scholarly journals Estimating inverse probability weights using super learner when weight-model specification is unknown in a marginal structural Cox model context

2017 ◽  
Vol 36 (13) ◽  
pp. 2032-2047 ◽  
Author(s):  
Mohammad Ehsanul Karim ◽  
Robert W. Platt ◽  
Keyword(s):  
Cox Model ◽  
Model Specification ◽  
Inverse Probability ◽  
Inverse Probability Weights ◽  
Super Learner ◽  
Model Context ◽  
Weight Model

scholarly journals Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets

2014 ◽  
Vol 34 (1) ◽  
pp. 106-117 ◽  
Author(s):  
Susan Gruber ◽  
Roger W. Logan ◽  
Inmaculada Jarrín ◽  
Susana Monge ◽  
Miguel A. Hernán
Keyword(s):  
Ensemble Learning ◽  
Structural Modeling ◽  
Inverse Probability ◽  
Inverse Probability Weights ◽  
Marginal Structural Modeling

scholarly journals Tackling missing radiographic progression data: multiple imputation technique compared with inverse probability weights and complete case analysis

Rheumatology ◽  
2012 ◽  
Vol 52 (2) ◽  
pp. 331-336 ◽  
Author(s):  
M. A. Descalzo ◽  
V. V. Garcia ◽  
I. Gonzalez-Alvaro ◽  
J. Carbonell ◽  
A. Balsa ◽  
...  
Keyword(s):  
Multiple Imputation ◽  
Radiographic Progression ◽  
Case Analysis ◽  
Complete Case Analysis ◽  
Complete Case ◽  
Inverse Probability ◽  
Inverse Probability Weights

Association Effects Of Comorbid Versus Uncomorbid Diabetes And Blood Glucose Concentrations On Short-term Outcomes In Patients With Critical Illness: A Real-World Study Based On Propensity Score Analyses

2020 ◽  
Author(s):  
Shan Lin ◽  
Shanhui Ge ◽  
Wanmei He ◽  
Mian Zeng
Keyword(s):  
Critical Illness ◽  
Glycemic Control ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Cox Model ◽  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Optimal Range ◽  
Short Term ◽  
Inverse Probability

Abstract Background: The effects of combined diabetes and glycemic control strategies on the short-term prognosis in patients with a critical illness are currently ambiguous. The objectives of our study were to determine whether comorbid diabetes affects short-term prognosis and the optimal range of glycemic control in critically ill patients.Methods: We performed this study with the critical care database. The primary outcomes were 28-day mortality in critically ill patients with comorbid diabetes and the optimal range of glycemic control. Association of comorbid diabetes with 28-day mortality was assessed by multivariable Cox regression model with inverse probability weighting. Smooth curves were applied to fit the association for glucose and 28-day mortality.Results: Of the 33,680 patients enrolled in the study, 8,701 (25.83%) had diabetic comorbidity. Cox model with inverse probability weighting showed that the 28-day mortality rate was reduced by 29% (HR=0.71, 95% CI 0.67-0.76) in the group with diabetes in comparison to the group without diabetes. The E value of 2.17 indicated robustness to unmeasured confounders. The effect of the association between comorbid diabetes and 28-day mortality was generally in line for all subgroup variables, significant interactions were observed for glucose on first day, admission type, and use of insulin or not (Interaction P <0.05). A V-shaped relationship was observed between glucose concentrations and 28-day mortality in patients without diabetes, with the lowest 28-day mortality corresponding to the glucose level was 101.75 mg/dl (95% CI 94.64-105.80 mg/dl); whereas in patients with comorbid diabetes, the effect of glucose concentration on 28-day mortality was structurally softer than in those with uncomorbid diabetes. Lastly, of all patients, hyperglycemia had the greatest deleterious effect on patients admitted to CSRU.Conclusions: Our study further confirmed the protective effect of comorbid diabetes on the short-term prognosis of critically ill patients, resulting in an approximately 29% reduction in 28-day mortality. Besides, we also demonstrated the personalized glycemic control strategy for critically ill patients. Lastly, clinicians should be aware of the occurrence and the prompt management of hyperglycemia in critically ill patients admitted to the CSRU.

scholarly journals Comparison of Parametric and Nonparametric Estimators for the Association Between Incident Prepregnancy Obesity and Stillbirth in a Population-Based Cohort Study

