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Nested case–control design (NCC) is a cost-effective outcome-dependent design in epidemiology that collects all cases and a fixed number of controls at the time of case diagnosis from a large cohort. Due to inefficiency relative to full cohort studies, previous research developed various estimation methodologies but changing designs in the formulation of risk sets was considered only in view of potential bias in the partial likelihood estimation. In this paper, we study a modified design that excludes previously selected controls from risk sets in view of efficiency improvement as well as bias. To this end, we extend the inverse probability weighting method of Samuelsen which was shown to outperform the partial likelihood estimator in the standard setting. We develop its asymptotic theory and a variance estimation of both regression coefficients and the cumulative baseline hazard function that takes account of the complex feature of the modified sampling design. In addition to good finite sample performance of variance estimation, simulation studies show that the modified design with the proposed estimator is more efficient than the standard design. Examples are provided using data from NIH-AARP Diet and Health Cohort Study. [ABSTRACT FROM AUTHOR]

One-shot devices are products that can only be used once. Typical one-shot devices include airbags, fire extinguishers, inflatable life vests, ammo, and handheld flares. Most of them are life-saving products and should be highly reliable in an emergency. Quality control of those productions and predicting their reliabilities over time is critically important. To assess the reliability of the products, manufacturers usually test them in controlled conditions rather than user conditions. We may rely on public datasets that reflect their reliability in actual use, but the datasets often come with missing observations. The experimenter may lose information on covariate readings due to human errors. Traditional missing-data-handling methods may not work well in handling one-shot device data as they only contain their survival statuses. In this research, we propose Multiple Imputation with Unsupervised Learning (MIUL) to impute the missing data using Hierarchical Clustering, k-prototype, and density-based spatial clustering of applications with noise (DBSCAN). Our simulation study shows that MIUL algorithms have superior performance. We also illustrate the method using datasets from the Crash Report Sampling System (CRSS) of the National Highway Traffic Safety Administration (NHTSA). [ABSTRACT FROM AUTHOR]

A dynamic treatment regime (DTR) is a mathematical representation of a multistage decision process. When applied to sequential treatment selection in medical settings, DTRs are useful for identifying optimal therapies for chronic diseases such as AIDs, mental illnesses, substance abuse, and many cancers. Sequential multiple assignment randomized trials (SMARTs) provide a useful framework for constructing DTRs and providing unbiased between-DTR comparisons. A limitation of SMARTs is that they ignore data from past patients that may be useful for reducing the probability of exposing new patients to inferior treatments. In practice, this may result in decreased treatment adherence or dropouts. To address this problem, we propose a generalized outcome-adaptive (GO) SMART design that adaptively unbalances stage-specific randomization probabilities in favor of treatments observed to be more effective in previous patients. To correct for bias induced by outcome adaptive randomization, we propose G-estimators and inverse-probability-weighted estimators of DTR effects embedded in a GO-SMART and show analytically that they are consistent. We report simulation results showing that, compared to a SMART, Response-Adaptive SMART and SMART with adaptive randomization, a GO-SMART design treats significantly more patients with the optimal DTR and achieves a larger number of total responses while maintaining similar or better statistical power. [ABSTRACT FROM AUTHOR]

We estimate relative hazards and absolute risks (or cumulative incidence or crude risk) under cause-specific proportional hazards models for competing risks from double nested case-control (DNCC) data. In the DNCC design, controls are time-matched not only to cases from the cause of primary interest, but also to cases from competing risks (the phase-two sample). Complete covariate data are available in the phase-two sample, but other cohort members only have information on survival outcomes and some covariates. Design-weighted estimators use inverse sampling probabilities computed from Samuelsen-type calculations for DNCC. To take advantage of additional information available on all cohort members, we augment the estimating equations with a term that is unbiased for zero but improves the efficiency of estimates from the cause-specific proportional hazards model. We establish the asymptotic properties of the proposed estimators, including the estimator of absolute risk, and derive consistent variance estimators. We show that augmented design-weighted estimators are more efficient than design-weighted estimators. Through simulations, we show that the proposed asymptotic methods yield nominal operating characteristics in practical sample sizes. We illustrate the methods using prostate cancer mortality data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Study of the National Cancer Institute. [ABSTRACT FROM AUTHOR]

