scholarly journals G-computation and doubly robust standardisation for continuous-time data: A comparison with inverse probability weighting

2021 ◽  
pp. 096228022110473
Author(s):  
Arthur Chatton ◽  
Florent Le Borgne ◽  
Clémence Leyrat ◽  
Yohann Foucher
Keyword(s):  
Continuous Time ◽  
Inverse Probability Weighting ◽  
Model Specification ◽  
Practical Implementation ◽  
Probability Weighting ◽  
Survival Times ◽  
Time Data ◽  
Inverse Probability ◽  
Real World Datasets ◽  
Doubly Robust

In time-to-event settings, g-computation and doubly robust estimators are based on discrete-time data. However, many biological processes are evolving continuously over time. In this paper, we extend the g-computation and the doubly robust standardisation procedures to a continuous-time context. We compare their performance to the well-known inverse-probability-weighting estimator for the estimation of the hazard ratio and restricted mean survival times difference, using a simulation study. Under a correct model specification, all methods are unbiased, but g-computation and the doubly robust standardisation are more efficient than inverse-probability-weighting. We also analyse two real-world datasets to illustrate the practical implementation of these approaches. We have updated the R package RISCA to facilitate the use of these methods and their dissemination.

scholarly journals A Case Study of the Impact of Data-Adaptive Versus Model-Based Estimation of the Propensity Scores on Causal Inferences from Three Inverse Probability Weighting Estimators

2016 ◽  
Vol 12 (1) ◽  
pp. 131-155 ◽  
Author(s):  
Romain Neugebauer ◽  
Julie A. Schmittdiel ◽  
Mark J. van der Laan
Keyword(s):  
Propensity Scores ◽  
Adaptive Estimation ◽  
Inverse Probability Weighting ◽  
Model Specification ◽  
Probability Weighting ◽  
Consistent Estimation ◽  
Causal Inferences ◽  
Inverse Probability ◽  
Estimation Strategy ◽  
Data Adaptive

Abstract:Objective: Consistent estimation of causal effects with inverse probability weighting estimators is known to rely on consistent estimation of propensity scores. To alleviate the bias expected from incorrect model specification for these nuisance parameters in observational studies, data-adaptive estimation and in particular an ensemble learning approach known as Super Learning has been proposed as an alternative to the common practice of estimation based on arbitrary model specification. While the theoretical arguments against the use of the latter haphazard estimation strategy are evident, the extent to which data-adaptive estimation can improve inferences in practice is not. Some practitioners may view bias concerns over arbitrary parametric assumptions as academic considerations that are inconsequential in practice. They may also be wary of data-adaptive estimation of the propensity scores for fear of greatly increasing estimation variability due to extreme weight values. With this report, we aim to contribute to the understanding of the potential practical consequences of the choice of estimation strategy for the propensity scores in real-world comparative effectiveness research.Method: We implement secondary analyses of Electronic Health Record data from a large cohort of type 2 diabetes patients to evaluate the effects of four adaptive treatment intensification strategies for glucose control (dynamic treatment regimens) on subsequent development or progression of urinary albumin excretion. Three Inverse Probability Weighting estimators are implemented using both model-based and data-adaptive estimation strategies for the propensity scores. Their practical performances for proper confounding and selection bias adjustment are compared and evaluated against results from previous randomized experiments.Conclusion: Results suggest both potential reduction in bias and increase in efficiency at the cost of an increase in computing time when using Super Learning to implement Inverse Probability Weighting estimators to draw causal inferences.

scholarly journals Analysis of Incomplete Data Using Inverse Probability Weighting and Doubly Robust Estimators

Methodology ◽  
2010 ◽  
Vol 6 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Stijn Vansteelandt ◽  
James Carpenter ◽  
Michael G. Kenward
Keyword(s):  
Incomplete Data ◽  
Missing At Random ◽  
Inverse Probability Weighting ◽  
Estimation Methods ◽  
Data Sets ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Doubly Robust Estimation ◽  
Doubly Robust ◽  
Standard Software

This article reviews inverse probability weighting methods and doubly robust estimation methods for the analysis of incomplete data sets. We first consider methods for estimating a population mean when the outcome is missing at random, in the sense that measured covariates can explain whether or not the outcome is observed. We then sketch the rationale of these methods and elaborate on their usefulness in the presence of influential inverse weights. We finally outline how to apply these methods in a variety of settings, such as for fitting regression models with incomplete outcomes or covariates, emphasizing the use of standard software programs.

