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Journal articles on the topic 'Targeted Maximum Likelihood Estimation'

Author: Grafiati

Published: 25 January 2025

Last updated: 31 July 2025

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1

Pang, Menglan, Tibor Schuster, Kristian B. Filion, Maria Eberg, and Robert W. Platt. "Targeted Maximum Likelihood Estimation for Pharmacoepidemiologic Research." Epidemiology 27, no. 4 (2016): 570–77. http://dx.doi.org/10.1097/ede.0000000000000487.

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Lendle, Samuel D., Bruce Fireman, and Mark J. van der Laan. "Targeted maximum likelihood estimation in safety analysis." Journal of Clinical Epidemiology 66, no. 8 (2013): S91—S98. http://dx.doi.org/10.1016/j.jclinepi.2013.02.017.

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3

Zheng, Wenjing, and Mark J. van der Laan. "Targeted Maximum Likelihood Estimation of Natural Direct Effects." International Journal of Biostatistics 8, no. 1 (2012): 1–40. http://dx.doi.org/10.2202/1557-4679.1361.

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4

Dijkhuis, Talko B., and Frank J. Blaauw. "Transfering Targeted Maximum Likelihood Estimation for Causal Inference into Sports Science." Entropy 24, no. 8 (2022): 1060. http://dx.doi.org/10.3390/e24081060.

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Although causal inference has shown great value in estimating effect sizes in, for instance, physics, medical studies, and economics, it is rarely used in sports science. Targeted Maximum Likelihood Estimation (TMLE) is a modern method for performing causal inference. TMLE is forgiving in the misspecification of the causal model and improves the estimation of effect sizes using machine-learning methods. We demonstrate the advantage of TMLE in sports science by comparing the calculated effect size with a Generalized Linear Model (GLM). In this study, we introduce TMLE and provide a roadmap for

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5

Schuler, Megan S., and Sherri Rose. "Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies." American Journal of Epidemiology 185, no. 1 (2016): 65–73. http://dx.doi.org/10.1093/aje/kww165.

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6

Luque-Fernandez, Miguel Angel, Michael Schomaker, Bernard Rachet, and Mireille E. Schnitzer. "Targeted maximum likelihood estimation for a binary treatment: A tutorial." Statistics in Medicine 37, no. 16 (2018): 2530–46. http://dx.doi.org/10.1002/sim.7628.

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Cho, Sunyoung, Heejo Koo, Beom Kyung Kim, and Euna Han. "Causal Analyses of Statin to Prevent Liver Disease Progression: A Nationwide Study Using Superlearning Targeted Maximum Likelihood Estimation." Yakhak Hoeji 68, no. 1 (2024): 44–55. http://dx.doi.org/10.17480/psk.2024.68.1.44.

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Many studies have shown that statins reduce the risk of progression to liver cirrhosis (LC) and hepatocellular carcinoma (HCC) among at-risk populations. However, causality has not been proved. This study examined whether statins could prevent LC and HCC in patients with progressive and worsening chronic liver disease, using a robust methodology for causality. Between 2002 and 2013, 52,145 patients with chronic liver diseases were identified from the National Health Insurance Service database in South Korea. The inverse probability weighting (IPW) and superlearning targeted maximum likelihood

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Cai, Weixin, and Mark J. Laan. "One‐step targeted maximum likelihood estimation for time‐to‐event outcomes." Biometrics 76, no. 3 (2019): 722–33. http://dx.doi.org/10.1111/biom.13172.

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9

Stitelman, Ori M., C. William Wester, Victor De Gruttola, and Mark J. van der Laan. "Targeted Maximum Likelihood Estimation of Effect Modification Parameters in Survival Analysis." International Journal of Biostatistics 7, no. 1 (2011): 1–34. http://dx.doi.org/10.2202/1557-4679.1307.

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10

Grossman, J., M. Ghadessi, A. Contijoch, et al. "MSR75 Correlate: Assessing Dose Effect Using Targeted Maximum Likelihood Estimation (TMLE)." Value in Health 26, no. 12 (2023): S407. http://dx.doi.org/10.1016/j.jval.2023.09.2134.

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11

Moore, K. L., and M. J. van der Laan. "Covariate adjustment in randomized trials with binary outcomes: Targeted maximum likelihood estimation." Statistics in Medicine 28, no. 1 (2009): 39–64. http://dx.doi.org/10.1002/sim.3445.