We propose an estimator for the population mean θ0=피(Y) under the semi‐supervised learning setting with the Missing at Random (MAR) assumption. This setting assumes that the probability of observing Y$$ Y $$, denoted by πM∗$$ {\pi}_M^{\ast } $$, depends on the total sample size M$$ M $$ and satisfies πM∗=o(1)$$ {\pi}_M^{\ast }=o(1) $$. To efficiently estimate θ0$$ {\theta}_0 $$, we introduce an adaptive estimator based on inverse probability weighting and cross‐fitting. Theoretical analysis reveals that our proposed estimator is consistent and efficient, with a convergence rate of MπM∗$$ \sqrt{M{\pi}_M^{\ast }} $$, slower than the typical M$$ \sqrt{M} $$ rate, due to the diminishing proportion of labelled data as the sample size M$$ M $$ increases in the semi‐supervised setting. We also prove the consistency of inverse probability weighting (IPW)–Nadaraya–Watson density function estimators. Extensive simulations and an application to the Los Angeles homeless data validate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Background: People with HIV (PWH) have higher risk of COVID-19 mortality. SARS-CoV-2 vaccination is highly effective among PWH, although vaccine hesitancy could limit the population-level impact. Setting: From February 2021 to April 2022, PWH from 8 sites in the Centers for AIDS Research Network of Integrated Clinical Systems completed a vaccine hesitancy instrument as part of routine care. Methods: Participants were defined as vaccine hesitant if they had not received the SARS-CoV-2 vaccine and would probably/definitely not receive it. We assessed factors associated with SARS-CoV-2 vaccine hesitancy using logistic regression adjusted for demographics, unsuppressed viral load (VL > 200 copies/mL), month, and time on ART; using inverse probability weighting for survey nonresponse. Results: Overall, 3288 PWH with a median age of 55 were included; 18% were female and 94% were virally suppressed. At the time of survey, 27% reported they had not received the SARS-CoV-2 vaccine, and 9% (n = 279) reported vaccine hesitancy. Factors associated with vaccine hesitancy included female sex (adjusted odds ratio [AOR] = 2.3; 95% confidence interval (CI): 1.6-3.2), Black vs. White race (AOR 1.7; 95% CI: 1.2 to 2.4), younger age (AOR 1.4; 95% CI: 1.2 to 1.5), and unsuppressed VL (AOR 1.9; 95% CI: 1.3 to 3.0). Conclusion: Overall, over one-quarter of PWH in this multisite cohort were unvaccinated for SARS-CoV-2 when interviewed February 21-April 22. Vaccine hesitancy was reported by approximately 9% of PWH and was higher among women, Black PWH, younger PWH, PWH with unsuppressed VL, and those in the South/Midwest. Renewed efforts are needed to address concerns of PWH about vaccinations against COVID-19 as the pandemic evolves, and vaccines in general, given the potential for future pandemics. [ABSTRACT FROM AUTHOR]

Abstract This paper studies the long-term consequences of unemployment on different dimensions of social exclusion. Based on longitudinal linked survey and administrative data from Germany and an event study analysis combined with inverse propensity score weighting, I document that becoming unemployed has lasting adverse effects on both individuals’ material well-being and their subjective perception of social status and integration, persisting even after four years. An examination of effect heterogeneity underscores that the enduring effects of job loss are more pronounced for individuals confronted with challenging labor market conditions, those with a history of repeated unemployment, and individuals with lower levels of educational attainment.

Abstract Background Throughout the Covid-19 pandemic, researchers have made use of electronic health records to research this disease in a rapidly evolving environment of questions and discoveries. These studies are prone to collider bias as they restrict the population of Covid-19 patients to only those with severe disease. Inverse probability weighting is typically used to correct for this bias but requires information from the unrestricted population. Using electronic health records from a South London NHS trust, this work demonstrates a method to correct for collider bias using externally sourced data while examining the relationship between minority ethnicities and poor Covid-19 outcomes. Methods The probability of inclusion within the observed hospitalised cohort was modelled based on estimates from published national data. The model described the relationship between patient ethnicity, hospitalisation, and death due to Covid-19 – a relationship suggested to be susceptible to collider bias. The obtained probabilities (as applied to the observed patient cohort) were used as inverse probability weights in survival analysis examining ethnicity (and covariates) as a risk factor for death due to Covid-19. Results Within the observed cohort, unweighted analysis of survival suggested a reduced risk of death in those of Black ethnicity – differing from the published literature. Applying inverse probability weights to this analysis amended this aberrant result to one more compatible with the literature. This effect was consistent when the analysis was applied to patients within only the first wave of Covid-19 and across two waves of Covid-19 and was robust against adjustments to the modelled relationship between hospitalisation, patient ethnicity, and death due to Covid-19 made as part of a sensitivity analysis. Conclusions In conclusion, this analysis demonstrates the feasibility of using external publications to correct for collider bias (or other forms of selection bias) induced by the restriction of a population to a hospitalised cohort using an example from the recent Covid-19 pandemic.

