The inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic: an unbiased estimator in the presence of independent censoring.
Ontology highlight
ABSTRACT:
SUBMITTER: Dong G
PROVIDER: S-EPMC7538385 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundIn the LATITUDE study (ClinicalTrials.gov, NCT01715285), compared with placebos, abiraterone acetate plus prednisone (AAP) with androgen deprivation therapy (ADT) provided significant overall survival (OS) benefit in high-risk metastatic castration-sensitive prostate cancer (mCSPC) patients. It is controversial whether survival benefits would remain if all patients in the placebo group subsequently received life-extending therapies.ObjectiveTo estimate treatment effect in the case of all patients in the placebo group receiving life-extending subsequent therapies.Design setting and participantsA post hoc analysis of LATITUDE final-analysis data was carried out (setting and participants have been reported previously).InterventionAAP or placebos plus ADT.Outcome measurements and statistical analysisWe applied the inverse probability of censoring weighting (IPCW) method to represent the situation in which all patients in the placebo group would have received life-extending subsequent therapies. The OS hazard ratio (HR) of AAP versus placebos and associated 95% confidence interval (CI) were estimated using a Cox proportional hazards model.Results and limitationsOf the 581 eligible patients in the placebo group, 237 (40.8%) did not receive life-extending subsequent therapies. From the unadjusted intention-to-treat analysis, the HR for OS for AAP versus placebos was 0.661 (95% CI 0.564-0.775). Using IPCW to adjust for patients in the placebo group without life-extending subsequent therapies, the HR was 0.732 (95% CI 0.604-0.887). A limitation is a lack of proof that the Cox proportional hazards model for the absence of life-extending subsequent therapy is correctly specified for the IPCW method.ConclusionsTreatment with AAP exerts OS benefit over placebos in high-risk mCSPC patients, regardless of whether life-extending subsequent therapy is given.Patient summaryIn a previous study, high-risk metastatic castration-sensitive prostate cancer patients who received abiraterone acetate plus prednisone (AAP) with androgen deprivation therapy generally survived longer than those given placebos. The benefit of adding AAP continues regardless of whether life-extending subsequent therapy is given.
| S-EPMC8783036 | biostudies-literature
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.
| S-EPMC4006991 | biostudies-literature
Project description:Background and aimThis study aimed to elucidate the clinical importance of muscle volume loss (pre-sarcopenia) in patients receiving lenvatinib as treatment for unresectable hepatocellular carcinoma (u-HCC).MethodsOf 437 u-HCC patients treated with lenvatinib at specific institutions in Japan between March 2018 and May 2020, 151 with available computed tomography imaging data from the time of lenvatinib introduction were enrolled. Pre-sarcopenia was diagnosed based on a previously reported cut-off value calculation formula [psoas muscle area at level of middle of third lumbar vertebra (cm2 )/height (m)2 ]. Clinical features and prognostic factors for overall survival (OS) with inverse probability weighting were investigated retrospectively for their relationship with pre-sarcopenia.ResultsCox hazard multivariate analysis showed alpha-fetoprotein (≥400 ng/mL) (hazard ratio [HR] 2.271, P < 0.001), Barcelona Clinic Liver Cancer stage (C and D) (HR 1.625, P = 0.018), and positive for pre-sarcopenia (HR 1.652, P = 0.042) to be significant prognostic factors. OS rates for the pre-sarcopenia group (n = 41) were worse than those for the non-pre-sarcopenia group (n = 110) (0.5-, 1-, and 1.5-year OS: 72.5%, 27.9%, and 7.0% vs 80.7%, 56.7%, and 46.1%, respectively; P < 0.001), as was progression-free survival (P = 0.025). Time to stopping lenvatinib or disease progression was better in the non-pre-sarcopenia group (0.5-, 1-, and 1.5-year OS: 48.0%, 24.5%, and 8.4% vs 20.0%, 10.3%, and 4.2%, respectively; P < 0.001). Also, the frequency of the adverse event appetite loss (any grade) was greater in the pre-sarcopenia group (43.9% vs 18.2%, P = 0.003).ConclusionPre-sarcopenia was shown to be a significant prognostic factor in patients treated with lenvatinib for u-HCC.
