The risk of treatment-related toxicities with PD-1/PD-L1 inhibitors in patients with lung cancer.

The risk of treatment-related toxicities with programmed cell death 1 and its ligand (PD-1/PD-L1) inhibitors in patients with lung cancer is unclear and inconclusive. PubMed, EMBASE, and the Cochrane Library databases were systematically searched without language restrictions from inception to May 31, 2024 to identify Phase 3 randomized controlled trials of lung cancer comparing PD-1/PD-L1 inhibitors versus placebo/best supportive care (alone or in combination with nontargeted chemotherapy) that had available data regarding treatment-related adverse events (TRAEs) or incidence and sample size. Random-effect models were employed to study the pooled relative risk (RR) and 95% confidence intervals (CIs). Finally, 36 trials, involving 19,693 participants, fulfilled the inclusion criteria. PD-1/PD-L1 inhibitors significantly augmented the likelihood of developing all-grade (RR, 1.03; 95% CI, 1.01-1.04, p < .01) and grade ≥3 TRAEs (RR, 1.16; 95% CI, 1.10 to 1.23, p < .01). PD-1/PD-L1 inhibitors substantially augmented the odds of developing treatment-related serious adverse events (SAEs) (RR, 1.48; 95% CI, 1.27-1.71, p < .01) and fatal adverse events (FAEs) (RR, 1.42; 95% CI, 1.11-1.82, p < .01). Subgroup analyses indicated that the RR of SAEs and FAEs were generally consistent, regardless of treatment type, tumor type, treatment setting, PD-1/PD-L1 inhibitors type and study design. The most common causes of FAEs were respiratory failure/insufficiency (33.3%), cardiac events (16.1%), and hematological disorders (10.1%). We demonstrated that PD-1/PD-L1 inhibitors were significantly correlated with higher possibility of developing treatment-related toxicities, especially SAEs and FAEs, compared with placebo/best supportive care controls.

Prediction of Cancer Proneness under Influence of X-rays with Four DNA Mutability and/or Three Cellular Proliferation Assays.

Context: Although carcinogenesis is a multi-factorial process, the mutability and the capacity of cells to proliferate are among the major features of the cells that contribute together to the initiation and promotion steps of cancer formation. Particularly, mutability can be quantified by hyper-recombination rate assessed with specific plasmid assay, hypoxanthine-guanine phosphoribosyltransferase (HPRT) mutations frequency rate, or MRE11 nuclease activities. Cell proliferation can be assessed by flow cytometry by quantifying G2/M, G1 arrests, or global cellular evasion. METHODS: All these assays were applied to skin untransformed fibroblasts derived from eight major cancer syndromes characterized by their excess of relative cancer risk (ERR). RESULTS: Significant correlations with ERR were found between hyper-recombination assessed by the plasmid assay and G2/M arrest and described a third-degree polynomial ERR function and a sigmoidal ERR function, respectively. The product of the hyper-recombination rate and capacity of proliferation described a linear ERR function that permits one to better discriminate each cancer syndrome. CONCLUSIONS: Hyper-recombination and cell proliferation were found to obey differential equations that better highlight the intrinsic bases of cancer formation. Further investigations to verify their relevance for cancer proneness induced by exogenous agents are in progress.

The short-term association between environmental variables and daily pediatric asthma patient visits in Hangzhou, China: A time-series study.

Background: To date, a large number of studies have shown correlations between environmental variables and pediatric asthma in short-term lag time. However, their results are inconsistent. Therefore, we aimed to evaluate the short-term impact of environmental variables on daily pediatric asthma patients' visits (DPAPV) in Hangzhou, China, and find the most important risk factor. Methods: Generalized additive distribution lag non-linear model (GAM-DLNM) was applied to explore the effect of environmental variables on DPAPV in single- and multi-variable models in Hangzhou, China from 2014 to 2021. Then, risk factors of pediatric asthma were selected (p < 0.05 both in single- and multi-variable models) and used weighted quantile sum (WQS) regression model to evaluate their relative importance. Results: There were 313,296 pediatric asthma patient records between 2014 and 2021. Both in single- and multi-variable models, PM2.5, PM10, and NO2 exhibited significant positive correlations in short-term lag time and these correlations reached their maximum in lag day 2 (RR = 1.00, 95%CI:1.00 to 1.01), lag day 2 (RR = 1.00, 95%CI:1.00 to 1.01), and lag day 3 (RR = 1.04, 95%CI:1.02 to1.05), respectively. The WQS index showed that NO2 had the greatest relative importance (weight over 70 %). The relative importance of NO2 increased with time passing. Males were more susceptible to the adverse effects of NO2. Conclusion: PM2.5, PM10, and NO2 had significant adverse effects on pediatric asthma. Among them, NO2 presented the greatest and most important adverse effect on the disease. Therefore, parents could give priority to paying attention to NO2 to control children's asthma.