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12

Schomaker, M., M. A. Luque‐Fernandez, V. Leroy, and M. A. Davies. "Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions." Statistics in Medicine 38, no. 24 (2019): 4888–911. http://dx.doi.org/10.1002/sim.8340.

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13

Petersen, Maya, Joshua Schwab, Susan Gruber, Nello Blaser, Michael Schomaker, and Mark van der Laan. "Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models." Journal of Causal Inference 2, no. 2 (2014): 147–85. http://dx.doi.org/10.1515/jci-2013-0007.

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AbstractThis paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific count

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van der Laan, Mark, and Susan Gruber. "One-Step Targeted Minimum Loss-based Estimation Based on Universal Least Favorable One-Dimensional Submodels." International Journal of Biostatistics 12, no. 1 (2016): 351–78. http://dx.doi.org/10.1515/ijb-2015-0054.

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AbstractConsider a study in which one observesnindependent and identically distributed random variables whose probability distribution is known to be an element of a particular statistical model, and one is concerned with estimation of a particular real valued pathwise differentiable target parameter of this data probability distribution. The targeted maximum likelihood estimator (TMLE) is an asymptotically efficient substitution estimator obtained by constructing a so called least favorable parametric submodel through an initial estimator with score, at zero fluctuation of the initial estimat

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15

Baumann, Philipp F. M., Michael Schomaker, and Enzo Rossi. "Estimating the effect of central bank independence on inflation using longitudinal targeted maximum likelihood estimation." Journal of Causal Inference 9, no. 1 (2021): 109–46. http://dx.doi.org/10.1515/jci-2020-0016.

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Abstract The notion that an independent central bank reduces a country’s inflation is a controversial hypothesis. To date, it has not been possible to satisfactorily answer this question because the complex macroeconomic structure that gives rise to the data has not been adequately incorporated into statistical analyses. We develop a causal model that summarizes the economic process of inflation. Based on this causal model and recent data, we discuss and identify the assumptions under which the effect of central bank independence on inflation can be identified and estimated. Given these and al

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Schnitzer, Mireille E., Erica E. M. Moodie, and Robert W. Platt. "Targeted maximum likelihood estimation for marginal time-dependent treatment effects under density misspecification." Biostatistics 14, no. 1 (2012): 1–14. http://dx.doi.org/10.1093/biostatistics/kxs024.

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17

Hu, Liangyuan, Chenyang Gu, Michael Lopez, Jiayi Ji, and Juan Wisnivesky. "Estimation of causal effects of multiple treatments in observational studies with a binary outcome." Statistical Methods in Medical Research 29, no. 11 (2020): 3218–34. http://dx.doi.org/10.1177/0962280220921909.

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There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and evaluate the use of Bayesian additive regression trees in such settings. First, we compare Bayesian additive regression trees to several approaches that have been proposed for continuous outcomes, including inverse probability of treatment weighting, targeted maximum likelihood estimator, vector matching, and regression adjustment. Results suggest that under conditions of non-linearity and non-additivity of both the

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18

Gruber, Susan, and Mark J. van der Laan. "An Application of Targeted Maximum Likelihood Estimation to the Meta-Analysis of Safety Data." Biometrics 69, no. 1 (2013): 254–62. http://dx.doi.org/10.1111/j.1541-0420.2012.01829.x.

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Decker, Anna L., Alan Hubbard, Catherine M. Crespi, Edmund Y. W. Seto, and May C. Wang. "Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package." Journal of Causal Inference 2, no. 1 (2014): 95–108. http://dx.doi.org/10.1515/jci-2013-0025.

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AbstractWhile child and adolescent obesity is a serious public health concern, few studies have utilized parameters based on the causal inference literature to examine the potential impacts of early intervention. The purpose of this analysis was to estimate the causal effects of early interventions to improve physical activity and diet during adolescence on body mass index (BMI), a measure of adiposity, using improved techniques. The most widespread statistical method in studies of child and adolescent obesity is multivariable regression, with the parameter of interest being the coefficient on

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Lim, Sungwoo, Marisol Tellez, and Amid I. Ismail. "Estimating a Dynamic Effect of Soda Intake on Pediatric Dental Caries Using Targeted Maximum Likelihood Estimation Method." Caries Research 53, no. 5 (2019): 532–40. http://dx.doi.org/10.1159/000497359.

