Application of targeted maximum likelihood estimation technique to assess the impact of prenatal exposure to nitrogen dioxide and ozone on stillbirth in a California cohort study

2016 ◽  
Vol 2016 (1) ◽  
Author(s):  
Varada Sarovar* ◽  
Rupa Basu ◽  
Brian Malig ◽  
Mark van der Laan ◽  
Maya Petersen
Keyword(s):  
Cohort Study ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Nitrogen Dioxide ◽  
Prenatal Exposure ◽  
Likelihood Estimation ◽  
Estimation Technique ◽  
Targeted Maximum Likelihood Estimation ◽  
Targeted Maximum Likelihood ◽  
The Impact

scholarly journals The Impact of Unmeasured Confounding in Observational Studies: a Plasmode Simulation Study of Targeted Maximum Likelihood Estimation

2021 ◽  
Author(s):  
Lateef Amusa ◽  
Temesgen Zewotir ◽  
Delia North
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Simulation Study ◽  
Observational Studies ◽  
Likelihood Estimation ◽  
Unmeasured Confounding ◽  
Targeted Maximum Likelihood Estimation ◽  
Targeted Maximum Likelihood ◽  
The Impact ◽  
Unmeasured Confounders

Abstract Unmeasured confounding can cause considerable problems in observational studies and may threaten the validity of the estimates of causal treatment effects. There has been discussion on the amount of bias in treatment effect estimates that can occur due to unmeasured confounding. We investigate the robustness of a relatively new causal inference technique, targeted maximum likelihood estimation (TMLE), in terms of its robustness to the impact of unmeasured confounders. We benchmark TMLE’s performance with the inverse probability of treatment weighting (IPW) method. We utilize a plasmode-like simulation based on variables and parameters from the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT). We evaluated the accuracy and precision of the estimated treatment effects. Though TMLE performed better in most of the scenarios considered, our simulation study results suggest that both methods performed reasonably well in estimating the marginal odds ratio, in the presence of unmeasured confounding. Nonetheless, the only remedy to unobserved confounding is controlling for as many as available covariates in an observational study, because not even TMLE can provide safeguard against bias from unmeasured confounders.

scholarly journals The causal effect and impact of reproductive factors on breast cancer using super learner and targeted maximum likelihood estimation: a case-control study in Fars Province, Iran

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Amir Almasi-Hashiani ◽  
Saharnaz Nedjat ◽  
Reza Ghiasvand ◽  
Saeid Safiri ◽  
Maryam Nazemipour ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Case Control Study ◽  
Causal Effect ◽  
Reproductive Factors ◽  
Likelihood Estimation ◽  
Case Control ◽  
Targeted Maximum Likelihood Estimation ◽  
Targeted Maximum Likelihood ◽  
Control Study

Abstract Objectives The relationship between reproductive factors and breast cancer (BC) risk has been investigated in previous studies. Considering the discrepancies in the results, the aim of this study was to estimate the causal effect of reproductive factors on BC risk in a case-control study using the double robust approach of targeted maximum likelihood estimation. Methods This is a causal reanalysis of a case-control study done between 2005 and 2008 in Shiraz, Iran, in which 787 confirmed BC cases and 928 controls were enrolled. Targeted maximum likelihood estimation along with super Learner were used to analyze the data, and risk ratio (RR), risk difference (RD), andpopulation attributable fraction (PAF) were reported. Results Our findings did not support parity and age at the first pregnancy as risk factors for BC. The risk of BC was higher among postmenopausal women (RR = 3.3, 95% confidence interval (CI) = (2.3, 4.6)), women with the age at first marriage ≥20 years (RR = 1.6, 95% CI = (1.3, 2.1)), and the history of oral contraceptive (OC) use (RR = 1.6, 95% CI = (1.3, 2.1)) or breastfeeding duration ≤60 months (RR = 1.8, 95% CI = (1.3, 2.5)). The PAF for menopause status, breastfeeding duration, and OC use were 40.3% (95% CI = 39.5, 40.6), 27.3% (95% CI = 23.1, 30.8) and 24.4% (95% CI = 10.5, 35.5), respectively. Conclusions Postmenopausal women, and women with a higher age at first marriage, shorter duration of breastfeeding, and history of OC use are at the higher risk of BC.