2019 ◽  
Vol 188 (7) ◽  
pp. 1328-1336 ◽  
Author(s):  
Ya-Hui Yu ◽  
Lisa M Bodnar ◽  
Maria M Brooks ◽  
Katherine P Himes ◽  
Ashley I Naimi
Keyword(s):  
Inverse Probability Weighting ◽  
Population Based ◽  
Parametric Models ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Nonparametric Estimators ◽  
Inverse Probability Weights ◽  
Highly Sensitive ◽  
Prepregnancy Obesity ◽  
Index Weight

AbstractWhile prepregnancy obesity increases risk of stillbirth, few studies have evaluated the role of newly developed obesity independent of long-standing obesity. Additionally, researchers have relied almost exclusively on parametric models, which require correct specification of an unknown function for consistent estimation. We estimated the association between incident obesity and stillbirth in a cohort constructed from linked birth and death records in Pennsylvania (2003–2013). Incident obesity was defined as body mass index (weight (kg)/height (m)2) greater than or equal to 30. We used parametric G-computation, semiparametric inverse-probability weighting, and parametric/nonparametric targeted minimum loss-based estimation (TMLE) to estimate the association between incident prepregnancy obesity and stillbirth. Compared with pregnancies from women who stayed nonobese, women who became obese prior to their next pregnancy were estimated to have 2.0 (95% confidence interval (CI): 0.5, 3.5) more stillbirths per 1,000 pregnancies using parametric G-computation. However, despite well-behaved stabilized inverse probability weights, risk differences estimated from inverse-probability weighting, nonparametric TMLE, and parametric TMLE represented 6.9 (95% CI: 3.7, 10.0), 0.4 (95% CI: 0.1, 0.7), and 2.9 (95% CI: 1.5, 4.2) excess stillbirths per 1,000 pregnancies, respectively. These results, particularly those derived from nonparametric TMLE, were highly sensitive to covariates included in the propensity score models. Our results suggest that caution is warranted when using nonparametric estimators to quantify exposure effects.

scholarly journals Early Experience With Neutralizing Monoclonal Antibody Therapy For COVID-19

2021 ◽  
Author(s):  
Mark Jarrett ◽  
Warren B. Licht ◽  
Kevin Bock ◽  
Zenobia Brown ◽  
Jamie S. Hirsch ◽  
...  
Keyword(s):  
Monoclonal Antibody ◽  
High Risk ◽  
Hospital Mortality ◽  
Primary Outcome ◽  
Cox Model ◽  
Adult Patients ◽  
Infusion Therapy ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Neutralizing Monoclonal Antibody

ABSTRACTImportanceNeutralizing monoclonal antibody (MAB) therapies may benefit patients with mild to moderate COVID-19 at high risk for progressing to severe COVID-19 and/or hospitalization. Studies documenting approaches to deliver MAB infusions as well as demonstrating their efficacy are lacking.ObjectiveWe describe our experience and patient outcomes of almost 3,000 patients who received MAB infusion therapy at Northwell Health, a large integrated health care system in New York.Design, Setting, and ParticipantsThis is a descriptive study of adult patients who received MAB therapy between November 20, 2020, to January 31, 2021, and a retrospective cohort survival analysis comparing patients who received MAB therapy prior to admission versus those who did not. A multivariable Cox model with inverse probability weighting according to the propensity score including covariates (sociodemographic, comorbidities, and presenting vital signs) was used.Main outcomes and measuresThe primary outcome was in-hospital mortality; additional evaluations included ED utilization and hospitalization within 28 days of a positive COVID-19 test for patients who received MAB therapy.ResultsDuring the study period, 2818 adult patients received MAB infusion. Following therapy and within 28 days of COVID-19 test, 123 patients (4.4%) presented to the ED and were released and 145 patients (5.1%) were hospitalized. These 145 patients were compared with 200 controls who were eligible for but did not receive MAB therapy, and were hospitalized. In the MAB group, 16 (11%) patients met the primary outcome of in-hospital mortality, versus 21 (10.5%) in the control group. In an unadjusted Cox model, the hazard ratio (HR) for time to in-hospital mortality for the MAB group was 1.38 (95% confidence interval [95% CI] 0.696-2.719). Models adjusting for demographics (HR 1.1, 95% CI 0.53-2.23), demographics and Charlson Comorbidity Index (CCI) (HR 1.22, 95% CI 0.573-2.59), and with inverse probability weighting according to propensity scores (HR 1.19, 95% CI 0.619-2.29) did not demonstrate significance. The hospitalization rate was 4.4% for patients who received MAB therapy within 0-4 days, 5% within 5-7 days, and 6.1% within ≥8 days of symptom onset (p-value = 0.15).Conclusions and relevanceEstablishing the capability to provide neutralizing MAB infusion therapy requires significant planning and coordination. While this therapy may be an important treatment option for early mild to moderate COVID-19 in high-risk patients, further investigations are needed to define the optimal timing of MAB treatment in order to reduce hospitalization and mortality.