Avoided and Avoidable Deaths with the Use of COVID-19 Convalescent Plasma in Italy during the First Two Years of Pandemic.

Italy was the first western country to be hit by the COVID-19 pandemic and has suffered nearly 200,000 deaths so far during the four years of the pandemic. In March 2020, Italy first deployed COVID-19 convalescent plasma (CCP) to treat hospitalized patients. Despite this initial effort, the proportion of COVID-19 patients treated with CCP during the first two years of the pandemic (2020-2021) was very low (approximately 2% of individuals hospitalized for COVID-19). In this study, we estimated the number of actual inpatient lives saved by CCP treatment in Italy using national mortality data, and CCP mortality reduction data from meta-analyses of randomized controlled trials and real-world data. We also estimated the potential number of lives saved if CCP had been deployed to 100% of hospitalized patients or used in 15% to 75% of outpatients. According to these models, CCP usage in 2020-2021 saved between 385-1304 lives, but this number would have increased to 17,751-60,079 if 100% of inpatients had been transfused with CCP. Similarly, broader (15-75%) usage in outpatients could have prevented 21,187-190,689 hospitalizations (desaturating hospitals) and 6144-81,926 deaths. These data have important implications for convalescent plasma use in future infectious disease emergencies.

Association between P M 10 and respiratory diseases admission in peninsula Malaysia during haze.

Numerous studies have been conducted in other countries on the health effects of exposure to particulate matter with a diameter of 10 microns or less P M 10 , but little research has been conducted in Malaysia, particularly during the haze season. This study intends to investigate how exposure of P M 10 influenced hospital admissions for respiratory diseases during the haze period in peninsula Malaysia and it was further stratified by age group, gender and respiratory diseases categories. The study includes data from all patients with respiratory diseases in 92 government hospitals, as well as P M 10 concentration and meteorological data from 92 monitoring stations in Peninsula Malaysia starting from 1st January 2000 to 31st December 2019. A quasi-poison time series regression with distributed lag nonlinear model (DLNM) was employed in this study to examine the relationship between exposure of P M 10 and hospital admissions for respiratory diseases during the haze period. Haze period for this study has been defined from June to September each year. According to the findings of this study, P M 10 was positively associated with hospitalisation of respiratory disease within 30 lag days under various lag patterns, with lag 25 showing the strongest association (RR = 1.001742, CI 1.001029,1.002456). Using median as a reference, it was discovered that females were more likely than males to be hospitalized for P M 10 exposure. Working age group will be the most affected by the increase in P M 10 exposure with a significant cumulative RR from lag 010 to lag 030. The study found that P M 10 had a significant influence on respiratory hospitalisation in peninsula Malaysia, particularly for lung diseases caused by external agents(CD5). Therefore, it is important to implement effective intervention measures to control P M 10 and reduce the burden of respiratory disease admissions.

Nuances of Cohort Studies and Risk Ratio.

Post-graduate students and faculty usually conduct case-control studies. However, sometimes they conduct cohort studies that are short-duration. Most investigators enroll the participants in two groups according to the exposure. Then, follow the participants for some duration. At the end of the study, relative risk is calculated, and the work is published in some journal. The exposure may be one time, which may or may not be quantified. The follow-up duration may not be participant-specific, and differential follow-up does not exist. The author has given three examples: the first example of consanguineous marriages and congenital disabilities, the second example of the ABO blood group system and childhood asthma, and the third example of insecticide spraying and stillbirth. In the given examples, cumulative or density incidence cannot be calculated in a true sense and, therefore, risk ratio. Even estimating the incidence of outcome variables in some studies is not appropriate. Risk ratio calculation in such scenarios is questionable because exposure quantification, follow-up period, and combination are the limiting factors. In case-control studies, the prevalence ratio is calculated, which is analogous to relative risk. The author suggests that, in such circumstances, prevalence ratio calculation will be more appropriate.

