Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Inverse probability weighted Cox model in multi-site studies without sharing individual-level data.

ABSTRACT: The inverse probability weighted Cox proportional hazards model can be used to estimate the marginal hazard ratio. In multi-site studies, it may be infeasible to pool individual-level datasets due to privacy and other considerations. We propose three methods for making inference on hazard ratios without the need for pooling individual-level datasets across sites. The first method requires a summary-level eight-column risk-set table to produce the same hazard ratio estimate and robust sandwich variance estimate as those from the corresponding pooled individual-level data analysis (reference analysis). The second and third methods, which are based on two bootstrap re-sampling strategies, require a summary-level four-column risk-set table and bootstrap-based risk-set tables from each site to produce the same hazard ratio and bootstrap variance estimates as those from their reference analyses. All three methods require only one file transfer between the data-contributing sites and the analysis center. We justify these methods theoretically, illustrate their use, and demonstrate their statistical performance using both simulated and real-world data.

SUBMITTER: Shu D

PROVIDER: S-EPMC7042068 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Inverse probability weighted Cox regression for doubly truncated data.

Project description:Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. The approach is used to study the association between candidate single nucleotide polymorphisms and age of onset of Parkinson's disease.

| S-EPMC5843502 | biostudies-literature

Worth the weight: using inverse probability weighted Cox models in AIDS research.

Project description:In an observational study with a time-to-event outcome, the standard analytical approach is the Cox proportional hazards regression model. As an alternative to the standard Cox model, in this article we present a method that uses inverse probability (IP) weights to estimate the effect of a baseline exposure on a time-to-event outcome. IP weighting can be used to adjust for multiple measured confounders of a baseline exposure in order to estimate marginal effects, which compare the distribution of outcomes when the entire population is exposed versus when the entire population is unexposed. For example, IP-weighted Cox models allow for estimation of the marginal hazard ratio and marginal survival curves. IP weights can also be employed to adjust for selection bias due to loss to follow-up. This approach is illustrated using an example that estimates the effect of injection drug use on time until AIDS or death among HIV-infected women.

| S-EPMC4250953 | biostudies-literature

Inverse-Probability-Weighted Estimation for Monotone and Nonmonotone Missing Data.

Project description:Missing data is a common occurrence in epidemiologic research. In this paper, 3 data sets with induced missing values from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are provided as examples of prototypical epidemiologic studies with missing data. Our goal was to estimate the association of maternal smoking behavior with spontaneous abortion while adjusting for numerous confounders. At the same time, we did not necessarily wish to evaluate the joint distribution among potentially unobserved covariates, which is seldom the subject of substantive scientific interest. The inverse probability weighting (IPW) approach preserves the semiparametric structure of the underlying model of substantive interest and clearly separates the model of substantive interest from the model used to account for the missing data. However, IPW often will not result in valid inference if the missing-data pattern is nonmonotone, even if the data are missing at random. We describe a recently proposed approach to modeling nonmonotone missing-data mechanisms under missingness at random to use in constructing the weights in IPW complete-case estimation, and we illustrate the approach using 3 data sets described in a companion article (Am J Epidemiol. 2018;187(3):568-575).

| S-EPMC5860553 | biostudies-literature

Inverse probability weighting methods for Cox regression with right-truncated data.

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.

| S-EPMC7162718 | biostudies-literature

Inverse probability weighted estimation for recurrent events data with missing category.

Project description:Modeling recurrent event data with multiple event types has drawn interest in recent biomedical studies due to its flexibility for understanding different risk factors for multiple recurrent event processes. However, in such data type, missing event type appears frequently because of various reasons such as recording ignorance or resource limitation. In this study, we aim to propose an inverse probability weighted estimation that is commonly used in the missing data literature to correct possibly biased estimation by a complete-case analysis. This approach is not limited to a specific form of the recurrent event model. We derive the large sample theory in a general form. We demonstrate that our approach can be applied to either multiplicative or additive rates model with practical sample size via comprehensive simulations. Nonmucoid and mucoid Pseudomonas aeruginosa infections of 14 888 patients in 2016 Cystic Fibrosis Foundation Patient Registry data are analyzed to show that, without including 12% events with missing event type in the analysis, several factors may be misidentified as risk factors for the nonmucoid type of infections.

| S-EPMC8269380 | biostudies-literature

Inverse probability of censoring weighted estimates of Kendall's ? for gap time analyses.

Project description:In life history studies, interest often lies in the analysis of the interevent, or gap times and the association between event times. Gap time analyses are challenging however, even when the length of follow-up is determined independently of the event process, because associations between gap times induce dependent censoring for second and subsequent gap times. This article discusses nonparametric estimation of the association between consecutive gap times based on Kendall's ? in the presence of this type of dependent censoring. A nonparametric estimator that uses inverse probability of censoring weights is provided. Estimates of conditional gap time distributions can be obtained following specification of a particular copula function. Simulation studies show the estimator performs well and compares favorably with an alternative estimator. Generalizations to a piecewise constant Clayton copula are given. Several simulation studies and illustrations with real data sets are also provided.

| S-EPMC3895492 | biostudies-literature

AIPW: An R Package for Augmented Inverse Probability-Weighted Estimation of Average Causal Effects.