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An effect of soda intake on dental caries in young children (birth to 5 years) may vary over time. Estimating a dynamic effect may be challenging due to time-varying confounding and loss to follow-up. The purpose of this paper is to demonstrate utility of targeted maximum likelihood estimation (TMLE) method in addressing longitudinal data analysis challenges and estimating a dynamic effect of soda intake on pediatric caries. Data came from the Detroit Dental Health Project, a 4-year cohort study of low-income African-American children and caregivers. The sample included 995 child–caregiver pa

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van der Laan, Mark J. "Causal Inference for a Population of Causally Connected Units." Journal of Causal Inference 2, no. 1 (2014): 13–74. http://dx.doi.org/10.1515/jci-2013-0002.

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AbstractSuppose that we observe a population of causally connected units. On each unit at each time-point on a grid we observe a set of other units the unit is potentially connected with, and a unit-specific longitudinal data structure consisting of baseline and time-dependent covariates, a time-dependent treatment, and a final outcome of interest. The target quantity of interest is defined as the mean outcome for this group of units if the exposures of the units would be probabilistically assigned according to a known specified mechanism, where the latter is called a stochastic intervention.

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22

Gianfrancesco, M. A., L. Balzer, K. E. Taylor, et al. "Genetic risk and longitudinal disease activity in systemic lupus erythematosus using targeted maximum likelihood estimation." Genes & Immunity 17, no. 6 (2016): 358–62. http://dx.doi.org/10.1038/gene.2016.33.

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23

Pirracchio, Romain, John K. Yue, Geoffrey T. Manley, Mark J. van der Laan, and Alan E. Hubbard. "Collaborative targeted maximum likelihood estimation for variable importance measure: Illustration for functional outcome prediction in mild traumatic brain injuries." Statistical Methods in Medical Research 27, no. 1 (2016): 286–97. http://dx.doi.org/10.1177/0962280215627335.

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Standard statistical practice used for determining the relative importance of competing causes of disease typically relies on ad hoc methods, often byproducts of machine learning procedures (stepwise regression, random forest, etc.). Causal inference framework and data-adaptive methods may help to tailor parameters to match the clinical question and free one from arbitrary modeling assumptions. Our focus is on implementations of such semiparametric methods for a variable importance measure (VIM). We propose a fully automated procedure for VIM based on collaborative targeted maximum likelihood

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24

Bembom, Oliver, Maya L. Petersen, Soo-Yon Rhee, et al. "Biomarker discovery using targeted maximum-likelihood estimation: Application to the treatment of antiretroviral-resistant HIV infection." Statistics in Medicine 28, no. 1 (2008): 152–72. http://dx.doi.org/10.1002/sim.3414.

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Ju, Cheng, Joshua Schwab, and Mark J. van der Laan. "On adaptive propensity score truncation in causal inference." Statistical Methods in Medical Research 28, no. 6 (2018): 1741–60. http://dx.doi.org/10.1177/0962280218774817.

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The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator. One of the consequences of practical violations of the positivity assumption is extreme values in the estimated propensity score (PS). A common practice to address this issue is truncating the PS estimate when constructing PS-based estimators. In this study, we propose a novel adaptive truncation method, Pos

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26

McCoy, David, Alan Hubbard, and Mark Van der Laan. "CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R." Journal of Open Source Software 8, no. 82 (2023): 4181. http://dx.doi.org/10.21105/joss.04181.

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27

Kreif, Noémi, Susan Gruber, Rosalba Radice, Richard Grieve, and Jasjeet S. Sekhon. "Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching." Statistical Methods in Medical Research 25, no. 5 (2016): 2315–36. http://dx.doi.org/10.1177/0962280214521341.

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Zheng, Wenjing, Maya Petersen, and Mark J. van der Laan. "Doubly Robust and Efficient Estimation of Marginal Structural Models for the Hazard Function." International Journal of Biostatistics 12, no. 1 (2016): 233–52. http://dx.doi.org/10.1515/ijb-2015-0036.

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Abstract In social and health sciences, many research questions involve understanding the causal effect of a longitudinal treatment on mortality (or time-to-event outcomes in general). Often, treatment status may change in response to past covariates that are risk factors for mortality, and in turn, treatment status may also affect such subsequent covariates. In these situations, Marginal Structural Models (MSMs), introduced by Robins (1997. Marginal structural models Proceedings of the American Statistical Association. Section on Bayesian Statistical Science, 1–10), are well-established and w

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29

Balzer, Laura B., Wenjing Zheng, Mark J. van der Laan, and Maya L. Petersen. "A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure." Statistical Methods in Medical Research 28, no. 6 (2018): 1761–80. http://dx.doi.org/10.1177/0962280218774936.