scholarly journals Exercise During the First Trimester and Infant Size at Birth: Targeted Maximum Likelihood Estimation of the Causal Risk Difference

2019 ◽  
Vol 189 (2) ◽  
pp. 133-145 ◽  
Author(s):  
Samantha F Ehrlich ◽  
Romain S Neugebauer ◽  
Juanran Feng ◽  
Monique M Hedderson ◽  
Assiamira Ferrara
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Gestational Age ◽  
Weight Status ◽  
Likelihood Estimation ◽  
First Trimester ◽  
Normal Weight ◽  
Targeted Maximum Likelihood Estimation ◽  
Increased Risk ◽  
Targeted Maximum Likelihood

Abstract This cohort study sought to estimate the differences in risk of delivering infants who were small or large for gestational age (SGA or LGA, respectively) according to exercise during the first trimester of pregnancy (vs. no exercise) among 2,286 women receiving care at Kaiser Permanente Northern California in 2013–2017. Exercise was assessed by questionnaire. SGA and LGA were determined by the sex- and gestational-age-specific birthweight distributions of the 2017 US Natality file. Risk differences were estimated by targeted maximum likelihood estimation, with and without data-adaptive prediction (machine learning). Analyses were also stratified by prepregnancy weight status. Overall, exercise at the cohort-specific 75th percentile was associated with an increased risk of SGA of 4.5 (95% CI: 2.1, 6.8) per 100 births, and decreased risk of LGA of 2.8 (95% CI: 0.5, 5.1) per 100 births; similar findings were observed among the underweight and normal-weight women, but no associations were found among those with overweight or obesity. Meeting Physical Activity Guidelines was associated with increased risk of SGA and decreased risk of LGA but only among underweight and normal-weight women. Any vigorous exercise reduced the risk of LGA in underweight and normal-weight women only and was not associated with SGA risk.

scholarly journals A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure

2018 ◽  
Vol 28 (6) ◽  
pp. 1761-1780 ◽  
Author(s):  
Laura B Balzer ◽  
Wenjing Zheng ◽  
Mark J van der Laan ◽  
Maya L Petersen
Keyword(s):  
Maximum Likelihood ◽  
Causal Model ◽  
Causal Effect ◽  
Likelihood Estimation ◽  
Biological Interactions ◽  
Individual Level ◽  
Targeted Maximum Likelihood Estimation ◽  
Targeted Maximum Likelihood ◽  
The Impact ◽  
Cluster Level

We often seek to estimate the impact of an exposure naturally occurring or randomly assigned at the cluster-level. For example, the literature on neighborhood determinants of health continues to grow. Likewise, community randomized trials are applied to learn about real-world implementation, sustainability, and population effects of interventions with proven individual-level efficacy. In these settings, individual-level outcomes are correlated due to shared cluster-level factors, including the exposure, as well as social or biological interactions between individuals. To flexibly and efficiently estimate the effect of a cluster-level exposure, we present two targeted maximum likelihood estimators (TMLEs). The first TMLE is developed under a non-parametric causal model, which allows for arbitrary interactions between individuals within a cluster. These interactions include direct transmission of the outcome (i.e. contagion) and influence of one individual’s covariates on another’s outcome (i.e. covariate interference). The second TMLE is developed under a causal sub-model assuming the cluster-level and individual-specific covariates are sufficient to control for confounding. Simulations compare the alternative estimators and illustrate the potential gains from pairing individual-level risk factors and outcomes during estimation, while avoiding unwarranted assumptions. Our results suggest that estimation under the sub-model can result in bias and misleading inference in an observational setting. Incorporating working assumptions during estimation is more robust than assuming they hold in the underlying causal model. We illustrate our approach with an application to HIV prevention and treatment.

Sign in / Sign up

Export Citation Format

Share Document