Financial inclusion, land title and credit: evidence from China

2020 ◽  
Vol 12 (2) ◽  
pp. 257-273
Author(s):  
Meishan Jiang ◽  
Krishna P. Paudel ◽  
Donghui Peng ◽  
Yunsheng Mi
Keyword(s):  
Design Methodology ◽  
Financial Inclusion ◽  
Content Type ◽  
Inverse Probability ◽  
Rural Credit ◽  
Inverse Probability Weights ◽  
Land Title ◽  
Land Titling ◽  
Doubly Robust ◽  
Rural Credit Market

Purpose The purpose of this paper is to study land title’s credit effect from a financial inclusion perspective in China. The focus is both small land holding and poor farmers. Formal and informal finances are considered to test their differences in land title’s credit effect. Design/methodology/approach The authors use augmented inverse-probability weights of the doubly robust method to test the effect of land titling on the rural credit market by addressing self-selection, endogeneity and heterogeneity concerns. Findings Results show that the poor, non-poor and small land holders with land titles are willing to borrow more from formal financial institutions. Land titling increases loan accessibility for non-poor and small land holding farmers. As for informal financing, large land holding and non-poor farmers show a decrease in informal lending. Land titling has a financial inclusion effect for some farmers, but poor farmers’ credit restrictions are not entirely solved by land titling. Originality/value This is the first study that focuses on the financial inclusion effect of farm land titling in China.

scholarly journals Association between Hydroxyzine Use and Reduced Mortality in Patients Hospitalized for Coronavirus Disease 2019: Results from a multicenter observational study

2020 ◽  
Author(s):  
Nicolas Hoertel ◽  
Marina Sánchez ◽  
Raphaël Vernet ◽  
Nathanaël Beeker ◽  
Antoine Neuraz ◽  
...  
Keyword(s):  
Observational Study ◽  
Cox Model ◽  
Inverse Probability Weighting ◽  
Dose Effect ◽  
Sensitivity Analyses ◽  
Study Endpoint ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Effect Relationship ◽  
Anti Inflammatory

ABSTRACTObjectiveTo examine the association between hydroxyzine use and mortality in patients hospitalized for COVID-19, based on its anti-inflammatory and antiviral properties.DesignMulticenter observational retrospective cohort study.SettingGreater Paris University hospitals, France.Participants7,345 adults hospitalized for COVID-19 between 24 January and 1 April 2020, including 138 patients (1.9%) who received hydroxyzine during the visit at a mean dose of 49.8 mg (SD=51.5) for an average of 22.4 days (SD=25.9).Data sourceAssistance Publique-Hôpitaux de Paris Health Data Warehouse.Main outcome measuresThe study endpoint was death. We compared this endpoint between patients who received hydroxyzine and those who did not in time-to-event analyses adjusting for patient characteristics (such as age, sex, and comorbidities), clinical and biological markers of disease’s severity, and use of other medications. The primary analysis was a multivariable Cox model with inverse probability weighting. Sensitivity analyses included a multivariable Cox model and a univariate Cox regression model in a matched analytic sample in a 1:1 ratio.ResultsOver a mean follow-up of 20.3 days (SD=27.5), 994 patients (13.5%) had a primary end-point event. The primary multivariable analysis with inverse probability weighting showed a significant association between hydroxyzine use and reduced mortality (HR, 0.42; 95% CI, 0.25 to 0.71; p=0.001) with a significant dose-effect relationship (HR, 0.10; 95% CI, 0.02 to 0.45; p=0.003). This association was similar in sensitivity analyses. In secondary analyses conducted among subsamples of patients, we found a significant association between hydroxyzine use and a faster decrease in biological inflammatory markers associated with COVID-19-related mortality, including neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-C-reactive protein ratio (LCRP), and circulating interleukin 6 levels (IL-6) (all p<0.016), with a significant dose-effect relationship for NLR and LCRP (both p<0.037).ConclusionsIn this retrospective observational study, hydroxyzine use was associated with reduced mortality in patients hospitalized for COVID-19. This association may be partially mediated by specific anti-inflammatory properties of H1 antihistamines. Double-blind controlled randomized clinical trials of hydroxyzine for COVID-19 are needed to confirm these results.

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