Cardiometabolic multimorbidity in Mexican adults: a cross-sectional analysis of a national survey.

Background: Cardiometabolic multimorbidity is a rising phenomenon that has been barely explored in middle-income countries such as Mexico. Objective: This study aimed to estimate the prevalence, associated factors, and patterns of cardiometabolic multimorbidity (2 and 3+ diseases) in Mexican adults (≥20 years old) by age group. Methods: A cross-sectional and secondary analysis of Mexico's National Health and Nutrition Survey 2018-2019 was conducted. Information on eight diseases and other sociodemographic and health/lifestyle characteristics was obtained through self-reporting. Descriptive analyses were performed, and multinomial logistic regression models were calculated to identify the variables associated with cardiometabolic multimorbidity. Factor analysis and latent classes were estimated to determine disease patterns. Results: The prevalence of cardiometabolic multimorbidity for the total population study was 27.6% (13.7% for people with 2 diseases and 13.9% for people with 3+ diseases). By age group, the prevalence of 2+ diseases was 12.5% in the age group of 20-39 years, 35.2% in the age group of 40-59 years, and 44.5% in the age group of 60 years and older. The variables of depressive symptomatology and having functional limitations (1+) were statistically associated with cardiometabolic multimorbidity in almost all age groups. Patterns of cardiometabolic multimorbidity varied among adults in different age groups. Understanding the behavior of cardiometabolic multimorbidity at various stages of adulthood is a resource that could be used to design and implement intervention strategies. Such strategies should correspond to the population's sociodemographic, health, and lifestyle characteristics and the specific disease patterns of each age group.

Choice of needleless connector technology as a risk reduction strategy for catheter related bloodstream infection, mortality, and cost: A secondary data analysis.

OBJECTIVE: To compare relative risk (RR) of central line-associated bloodstream infection (CLABSI) between hospitals using Clave needleless connector technologies (NCT) and comparator hospitals using non-Clave NCT. And, to estimate avoided CLABSIs, mortality, and cost savings. BACKGROUND: Needleless connectors, while protective against needlestick injury, have long been implicated as a potential risk for CLABSI. Significant RR reduction of CLABSI among the many NCT has not been clinically demonstrated. METHODS: The U.S. Healthcare-Associated Infections database was accessed for CLABSI data for calendar year 2019 via the Centers for Medicare and Medicaid Services website. This dataset was merged with the Clave NCT manufacturer's 2019 database to identify hospitals purchasing Clave NCT (MicroClave™, NanoClave™, Clave Neutron™, Clave™, Microclave™ Clear) and non-Clave NCT comparator hospitals. Sub-analysis of Clave NCT hospitals included: (1) Clave NCT mixed-use and (2) Clave high-volume use hospitals. The standardized infection ratio (SIR) was generated to estimate CLABSI RR after adjusting for intern/resident-to-bed-ratio (IRB), care location, and hospital demographics. Using the RR, avoided CLABSIs, mortality, and cost savings were calculated. RESULTS: A total of 2987 eligible hospitals (1288 Clave NCT, 1699 non-Clave NCT) with 17,452,575 central line-days were evaluated. All three Clave NCT hospital groups showed a statistically significant reduction in RR compared to the non-Clave NCT hospitals. The RR in Clave NCT hospitals was 0.93, a 7% decrease in CLABSI risk (p = 0.02). In the Clave NCT subgroups, mixed-use hospitals RR was 0.93, a 7% reduction (p = 0.04), while the Clave NCT high-volume hospitals experienced a 19% reduction, RR 0.81 (p = 0.04). An estimated 563 CLABSIs and 84 related deaths were avoided with use of Clave NCT and $27,095,231 in cost savings. CONCLUSION: The use of the Clave NCT, in and of itself, is an effective risk reduction strategy for CLABSI prevention, reduced mortality, and substantial cost savings.

Impact of deforestation and climate on spatio-temporal spread of dengue fever in Mexico.

Dengue prevalence results from the interaction of multiple socio-environmental variables which influence its spread. This study investigates the impact of forest loss, precipitation, and temperature on dengue incidence in Mexico from 2010 to 2020 using a Bayesian hierarchical spatial model. Three temporal structures-AR1, RW1, and RW2-were compared, with RW2 showing superior performance. Findings indicate that a 1 % loss of municipal forest cover correlates with a 16.9 % increase in dengue risk. Temperature also significantly affects the vectors' ability to initiate and maintain outbreaks, highlighting the significant role of environmental factors. The research emphasizes the importance of multilevel modeling, finer temporal data resolution, and understanding deforestation causes to enhance the predictability and effectiveness of public health interventions. As dengue continues affecting global populations, particularly in tropical and subtropical regions, this study contributes insights, advocating for an integrated approach to health and environmental policy to mitigate the impact of vector-borne diseases.