| S-EPMC8359359 | biostudies-literature
Project description:In time-to-event analyses, artificial censoring with correction for induced selection bias using inverse probability-of-censoring weights can be used to 1) examine the natural history of a disease after effective interventions are widely available, 2) correct bias due to noncompliance with fixed or dynamic treatment regimens, and 3) estimate survival in the presence of competing risks. Artificial censoring entails censoring participants when they meet a predefined study criterion, such as exposure to an intervention, failure to comply, or the occurrence of a competing outcome. Inverse probability-of-censoring weights use measured common predictors of the artificial censoring mechanism and the outcome of interest to determine what the survival experience of the artificially censored participants would be had they never been exposed to the intervention, complied with their treatment regimen, or not developed the competing outcome. Even if all common predictors are appropriately measured and taken into account, in the context of small sample size and strong selection bias, inverse probability-of-censoring weights could fail because of violations in assumptions necessary to correct selection bias. The authors used an example from the Multicenter AIDS Cohort Study, 1984-2008, regarding estimation of long-term acquired immunodeficiency syndrome-free survival to demonstrate the impact of violations in necessary assumptions. Approaches to improve correction methods are discussed.
| S-EPMC3105434 | biostudies-literature
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.
| S-EPMC3777387 | biostudies-literature
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.
| S-EPMC5948129 | biostudies-literature
Project description:This article discusses the augmented inverse propensity weighted (AIPW) estimator as an estimator for average treatment effects. The AIPW combines both the properties of the regression-based estimator and the inverse probability weighted (IPW) estimator and is therefore a "doubly robust" method in that it requires only either the propensity or outcome model to be correctly specified but not both. Even though this estimator has been known for years, it is rarely used in practice. After explaining the estimator and proving the double robustness property, I conduct a simulation study to compare the AIPW efficiency with IPW and regression under different scenarios of misspecification. In 2 real-world examples, I provide a step-by-step guide on implementing the AIPW estimator in practice. I show that it is an easily usable method that extends the IPW to reduce variability and improve estimation accuracy.[Box: see text].
| S-EPMC8793316 | biostudies-literature
Project description:Models for predicting the probability of experiencing various health outcomes or adverse events over a certain time frame (e.g., having a heart attack in the next 5years) based on individual patient characteristics are important tools for managing patient care. Electronic health data (EHD) are appealing sources of training data because they provide access to large amounts of rich individual-level data from present-day patient populations. However, because EHD are derived by extracting information from administrative and clinical databases, some fraction of subjects will not be under observation for the entire time frame over which one wants to make predictions; this loss to follow-up is often due to disenrollment from the health system. For subjects without complete follow-up, whether or not they experienced the adverse event is unknown, and in statistical terms the event time is said to be right-censored. Most machine learning approaches to the problem have been relatively ad hoc; for example, common approaches for handling observations in which the event status is unknown include (1) discarding those observations, (2) treating them as non-events, (3) splitting those observations into two observations: one where the event occurs and one where the event does not. In this paper, we present a general-purpose approach to account for right-censored outcomes using inverse probability of censoring weighting (IPCW). We illustrate how IPCW can easily be incorporated into a number of existing machine learning algorithms used to mine big health care data including Bayesian networks, k-nearest neighbors, decision trees, and generalized additive models. We then show that our approach leads to better calibrated predictions than the three ad hoc approaches when applied to predicting the 5-year risk of experiencing a cardiovascular adverse event, using EHD from a large U.S. Midwestern healthcare system.
| S-EPMC4893987 | biostudies-literature
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.
| S-EPMC6051732 | biostudies-literature
Project description:Recurrent events-outcomes that an individual can experience repeatedly over the course of follow-up-are common in epidemiologic and health services research. Studies involving recurrent events often focus on time to first occurrence or on event rates, which assume constant hazards over time. In this paper, we contextualize recurrent event parameters of interest using counterfactual theory in a causal inference framework and describe an approach for estimating a target parameter referred to as the mean cumulative count. This approach leverages inverse probability weights to control measured confounding with an existing (and underutilized) nonparametric estimator of recurrent event burden first proposed by Dong et al. in 2015. We use simulations to demonstrate the unbiased estimation of the mean cumulative count using the weighted Dong-Yasui estimator in a variety of scenarios. The weighted Dong-Yasui estimator for the mean cumulative count allows researchers to use observational data to flexibly estimate and contrast the expected number of cumulative events experienced per individual by a given time point under different exposure regimens. We provide code to ease application of this method.
| S-EPMC10423633 | biostudies-literature