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Journal articles on the topic 'Inverse probability (IP) weighting'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Switkowski, Karen M., Izzuddin M. Aris, Véronique Gingras, Emily Oken, and Jessica G. Young. "Estimated causal effects of complementary feeding behaviors on early childhood diet quality in a US cohort." American Journal of Clinical Nutrition 115, no. 4 (January 14, 2022): 1105–14. http://dx.doi.org/10.1093/ajcn/nqac003.

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ABSTRACT Background Complementary feeding (CF) provides an opportunity to shape children's future dietary habits, setting the foundation for good nutrition and health. Objectives We estimated effects of 3 CF behaviors on early childhood diet quality using inverse probability (IP) weighting of marginal structural models (MSMs). Methods Among 1041 children from the Boston-area Project Viva cohort, we estimated effects on the mean Youth Healthy Eating Index (YHEI) score in early childhood of 1) delayed (≥12 mo) compared with early (<12 mo) introduction of sweets and fruit juice; 2) continued compared with ceased offering of initially refused foods; and 3) early (<12 mo) compared with late (≥12 mo) introduction of flavor/texture variety. Mothers reported CF behaviors at 1 y and completed FFQs for children in early childhood (median age: 3.1 y). We estimated average treatment effects (ATEs) using IP weighting of MSMs to adjust for both confounding and selection bias due to censored outcomes and examined effect modification by child sex and breastfeeding compared with formula feeding at 6 mo. Results Twelve percent of mothers delayed introducing sweets/fruit juice, 93% continued offering initially refused foods, and 32% introduced flavor/texture variety early. The mean ± SD YHEI score was 52.8 ± 9.2 points. In adjusted models, we estimated a higher mean YHEI score with delayed (compared with early) sweets and fruit juice among breastfeeding children (ATE: 4.5 points; 95% CI: 1.0, 7.4 points), as well as with continued (compared with ceased) offering of refused foods among females (ATE: 5.4 points; 95% CI: 0.8, 9.1 points). The ATE for early (compared with late) flavor/texture variety was 1.7 points (95% CI: 0.3, 3.2 points) overall and stronger (2.8 points; 95% CI: 0.7, 5.1 points) among the formula-fed group. Conclusions Delayed introduction of sweets/juice, continued offering of refused foods, and early flavor/texture variety may all result in higher childhood diet quality. Effects may depend on child sex and infant breastfeeding status.

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Hurvitz, Sara A., Annie Guerin, Melissa G. Brammer, Ellie Guardino, Zheng-Yi Zhou, Michael S. Kaminsky, Eric Q. Wu, and Deepa Lalla. "Comprehensive investigation of adverse event (AE)-related costs in patients with metastatic breast cancer (MBC) treated with first- and second-line chemotherapies." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 1037. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.1037.

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1037 Background: MBC is incurable and managed with ongoing therapy. This study examined the incremental costs of chemotherapy-associated AEs in MBC. Methods: The PharMetrics Integrated Database (2000-2010) was used to identify MBC pts treated either 1st or 2nd line with a taxane (T) (paclitaxel or docetaxel) or capecitabine (C)-based regimen for ≥30 days (defined as a treatment episode (TE)). Incremental costs attributable to AEs were assessed by comparing costs incurred during TEs with and without AEs. AEs were identified using medical claims with a diagnosis for ≥1 event of interest (e.g., infections, fatigue, anemia, neutropenia). Pt characteristics were balanced between comparison groups (with and w/o AEs) using inverse probability weighting method. Incremental monthly costs due to AEs were estimated during the TEs and included the following cost components: inpt (IP), outpt (OP), emergency room (ER), other medical service, pharmacy costs (chemotherapy and other drugs), and total healthcare costs. Statistical comparisons were conducted using Wilcoxon tests. Results: 3,222 women (mean age=57) received a T or C as 1st or 2nd-line therapy for MBC. Of the 2,678 1st-line pts, 69.7% received T and 30.3% with C; average monthly total costs ranged from $9,159 to $10,298. AEs were commonly seen in pts treated with 1st-line T and C (94.6% and 83.7%). On average, the total monthly incremental cost associated with AEs was 38% higher ($3,547) for T and 9% higher ($854) for C. IP and other drug costs accounted for a majority of these costs. Of 1,084 2nd-line pts, 66% received T and 34% C, with average monthly total costs ranging from $5,950 to $12,979. 94.4% of T pts and 84% of C pts in the 2nd-line had an AE. The average total monthly incremental cost associated with AEs for T was $5,320 and $4,933 for C (69.5% and 82.9% higher vs pts w/o AEs). Pharmacy costs accounted for a majority of increased costs seen in pts with AEs treated with T; IP and OP accounted for a majority of these costs in pts treated with C. Conclusions: This is the 1st study assessing costs associated with AEs for tx of mBC. AEs are associated with a substantial economic burden that is mainly explained by increased IP, OP, and pharmacy costs.