Summary Probability weights have been widely used in addressing selection bias arising from a variety of contexts. Common examples of probability weights include sampling weights, missing data weights, and propensity score weights. Frequency weights, which are used to control for varying variabilities of aggregated outcomes, are both conceptually and analytically different from probability weights. Popular software such as R, SAS and STATA support both types of weights. Many users, including professional statisticians, become bewildered when they see identical estimates, but different standard errors and p$$ p $$‐values when probability weights are treated as frequency weights. Some even completely ignore the difference between the two types of weights and treat them as the same. Although a large body of literature exists on each type of weights, we have found little, if any, discussion that provides head‐to‐head comparisons of the two types of weights and associated inference methods. In this paper, we unveil the conceptual and analytic differences between the two types of weights within the context of parametric and semi‐parametric generalised linear models (GLM) and discuss valid inference for each type of weights. To the best of our knowledge, this is the first paper that looks into such differences by identifying the conditions under which the two types of weights can be treated the same analytically and providing clear guidance on the appropriate statistical models and inference procedures for each type of weights. We illustrate these considerations using real study data. [ABSTRACT FROM AUTHOR]

Inverse probability weighting (IPW) is frequently used to reduce or minimize the observed confounding in observational studies. IPW creates a pseudo-sample by weighting each individual by the inverse of the conditional probability of receiving the treatment level that he/she has actually received. In the pseudo-sample there is no variation among the multiple individuals generated by weighting the same individual in the original sample. This would reduce the variability of the data and therefore bias the variance estimate in the target population. Conventional variance estimation methods for IPW estimators generally ignore this underestimation and tend to produce biased estimates of variance. We here propose a more reasonable method that incorporates this source of variability by using parametric bootstrapping based on intra-stratum variability estimates. This approach firstly uses propensity score stratification and intra-stratum standard deviation to approximate the variability among multiple individuals generated based on a single individual whose propensity score falls within the corresponding stratum. The parametric bootstrapping is then used to incorporate the target variability by re-generating outcomes after adding a random error term to the original data. The performance of the proposed method is compared with three existing methods including the naïve model-based variance estimator, the nonparametric bootstrap variance estimator, and the robust variance estimator in the simulation section. An example of patients with sarcopenia is used to illustrate the implementation of the proposed approach. According to the results, the proposed approach has desirable statistical properties and can be easily implemented using the provided R code. [ABSTRACT FROM AUTHOR]

Background Empirical evaluation of inverse probability weighting (IPW) for self-selection bias correction is inaccessible without the full source population. We aimed to: (i) investigate how self-selection biases frequency and association measures and (ii) assess self-selection bias correction using IPW in a cohort with register linkage. Methods The source population included 17 936 individuals invited to the Copenhagen Aging and Midlife Biobank during 2009–11 (ages 49–63 years). Participants counted 7185 (40.1%). Register data were obtained for every invited person from 7 years before invitation to the end of 2020. The association between education and mortality was estimated using Cox regression models among participants, IPW participants and the source population. Results Participants had higher socioeconomic position and fewer hospital contacts before baseline than the source population. Frequency measures of participants approached those of the source population after IPW. Compared with primary/lower secondary education, upper secondary, short tertiary, bachelor and master/doctoral were associated with reduced risk of death among participants (adjusted hazard ratio [95% CI]: 0.60 [0.46; 0.77], 0.68 [0.42; 1.11], 0.37 [0.25; 0.54], 0.28 [0.18; 0.46], respectively). IPW changed the estimates marginally (0.59 [0.45; 0.77], 0.57 [0.34; 0.93], 0.34 [0.23; 0.50], 0.24 [0.15; 0.39]) but not only towards those of the source population (0.57 [0.51; 0.64], 0.43 [0.32; 0.60], 0.38 [0.32; 0.47], 0.22 [0.16; 0.29]). Conclusions Frequency measures of study participants may not reflect the source population in the presence of self-selection, but the impact on association measures can be limited. IPW may be useful for (self-)selection bias correction, but the returned results can still reflect residual or other biases and random errors. [ABSTRACT FROM AUTHOR]

Using administrative patient-care data such as Electronic Health Records (EHR) and medical/pharmaceutical claims for population-based scientific research has become increasingly common. With vast sample sizes leading to very small standard errors, researchers need to pay more attention to potential biases in the estimates of association parameters of interest, specifically to biases that do not diminish with increasing sample size. Of these multiple sources of biases, in this paper, we focus on understanding selection bias. We present an analytic framework using directed acyclic graphs for guiding applied researchers to dissect how different sources of selection bias may affect estimates of the association between a binary outcome and an exposure (continuous or categorical) of interest. We consider four easy-to-implement weighting approaches to reduce selection bias with accompanying variance formulae. We demonstrate through a simulation study when they can rescue us in practice with analysis of real-world data. We compare these methods using a data example where our goal is to estimate the well-known association of cancer and biological sex, using EHR from a longitudinal biorepository at the University of Michigan Healthcare system. We provide annotated R codes to implement these weighted methods with associated inference. [ABSTRACT FROM AUTHOR]