Combining Multiple Imputation and Inverse-Probability Weighting for Analyzing Response with Missing in the Presence of Covariates

2020 ◽  
Author(s):  
Freshteh Osmani ◽  
Ebrahim Hajizadeh
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Clinical Trial Data ◽  
Real Data ◽  
Inverse Probability Weighting ◽  
Trial Data ◽  
Data Sets ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Doubly Robust

Introduction: Missing values are frequently seen in data sets of research studiesespecially in medical studies.Therefore, it is essential that the data, especially in medical research should evaluate in terms of the structure of missingness.This study aims to provide new statistical methods for analyzing such data. Methods:Multiple imputation (MI) and inverse-probability weighting (IPW)aretwo common methods whichused to deal with missing data. MI method is more effectiveand complexthan IPW.MI requires a model for the joint distribution of the missing data given the observed data.While IPW need only a model for the probability that a subject has fulldata .Inefficacy in each of these models may causeto serious bias if missingness in dataset is large .Anothermethod that combines these approaches to give a doubly robust estimator.In addition, using of these methodswill demonstrate in the clinical trial data related to postpartum bleeding. Results:In this article, we examine the performance of IPW/MI relative to MI and IPW alone in terms of bias and efficiency.According to the results of simulation can be said that that IPW/MI have advantages over alternatives.Also results of real data showed that,results of MI/MI doesnot differ with the results ofIPW/MIsignificantly. Conclusion:Problem of missing data are in many studies that causes bias and decreasing efficacy inmodel.In this study, after comparing the results of these techniques,it was concludedthat IPW/MI method has better performance than other methods.

scholarly journals A comparison of confounding adjustment methods with an application to early life determinants of childhood obesity

2014 ◽  
Vol 5 (6) ◽  
pp. 435-447 ◽  
Author(s):  
L. Li ◽  
K. Kleinman ◽  
M. W. Gillman
Keyword(s):  
Cesarean Section ◽  
Inverse Probability Weighting ◽  
The Body ◽  
Birth Cohort Study ◽  
Probability Weighting ◽  
Regression Estimate ◽  
Inverse Probability ◽  
Doubly Robust Estimation ◽  
Doubly Robust ◽  
Confounding Adjustment

We implemented six confounding adjustment methods: (1) covariate-adjusted regression, (2) propensity score (PS) regression, (3) PS stratification, (4) PS matching with two calipers, (5) inverse probability weighting and (6) doubly robust estimation to examine the associations between the body mass index (BMI) z-score at 3 years and two separate dichotomous exposure measures: exclusive breastfeeding v. formula only (n=437) and cesarean section v. vaginal delivery (n=1236). Data were drawn from a prospective pre-birth cohort study, Project Viva. The goal is to demonstrate the necessity and usefulness, and approaches for multiple confounding adjustment methods to analyze observational data. Unadjusted (univariate) and covariate-adjusted linear regression associations of breastfeeding with BMI z-score were −0.33 (95% CI −0.53, −0.13) and −0.24 (−0.46, −0.02), respectively. The other approaches resulted in smaller n (204–276) because of poor overlap of covariates, but CIs were of similar width except for inverse probability weighting (75% wider) and PS matching with a wider caliper (76% wider). Point estimates ranged widely, however, from −0.01 to −0.38. For cesarean section, because of better covariate overlap, the covariate-adjusted regression estimate (0.20) was remarkably robust to all adjustment methods, and the widths of the 95% CIs differed less than in the breastfeeding example. Choice of covariate adjustment method can matter. Lack of overlap in covariate structure between exposed and unexposed participants in observational studies can lead to erroneous covariate-adjusted estimates and confidence intervals. We recommend inspecting covariate overlap and using multiple confounding adjustment methods. Similar results bring reassurance. Contradictory results suggest issues with either the data or the analytic method.

scholarly journals Impact of Adopting Improved Seeds on Access to Broader Food Groups Among Small-Scale Sorghum Producers in Tanzania

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402097999
Author(s):  
Aloyce R. Kaliba ◽  
Anne G. Gongwe ◽  
Kizito Mazvimavi ◽  
Ashagre Yigletu
Keyword(s):  
Food Security ◽  
Dietary Diversity ◽  
Sampling Technique ◽  
Inverse Probability Weighting ◽  
Reproductive Age ◽  
Small Scale ◽  
Probability Weighting ◽  
Food Groups ◽  
Inverse Probability ◽  
Double Robust

In this study, we use double-robust estimators (i.e., inverse probability weighting and inverse probability weighting with regression adjustment) to quantify the effect of adopting climate-adaptive improved sorghum varieties on household and women dietary diversity scores in Tanzania. The two indicators, respectively, measure access to broader food groups and micronutrient and macronutrient availability among children and women of reproductive age. The selection of sample households was through a multistage sampling technique, and the population was all households in the sorghum-producing regions of Central, Northern, and Northwestern Tanzania. Before data collection, enumerators took part in a 1-week training workshop and later collected data from 822 respondents using a structured questionnaire. The main results from the study show that the adoption of improved sorghum seeds has a positive effect on both household and women dietary diversity scores. Access to quality food groups improves nutritional status, food security adequacy, and general welfare of small-scale farmers in developing countries. Agricultural projects that enhance access to improved seeds are, therefore, likely to generate a positive and sustainable effect on food security and poverty alleviation in sorghum-producing regions of Tanzania.

scholarly journals Inverse probability weighting for clustered nonresponse

Biometrika ◽  
2011 ◽  
Vol 98 (4) ◽  
pp. 953-966 ◽  
Author(s):  
C. J. Skinner ◽  
D'arrigo
Keyword(s):  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Inverse Probability
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