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3

Halpern, Elkan F. "Behind the Numbers: Inverse Probability Weighting." Radiology 271, no. 3 (June 2014): 625–28. http://dx.doi.org/10.1148/radiol.14140035.

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4

Skinner, C. J., and D'arrigo. "Inverse probability weighting for clustered nonresponse." Biometrika 98, no. 4 (November 24, 2011): 953–66. http://dx.doi.org/10.1093/biomet/asr058.

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5

Ma, Xinwei, and Jingshen Wang. "Robust Inference Using Inverse Probability Weighting." Journal of the American Statistical Association 115, no. 532 (October 16, 2019): 1851–60. http://dx.doi.org/10.1080/01621459.2019.1660173.

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6

Zhou, Yunji, Roland A. Matsouaka, and Laine Thomas. "Propensity score weighting under limited overlap and model misspecification." Statistical Methods in Medical Research 29, no. 12 (July 21, 2020): 3721–56. http://dx.doi.org/10.1177/0962280220940334.

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Propensity score weighting methods are often used in non-randomized studies to adjust for confounding and assess treatment effects. The most popular among them, the inverse probability weighting, assigns weights that are proportional to the inverse of the conditional probability of a specific treatment assignment, given observed covariates. A key requirement for inverse probability weighting estimation is the positivity assumption, i.e. the propensity score must be bounded away from 0 and 1. In practice, violations of the positivity assumption often manifest by the presence of limited overlap in the propensity score distributions between treatment groups. When these practical violations occur, a small number of highly influential inverse probability weights may lead to unstable inverse probability weighting estimators, with biased estimates and large variances. To mitigate these issues, a number of alternative methods have been proposed, including inverse probability weighting trimming, overlap weights, matching weights, and entropy weights. Because overlap weights, matching weights, and entropy weights target the population for whom there is equipoise (and with adequate overlap) and their estimands depend on the true propensity score, a common criticism is that these estimators may be more sensitive to misspecifications of the propensity score model. In this paper, we conduct extensive simulation studies to compare the performances of inverse probability weighting and inverse probability weighting trimming against those of overlap weights, matching weights, and entropy weights under limited overlap and misspecified propensity score models. Across the wide range of scenarios we considered, overlap weights, matching weights, and entropy weights consistently outperform inverse probability weighting in terms of bias, root mean squared error, and coverage probability.

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Seaman, Shaun R., Ian R. White, Andrew J. Copas, and Leah Li. "Combining Multiple Imputation and Inverse‐Probability Weighting." Biometrics 68, no. 1 (November 3, 2011): 129–37. http://dx.doi.org/10.1111/j.1541-0420.2011.01666.x.

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8

McCaffrey, D. F., J. R. Lockwood, and C. M. Setodji. "Inverse probability weighting with error-prone covariates." Biometrika 100, no. 3 (June 24, 2013): 671–80. http://dx.doi.org/10.1093/biomet/ast022.

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Avagyan, Vahe, and Stijn Vansteelandt. "Stable inverse probability weighting estimation for longitudinal studies." Scandinavian Journal of Statistics 48, no. 3 (July 8, 2021): 1046–67. http://dx.doi.org/10.1111/sjos.12542.

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10

Sjölander, Arvid. "Estimation of attributable fractions using inverse probability weighting." Statistical Methods in Medical Research 20, no. 4 (March 11, 2010): 415–28. http://dx.doi.org/10.1177/0962280209349880.

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11

Sheikh, Kazim. "Investigation of selection bias using inverse probability weighting." European Journal of Epidemiology 22, no. 5 (May 5, 2007): 349–50. http://dx.doi.org/10.1007/s10654-007-9131-4.