Weighting methods are widely used for causal effect estimation in non‐randomised studies. In general, these methods use the propensity score (PS), the probability of receiving the treatment given the covariates, to arrive at the respective weights. All of these "modelling" methods actually optimize prediction of the respective outcome, which is, in the PS model, treatment assignment. However, this does not match with the actual aim of weighting, which is eliminating the association between covariates and treatment assignment. In the "balancing" approach, covariates are thus balanced directly by solving systems of numerical equations, explicitly without fitting a PS model. To compare modelling, balancing and hybrid approaches to weighting we performed a large simulation study for a binary treatment and a survival outcome. For maximal practical relevance all simulation parameters were selected after a systematic review of medical studies that used PS methods for analysis. We also introduce a new hybrid method that uses the idea of the covariate balancing propensity score and matching weights, thus avoiding extreme weights. In addition, we present a corrected robust variance estimator for some of the methods. Overall, our simulations results indicate that balancing approach methods work worse than expected. However, among the considered balancing methods, entropy balancing consistently outperforms the variance balancing approach. All methods estimating the average treatment effect in the overlap population perform well with very little bias and small standard errors even in settings with misspecified propensity score models. Finally, the coverage using the standard robust variance estimator was too high for all methods, with the proposed corrected robust variance estimator improving coverage in a variety of settings. [ABSTRACT FROM AUTHOR]

This paper studies the long-term consequences of unemployment on different dimensions of social exclusion. Based on longitudinal linked survey and administrative data from Germany and an event study analysis combined with inverse propensity score weighting, I document that becoming unemployed has lasting adverse effects on both individuals' material well-being and their subjective perception of social status and integration, persisting even after four years. An examination of effect heterogeneity underscores that the enduring effects of job loss are more pronounced for individuals confronted with challenging labor market conditions, those with a history of repeated unemployment, and individuals with lower levels of educational attainment. [ABSTRACT FROM AUTHOR]

Recurrent events, including cardiovascular events, are commonly observed in biomedical studies. Understanding the effects of various treatments on recurrent events and investigating the underlying mediation mechanisms by which treatments may reduce the frequency of recurrent events are crucial tasks for researchers. Although causal inference methods for recurrent event data have been proposed, they cannot be used to assess mediation. This study proposed a novel methodology of causal mediation analysis that accommodates recurrent outcomes of interest in a given individual. A formal definition of causal estimands (direct and indirect effects) within a counterfactual framework is given, and empirical expressions for these effects are identified. To estimate these effects, a semiparametric estimator with triple robustness against model misspecification was developed. The proposed methodology was demonstrated in a real‐world application. The method was applied to measure the effects of two diabetes drugs on the recurrence of cardiovascular disease and to examine the mediating role of kidney function in this process. [ABSTRACT FROM AUTHOR]

Background: Throughout the Covid-19 pandemic, researchers have made use of electronic health records to research this disease in a rapidly evolving environment of questions and discoveries. These studies are prone to collider bias as they restrict the population of Covid-19 patients to only those with severe disease. Inverse probability weighting is typically used to correct for this bias but requires information from the unrestricted population. Using electronic health records from a South London NHS trust, this work demonstrates a method to correct for collider bias using externally sourced data while examining the relationship between minority ethnicities and poor Covid-19 outcomes. Methods: The probability of inclusion within the observed hospitalised cohort was modelled based on estimates from published national data. The model described the relationship between patient ethnicity, hospitalisation, and death due to Covid-19 – a relationship suggested to be susceptible to collider bias. The obtained probabilities (as applied to the observed patient cohort) were used as inverse probability weights in survival analysis examining ethnicity (and covariates) as a risk factor for death due to Covid-19. Results: Within the observed cohort, unweighted analysis of survival suggested a reduced risk of death in those of Black ethnicity – differing from the published literature. Applying inverse probability weights to this analysis amended this aberrant result to one more compatible with the literature. This effect was consistent when the analysis was applied to patients within only the first wave of Covid-19 and across two waves of Covid-19 and was robust against adjustments to the modelled relationship between hospitalisation, patient ethnicity, and death due to Covid-19 made as part of a sensitivity analysis. Conclusions: In conclusion, this analysis demonstrates the feasibility of using external publications to correct for collider bias (or other forms of selection bias) induced by the restriction of a population to a hospitalised cohort using an example from the recent Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