Effects of major air pollutants on angina hospitalizations: a correlation study.

BACKGROUND: Angina is a crucial risk signal for cardiovascular disease. However, few studies have evaluated the effects of ambient air pollution exposure on angina. OBJECTIVE: We aimed to explore the short-term effects of air pollution on hospitalization for angina and its lag effects. METHODS: We collected data on air pollutant concentrations and angina hospitalizations from 2013 to 2020. Distributed lag nonlinear model (DLNM) was used to evaluate the short-term effects of air pollutants on angina hospitalization under different lag structures. Stratified analysis by sex, age and season was obtained. RESULTS: A total of 39,110 cases of angina hospitalization were included in the study. The results showed a significant positive correlation between PM2.5, SO2, NO2, and CO and angina hospitalization. Their maximum harmful effects were observed at lag0-7 (RR = 1.042; 95% CI: 1.017, 1.068), lag0-3 (RR = 1.067; 95% CI: 1.005, 1.133), lag0-6 (RR = 1.078; 95% CI: 1.041, 1.117), and lag0-6 (RR = 1.244; 95% CI: 1.109, 1.397), respectively. PM10 did not have an overall risk effect on angina hospitalization, but it did have a risk effect on women and the elderly. O3 was significantly negatively correlated with angina hospitalization, with the most pronounced effect observed at lag0-6 (RR = 0.960; 95% CI: 0.940, 0.982). Stratified analysis results showed that women and the elderly were more susceptible to pollutants, and the adverse effects of pollutants were stronger in the cold season. CONCLUSION: Short-term exposure to PM2.5, SO2, NO2, and CO increases the risk of hospitalization for angina.

Estimating causal effects for binary outcomes using per-decision inverse probability weighting.

Micro-randomized trials are commonly conducted for optimizing mobile health interventions such as push notifications for behavior change. In analyzing such trials, causal excursion effects are often of primary interest, and their estimation typically involves inverse probability weighting (IPW). However, in a micro-randomized trial, additional treatments can often occur during the time window over which an outcome is defined, and this can greatly inflate the variance of the causal effect estimator because IPW would involve a product of numerous weights. To reduce variance and improve estimation efficiency, we propose two new estimators using a modified version of IPW, which we call "per-decision IPW." The second estimator further improves efficiency using the projection idea from the semiparametric efficiency theory. These estimators are applicable when the outcome is binary and can be expressed as the maximum of a series of sub-outcomes defined over sub-intervals of time. We establish the estimators' consistency and asymptotic normality. Through simulation studies and real data applications, we demonstrate substantial efficiency improvement of the proposed estimator over existing estimators. The new estimators can be used to improve the precision of primary and secondary analyses for micro-randomized trials with binary outcomes.

Modified Atkins Diet in Adolescents and Adults with Drug Resistant Epilepsy: A Systematic Review and Meta-Analysis.

Epilepsy is one of the common neurological diseases which affects 65-70 million people worldwide. Modified Atkins diet (MAD) as a therapy is used as one of the treatments to reduce the seizures occurrence in epileptic patients. The purpose of this purpose is to review all evidence regarding the efficacy of the MAD from randomized controlled trials (RCTs) in adolescents and adults with drug resistant epilepsy (DRE). The total of three databases were searched (PubMed, Embase, and Cochrane Library) till 31 January 2023. Only RCTs with MAD as a one of the treatment arms were included in meta-analysis. The proportion of reduction of seizures in patients with epilepsy and relative risk to identify the relationship between MAD (as risk) to decrease the epileptic seizure was used as outcomes. The Jadad score with three domains was used to estimate the quality of RCTs included for meta-analysis. Only three RCTs were included following the stringent inclusion criteria in current meta-analysis. The pooled proportion from 142 patients going through MAD therapy shows the reduction in epileptic seizure ≥50%, by the random effect model was 0.23 (95% confidence interval [CI], 0.10 to 0.37). Our meta-analysis underlines a significant efficacy of MAD compared to the control group in seizure reduction ≥50%, The pooled relative risk was 6.47 (95% CI, 1.60 to 26.14; p-value <0.05). MAD therapy was efficacious and had better compliance for seizure reduction in subjects with DRE.

