Objective To compare the efficacy of covid-19 vaccines between immunocompromised and immunocompetent people. Design Systematic review and meta-analysis. Data sources PubMed, Embase, Central Register of Controlled Trials, COVID-19 Open Research Dataset Challenge (CORD-19), and WHO covid-19 databases for studies published between 1 December 2020 and 5 November 2021. ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform were searched in November 2021 to identify registered but as yet unpublished or ongoing studies. Study selection Prospective observational studies comparing the efficacy of covid-19 vaccination in immunocompromised and immunocompetent participants. Methods A frequentist random effects meta-analysis was used to separately pool relative and absolute risks of seroconversion after the first and second doses of a covid-19 vaccine. Systematic review without meta-analysis of SARS-CoV-2 antibody titre levels was performed after first, second, and third vaccine doses and the seroconversion rate after a third dose. Risk of bias and certainty of evidence were assessed. Results 82 studies were included in the meta-analysis. Of these studies, 77 (94%) used mRNA vaccines, 16 (20%) viral vector vaccines, and 4 (5%) inactivated whole virus vaccines. 63 studies were assessed to be at low risk of bias and 19 at moderate risk of bias. After one vaccine dose, seroconversion was about half as likely in patients with haematological cancers (risk ratio 0.40, 95% confidence interval 0.32 to 0.50, I 2 =80%; absolute risk 0.29, 95% confidence interval 0.20 to 0.40, I 2 =89%), immune mediated inflammatory disorders (0.53, 0.39 to 0.71, I 2 =89%; 0.29, 0.11 to 0.58, I 2 =97%), and solid cancers (0.55, 0.46 to 0.65, I 2 =78%; 0.44, 0.36 to 0.53, I 2 =84%) compared with immunocompetent controls, whereas organ transplant recipients were 16 times less likely to seroconvert (0.06, 0.04 to 0.09, I 2 =0%; 0.06, 0.04 to 0.08, I 2 =0%). After a second dose, seroconversion remained least likely in transplant recipients (0.39, 0.32 to 0.46, I 2 =92%; 0.35, 0.26 to 0.46), with only a third achieving seroconversion. Seroconversion was increasingly likely in patients with haematological cancers (0.63, 0.57 to 0.69, I 2 =88%; 0.62, 0.54 to 0.70, I 2 =90%), immune mediated inflammatory disorders (0.75, 0.69 to 0.82, I 2 =92%; 0.77, 0.66 to 0.85, I 2 =93%), and solid cancers (0.90, 0.88 to 0.93, I 2 =51%; 0.89, 0.86 to 0.91, I 2 =49%). Seroconversion was similar between people with HIV and immunocompetent controls (1.00, 0.98 to 1.01, I 2 =0%; 0.97, 0.83 to 1.00, I 2 =89%). Systematic review of 11 studies showed that a third dose of a covid-19 mRNA vaccine was associated with seroconversion among vaccine non-responders with solid cancers, haematological cancers, and immune mediated inflammatory disorders, although response was variable in transplant recipients and inadequately studied in people with HIV and those receiving non-mRNA vaccines. Conclusion Seroconversion rates after covid-19 vaccination were significantly lower in immunocompromised patients, especially organ transplant recipients. A second dose was associated with consistently improved seroconversion across all patient groups, albeit at a lower magnitude for organ transplant recipients. Targeted interventions for immunocompromised patients, including a third (booster) dose, should be performed. Systematic review registration PROSPERO CRD42021272088.