The use of structural equation models for causal inference from panel data is critiqued in the causal inference literature for unnecessarily relying on a large number of parametric assumptions, and alternative methods originating from the potential outcomes framework have been recommended, such as inverse probability weighting (IPW) estimation of marginal structural models (MSMs). To better understand this criticism, we describe three phases of causal research. We explain (differences in) the assumptions that are made throughout these phases for structural equation modeling (SEM) and IPW-MSM approaches using an empirical example. Second, using simulations we compare the finite sample performance of SEM and IPW-MSM for the estimation of time-varying exposure effects on an end-of-study outcome under violations of parametric assumptions. Although increased reliance on parametric assumptions does not always translate to increased bias (even under model misspecification), researchers are still well-advised to acquaint themselves with causal methods from the potential outcomes framework. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the causal effects of a set of active labor market programs for male unemployed refugees on welfare who entered Germany between, 2013 and September, 2016. Using rich administrative data, we employ covariate balancing propensity scores combined with inverse probability weighting to estimate effects up to 33 months after the start of treatment. Our results show that relatively short-term training in the form of Schemes by Providers and In-Firm Training, as well as longer-term Further Vocational Training programs have a positive impact on both the employment chances as well as labor market earnings of refugees in the medium run. So-called "One Euro Jobs", a public employment program, does not yield positive effects on employment or earnings. Sensitivity analyses confirm that our results are unlikely to be driven by unobserved confounding. [ABSTRACT FROM AUTHOR]

AbstractData subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets. We contribute to the development of adaptive design for estimation of finite population characteristics, using active learning and adaptive importance sampling. We propose an active sampling strategy that iterates between estimation and data collection with optimal subsamples, guided by machine learning predictions on yet unseen data. The method is illustrated on virtual simulation-based safety assessment of advanced driver assistance systems. Substantial performance improvements are demonstrated compared to traditional sampling methods. [ABSTRACT FROM AUTHOR]

Introduction: Antihypertensive drugs are used preventatively to lower the risk of cardiovascular disease events. Comparative effectiveness studies on angiotensinconverting enzyme inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), beta-blockers (BBs), calcium channel blockers (CCBs), and thiazides have yielded inconsistent results and given little consideration to patient adherence. Using a longitudinal cohort and considering time-varying adherence and confounding factors, we aimed to estimate the real-world effectiveness of five major antihypertensive drug monotherapies in the primary prevention of cardiovascular events. Methods: Eligible patients for a retrospective inception cohort study were selected using information obtained from the University of Groningen IADB.nl pharmacy prescription database. Cohort 1 comprised adherent patients with a follow-up time exceeding 1 year, and cohort 2 comprised all patients independent of adherence. The exposures were ACEIs, ARBs, BBs, CCBs, and thiazides. The primary outcome was the time to the first prescription for an acute cardiac drug therapy (CDT) measured using valid drug proxies to identify the first major cardiovascular event. A per-protocol analytical approach was adopted with inverse probability of treatment weighted (IPTW), time-varying Cox regression analysis to obtain the hazard ratios (HRs) and 95% confidence intervals (CIs). Results: In cohort 1 (n = 22,441), 1,294 patients (5.8%) were prescribed an acute CDT with an average follow-up time of 4.2 ± 2.8 years. Following IPTW, the hazard measures of ARBs and thiazides were lower than those of BBs (HRs: 0.79 and 0.80, respectively; 95% CIs: 0.64-0.97 and 0.69-0.94, respectively). Among drug-treated diabetic patients, the hazard measures were even lower, with HR point estimates of 0.43 (CI: 0.19-0.98) for ARBs and 0.32 (CI: 0.13-0.82) for thiazides. In cohort 2 (n = 33,427) and sensitivity analysis, the comparative effectiveness results for thiazides and BBs were similar to those for cohort 1. Conclusion: The findings of this real-world analysis suggest that the incidence of CDT associated with long-term thiazide or ARB monotherapy is lower than the incidence of CDTwith BBs, notably among high-risk patients. Incidences of CDT associated with ACEIs and CCBs were comparable relative to those associated with BBs. [ABSTRACT FROM AUTHOR]

The marginal structure quantile model (MSQM) provides a unique lens to understand the causal effect of a time-varying treatment on the full distribution of potential outcomes. Under the semiparametric framework, we derive the efficiency influence function for the MSQM, from which a new doubly robust estimator is proposed for point estimation and inference. We show that the doubly robust estimator is consistent if either of the models associated with treatment assignment or the potential outcome distributions is correctly specified, and is semiparametric efficient if both models are correct. To implement the doubly robust MSQM estimator, we propose to solve a smoothed estimating equation to facilitate efficient computation of the point and variance estimates. In addition, we develop a confounding function approach to investigate the sensitivity of several MSQM estimators when the sequential ignorability assumption is violated. Extensive simulations are conducted to examine the finite-sample performance characteristics of the proposed methods. We apply the proposed methods to the Yale New Haven Health System Electronic Health Record data to study the effect of antihypertensive medications to patients with severe hypertension and assess the robustness of the findings to unmeasured baseline and time-varying confounding. [ABSTRACT FROM AUTHOR]