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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 efficient

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Kreif, Noémi, Linh Tran, Richard Grieve, Bianca De Stavola, Robert C. Tasker, and Maya Petersen. "Estimating the Comparative Effectiveness of Feeding Interventions in the Pediatric Intensive Care Unit: A Demonstration of Longitudinal Targeted Maximum Likelihood Estimation." American Journal of Epidemiology 186, no. 12 (2017): 1370–79. http://dx.doi.org/10.1093/aje/kwx213.

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31

Brooks, Jordan C., Mark J. van der Laan, Daniel E. Singer, and Alan S. Go. "Targeted Minimum Loss-Based Estimation of Causal Effects in Right-Censored Survival Data with Time-Dependent Covariates: Warfarin, Stroke, and Death in Atrial Fibrillation." Journal of Causal Inference 1, no. 2 (2013): 235–54. http://dx.doi.org/10.1515/jci-2013-0001.

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AbstractCausal effects in right-censored survival data can be formally defined as the difference in the marginal cumulative event probabilities under particular interventions. Conventional estimators, such as the Kaplan-Meier (KM), fail to consistently estimate these marginal parameters under dependent treatment assignment or dependent censoring. Several modern estimators have been developed that reduce bias under both dependent treatment assignment and dependent censoring by incorporating information from baseline and time-dependent covariates. In the present article we describe a recently de

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Rodríguez-Molina, Daloha, Swaantje Barth, Ronald Herrera, Constanze Rossmann, Katja Radon, and Veronika Karnowski. "An educational intervention to improve knowledge about prevention against occupational asthma and allergies using targeted maximum likelihood estimation." International Archives of Occupational and Environmental Health 92, no. 5 (2019): 629–38. http://dx.doi.org/10.1007/s00420-018-1397-1.

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Ehrlich, Samantha F., Romain S. Neugebauer, Juanran Feng, Monique M. Hedderson, and Assiamira Ferrara. "Exercise During the First Trimester and Infant Size at Birth: Targeted Maximum Likelihood Estimation of the Causal Risk Difference." American Journal of Epidemiology 189, no. 2 (2019): 133–45. http://dx.doi.org/10.1093/aje/kwz213.

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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-adaptiv

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Ekwaru, J. P., S. McMullen, T. Cowling, M. Bhutani, and M. van der Laan. "PT41 Benefits of Inhaled Corticosteroids (ICS) in COPD Maintenance Combinations: Real-World Evidence Using Longitudinal Targeted Maximum Likelihood Estimation." Value in Health 27, no. 12 (2024): S486. https://doi.org/10.1016/j.jval.2024.10.3840.

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Zakhidov, Dilshodbek, Zulfiya Sagdillayeva, and Ali R. A. Moursy. "Dividing Social Networks into Two Communities Using the Maximum Likelihood Method: Application to ESG." E3S Web of Conferences 574 (2024): 03007. http://dx.doi.org/10.1051/e3sconf/202457403007.

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This article explores the application of the Maximum Likelihood Estimation method (MLE) for community detection in environmental, social, and governance (ESG) networks. ESG factors are important in assessing the sustainability and ethical impact of investments. By understanding the structure of social networks that discuss and promote ESG practices, we can gain important insights. It proposes a probabilistic framework for identifying community structures by dividing the network into two distinct groups based on connectivity patterns using the MLE method. The network structure is analyzed, and

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Fayomi, Aisha, and Hamdah Al-Shammari. "Estimating the Parameters of the Exponential-Geometric distribution based on progressively type-II censored data." International Journal of Advanced Statistics and Probability 6, no. 1 (2018): 37. http://dx.doi.org/10.14419/ijasp.v6i1.10450.

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This paper deals with the problem of parameters estimation of the Exponential-Geometric (EG) distribution based on progressive type-II censored data. It turns out that the maximum likelihood estimators for the distribution parameters have no closed forms, therefore the EM algorithm are alternatively used. The asymptotic variance of the MLEs of the targeted parameters under progressive type-II censoring is computed along with the asymptotic confidence intervals. Finally, a simple numerical example is given to illustrate the obtained results.

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Siddique, Arman Alam, Mireille E. Schnitzer, Asma Bahamyirou, et al. "Causal inference with multiple concurrent medications: A comparison of methods and an application in multidrug-resistant tuberculosis." Statistical Methods in Medical Research 28, no. 12 (2018): 3534–49. http://dx.doi.org/10.1177/0962280218808817.