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12

Mao, Huzhang, Liang Li, and Tom Greene. "Propensity score weighting analysis and treatment effect discovery." Statistical Methods in Medical Research 28, no. 8 (June 19, 2018): 2439–54. http://dx.doi.org/10.1177/0962280218781171.

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Inverse probability weighting can be used to estimate the average treatment effect in propensity score analysis. When there is lack of overlap in the propensity score distributions between the treatment groups under comparison, some weights may be excessively large, causing numerical instability and bias in point and variance estimation. We study a class of modified inverse probability weighting estimators that can be used to avoid this problem. These weights cause the estimand to deviate from the average treatment effect. We provide some justification for this deviation from the perspective of treatment effect discovery. We show that when lack of overlap occurs, the modified weights can achieve substantial gains in statistical power compared with inverse probability weighting and other propensity score methods. We develop analytical variance estimates that properly adjust for the sampling variability of the estimated propensity scores, and augment the modified inverse probability weighting estimator with outcome models for improved efficiency, a property that resembles double robustness. Results from extensive simulations and a real data application support our conclusions. The proposed methodology is implemented in R package PSW.

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Rostami, Mehdi, and Olli Saarela. "Normalized Augmented Inverse Probability Weighting with Neural Network Predictions." Entropy 24, no. 2 (January 25, 2022): 179. http://dx.doi.org/10.3390/e24020179.

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The estimation of average treatment effect (ATE) as a causal parameter is carried out in two steps, where in the first step, the treatment and outcome are modeled to incorporate the potential confounders, and in the second step, the predictions are inserted into the ATE estimators such as the augmented inverse probability weighting (AIPW) estimator. Due to the concerns regarding the non-linear or unknown relationships between confounders and the treatment and outcome, there has been interest in applying non-parametric methods such as machine learning (ML) algorithms instead. Some of the literature proposes to use two separate neural networks (NNs) where there is no regularization on the network’s parameters except the stochastic gradient descent (SGD) in the NN’s optimization. Our simulations indicate that the AIPW estimator suffers extensively if no regularization is utilized. We propose the normalization of AIPW (referred to as nAIPW) which can be helpful in some scenarios. nAIPW, provably, has the same properties as AIPW, that is, the double-robustness and orthogonality properties. Further, if the first-step algorithms converge fast enough, under regulatory conditions, nAIPW will be asymptotically normal. We also compare the performance of AIPW and nAIPW in terms of the bias and variance when small to moderate L1 regularization is imposed on the NNs.

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14

Støer, Nathalie,C, and Sven,Ove Samuelsen. "multipleNCC: Inverse Probability Weighting of Nested Case-Control Data." R Journal 8, no. 2 (2016): 5. http://dx.doi.org/10.32614/rj-2016-030.

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15

Hernan, Miguel A., Emilie Lanoy, Dominique Costagliola, and James M. Robins. "Comparison of Dynamic Treatment Regimes via Inverse Probability Weighting." Basic Clinical Pharmacology Toxicology 98, no. 3 (March 2006): 237–42. http://dx.doi.org/10.1111/j.1742-7843.2006.pto_329.x.

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Karp, Igor. "Inverse Probability Weighting With Time-varying Confounding and Nonpositivity." Epidemiology 23, no. 1 (January 2012): 178–79. http://dx.doi.org/10.1097/ede.0b013e31823ac960.

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Naimi, Ashley I., Stephen R. Cole, Daniel J. Westreich, and David B. Richardson. "Inverse Probability Weighting With Time-varying Confounding and Nonpositivity." Epidemiology 23, no. 1 (January 2012): 179. http://dx.doi.org/10.1097/ede.0b013e31823acc73.

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18

Shen, Changyu, Xiaochun Li, and Lingling Li. "Inverse probability weighting for covariate adjustment in randomized studies." Statistics in Medicine 33, no. 4 (September 9, 2013): 555–68. http://dx.doi.org/10.1002/sim.5969.

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19

Huber, Martin. "IDENTIFYING CAUSAL MECHANISMS (PRIMARILY) BASED ON INVERSE PROBABILITY WEIGHTING." Journal of Applied Econometrics 29, no. 6 (June 30, 2013): 920–43. http://dx.doi.org/10.1002/jae.2341.