Background Biobanks typically rely on volunteer-based sampling. This results in large samples (power) at the cost of representativeness (bias). The problem of volunteer bias is debated. Here, we (i) show that volunteering biases associations in UK Biobank (UKB) and (ii) estimate inverse probability (IP) weights that correct for volunteer bias in UKB. Methods Drawing on UK Census data, we constructed a subsample representative of UKB's target population, which consists of all individuals invited to participate. Based on demographic variables shared between the UK Census and UKB, we estimated IP weights (IPWs) for each UKB participant. We compared 21 weighted and unweighted bivariate associations between these demographic variables to assess volunteer bias. Results Volunteer bias in all associations, as naively estimated in UKB, was substantial—in some cases so severe that unweighted estimates had the opposite sign of the association in the target population. For example, older individuals in UKB reported being in better health, in contrast to evidence from the UK Census. Using IPWs in weighted regressions reduced 87% of volunteer bias on average. Volunteer-based sampling reduced the effective sample size of UKB substantially, to 32% of its original size. Conclusions Estimates from large-scale biobanks may be misleading due to volunteer bias. We recommend IP weighting to correct for such bias. To aid in the construction of the next generation of biobanks, we provide suggestions on how to best ensure representativeness in a volunteer-based design. For UKB, IPWs have been made available. [ABSTRACT FROM AUTHOR]

This paper studies the single-index varyingcoefficient quantile model with missing covariates at random. Firstly, some estimators of index parameters and their corresponding linkage function are given by using the inverse probability weighting method for missing data in two cases including parameter estimation and non-parametric estimation for the single-index varying-coefficient quantile regression model. In particular, the latter case focuses on the study of both known and unknown probability functions. Secondly, the established estimators are proved to be asymptotic normal under some suitable regularity conditions. Finally, the simulation studies are conducted to demonstrate the finite sample performance of the proposed method. [ABSTRACT FROM AUTHOR]

Cardiac complications after major noncardiac surgery are common and associated with high morbidity and mortality. How preoperative use of beta-blockers may impact perioperative cardiac complications remains unclear. In a multicentre prospective cohort study, preoperative beta-blocker use was ascertained in consecutive patients at elevated cardiovascular risk undergoing major noncardiac surgery. Cardiac complications were prospectively monitored and centrally adjudicated by two independent experts. The primary endpoint was perioperative myocardial infarction or injury attributable to a cardiac cause (cardiac PMI) within the first three postoperative days. The secondary endpoints were major adverse cardiac events (MACE), defined as a composite of myocardial infarction, acute heart failure, life-threatening arrhythmia, and cardiovascular death and all-cause death after 365 days. We used inverse probability of treatment weighting to account for differences between patients receiving beta-blockers and those who did not. A total of 3839/10 272 (37.4%) patients (mean age 74 yr; 44.8% female) received beta-blockers before surgery. Patients on beta-blockers were older, and more likely to be male with established cardiorespiratory and chronic kidney disease. Cardiac PMI occurred in 1077 patients, with a weighted odds ratio of 1.03 (95% confidence interval [CI] 0.94–1.12, P =0.55) for patients on beta-blockers. Within 365 days of surgery, 971/10 272 (9.5%) MACE had occurred, with a weighted hazard ratio of 0.99 (95% CI 0.83–1.18, P =0.90) for patients on beta-blockers. Preoperative use of beta-blockers was not associated with decreased cardiac complications including cardiac perioperative myocardial infarction or injury and major adverse cardiac event. Additionally, preoperative use of beta-blockers was not associated with increased all-cause death within 30 and 365 days. NCT02573532. [ABSTRACT FROM AUTHOR]

In this paper, we consider parameter estimation, kink points testing and statistical inference for a longitudinal multi-kink expectile regression model with nonignorable dropout. In order to accommodate both within-subject correlations and nonignorable dropout, the bias-corrected generalized estimating equations are constructed by combining the inverse probability weighting and quadratic inference function approaches. The estimators for the kink locations and regression coefficients are obtained by using the generalized method of moments. A selection procedure based on a modified BIC is applied to estimate the number of kink points. We theoretically demonstrate the number selection consistency of kink points and the asymptotic normality of all estimators. A weighted cumulative sum type statistic is proposed to test the existence of kink effects at a given expectile, and its limiting distributions are derived under both the null and the local alternative hypotheses. Simulation studies show that the proposed estimators and test have desirable finite sample performance in both homoscedastic and heteroscedastic errors. An application to the Nation Growth, Lung and Health Study dataset is also presented.

