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To compare the performance of a targeted maximum likelihood estimator (TMLE) and a collaborative TMLE (CTMLE) to other estimators in a drug safety analysis, including a regression-based estimator, propensity score (PS)–based estimators, and an alternate doubly robust (DR) estimator in a real example and simulations. The real data set is a subset of observational data from Kaiser Permanente Northern California formatted for use in active drug safety surveillance. Both the real and simulated data sets include potential confounders, a treatment variable indicating use of one of two antidiabetic treatments and an outcome variable indicating occurrence of an acute myocardial infarction (AMI). In the real data example, there is no difference in AMI rates between treatments. In simulations, the double robustness property is demonstrated: DR estimators are consistent if either the initial outcome regression or PS estimator is consistent, whereas other estimators are inconsistent if the initial estimator is not consistent. In simulations with near-positivity violations, CTMLE performs well relative to other estimators by adaptively estimating the PS. Each of the DR estimators was consistent, and TMLE and CTMLE had the smallest mean squared error in simulations.

We develop robust targeted maximum likelihood estimators (TMLEs) for transporting intervention effects from one population to another. Specifically, we develop TMLEs for three transported estimands: the ¡ntent-to-treat average treatment effect (ATE) and complier ATE, which are relevant for encouragement design interventions and instrumental variable analyses, and the ATE of the exposure on the outcome, which is applicable to any randomized or observational study. We demonstrate finite sample performance of these TMLEs by using simulation, including in the presence of practical violations of the positivity assumption. We then apply these methods to the 'Moving to opportunity' trial: a multisite, encouragement design intervention in which families in public housing were randomized to receive housing vouchers and logistical support to move to low poverty neighbourhoods. This application sheds light on whether effect differences across sites can be explained by differences in population composition.

Objectives To have confidence in one's interpretation of treatment effects assessed by comparing trial results to external controls, minimizing bias is a critical step. We sought to investigate different methods for causal inference in simulated data sets with measured and unmeasured confounders. Methods The simulated data included three types of outcomes (continuous, binary, and time-to-event), treatment assignment, two measured baseline confounders, and one unmeasured confounding factor. Three scenarios were set to create different intensities of confounding effect (e.g., small and blocked confounding paths, medium and blocked confounding paths, and one large unblocked confounding path for scenario 1 to 3, respectively) caused by the unmeasured confounder. The methods of g-computation (GC), inverse probability of treatment weighting (IPTW), overlap weighting (OW), standardized mortality/morbidity ratio (SMR), and targeted maximum likelihood estimation (TMLE) were used to estimate average treatment effects and reduce potential biases. Results The results with the greatest extent of biases were from the raw model that ignored all the potential confounders. In scenario 2, the unmeasured factor indirectly influenced the treatment assignment through a measured controlling factor and led to medium confounding. The methods of GC, IPTW, OW, SMR, and TMLE removed most of bias observed in average treatment effects for all three types of outcomes from the raw model. Similar results were found in scenario 1, but the results tended to be biased in scenario 3. GC had the best performance followed by OW. Conclusions The aforesaid methods can be used for causal inference in externally controlled studies when there is no large, unblockable confounding path for an unmeasured confounder. GC and OW are the preferable approaches.

Estimating the causal effect of an intervention on a population typically involves defining parameters in a nonparametric structural equation model (Pearl, 2000, Causality: Models, Reasoning, and Inference) in which the treatment or exposure is deterministically assigned in a static or dynamic way. We define a new causal parameter that takes into account the fact that intervention policies can result in stochastically assigned exposures. The statistical parameter that identifies the causal parameter of interest is established. Inverse probability of treatment weighting (IPTW), augmented IPTW (A‐IPTW), and targeted maximum likelihood estimators (TMLE) are developed. A simulation study is performed to demonstrate the properties of these estimators, which include the double robustness of the A‐IPTW and the TMLE. An application example using physical activity data is presented.

Second-generation antipsychotics (SGAs) cause metabolic side effects. However, patients' metabolic profiles were influenced by time-invariant and time-varying confounders. Real-world evidence on the long-term, dynamic effects of SGAs (e.g. different treatment sequences) are limited. We employed advanced causal inference methods to evaluate the metabolic impact of SGAs in a naturalistic cohort. We followed 696 Chinese patients with schizophrenia-spectrum disorders receiving SGAs. Longitudinal targeted maximum likelihood estimation (LTMLE) was used to estimate the average treatment effects (ATEs) of continuous SGA treatment versus 'no treatment' on metabolic outcomes, including total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglyceride (TG), fasting glucose (FG), and body mass index (BMI), over 6-18 months at 3-month intervals. LTMLE accounted for time-invariant and time-varying confounders. Post-SGA discontinuation side effects were also assessed. The ATEs of continuous SGA treatment on BMI and TG showed an inverted U-shaped pattern, peaking at 12 months and declining afterwards. Similar patterns were observed for TC and LDL, albeit the ATEs peaked at 15 months. For FG and HDL, the ATEs peaked at ~6 months. The adverse impact of SGAs on BMI persisted even after medication discontinuation, yet other metabolic parameters did not show such lingering side effects. Clozapine and olanzapine exhibited greater metabolic side effects compared to other SGAs. Our real-world study suggests that metabolic side effects may stabilize with prolonged continuous treatment. Clozapine and olanzapine confer higher cardiometabolic risks than other SGAs. The side effects of SGAs on BMI may persist after drug discontinuation. These insights may guide antipsychotic choice and improve management of metabolic side effects.