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Liu, Li, Daniel Nevo, Reiko Nishihara, Yin Cao, Mingyang Song, Tyler S. Twombly, Andrew T. Chan, et al. "Utility of inverse probability weighting in molecular pathological epidemiology." European Journal of Epidemiology 33, no. 4 (December 20, 2017): 381–92. http://dx.doi.org/10.1007/s10654-017-0346-8.

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Shen, Changyu, Xiaochun Li, Lingling Li, and Martin C. Were. "Sensitivity analysis for causal inference using inverse probability weighting." Biometrical Journal 53, no. 5 (July 19, 2011): 822–37. http://dx.doi.org/10.1002/bimj.201100042.

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22

Dong He, Xue, Roy Kouwenberg, and Xun Yu Zhou. "Inverse S-shaped probability weighting and its impact on investment." Mathematical Control & Related Fields 8, no. 3 (2018): 679–706. http://dx.doi.org/10.3934/mcrf.2018029.

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Seaman, Shaun R., and Ian R. White. "Review of inverse probability weighting for dealing with missing data." Statistical Methods in Medical Research 22, no. 3 (January 10, 2011): 278–95. http://dx.doi.org/10.1177/0962280210395740.

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Sun, BaoLuo, and Eric J. Tchetgen Tchetgen. "On Inverse Probability Weighting for Nonmonotone Missing at Random Data." Journal of the American Statistical Association 113, no. 521 (December 1, 2017): 369–79. http://dx.doi.org/10.1080/01621459.2016.1256814.

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Dombi, József, and Tamás Jónás. "Towards a general class of parametric probability weighting functions." Soft Computing 24, no. 21 (September 24, 2020): 15967–77. http://dx.doi.org/10.1007/s00500-020-05335-3.

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Abstract In this study, we present a novel methodology that can be used to generate parametric probability weighting functions, which play an important role in behavioral economics, by making use of the Dombi modifier operator of continuous-valued logic. Namely, we will show that the modifier operator satisfies the requirements for a probability weighting function. Next, we will demonstrate that the application of the modifier operator can be treated as a general approach to create parametric probability weighting functions including the most important ones such as the Prelec and the Ostaszewski, Green and Myerson (Lattimore, Baker and Witte) probability weighting function families. Also, we will show that the asymptotic probability weighting function induced by the inverse of the so-called epsilon function is none other than the Prelec probability weighting function. Furthermore, we will prove that, by using the modifier operator, other probability weighting functions can be generated from the dual generator functions and from transformed generator functions. Finally, we will show how the modifier operator can be used to generate strictly convex (or concave) probability weighting functions and introduce a method for fitting a generated probability weighting function to empirical data.

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Dodd, Susanna, Paula Williamson, and Ian R. White. "Adjustment for treatment changes in epilepsy trials: A comparison of causal methods for time-to-event outcomes." Statistical Methods in Medical Research 28, no. 3 (November 8, 2017): 717–33. http://dx.doi.org/10.1177/0962280217735560.

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Background When trials are subject to departures from randomised treatment, simple statistical methods that aim to estimate treatment efficacy, such as per protocol or as treated analyses, typically introduce selection bias. More appropriate methods to adjust for departure from randomised treatment are rarely employed, primarily due to their complexity and unfamiliarity. We demonstrate the use of causal methodologies for the production of estimands with valid causal interpretation for time-to-event outcomes in the analysis of a complex epilepsy trial, as an example to guide non-specialist analysts undertaking similar analyses. Methods Two causal methods, the structural failure time model and inverse probability of censoring weighting, are adapted to allow for skewed time-varying confounders, competing reasons for treatment changes and a complicated time to remission outcome. We demonstrate the impact of various factors: choice of method (structural failure time model versus inverse probability of censoring weighting), model for inverse probability of censoring weighting (pooled logistic regression versus Cox models), time interval (for creating panel data for time-varying confounders and outcome), choice of confounders and (in pooled logistic regression) use of splines to estimate underlying risk. Results The structural failure time model could adjust for switches between trial treatments but had limited ability to adjust for the other treatment changes that occurred in this epilepsy trial. Inverse probability of censoring weighting was able to adjust for all treatment changes and demonstrated very similar results with Cox and pooled logistic regression models. Accounting for increasing numbers of time-varying confounders and reasons for treatment change suggested a more pronounced advantage of the control treatment than that obtained using intention to treat. Conclusions In a complex trial featuring a remission outcome, underlying assumptions of the structural failure time model are likely to be violated, and inverse probability of censoring weighting may provide the most useful option, assuming availability of appropriate data and sufficient sample sizes. Recommendations are provided for analysts when considering which of these methods should be applied in a given trial setting.

