Augmented Inverse Probability Weighting and the Double Robustness Property.
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ABSTRACT:
SUBMITTER: Kurz CF
PROVIDER: S-EPMC8793316 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Inverse probability-weighted estimators are widely used in applications where data are missing due to nonresponse or censoring and in the estimation of causal effects from observational studies. Current estimators rely on ignorability assumptions for response indicators or treatment assignment and outcomes being conditional on observed covariates which are assumed to be measured without error. However, measurement error is common for the variables collected in many applications. For example, in studies of educational interventions, student achievement as measured by standardized tests is almost always used as the key covariate for removing hidden biases, but standardized test scores may have substantial measurement errors. We provide several expressions for a weighting function that can yield a consistent estimator for population means using incomplete data and covariates measured with error. We propose a method to estimate the weighting function from data. The results of a simulation study show that the estimator is consistent and has no bias and small variance.
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Project description:BackgroundAcross the world, many young people are supplying unpaid, informal care. There is growing evidence of the impact of this caring role on the lives of young informal carers, however there has been little quantitative analysis of the mental health impacts. This research aimed to estimate the effect of informal caring at age 14/15 years on mental health at age 18/19 years.MethodData was drawn from Waves 5, 6, 8 (2012-2018) of the Longitudinal Study of Australian Children. Effects of caring on mental health were assessed using augmented inverse probability weighting, with adjustment for potential confounders, and using the Kessler-10 measure of mental health. Caring was assessed with both a binary (any caring vs none), and a three category variable (no caring, less than daily caring, daily caring). Multiple imputation was carried out using chained equations, and analysis was conducted on both complete case (n=2165) and the imputed dataset (n=3341).OutcomesIn complete case models, any caring at age 14/15 years was associated with poorer mental health at age 18/19 years compared to those reporting no caring, with an average treatment effect (ATE) of 1.10 (95%CI 0.37, 1.83). The ATE of daily caring compared to no caring at age 14/15 years of age was 1.94 (95%CI 0.48, 3.39), and caring less than daily (compared to no caring) was associated with a treatment effect of 0.83(95%CI 0.06, 1.61). Associations were robust to several sensitivity analyses.InterpretationThese results suggest there is a mental health impact of caring in adolescence on mental health four years later. This highlights the need for support for young informal carers, particularly for those providing more intensive caring.FundingThis study was supported by an Australian Research Council Discovery Early Career Award (TK, DE200100607).
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Project description:Evaluation of impact of potential uncontrolled confounding is an important component for causal inference based on observational studies. In this article, we introduce a general framework of sensitivity analysis that is based on inverse probability weighting. We propose a general methodology that allows both non-parametric and parametric analyses, which are driven by two parameters that govern the magnitude of the variation of the multiplicative errors of the propensity score and their correlations with the potential outcomes. We also introduce a specific parametric model that offers a mechanistic view on how the uncontrolled confounding may bias the inference through these parameters. Our method can be readily applied to both binary and continuous outcomes and depends on the covariates only through the propensity score that can be estimated by any parametric or non-parametric method. We illustrate our method with two medical data sets.
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Project description:As one of causal inference methodologies, the inverse probability weighting (IPW) method has been utilized to address confounding and account for missing data when subjects with missing data cannot be included in a primary analysis. The transdisciplinary field of molecular pathological epidemiology (MPE) integrates molecular pathological and epidemiological methods, and takes advantages of improved understanding of pathogenesis to generate stronger biological evidence of causality and optimize strategies for precision medicine and prevention. Disease subtyping based on biomarker analysis of biospecimens is essential in MPE research. However, there are nearly always cases that lack subtype information due to the unavailability or insufficiency of biospecimens. To address this missing subtype data issue, we incorporated inverse probability weights into Cox proportional cause-specific hazards regression. The weight was inverse of the probability of biomarker data availability estimated based on a model for biomarker data availability status. The strategy was illustrated in two example studies; each assessed alcohol intake or family history of colorectal cancer in relation to the risk of developing colorectal carcinoma subtypes classified by tumor microsatellite instability (MSI) status, using a prospective cohort study, the Nurses' Health Study. Logistic regression was used to estimate the probability of MSI data availability for each cancer case with covariates of clinical features and family history of colorectal cancer. This application of IPW can reduce selection bias caused by nonrandom variation in biospecimen data availability. The integration of causal inference methods into the MPE approach will likely have substantial potentials to advance the field of epidemiology.