In ecology, there is a growing need to move beyond correlations to uncovering causal effects from observational data. With the parallel increase in big data and machine learning algorithms, the opportunity now exists to benefit from causal machine learning methodologies. This paper presents an accessible overview of four causal machine learning methods, double machine learning (DML), targeted maximum likelihood estimation (TMLE), deep instrumental variables (Deep IV) and causal forests, that can be applied across ecological contexts. DML and TMLE leverage machine learning to estimate causal effects in the presence of known confounders. Deep IV offers a robust solution for addressing unmeasured confounding or bidirectional relationships by pairing valid instruments with deep neural networks. Causal forests uncover heterogeneity in causal effects, shedding light on context‐dependent ecological responses. Adding these causal machine learning techniques to an ecologist's broader causal toolkit will increase the options researchers have for estimating causal relationships, particularly when dealing with complex and large‐scale observational data.

Background Causal inference from observational studies is an area of interest to researchers, advancing rapidly over the years and with it, the methods for causal effect estimation. Among them, Targeted Maximum Likelihood estimation (TMLE) possesses arguably the most outstanding statistical properties, and with no outright treatment for COVID-19, there was an opportunity to estimate the causal effect of dexamethasone versus hydrocortisone upon the neutrophil-lymphocyte ratio (NLR), a vital indicator for disease progression among critically ill COVID-19 patients. Methods TMLE variations were used in the analysis. Super Learner (SL), Bayesian Additive Regression Trees (BART) and parametric regression (PAR) were implemented to estimate the average treatment effect (ATE). Results The study had 168 participants, 128 on dexamethasone and 40 on hydrocortisone. The mean causal difference in NLR on day 5; ATE [95% CI]: from SL-TMLE was − 0.309 [-3.800, 3.182] BART-TMLE 0.246 [-3.399, 3.891] and PAR-TMLE 1.245 [-1.882, 4372]. The ATE of dexamethasone versus hydrocortisone on NLR was not statistically significant since the confidence interval included zero. Conclusion The effect of dexamethasone is not significantly different from that of hydrocortisone on NLR in critically ill COVID-19 patients admitted to ICU. This implies that the difference in effect on NLR between the two drugs is due to random chance. TMLE remains an outstanding approach for causal analysis of observational studies with the ability to be augmented with multiple prediction approaches.

BACKGROUND:Migration of adult children may impact the health of aging parents who remain in low- and middle-income countries. Prior studies have uncovered mixed associations between adult child migration status and physical functioning of older parents; none to our knowledge has examined the impact on unmet caregiving needs. METHODS:Data come from a population-based study of Mexican adults ≥50 years. We used longitudinal targeted maximum likelihood estimation to estimate associations between having an adult child US migrant and lower-body functional limitations, and both needs and unmet needs for assistance with basic or instrumental activities of daily living (ADLs/IADLs) for 11,806 respondents surveyed over an 11-year period. RESULTS:For women, having an adult child US migrant at baseline and 2-year follow-up was associated with fewer lower-body functional limitations [marginal risk difference (RD) = −0.14, 95% confidence interval (CI) = −0.26, −0.01] and ADLs/IADLs (RD = −0.08, 95% CI = −0.16, −0.001) at 2-year follow-up. Having an adult child US migrant at all waves was associated with a higher prevalence of functional limitations at 11-year follow-up (RD = 0.04, 95% CI = 0.01, 0.06). Having an adult child US migrant was associated with a higher prevalence of unmet needs for assistance at 2 (RD = 0.13, 95% CI = 0.04, 0.21) and 11-year follow-up for women (RD = 0.07, 95% CI = −0.02, 0.15) and 11-year follow-up for men (RD = 0.08, 95% CI = 0.00, 0.16). CONCLUSION:Having an adult child US migrant had mixed associations with physical functioning, but substantial adverse associations with unmet caregiving needs for a cohort of older adults in Mexico.