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Vakulenko‐Lagun, Bella, Micha Mandel, and Rebecca A. Betensky. "Inverse probability weighting methods for Cox regression with right‐truncated data." Biometrics 76, no. 2 (November 11, 2019): 484–95. http://dx.doi.org/10.1111/biom.13162.

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Seaman, Shaun, and Ian White. "Inverse Probability Weighting with Missing Predictors of Treatment Assignment or Missingness." Communications in Statistics - Theory and Methods 43, no. 16 (July 24, 2014): 3499–515. http://dx.doi.org/10.1080/03610926.2012.700371.

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Breskin, Alexander, Stephen R. Cole, and Daniel Westreich. "Exploring the Subtleties of Inverse Probability Weighting and Marginal Structural Models." Epidemiology 29, no. 3 (May 2018): 352–55. http://dx.doi.org/10.1097/ede.0000000000000813.

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Zhao, Qingyuan, Dylan S. Small, and Bhaswar B. Bhattacharya. "Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 81, no. 4 (June 5, 2019): 735–61. http://dx.doi.org/10.1111/rssb.12327.

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Matsouaka, Roland A., and Folefac D. Atem. "Regression with a right‐censored predictor using inverse probability weighting methods." Statistics in Medicine 39, no. 27 (August 10, 2020): 4001–15. http://dx.doi.org/10.1002/sim.8704.

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Wanis, Kerollos Nashat, Arin L. Madenci, Miguel A. Hernán, and Eleanor J. Murray. "Adjusting for adherence in randomized trials when adherence is measured as a continuous variable: An application to the Lipid Research Clinics Coronary Primary Prevention Trial." Clinical Trials 17, no. 5 (May 15, 2020): 570–75. http://dx.doi.org/10.1177/1740774520920893.

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Background: Clinicians and patients may be more interested in per-protocol effect estimates than intention-to-treat effect estimates from randomized trials. However, per-protocol effect estimates may be biased due to insufficient adjustment for prognostic factors that predict adherence. Adjustment for this bias is possible when appropriate methods, such as inverse probability weighting, are used. But, when adherence is measured as a continuous variable, constructing these weights can be challenging. Methods: In the placebo arm of the Lipid Research Clinics Coronary Primary Prevention Trial, we estimated the 7-year cumulative incidence of coronary heart disease under 100% adherence and 0% adherence to placebo. We used dose-response discrete-hazards models with inverse probability weighting to adjust for pre- and post-randomization covariates. We considered several continuous distributions for constructing the inverse probability weights. Results: The risk difference estimate for 100% adherence compared with 0% adherence ranged from −7.7 to −6.1 percentage points without adjustment for baseline and post-baseline covariates, and ranged from −1.8 to 2.2 percentage points with adjustment using inverse probability weights, depending on the dose-response model and inverse probability weight distribution used. Conclusions: Methods which appropriately adjust for time-varying post-randomization variables can explain away much of the bias in the “effect” of adherence to placebo. When considering continuous adherence, investigators should consider several models as estimates may be sensitive to the model chosen.

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Jacqmin-Gadda, Hélène, Paul Blanche, Emilie Chary, Célia Touraine, and Jean-François Dartigues. "Receiver operating characteristic curve estimation for time to event with semicompeting risks and interval censoring." Statistical Methods in Medical Research 25, no. 6 (September 30, 2016): 2750–66. http://dx.doi.org/10.1177/0962280214531691.