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This paper investigates different approaches for causal estimation under multiple concurrent medications. Our parameter of interest is the marginal mean counterfactual outcome under different combinations of medications. We explore parametric and non-parametric methods to estimate the generalized propensity score. We then apply three causal estimation approaches (inverse probability of treatment weighting, propensity score adjustment, and targeted maximum likelihood estimation) to estimate the causal parameter of interest. Focusing on the estimation of the expected outcome under the most preva

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Herrera, Ronald, Ursula Berger, Ondine von Ehrenstein, et al. "Estimating the Causal Impact of Proximity to Gold and Copper Mines on Respiratory Diseases in Chilean Children: An Application of Targeted Maximum Likelihood Estimation." International Journal of Environmental Research and Public Health 15, no. 1 (2017): 39. http://dx.doi.org/10.3390/ijerph15010039.

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Brown, Daniel, Maya Petersen, Mark van der Laan, et al. "0124 PM2.5 and Heart Disease in a Cohort of Aluminium Workers: An Application of Longitudinal Targeted Maximum Likelihood-based Estimation (TMLE)." Occupational and Environmental Medicine 71, Suppl 1 (2014): A14.2—A14. http://dx.doi.org/10.1136/oemed-2014-102362.44.

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Elduma, Adel Hussein, Kourosh Holakouie-Naieni, Amir Almasi-Hashiani, et al. "The Targeted Maximum Likelihood estimation to estimate the causal effects of the previous tuberculosis treatment in Multidrug-resistant tuberculosis in Sudan." PLOS ONE 18, no. 1 (2023): e0279976. http://dx.doi.org/10.1371/journal.pone.0279976.

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Introduction This study used Targeted Maximum Likelihood Estimation (TMLE) as a double robust method to estimate the causal effect of previous tuberculosis treatment history on the occurrence of multidrug-resistant tuberculosis (MDR-TB). TMLE is a method to estimate the marginal statistical parameters in case-control study design. The aim of this study was to estimate the causal effect of the previous tuberculosis treatment on the occurrence of MDR-TB using TMLE in Sudan. Method A case-control study design combined with TMLE was used to estimate parameters. Cases were MDR-TB patients and contr

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Winanda, Rara Sandhy, Yasyfin Ikrima Khairani, and Fajar Wisga Permana. "ASSESSING UNEMPLOYMENT RATES IN TANAH DATAR REGENCY: INSIGHTS FROM SMALL AREA ESTIMATION." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 2 (2025): 1433–44. https://doi.org/10.30598/barekengvol19iss2pp1433-1444.

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Unemployment is a significant issue in Indonesia's labor market. The unemployment rate is measured by the Open Unemployment Rate (OUR) through the National Labor Force Survey (SAKERNAS) conducted by BPS. In 2022, the OUR in Tanah Datar District reached its highest level in the past fifteen years. This rise in unemployment contrasts with the declining poverty rate, unlike other districts/cities in West Sumatra. To address the increasing unemployment, detailed information at the smallest administrative level is necessary. However, because the limited sample size in SAKERNAS does not allow for di

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Ahmed, Syed Ejaz, Reza Arabi Belaghi, Abdulkadir Hussein, and Alireza Safariyan. "New and Efficient Estimators of Reliability Characteristics for a Family of Lifetime Distributions under Progressive Censoring." Mathematics 12, no. 10 (2024): 1599. http://dx.doi.org/10.3390/math12101599.

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Estimation of reliability and stress–strength parameters is important in the manufacturing industry. In this paper, we develop shrinkage-type estimators for the reliability and stress–strength parameters based on progressively censored data from a rich class of distributions. These new estimators improve the performance of the commonly used Maximum Likelihood Estimators (MLEs) by reducing their mean squared errors. We provide analytical asymptotic and bootstrap confidence intervals for the targeted parameters. Through a detailed simulation study, we demonstrate that the new estimators have bet

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Rudolph, Kara E., Dana E. Goin, and Elizabeth A. Stuart. "The Peril of Power: A Tutorial on Using Simulation to Better Understand When and How We Can Estimate Mediating Effects." American Journal of Epidemiology 189, no. 12 (2020): 1559–67. http://dx.doi.org/10.1093/aje/kwaa083.