Project description:An increasing number of recent studies have suggested that doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, not all existing programs that implement doubly robust estimators support machine learning methods and cross-fitting, or provide estimates on multiplicative scales. To address these needs, we developed AIPW, a software package implementing augmented inverse probability weighting (AIPW) estimation of average causal effects in R (R Foundation for Statistical Computing, Vienna, Austria). Key features of the AIPW package include cross-fitting and flexible covariate adjustment for observational studies and randomized controlled trials (RCTs). In this paper, we use a simulated RCT to illustrate implementation of the AIPW estimator. We also perform a simulation study to evaluate the performance of the AIPW package compared with other doubly robust implementations, including CausalGAM, npcausal, tmle, and tmle3. Our simulation showed that the AIPW package yields performance comparable to that of other programs. Furthermore, we also found that cross-fitting substantively decreases the bias and improves the confidence interval coverage for doubly robust estimators fitted with machine learning algorithms. Our findings suggest that the AIPW package can be a useful tool for estimating average causal effects with machine learning methods in RCTs and observational studies.

| S-EPMC8796813 | biostudies-literature

Sensitivity analysis for calibrated inverse probability-of-censoring weighted estimators under non-ignorable dropout.

Project description:Inverse probability of censoring weighting is a popular approach to handling dropout in longitudinal studies. However, inverse probability-of-censoring weighted estimators (IPCWEs) can be inefficient and unstable if the weights are estimated by maximum likelihood. To alleviate these problems, calibrated IPCWEs have been proposed, which use calibrated weights that directly optimize covariate balance in finite samples rather than the weights from maximum likelihood. However, the existing calibrated IPCWEs are all based on the unverifiable assumption of sequential ignorability and sensitivity analysis strategies under non-ignorable dropout are lacking. In this paper, we fill this gap by developing an approach to sensitivity analysis for calibrated IPCWEs under non-ignorable dropout. A simple technique is proposed to speed up the computation of bootstrap and jackknife confidence intervals and thus facilitate sensitivity analyses. We evaluate the finite-sample performance of the proposed methods using simulations and apply our methods to data from an international inception cohort study of systemic lupus erythematosus. An R Markdown tutorial to demonstrate the implementation of the proposed methods is provided.

| S-EPMC9253927 | biostudies-literature

DataSHIELD: resolving a conflict in contemporary bioscience--performing a pooled analysis of individual-level data without sharing the data.

Project description:BackgroundContemporary bioscience sometimes demands vast sample sizes and there is often then no choice but to synthesize data across several studies and to undertake an appropriate pooled analysis. This same need is also faced in health-services and socio-economic research. When a pooled analysis is required, analytic efficiency and flexibility are often best served by combining the individual-level data from all sources and analysing them as a single large data set. But ethico-legal constraints, including the wording of consent forms and privacy legislation, often prohibit or discourage the sharing of individual-level data, particularly across national or other jurisdictional boundaries. This leads to a fundamental conflict in competing public goods: individual-level analysis is desirable from a scientific perspective, but is prevented by ethico-legal considerations that are entirely valid.MethodsData aggregation through anonymous summary-statistics from harmonized individual-level databases (DataSHIELD), provides a simple approach to analysing pooled data that circumvents this conflict. This is achieved via parallelized analysis and modern distributed computing and, in one key setting, takes advantage of the properties of the updating algorithm for generalized linear models (GLMs).ResultsThe conceptual use of DataSHIELD is illustrated in two different settings.ConclusionsAs the study of the aetiological architecture of chronic diseases advances to encompass more complex causal pathways-e.g. to include the joint effects of genes, lifestyle and environment-sample size requirements will increase further and the analysis of pooled individual-level data will become ever more important. An aim of this conceptual article is to encourage others to address the challenges and opportunities that DataSHIELD presents, and to explore potential extensions, for example to its use when different data sources hold different data on the same individuals.

| S-EPMC2972441 | biostudies-literature

Statin Treatment and Clinical Outcomes of Heart Failure Among Africans: An Inverse Probability Treatment Weighted Analysis.

Project description:Randomized control trials of statins have not demonstrated significant benefits in outcomes of heart failure (HF). However, randomized control trials may not always be generalizable. The aim was to determine whether statin and statin type-lipophilic or -hydrophilic improve long-term outcomes in Africans with HF.This was a retrospective longitudinal study of HF patients aged ≥18 years hospitalized at a tertiary healthcare center between January 1, 2009 and December 31, 2013 in Ghana. Patients were eligible if they were discharged from first admission for HF (index admission) and followed up to time of all-cause, cardiovascular, and HF mortality or end of study. Multivariable time-dependent Cox model and inverse-probability-of-treatment weighting of marginal structural model were used to estimate associations between statin treatment and outcomes. Adjusted hazard ratios were also estimated for lipophilic and hydrophilic statin compared with no statin use. The study included 1488 patients (mean age 60.3±14.2 years) with 9306 person-years of observation. Using the time-dependent Cox model, the 5-year adjusted hazard ratios with 95% CI for statin treatment on all-cause, cardiovascular, and HF mortality were 0.68 (0.55-0.83), 0.67 (0.54-0.82), and 0.63 (0.51-0.79), respectively. Use of inverse-probability-of-treatment weighting resulted in estimates of 0.79 (0.65-0.96), 0.77 (0.63-0.96), and 0.77 (0.61-0.95) for statin treatment on all-cause, cardiovascular, and HF mortality, respectively, compared with no statin use.Among Africans with HF, statin treatment was associated with significant reduction in mortality.

| S-EPMC5532994 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data