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Semicompeting risks and interval censoring are frequent in medical studies, for instance when a disease may be diagnosed only at times of visit and disease onset is in competition with death. To evaluate the ability of markers to predict disease onset in this context, estimators of discrimination measures must account for these two issues. In recent years, methods for estimating the time-dependent receiver operating characteristic curve and the associated area under the ROC curve have been extended to account for right censored data and competing risks. In this paper, we show how an approximation allows to use the inverse probability of censoring weighting estimator for semicompeting events with interval censored data. Then, using an illness-death model, we propose two model-based estimators allowing to rigorously handle these issues. The first estimator is fully model based whereas the second one only uses the model to impute missing observations due to censoring. A simulation study shows that the bias for inverse probability of censoring weighting remains modest and may be less than the one of the two parametric estimators when the model is misspecified. We finally recommend the nonparametric inverse probability of censoring weighting estimator as main analysis and the imputation estimator based on the illness-death model as sensitivity analysis.

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Doidge, James C. "Responsiveness-informed multiple imputation and inverse probability-weighting in cohort studies with missing data that are non-monotone or not missing at random." Statistical Methods in Medical Research 27, no. 2 (March 16, 2016): 352–63. http://dx.doi.org/10.1177/0962280216628902.

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Population-based cohort studies are invaluable to health research because of the breadth of data collection over time, and the representativeness of their samples. However, they are especially prone to missing data, which can compromise the validity of analyses when data are not missing at random. Having many waves of data collection presents opportunity for participants’ responsiveness to be observed over time, which may be informative about missing data mechanisms and thus useful as an auxiliary variable. Modern approaches to handling missing data such as multiple imputation and maximum likelihood can be difficult to implement with the large numbers of auxiliary variables and large amounts of non-monotone missing data that occur in cohort studies. Inverse probability-weighting can be easier to implement but conventional wisdom has stated that it cannot be applied to non-monotone missing data. This paper describes two methods of applying inverse probability-weighting to non-monotone missing data, and explores the potential value of including measures of responsiveness in either inverse probability-weighting or multiple imputation. Simulation studies are used to compare methods and demonstrate that responsiveness in longitudinal studies can be used to mitigate bias induced by missing data, even when data are not missing at random.

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Vansteelandt, Stijn, James Carpenter, and Michael G. Kenward. "Analysis of Incomplete Data Using Inverse Probability Weighting and Doubly Robust Estimators." Methodology 6, no. 1 (January 2010): 37–48. http://dx.doi.org/10.1027/1614-2241/a000005.

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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.

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Willems, SJW, A. Schat, MS van Noorden, and M. Fiocco. "Correcting for dependent censoring in routine outcome monitoring data by applying the inverse probability censoring weighted estimator." Statistical Methods in Medical Research 27, no. 2 (March 17, 2016): 323–35. http://dx.doi.org/10.1177/0962280216628900.

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Censored data make survival analysis more complicated because exact event times are not observed. Statistical methodology developed to account for censored observations assumes that patients’ withdrawal from a study is independent of the event of interest. However, in practice, some covariates might be associated to both lifetime and censoring mechanism, inducing dependent censoring. In this case, standard survival techniques, like Kaplan–Meier estimator, give biased results. The inverse probability censoring weighted estimator was developed to correct for bias due to dependent censoring. In this article, we explore the use of inverse probability censoring weighting methodology and describe why it is effective in removing the bias. Since implementing this method is highly time consuming and requires programming and mathematical skills, we propose a user friendly algorithm in R. Applications to a toy example and to a medical data set illustrate how the algorithm works. A simulation study was carried out to investigate the performance of the inverse probability censoring weighted estimators in situations where dependent censoring is present in the data. In the simulation process, different sample sizes, strengths of the censoring model, and percentages of censored individuals were chosen. Results show that in each scenario inverse probability censoring weighting reduces the bias induced in the traditional Kaplan–Meier approach where dependent censoring is ignored.

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Shao, Jun, and Lei Wang. "Semiparametric inverse propensity weighting for nonignorable missing data." Biometrika 103, no. 1 (January 28, 2016): 175–87. http://dx.doi.org/10.1093/biomet/asv071.