Background Diabetes is closely associated with dyslipidemia and inflammation. However, studies on the combined effects of inflammation and dyslipidemia on incident diabetes are lacking. Remnant cholesterol inflammation index (RCII) is a clinical indicator of inflammation and dyslipidemia. In this study, we aimed to investigate the longitudinal relationship between cumulative RCII (cumRCII) and incident diabetes. Methods Data were sourced from the China Health and Retirement Longitudinal Study from 2011 to 2018. The mean age of the study participants was 58 years. CumRCII was treated as the exposure variable and incident diabetes as the outcome variable. Restricted cubic splines and logistic regression were used to evaluate the relationship between cumRCII and incident diabetes, and longitudinal targeted maximum likelihood estimation used for sensitivity analysis. Results A total of 4,513 participants were included in the analysis. During a 7-year follow-up period, 302 cases of incident diabetes were diagnosed. Using the first quartile of cumRCII as the reference, the odds ratio (OR) for incident diabetes in the fourth quartile was 2.60 (OR = 2.60; 95% confidence interval [CI], 1.74–3.87; P  < 0.001). Each standard deviation increase in cumRCII, the OR for incident diabetes was 1.26 (OR = 1.26; 95% CI, 1.16–1.37; P  < 0.001). Sensitivity analysis yielded consistent conclusions, with the Net Reclassification Improvement (NRI) for the model incorporating cumRCII being 0.346 and the DeLong test showing statistical significance. We found a nonlinear relationship between cumRCII and incident diabetes, the threshold value of cumRCII with respect to incident diabetes was 43.39. Conclusions CumRCII is closely associated with incident diabetes in adults aged ≥ 45 years, and a significant correlation was found between cumRCII and incident diabetes in the population with normal triglyceride levels. Furthermore, the longitudinal relationship between cumulative RCII and incident diabetes was non-linear. CumRCII can be used as an early indicator of incident diabetes in a population with normal triglycerides. Highlight This study confirmed that cumulative remnant cholesterol inflammation index (cumRCII) is closely associated with incident diabetes in adults aged 45 years and older. Furthermore, a close correlation exists between cumRCII and incident diabetes in the population with normal triglyceride levels.

Importance Statin use prior to hospitalization for Coronavirus Disease 2019 (COVID-19) is hypothesized to improve inpatient outcomes including mortality, but prior findings from large observational studies have been inconsistent, due in part to confounding. Recent advances in statistics, including incorporation of machine learning techniques into augmented inverse probability weighting with targeted maximum likelihood estimation, address baseline covariate imbalance while maximizing statistical efficiency. Objective To estimate the association of antecedent statin use with progression to severe inpatient outcomes among patients admitted for COVD-19. Design, setting and participants We retrospectively analyzed electronic health records (EHR) from individuals ≥ 40-years-old who were admitted between March 2020 and September 2022 for ≥ 24 h and tested positive for SARS-CoV-2 infection in the 30 days before to 7 days after admission . Exposure Antecedent statin use—statin prescription ≥ 30 days prior to COVID-19 admission. Main outcome Composite end point of in-hospital death, intubation, and intensive care unit (ICU) admission. Results Of 15,524 eligible COVID-19 patients, 4412 (20%) were antecedent statin users. Compared with non-users, statin users were older (72.9 (SD: 12.6) versus 65.6 (SD: 14.5) years) and more likely to be male (54% vs. 51%), White (76% vs. 71%), and have ≥ 1 medical comorbidity (99% vs. 86%). Unadjusted analysis demonstrated that a lower proportion of antecedent users experienced the composite outcome (14.8% vs 19.3%), ICU admission (13.9% vs 18.3%), intubation (5.1% vs 8.3%) and inpatient deaths (4.4% vs 5.2%) compared with non-users. Risk differences adjusted for labs and demographics were estimated using augmented inverse probability weighting with targeted maximum likelihood estimation using Super Learner . Statin users still had lower rates of the composite outcome (adjusted risk difference: − 3.4%; 95% CI: − 4.6% to − 2.1%), ICU admissions (− 3.3%; − 4.5% to − 2.1%), and intubation (− 1.9%; − 2.8% to − 1.0%) but comparable inpatient deaths (0.6%; − 1.3% to 0.1%). Conclusions and relevance After controlling for confounding using doubly robust methods, antecedent statin use was associated with minimally lower risk of severe COVID-19-related outcomes, ICU admission and intubation, however, we were not able to corroborate a statin-associated mortality benefit. Key points Question Is statin use prior to hospital admission for COVID-19 associated with reducing severe inpatient outcomes? Findings In this observational study using electronic health records from a multi-hospital health system in Chicago, we used robust statistical methods to account for confounding and found that adults 40 years or older who were prescribed statins prior to admission for COVID-19 had minimally lower rates of intubation and admission to the intensive care unit. However, inpatient mortality was comparable between statins users and non-users. Meaning Consistent with current COVID-19 treatment guidelines, we did not find evidence supporting the utilization of statins for clinically significant reduction in severe inpatient COVID-19 outcomes.