Background: Immunization is the most effective intervention for reducing morbidity and mortality rates associated with vaccine-preventable diseases. Despite the Indonesian government's inclusion of several childhood vaccinations in the national immunization program (NIP), the number of unvaccinated or partially vaccinated children remains high. This observational study aimed to determine the completeness of childhood immunization and the factors influencing it in Indonesia. Methods: Data were extracted from the fifth wave of the Indonesia Family Life Survey (IFLS). The sample (n = 16,236) consists of children residing in 13 provinces, representing over 80% of Indonesia's population. The difference between groups was tested using the chi-square test. Logistic regression was performed to identify the variables associated with the completeness of basic immunization. Immunization is categorized as complete when children have received all the mandatory vaccines recommended by the Ministry of Health. We examined and compared the results using complete case analysis, inverse probability weighting, and multiple imputations. Results: The highest percentages of complete vaccinations were polio, tuberculosis, and DPT. Children who live in Sumatera and Kalimantan were more likely to be fully immunized, with ORs of 0.6 (95%CI 0.48–0.74) and 0.54 (0.37–0.80), respectively. Children who live in urban areas, have mothers who received the tetanus vaccine during pregnancy, have mothers with a higher level of education and health insurance, have fathers aged 41–50, and live with a large number of family members were more likely to be fully immunized (p

This paper discusses regression analysis of interval‐censored failure time data arising from semiparametric transformation models in the presence of missing covariates. Although some methods have been developed for the problem, they either apply only to limited situations or may have some computational issues. Corresponding to these, we propose a new and unified two‐step inference procedure that can be easily implemented using the existing or standard software. The proposed method makes use of a set of working models to extract partial information from incomplete observations and yields a consistent estimator of regression parameters assuming missing at random. An extensive simulation study is conducted and indicates that it performs well in practical situations. Finally, we apply the proposed approach to an Alzheimer's Disease study that motivated this study. [ABSTRACT FROM AUTHOR]

The Mann–Whitney–Wilcoxon rank sum test (MWWRST) is a widely used method for comparing two treatment groups in randomized control trials, particularly when dealing with highly skewed data. However, when applied to observational study data, the MWWRST often yields invalid results for causal inference. To address this limitation, Wu et al. (Causal inference for Mann–Whitney–Wilcoxon rank sum and other nonparametric statistics, Stat. Med. 33 (2014), pp. 1261–1271) introduced an approach that incorporates inverse probability weighting (IPW) into this rank-based statistic to mitigate confounding effects. Subsequently, Mao (On causal estimation using U-statistics, Biometrika 105 (2018), pp. 215–220), Zhang et al. (Estimating Mann Whitney-type causal effects, J. Causal Inference 7 (2019), ARTICLE ID 20180010), and Ai et al. (A Mann–Whitney test of distributional effects in a multivalued treatment, J. Stat. Plan. Inference 209 (2020), pp. 85–100) extended this IPW estimator to develop doubly robust estimators. Nevertheless, each of these approaches has notable limitations. Mao's method imposes stringent assumptions that may not align with real-world study data. Zhang et al.'s (Estimating Mann Whitney-type causal effects, J. Causal Inference 7 (2019), ARTICLE ID 20180010) estimators rely on bootstrap inference, which suffers from computational inefficiency and lacks known asymptotic properties. Meanwhile, Ai et al. (A Mann–Whitney test of distributional effects in a multivalued treatment, J. Stat. Plan. Inference 209 (2020), pp. 85–100) primarily focus on testing the null hypothesis of equal distributions between two groups, which is a more stringent assumption that may not be well-suited to the primary practical application of MWWRST. In this paper, we aim to address these limitations by leveraging functional response models (FRM) to develop doubly robust estimators. We demonstrate the performance of our proposed approach using both simulated and real study data. [ABSTRACT FROM AUTHOR]

This study addresses the problem of parameter estimation in spatial autoregressive models with missing data and measurement errors in covariates. Specifically, a corrected likelihood estimation approach is employed to rectify the bias in the log-maximum likelihood function induced by measurement errors. Additionally, a combination of inverse probability weighting (IPW) and mean imputation is utilized to mitigate the bias caused by missing data. Under several mild conditions, it is demonstrated that the proposed estimators are consistent and possess oracle properties. The efficacy of the proposed parameter estimation process is assessed through Monte Carlo simulation studies. Finally, the applicability of the proposed method is further substantiated using the Boston Housing Dataset. [ABSTRACT FROM AUTHOR]

We examine whether a country leader's diplomatic visit to the USA affects the Foreign Direct Investment inflow. The literature so far has found inconclusive results regarding diplomatic relations' effect on international flows. We use a dynamic Inverse Probability Weighting Regression Adjustment framework to examine this relationship and estimate the causal effect of foreign visits. Our results indicate that a visit to the US increases the country's total FDI inflows by up to one percentage point per annum, with a cumulative effect reaching 2.5 percentage points six years after the visit. However, this is a short-run effect as it disappears in the subsequent years. Furthermore, our first-stage results shed light on the profile of the leaders that visit the US. Our findings are consistent with the view that foreign visits act as signals to investors regarding the country's political risk. [ABSTRACT FROM AUTHOR]