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Abstract To estimate unknown population parameters based on data having nonignorable missing values with a semiparametric exponential tilting propensity, Kim & Yu (2011) assumed that the tilting parameter is known or can be estimated from external data, in order to avoid the identifiability issue. To remove this serious limitation on the methodology, we use an instrument, i.e., a covariate related to the study variable but unrelated to the missing data propensity, to construct some estimating equations. Because these estimating equations are semiparametric, we profile the nonparametric component using a kernel-type estimator and then estimate the tilting parameter based on the profiled estimating equations and the generalized method of moments. Once the tilting parameter is estimated, so is the propensity, and then other population parameters can be estimated using the inverse propensity weighting approach. Consistency and asymptotic normality of the proposed estimators are established. The finite-sample performance of the estimators is studied through simulation, and a real-data example is also presented.

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Allan, Victoria, Sreeram V. Ramagopalan, Jack Mardekian, Aaron Jenkins, Xiaoyan Li, Xianying Pan, and Xuemei Luo. "Propensity score matching and inverse probability of treatment weighting to address confounding by indication in comparative effectiveness research of oral anticoagulants." Journal of Comparative Effectiveness Research 9, no. 9 (June 2020): 603–14. http://dx.doi.org/10.2217/cer-2020-0013.

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After decades of warfarin being the only oral anticoagulant (OAC) widely available for stroke prevention in atrial fibrillation, four direct OACs (apixaban, dabigatran, edoxaban and rivaroxaban) were approved after demonstrating noninferior efficacy and safety versus warfarin in randomized controlled trials. Comparative effectiveness research of OACs based on real-world data provides complementary information to randomized controlled trials. Propensity score matching and inverse probability of treatment weighting are increasingly popular methods used to address confounding by indication potentially arising in comparative effectiveness research due to a lack of randomization in treatment assignment. This review describes the fundamentals of propensity score matching and inverse probability of treatment weighting, appraises differences between them and presents applied examples to elevate understanding of these methods within the atrial fibrillation field.

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Han, Peisong. "Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation." Scandinavian Journal of Statistics 43, no. 1 (August 25, 2015): 246–60. http://dx.doi.org/10.1111/sjos.12177.

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Zhao, Pu-Ying, Nian-Sheng Tang, and De-Peng Jiang. "Efficient inverse probability weighting method for quantile regression with nonignorable missing data." Statistics 51, no. 2 (December 27, 2016): 363–86. http://dx.doi.org/10.1080/02331888.2016.1268615.

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Lechner, Michael. "A Note on the Relation of Inverse-Probability-Weighting and Matching Estimators." Communications in Statistics - Theory and Methods 40, no. 4 (January 24, 2011): 674–83. http://dx.doi.org/10.1080/03610920903453434.

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Tchetgen Tchetgen, Eric J., M. Maria Glymour, Jennifer Weuve, and James Robins. "Specifying the Correlation Structure in Inverse-Probability- Weighting Estimation for Repeated Measures." Epidemiology 23, no. 4 (July 2012): 644–46. http://dx.doi.org/10.1097/ede.0b013e31825727b5.

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Thoemmes, Felix, and Anthony D. Ong. "A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models." Emerging Adulthood 4, no. 1 (December 16, 2015): 40–59. http://dx.doi.org/10.1177/2167696815621645.

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Sjolander, A., O. Nyren, R. Bellocco, and M. Evans. "Comparing Different Strategies for Timing of Dialysis Initiation Through Inverse Probability Weighting." American Journal of Epidemiology 174, no. 10 (October 7, 2011): 1204–10. http://dx.doi.org/10.1093/aje/kwr249.

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Edwards, Jessie K., Stephen R. Cole, Catherine R. Lesko, W. Christopher Mathews, Richard D. Moore, Michael J. Mugavero, and Daniel Westreich. "An Illustration of Inverse Probability Weighting to Estimate Policy-Relevant Causal Effects." American Journal of Epidemiology 184, no. 4 (July 28, 2016): 336–44. http://dx.doi.org/10.1093/aje/kwv339.

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Keskin, Kerim. "Inverse S-shaped probability weighting functions in first-price sealed-bid auctions." Review of Economic Design 20, no. 1 (October 19, 2015): 57–67. http://dx.doi.org/10.1007/s10058-015-0183-8.

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Chatton, Arthur, Florent Le Borgne, Clémence Leyrat, and Yohann Foucher. "G-computation and doubly robust standardisation for continuous-time data: A comparison with inverse probability weighting." Statistical Methods in Medical Research 31, no. 4 (December 3, 2021): 706–18. http://dx.doi.org/10.1177/09622802211047345.