PURPOSE The US Food and Drug Administration has granted regulatory approval for the use of nivolumab—an immune checkpoint inhibitor (ICI)—in the first-line treatment of advanced gastric or esophageal adenocarcinoma (GEAC), regardless of programmed death-ligand 1 (PD-L1) expression. However, the efficacy of ICIs in low PD-L1–expressing tumors remains unclear. MATERIALS AND METHODS This study aims to reconstruct unreported Kaplan-Meier (KM) plots of PD-L1 combined positive score (CPS) subgroups of randomized phase III trials comparing the addition of ICIs with conventional chemotherapy in the first-line treatment of GEAC. A graphical reconstructive algorithm was adopted to estimate time-to-event outcomes from reported overall survival and progression-free survival (OS and PFS) KM plots describing overall or subgroup cohorts. Using reconstructed time-to-event outcomes, KMSubtraction conducts bipartite matching of patients from the reported subgroup among the overall cohort. By excluding matched patients, KM plots and survival analyses of the unreported subgroups were retrieved. RESULTS CheckMate-649, KEYNOTE-062, and KEYNOTE-590 were included. Two PD-L1 subgroups were identified with data unreported in the primary manuscripts: PD-L1 CPS 1-4 from CheckMate-649 and PD-L1 CPS 1-9 from KEYNOTE-062. No significant differences in OS and PFS were demonstrated in ICI-chemotherapy combinations when compared with chemotherapy among CheckMate-649 PD-L1 CPS 1-4 (OS: hazard ratio [HR] = 0.950, 95% CI, 0.747 to 1.209, P = .678; PFS: HR = 0.958, 95% CI, 0.743 to 1.236, P = .743) and KEYNOTE-062 PD-L1 CPS 1-9 subgroups. In the KEYNOTE-062 PD-L1 CPS 1-9 subgroup, patients treated with pembrolizumab had an increased hazard of tumor progression (HR = 2.092, 95% CI, 1.661 to 2.635, P < .001). CONCLUSION Using KMSubtraction, data of PD-L1 subgroups previously unreported by primary manuscripts of pivotal clinical trials were retrieved. These data suggest the lack of benefit in the addition of ICI to chemotherapy in low PD-L1–expressing GEAC tumors.

Given a discrete maximization problem with a linear objective function where the coefficients are chosen randomly from a distribution, we would like to evaluate the expected optimal value and the marginal distribution of the optimal solution. We call this the persistency problem for a discrete optimization problem under uncertain objective, and the marginal probability mass function of the optimal solution is named the persistence value. In general, this is a difficult problem to solve, even if the distribution of the objective coefficient is well specified. In this paper, we solve a subclass of this problem when the distribution is assumed to belong to the class of distributions defined by given marginal distributions, or given marginal moment conditions. Under this model, we show that the persistency problem maximizing the expected objective value over the set of distributions can be solved via a concave maximization model. The persistency model solved using this formulation can be used to obtain important qualitative insights to the behavior of stochastic discrete optimization problems. We demonstrate how the approach can be used to obtain insights to problems in discrete choice modeling. Using a set of survey data from a transport choice modeling study, we calibrate the random utility model with choice probabilities obtained from the persistency model. Numerical results suggest that our persistency model is capable of obtaining estimates that perform as well, if not better, than classical methods, such as logit and cross-nested logit models. We can also use the persistency model to obtain choice probability estimates for more complex choice problems. We illustrate this on a stochastic knapsack problem, which is essentially a discrete choice problem under budget constraint.probability distribution, integer programming, utility preference, choice functions

We discuss some recent developments in smart city initiatives across the world to motivate the opportunities and challenges that such initiatives pose, and we categorize them into three themes: data access and collection, end-user utility, and economic viability of different solutions. We recognize that the academic literature that can help in addressing some of these challenges is at its nascent state and provide guidelines on how manufacturing and service operations management scholars can contribute to the global smart city movement.

In this article, we study the problem of finding tight bounds on the expected value of the kth-order statistic E @X k : n # under first and second moment information on n real-valued random variables+ Given means E @X i # ϭ µ i and variances, we show that the tight upper bound on the expected value of the highest-order statistic E @X n : n # can be computed with a bisection search algorithm+ An extremal discrete distribution is identified that attains the bound, and two closed-form bounds are proposed+ Under additional covariance information Cov@X i , X j # ϭ Q ij , we show that the tight upper bound on the expected value of the highest-order statistic can be computed with semidefinite optimization+ We generalize these results to find bounds on the expected value of the kth-order statistic under mean and variance information+ For k Ͻ n, this bound is shown to be tight under identical means and variances+ All of our results are distributionfree with no explicit assumption of independence made+ Particularly, using optimization methods, we develop tractable approaches to compute bounds on the expected value of order statistics+ Probability in the Engineering and Informational Sciences, 20, 2006, 667-686+ Printed in the U+S+A+