Associations between mosaic loss of sex chromosomes and incident hospitalization for atrial fibrillation in the United Kingdom.

Background: Mosaic loss of chromosome Y (mLOY) in leukocytes of men reflects genomic instability from aging, smoking, and environmental exposures. A similar mosaic loss of chromosome X (mLOX) occurs among women. However, the associations between mLOY, mLOX, and risk of incident heart diseases are unclear. Methods: We estimated associations between mLOY, mLOX, and risk of incident heart diseases requiring hospitalization, including atrial fibrillation, myocardial infarction, ischemic heart disease, cardiomyopathy, and heart failure. We analyzed 190,613 men and 224,853 women with genotyping data from the UK Biobank. Among these participants, we analyzed 37,037 men with mLOY and 13,978 women with mLOX detected using Mosaic Chromosomal Alterations caller. Multivariable Cox regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of each incident heart disease in relation to mLOY in men and mLOX in women. Additionally, Mendelian randomization (MR) was conducted to estimate causal associations. Results: Among men, detectable mLOY was associated with elevated risk of atrial fibrillation (HR=1.06, 95%CI:1.03-1.11). The associations were apparent in both never-smokers (HR=1.07, 95%:1.01-1.14) and ever-smokers (HR=1.05, 95%CI:1.01-1.11) as well as men > and ≤60 years of age. MR analyses supported causal associations between mLOY and atrial fibrillation (HRMR-PRESSO=1.15, 95%CI:1.13-1.18). Among post-menopausal women, we found a suggestive inverse association between detectable mLOX and atrial fibrillation risk (HR=0.90, 95%CI:0.83-0.98). However, associations with mLOY and mLOX were not found for other heart diseases. Conclusions: Our findings suggest that mLOY and mLOX reflect sex-specific biological processes or exposure profiles related to incident atrial fibrillation requiring hospitalization.

Estimating geographical spread of Streptococcus pneumoniae within Israel using genomic data.

Understanding how pathogens spread across geographical space is fundamental for control measures such as vaccination. Streptococcus pneumoniae (the pneumococcus) is a respiratory bacterium responsible for a large proportion of infectious disease morbidity and mortality globally. Even in the post-vaccination era, the rates of invasive pneumococcal disease (IPD) remain stable in most countries, including Israel. To understand the geographical spread of the pneumococcus in Israel, we analysed 1174 pneumococcal genomes from patients with IPD across multiple regions. We included the evolutionary distance between pairs of isolates inferred using whole-genome data within a relative risk (RR) ratio framework to capture the geographical structure of S. pneumoniae. While we could not find geographical structure at the overall lineage level, the extra granularity provided by whole-genome sequence data showed that it takes approximately 5 years for invasive pneumococcal isolates to become fully mixed across the country.This article contains data hosted by Microreact.

Learning about treatment effects in a new target population under transportability assumptions for relative effect measures.

Investigators often believe that relative effect measures conditional on covariates, such as risk ratios and mean ratios, are "transportable" across populations. Here, we examine the identification of causal effects in a target population using an assumption that conditional relative effect measures are transportable from a trial to the target population. We show that transportability for relative effect measures is largely incompatible with transportability for difference effect measures, unless the treatment has no effect on average or one is willing to make even stronger transportability assumptions that imply the transportability of both relative and difference effect measures. We then describe how marginal (population-averaged) causal estimands in a target population can be identified under the assumption of transportability of relative effect measures, when we are interested in the effectiveness of a new experimental treatment in a target population where the only treatment in use is the control treatment evaluated in the trial. We extend these results to consider cases where the control treatment evaluated in the trial is only one of the treatments in use in the target population, under an additional partial exchangeability assumption in the target population (i.e., an assumption of no unmeasured confounding in the target population with respect to potential outcomes under the control treatment in the trial). We also develop identification results that allow for the covariates needed for transportability of relative effect measures to be only a small subset of the covariates needed to control confounding in the target population. Last, we propose estimators that can be easily implemented in standard statistical software and illustrate their use using data from a comprehensive cohort study of stable ischemic heart disease.

Bayesian meta-analysis of penetrance for cancer risk.