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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.

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Li, Yan, and Liang Li. "Propensity score analysis methods with balancing constraints: A Monte Carlo study." Statistical Methods in Medical Research 30, no. 4 (February 1, 2021): 1119–42. http://dx.doi.org/10.1177/0962280220983512.

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The inverse probability weighting is an important propensity score weighting method to estimate the average treatment effect. Recent literature shows that it can be easily combined with covariate balancing constraints to reduce the detrimental effects of excessively large weights and improve balance. Other methods are available to derive weights that balance covariate distributions between the treatment groups without the involvement of propensity scores. We conducted comprehensive Monte Carlo experiments to study whether the use of covariate balancing constraints circumvent the need for correct propensity score model specification, and whether the use of a propensity score model further improves the estimation performance among methods that use similar covariate balancing constraints. We compared simple inverse probability weighting, two propensity score weighting methods with balancing constraints (covariate balancing propensity score, covariate balancing scoring rule), and two weighting methods with balancing constraints but without using the propensity scores (entropy balancing and kernel balancing). We observed that correct specification of the propensity score model remains important even when the constraints effectively balance the covariates. We also observed evidence suggesting that, with similar covariate balance constraints, the use of a propensity score model improves the estimation performance when the dimension of covariates is large. These findings suggest that it is important to develop flexible data-driven propensity score models that satisfy covariate balancing conditions.

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Kadir, Dler H. "Likelihood Approach for Bayesian Logistic Weighted Model." Cihan University-Erbil Scientific Journal 4, no. 2 (August 13, 2020): 9–12. http://dx.doi.org/10.24086/cuesj.v4n2y2020.pp9-12.

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Increasing the response rate and minimizing non-response rates represent the primary challenges to researchers in performing longitudinal and cohort research. This is most obvious in the area of paediatric medicine. When there are missing data, complete case analysis makes findings biased. Inverse Probability Weighting (IPW) is one of many available approaches for reducing the bias using a complete case analysis. Here, a complete case is weighted by probability inverse of complete cases. The data of this work is collected from the neonatal intensive care unit at Erbil maternity hospital for the years 2012 to 2017. In total, 570 babies (288 male and 282 females) were born very preterm. The aim of this paper is to use inverse probability weighting on the Bayesian logistic model developmental outcome. The Mental Development Index (MDI) approach is used for assessing the cognitive development of those born very preterm. Almost half of the information for the babies was missing, meaning that we do not know whether they have cognitive development issues or they have not. We obtained greater precision in results and standard deviation of parameter estimates which are less in the posterior weighted model in comparison with frequent analysis.

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Bianchi, Davide, and Licia Verde. "Confronting missing observations with probability weights: Fourier space and generalized formalism." Monthly Notices of the Royal Astronomical Society 495, no. 1 (May 15, 2020): 1511–29. http://dx.doi.org/10.1093/mnras/staa1267.

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ABSTRACT Due to instrumental limitations, spectroscopic galaxy surveys usually do not collect redshifts for the whole population of potential targets. Especially problematic is the entanglement between this incompleteness and the true cosmological signal, arising from the fact that the proportion of successful observations is typically lower in regions with higher galaxy density. The result is a fictitious suppression of the galaxy clustering that can impact severely on cosmological parameter inference. Recent developments have shown that an unbiased estimate of the two-point correlation in the presence of missing observations can be obtained by weighting each pair by its inverse probability of being targeted. In this work, we expand on the concept of probability weights by developing a more mature statistical formalism, which provides us with a deeper understanding of their fundamental properties. We take advantage of this novel perspective to handle the problem of estimating the inverse probability, specifically, we discuss how to efficiently determine the weights from a finite set of realizations of the targeting and how to model exactly the resulting sampling effects. This allows us to derive an inverse-probability-based power-spectrum estimator, which is the main result of this work, but also to improve robustness and computational efficiency of the already existing configuration-space estimator. Finally, we propose a strategy to further extend the inverse-probability prescription, providing examples of how traditional missing-observation countermeasures can be included in this more general picture. The effectiveness of models and weighting schemes discussed in this work is demonstrated using realizations of an idealized survey strategy.

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