity-score based sampling substantially balanced confounders between exposed and unexposed groups, enhancing study validity.

Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach

Background: Subgroup analyses are frequently conducted in randomized clinical trials to assess evidence of heterogeneous treatment effect across patient subpopulations. Although randomization balances covariates within subgroups in expectation, chance imbalance may be amplified in small subgroups and adversely impact the precision of subgroup analyses. Covariate adjustment in overall analysis of randomized clinical trial is often conducted, via either analysis of covariance or propensity score weighting, but covariate adjustment for subgroup analysis has been rarely discussed. In this article, we develop propensity score weighting methodology for covariate adjustment to improve the precision and power of subgroup analyses in randomized clinical trials. Methods: We extend the propensity score weighting methodology to subgroup analyses by fitting a logistic regression propensity model with pre-specified covariate–subgroup interactions. We show that, by construction, overlap weighting exactly balances the covariates with interaction terms in each subgroup. Extensive simulations were performed to compare the operating characteristics of unadjusted estimator, different propensity score weighting estimators and the analysis of covariance estimator. We apply these methods to the Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training trial to evaluate the effect of exercise training on 6-min walk test in several pre-specified subgroups. Results: Standard errors of the adjusted estimators are smaller than those of the unadjusted estimator. The propensity score weighting estimator is as efficient as analysis of covariance, and is often more efficient when subgroup sample size is small (e.g. <125), and/or when outcome model is misspecified. The weighting estimators with full-interaction propensity model consistently outperform the standard main-effect propensity model. Conclusion: Propensity score weighting is a transparent and objective method to adjust chance imbalance of important covariates in subgroup analyses of randomized clinical trials. It is crucial to include the full covariate–subgroup interactions in the propensity score model.

Considerations for using multiple imputation in propensity score-weighted analysis

We present our considerations for using multiple imputation to account for missing data in propensity score-weighted analysis with bootstrap percentile confidence interval. We outline the assumptions underlying each of the methods and discuss the methodological and practical implications of our choices and briefly point to alternatives. We made a number of choices a priori for example to use logistic regression-based propensity scores to produce standardized mortality ratio-weights and Substantive Model Compatible-Full Conditional Specification to multiply impute missing data (given no violation of underlying assumptions). We present a methodology to combine these methods by choosing the propensity score model based on covariate balance, using this model as the substantive model in the multiple imputation, producing and averaging the point estimates from each multiple imputed data set to give the estimate of association and computing the percentile confidence interval by bootstrapping. The described methodology is demanding in both work-load and in computational time, however, we do not consider the prior a draw-back: it makes some of the underlying assumptions explicit and the latter may be a nuisance that will diminish with faster computers and better implementations.

Robust Causal Estimation from Observational Studies Using Penalized Spline of Propensity Score for Treatment Comparison

Without randomization of treatments, valid inference of treatment effects from observational studies requires controlling for all confounders because the treated subjects generally differ systematically from the control subjects. Confounding control is commonly achieved using the propensity score, defined as the conditional probability of assignment to a treatment given the observed covariates. The propensity score collapses all the observed covariates into a single measure and serves as a balancing score such that the treated and control subjects with similar propensity scores can be directly compared. Common propensity score-based methods include regression adjustment and inverse probability of treatment weighting using the propensity score. We recently proposed a robust multiple imputation-based method, penalized spline of propensity for treatment comparisons (PENCOMP), that includes a penalized spline of the assignment propensity as a predictor. Under the Rubin causal model assumptions that there is no interference across units, that each unit has a non-zero probability of being assigned to either treatment group, and there are no unmeasured confounders, PENCOMP has a double robustness property for estimating treatment effects. In this study, we examine the impact of using variable selection techniques that restrict predictors in the propensity score model to true confounders of the treatment-outcome relationship on PENCOMP. We also propose a variant of PENCOMP and compare alternative approaches to standard error estimation for PENCOMP. Compared to the weighted estimators, PENCOMP is less affected by inclusion of non-confounding variables in the propensity score model. We illustrate the use of PENCOMP and competing methods in estimating the impact of antiretroviral treatments on CD4 counts in HIV+ patients.

COVID-19 in trauma: a propensity-matched analysis of COVID and non-COVID trauma patients

Purpose There is mounting evidence that surgical patients with COVID-19 have higher morbidity and mortality than patients without COVID-19. Infection is prevalent amongst the trauma population, but any effect of COVID-19 on trauma patients is unknown. We aimed to evaluate the effect of COVID-19 on a trauma population, hypothesizing increased mortality and pulmonary complications for COVID-19-positive (COVID) trauma patients compared to propensity-matched COVID-19-negative (non-COVID) patients. Methods A retrospective analysis of trauma patients presenting to 11 Level-I and II trauma centers in California between 1/1/2019–6/30/2019 and 1/1/2020–6/30/2020 was performed. A 1:2 propensity score model was used to match COVID to non-COVID trauma patients using age, blunt/penetrating mechanism, injury severity score, Glasgow Coma Scale score, systolic blood pressure, respiratory rate, and heart rate. Outcomes were compared between the two groups. Results A total of 20,448 trauma patients were identified during the study period. 53 COVID trauma patients were matched with 106 non-COVID trauma patients. COVID patients had higher rates of mortality (9.4% vs 1.9%, p = 0.029) and pneumonia (7.5% vs. 0.0%, p = 0.011), as well as a longer mean length of stay (LOS) (7.47 vs 3.28 days, p < 0.001) and intensive care unit LOS (1.40 vs 0.80 days, p = 0.008), compared to non-COVID patients. Conclusion This multicenter retrospective study found increased rates of mortality and pneumonia, as well as a longer LOS, for COVID trauma patients compared to a propensity-matched cohort of non-COVID patients. Further studies are warranted to validate these findings and to elucidate the underlying pathways responsible for higher mortality in COVID trauma patients.