Multi-gene panel testing allows many cancer susceptibility genes to be tested quickly at a lower cost making such testing accessible to a broader population. Thus, more patients carrying pathogenic germline mutations in various cancer-susceptibility genes are being identified. This creates a great opportunity, as well as an urgent need, to counsel these patients about appropriate risk-reducing management strategies. Counseling hinges on accurate estimates of age-specific risks of developing various cancers associated with mutations in a specific gene, ie, penetrance estimation. We propose a meta-analysis approach based on a Bayesian hierarchical random-effects model to obtain penetrance estimates by integrating studies reporting different types of risk measures (eg, penetrance, relative risk, odds ratio) while accounting for the associated uncertainties. After estimating posterior distributions of the parameters via a Markov chain Monte Carlo algorithm, we estimate penetrance and credible intervals. We investigate the proposed method and compare with an existing approach via simulations based on studies reporting risks for two moderate-risk breast cancer susceptibility genes, ATM and PALB2. Our proposed method is far superior in terms of coverage probability of credible intervals and mean square error of estimates. Finally, we apply our method to estimate the penetrance of breast cancer among carriers of pathogenic mutations in the ATM gene.

Rejoinder to the discussion on "Bayesian meta-analysis of penetrance for cancer risk".

The five discussions of our paper provide several modeling alternatives, extensions, and generalizations that can potentially guide future research in meta-analysis. In this rejoinder, we briefly summarize and comment on some of those points.

A Utilitarian Perspective on Risk Quantification for Clinical Significance in Binary Outcomes.

Null hypothesis significance testing (NHST) in medical research is increasingly being supplemented by estimation statistics, focusing on effect sizes (ESs) and confidence intervals (CIs). This study evaluates the expression of ESs and CIs for binary outcomes. A utilitarian framework is proposed, emphasizing the number of beneficiaries and the impact level. To evaluate clinical significance, minimal clinically important risk difference (MCIRD) is proposed based on event magnitude (EM). Within this framework, risk difference (RD) is introduced as the primary measure. To assess the performance of RD, we compared its statistical power against other measures (risk ratio, RR; odds ratio, OR; Cohen's h) in individual study scenarios, and visual information conveyance in meta-analysis scenarios. RDs maintain statistical power in comparison to other measures in individual studies. They provide clarity on the true impact of clinical interventions without compromising statistical integrity. Meta-analytic results indicate that using RDs directly enhances transparency, uncovers heterogeneity, and addresses misaligned assumptions. This approach, by quantifying clinical effectiveness under a utilitarian perspective, facilitates the applicability of research to patient care and encourages shared decision-making. The study advocates for reporting baseline risks (BRs) with RDs and recommends a standardized presentation of these statistics. In a utilitarian perspective, adopting RD as the preferred ES can foster a transparent, patient-focused research ethos. This aids in accurately presenting the magnitude and variability of treatment effects, offering a new direction in methodology.

The performance of marginal structural models for estimating risk differences and relative risks using weighted univariate generalized linear models.

We used Monte Carlo simulations to compare the performance of marginal structural models (MSMs) based on weighted univariate generalized linear models (GLMs) to estimate risk differences and relative risks for binary outcomes in observational studies. We considered four different sets of weights based on the propensity score: inverse probability of treatment weights with the average treatment effect as the target estimand, weights for estimating the average treatment effect in the treated, matching weights and overlap weights. We considered sample sizes ranging from 500 to 10,000 and allowed the prevalence of treatment to range from 0.1 to 0.9. We examined both the robust variance estimator when using generalized estimating equations with an independent working correlation matrix and a bootstrap variance estimator for estimating the standard error of the risk difference and the log-relative risk. The performance of these methods was compared with that of direct weighting. Both the direct weighting approach and MSMs based on weighted univariate GLMs resulted in the identical estimates of risk differences and relative risks. When sample sizes were small to moderate, the use of an MSM with a bootstrap variance estimator tended to result in the most accurate estimates of standard errors. When sample sizes were large, the direct weighting approach and an MSM with a bootstrap variance estimator tended to produce estimates of standard error with similar accuracy. When using a MSM to estimate risk differences and relative risks, in general it is preferable to use a bootstrap variance estimator than the robust variance estimator. We illustrate the application of the different methods for estimating risks differences and relative risks using an observational study on the effect on mortality of discharge prescribing of a beta-blocker in patients hospitalized with acute myocardial infarction.