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Abstract Mediation analyses are valuable for examining mechanisms underlying an association, investigating possible explanations for nonintuitive results, or identifying interventions that can improve health in the context of nonmanipulable exposures. However, designing a study for the purpose of answering a mediation-related research question remains challenging because sample size and power calculations for mediation analyses are typically not conducted or are crude approximations. Consequently, many studies are probably conducted without first establishing that they have the statistical pow

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Elduma, Adel Hussein, Kourosh Holakouie-Naieni, Amir Almasi-Hashiani, et al. "Correction: The Targeted Maximum Likelihood estimation to estimate the causal effects of the previous tuberculosis treatment in Multidrug-resistant tuberculosis in Sudan." PLOS ONE 19, no. 12 (2024): e0314954. https://doi.org/10.1371/journal.pone.0314954.

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45

Uchoa, Frederico. "An assessment about the relationship between educational inequality and economic growth in Brazilian Northeast region." Reflexões Econômicas 5, no. 1 (2020): 1–12. http://dx.doi.org/10.36113/rec.v5i1.2760.

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In this paper we analyze the impact of education inequality on the income of formal workers in Northeast Brazil. For this study, we analyzed the data collected from censuses data and estimate a dynamic panel data model. Statistical analyses were performed by using the quasi-maximum likelihood linear dynamic panel data estimation, an approach that produce consistent estimates with large n and small T. We found a negative and statistically significant impact of education inequality on economic growth, which is convergent with the literature that advocates that an unequal distribution of educatio

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Zhang, D., Y. Zhang, SW Ahn, et al. "MSR48 Utilization of High-Dimensional Propensity Score and Targeted Maximum Likelihood Estimation with Machine Learning to Improve Causal Effect Estimation in Patients with Nonvalvular Atrial Fibrillation and Hypertension." Value in Health 27, no. 6 (2024): S268. http://dx.doi.org/10.1016/j.jval.2024.03.1481.

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47

Balzer, Laura, Patrick Staples, Jukka-Pekka Onnela, and Victor DeGruttola. "Using a network-based approach and targeted maximum likelihood estimation to evaluate the effect of adding pre-exposure prophylaxis to an ongoing test-and-treat trial." Clinical Trials 14, no. 2 (2017): 201–10. http://dx.doi.org/10.1177/1740774516679666.

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Background: Several cluster-randomized trials are underway to investigate the implementation and effectiveness of a universal test-and-treat strategy on the HIV epidemic in sub-Saharan Africa. We consider nesting studies of pre-exposure prophylaxis within these trials. Pre-exposure prophylaxis is a general strategy where high-risk HIV– persons take antiretrovirals daily to reduce their risk of infection from exposure to HIV. We address how to target pre-exposure prophylaxis to high-risk groups and how to maximize power to detect the individual and combined effects of universal test-and-treat a

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Mozafar Saadati, Hossein, Yadollah Mehrabi, Siamak Sabour, Mohammad Ali Mansournia, and Seyed Saeed Hashemi Nazari. "Estimating the effects of body mass index and central obesity on stroke in diabetics and non‐diabetics using targeted maximum likelihood estimation: Atherosclerosis Risk in Communities study." Obesity Science & Practice 6, no. 6 (2020): 628–37. http://dx.doi.org/10.1002/osp4.447.

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Schnitzer, Mireille E., Erica E. M. Moodie, Mark J. van der Laan, Robert W. Platt, and Marina B. Klein. "Modeling the impact of hepatitis C viral clearance on end-stage liver disease in an HIV co-infected cohort with targeted maximum likelihood estimation." Biometrics 70, no. 1 (2013): 144–52. http://dx.doi.org/10.1111/biom.12105.

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Pabebang, Sriyanti Rahayu, and Jessy Yunus Dannari. "The Influence of Content Marketing on Instagram on Online Purchase Decisions for MsGlow Products with Purchase Intention as an Intervening Variable." Jurnal Indonesia Sosial Sains 5, no. 12 (2024): 3102–16. https://doi.org/10.59141/jiss.v5i12.1526.

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Abstract:

This research aims to examine the effect of Content Marketing on Instagram on purchasing decisions for Ms Glow products online, with Purchase Intention as an intermediary variable. The approach used in this research is quantitative with an explanatory research design. The population targeted by this study are Instagram users who have purchased Ms Glow products online or often shop at the MS Glow Makassar distributor store. The sample used consisted of 120 respondents, who were selected using the Maximum Likelihood Estimation (MLE) method. Several tests were conducted to ensure data validity an

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