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Project description:The development of coherent missing data models to account for nonmonotone missing at random (MAR) data by inverse probability weighting (IPW) remains to date largely unresolved. As a consequence, IPW has essentially been restricted for use only in monotone missing data settings. We propose a class of models for nonmonotone missing data mechanisms that spans the MAR model, while allowing the underlying full data law to remain unrestricted. For parametric specifications within the proposed class, we introduce an unconstrained maximum likelihood estimator for estimating the missing data probabilities which can be easily implemented using existing software. To circumvent potential convergence issues with this procedure, we also introduce a Bayesian constrained approach to estimate the missing data process which is guaranteed to yield inferences that respect all model restrictions. The efficiency of the standard IPW estimator is improved by incorporating information from incomplete cases through an augmented estimating equation which is optimal within a large class of estimating equations. We investigate the finite-sample properties of the proposed estimators in a simulation study and illustrate the new methodology in an application evaluating key correlates of preterm delivery for infants born to HIV infected mothers in Botswana, Africa.
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Project description:Since being introduced to epidemiology in 2000, marginal structural models have become a commonly used method for causal inference in a wide range of epidemiologic settings. In this brief report, we aim to explore three subtleties of marginal structural models. First, we distinguish marginal structural models from the inverse probability weighting estimator, and we emphasize that marginal structural models are not only for longitudinal exposures. Second, we explore the meaning of the word "marginal" in "marginal structural model." Finally, we show that the specification of a marginal structural model can have important implications for the interpretation of its parameters. Each of these concepts have important implications for the use and understanding of marginal structural models, and thus providing detailed explanations of them may lead to better practices for the field of epidemiology.
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Project description:Right-truncated data arise when observations are ascertained retrospectively, and only subjects who experience the event of interest by the time of sampling are selected. Such a selection scheme, without adjustment, leads to biased estimation of covariate effects in the Cox proportional hazards model. The existing methods for fitting the Cox model to right-truncated data, which are based on the maximization of the likelihood or solving estimating equations with respect to both the baseline hazard function and the covariate effects, are numerically challenging. We consider two alternative simple methods based on inverse probability weighting (IPW) estimating equations, which allow consistent estimation of covariate effects under a positivity assumption and avoid estimation of baseline hazards. We discuss problems of identifiability and consistency that arise when positivity does not hold and show that although the partial tests for null effects based on these IPW methods can be used in some settings even in the absence of positivity, they are not valid in general. We propose adjusted estimating equations that incorporate the probability of observation when it is known from external sources, which results in consistent estimation. We compare the methods in simulations and apply them to the analyses of human immunodeficiency virus latency.
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Project description:Study questionDoes inverse probability weighting (IPW) provide a more valid estimate of the cumulative incidence of live birth after multiple cycles of IVF?Summary answerIPW can provide a more accurate estimate of treatment success for counseling and decision-making regarding IVF.What is known alreadyDifferent approaches have been used to define and calculate IVF success; however, many of these approaches have limitations and potentially violate statistical assumptions. IPW can address potential selection bias that arises when people do not continue IVF treatment after a failed cycle.Study design, size, durationData were derived from a cohort study of women undergoing their first fresh embryo transfer IVF cycle at our institution between 1 January 1995 and 31 December 2014. All autologous cycles (fresh and frozen) were included, up to six total cycles.Participants/materials, setting, methodsWe identified 20 015 women who underwent 47 079 IVF cycles and had 10 031 live births during the study period. The cumulative incidence of live birth was calculated using three approaches. First, we used a standard Kaplan-Meier approach, 'the optimistic approach', censoring women when they dropped out of treatment. Second, we used a 'conservative' Kaplan-Meier approach that assumed women who dropped out of treatment did not achieve a live birth. Finally, we used IPW to calculate the probability of remaining in treatment, while accounting for differences in treatment drop out. IPW up-weights the data of those remaining under observation who resembled the women who dropped out of treatment, thereby decreasing the potential selection bias resulting from loss to follow-up. The IPW was incorporated into a Kaplan-Meier approach.Main results and the role of chanceThe cumulative incidence of live birth was 72.1% (95% CI: 71.0-73.1%) for the optimistic approach, 50.1% (49.4-50.8%) for the conservative approach and 66.8% (65.5-68.1%) for the IPW approach. Among women < 38 years of age, the cumulative incidence of live birth calculated by the IPW was slightly higher than that calculated by the optimistic approach. For women 41-42 years of age, the IPW cumulative incidence of live birth was slightly lower. The IPW was similar to the optimistic approach for the other age groups. The conservative estimate was lowest for all age groups.Limitations, reasons for cautionOnly clinical data recorded by the providers during an IVF cycle were used to generate weights for IPW. Covariates included: age, gravidity and year at the start of the cycle; primary infertility diagnosis; procedure type (i.e. whether a fresh or frozen embryo was transferred); number of mature oocytes retrieved; number of embryos transferred; cycle cancellation; pregnancy loss in the cycle; and insurance status. We were unable to determine exact reasons for treatment drop out (e.g. cessation of IVF treatment, transfer to another institution or spontaneous pregnancy). Our IPW model was moderately predictive based on the c-statistic from the calculation of the denominator of the weight; however, residual selection bias may remain due to the limited range of covariate data.Wider implications of the findingsIPW can be used in a variety of settings to address selection bias introduced by differential loss to follow up or treatment drop out.Study funding/competing interest(s)AMM was supported by National Institutes of Health (NIH) T32 HD052458-Boston University Reproductive, Perinatal and Pediatric Epidemiology Training Program. The authors report no conflicts of interest.Trial registration numberN/A.
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Project description:Commonly used semiparametric estimators of causal effects specify parametric models for the propensity score (PS) and the conditional outcome. An example is an augmented inverse probability weighting (IPW) estimator, frequently referred to as a doubly robust estimator, because it is consistent if at least one of the two models is correctly specified. However, in many observational studies, the role of the parametric models is often not to provide a representation of the data-generating process but rather to facilitate the adjustment for confounding, making the assumption of at least one true model unlikely to hold. In this paper, we propose a crude analytical approach to study the large-sample bias of estimators when the models are assumed to be approximations of the data-generating process, namely, when all models are misspecified. We apply our approach to three prototypical estimators of the average causal effect, two IPW estimators, using a misspecified PS model, and an augmented IPW (AIPW) estimator, using misspecified models for the outcome regression (OR) and the PS. For the two IPW estimators, we show that normalization, in addition to having a smaller variance, also offers some protection against bias due to model misspecification. To analyze the question of when the use of two misspecified models is better than one we derive necessary and sufficient conditions for when the AIPW estimator has a smaller bias than a simple IPW estimator and when it has a smaller bias than an IPW estimator with normalized weights. If the misspecification of the outcome model is moderate, the comparisons of the biases of the IPW and AIPW estimators show that the AIPW estimator has a smaller bias than the IPW estimators. However, all biases include a scaling with the PS-model error and we suggest caution in modeling the PS whenever such a model is involved. For numerical and finite sample illustrations, we include three simulation studies and corresponding approximations of the large-sample biases. In a dataset from the National Health and Nutrition Examination Survey, we estimate the effect of smoking on blood lead levels.
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