Background: Hepatolenticular degeneration [Wilson disease (WD)] is an autosomal recessive metabolic disease characterized by copper metabolism disorder. Cognitive impairment is a key neuropsychiatric symptom of WD. At present, there is no effective treatment for WD-related cognitive impairment. Methods: In this study, high-frequency repetitive transcranial magnetic stimulation (rTMS) was used to treat WD-related cognitive impairment, and inverse probability weighting of propensity scores was used to correct for confounding factors. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Auditory Verbal Learning Test (AVLT), Boston Naming Test (BNT), Clock Drawing Test (CDT) and Trail Making Test (TMT) were used to evaluate overall cognition and specific cognitive domains. Results: The MMSE, MoCA and CDT scores after treatment were significantly different from those before treatment (MMSE: before adjustment: OR = 1.404, 95% CI: 1.271-1.537; after adjustment: OR = 1.381, 95% CI: 1.265-1.497, p < 0.001; MoCA: before adjustment: OR = 1.306, 95% CI: 1.122-1.490; after adjustment: OR = 1.286, 95% CI: 1.104; AVLT: OR = 1.161, 95% CI: 1.074-1.248; after adjustment: OR = 1.145, 95% CI: 1.068-1.222, p < 0.05; CDT: OR = 1.524, 95% CI: 1.303-1.745; after adjustment: OR = 1.518, 95% CI: 1.294-1.742, p < 0.001). The BNT and TMT scores after adjustment were not significantly different from those before adjustment (BNT: before adjustment: OR = 1.048, 95% CI: 0.877-1.219; after adjustment: OR = 1.026, 95% CI: 0.863-1.189, p > 0.05; TMT: before adjustment: OR = 0.816, 95% CI: 1.122-1.490; after adjustment: OR = 0.791, 95% CI: 0.406-1.176, p > 0.05). Conclusion: High-frequency rTMS can effectively improve cognitive impairment, especially memory and visuospatial ability, in WD patients. The incidence of side effects is low, and the safety is good. [ABSTRACT FROM AUTHOR]

Background: Currently, the primary treatment modalities for colorectal neuroendocrine tumors (CRNET) with a diameter between 10mm and 20mm are surgical resection (SR) and endoscopic resection (ER). However, it remains unclear which surgical approach yields the greatest survival benefit for patients. Methods: This study included data from patients diagnosed with CRNET with tumor diameters ranging from 10mm to 20mm between the years 2004 and 2019, obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were categorized into ER and SR groups based on the respective surgical approaches. Inverse probability weighting (IPTW) was employed to mitigate selection bias. Kaplan-Meier analysis and log-rank tests were utilized to estimate overall survival (OS) and cancer-specific survival (CSS). Cox regression analysis (univariate and multivariate) was performed to evaluate potential factors influencing survival. Results: A total of 292 CRNET patients were included in this study (ER group: 108 individuals, SR group: 184 individuals). Prior to IPTW adjustment, Kaplan-Meier analysis and Cox proportional hazard regression analysis demonstrated that the OS and CSS of the SR group were inferior to those of the ER group. However, after IPTW adjustment, no statistically significant differences in prognosis were observed between the two groups. Subgroup analyses revealed that patients with muscular invasion, positive lymph nodes, or distant metastasis derived greater survival benefits from SR. Significant differences in OS and CSS between the two groups were also observed across different age groups. Conclusion: For patients with mucosal-limited lesions and without local lymph node or distant metastasis, ER is the preferred surgical approach. However, for patients with muscular invasion or positive lymph nodes/distant metastasis, SR offers a better prognosis. The choice of surgical approach should be based on the specific clinical characteristics of patients within different subgroups. [ABSTRACT FROM AUTHOR]

The goal of this study was to examine what happens to established associations between attention deficit hyperactivity disorder (ADHD) symptoms and cortical surface and thickness regions once we apply inverse probability of censoring weighting (IPCW) to address potential selection bias. Moreover, we illustrate how different factors that predict participation contribute to potential selection bias. Participants were 9‐ to 11‐year‐old children from the Generation R study (N = 2707). Cortical area and thickness were measured with magnetic resonance imaging (MRI) and ADHD symptoms with the Child Behavior Checklist. We examined how associations between ADHD symptoms and brain morphology change when we weight our sample back to either follow‐up (ages 9–11), baseline (cohort at birth), or eligible (population of Rotterdam at time of recruitment). Weights were derived using IPCW or raking and missing predictors of participation used to estimate weights were imputed. Weighting analyses to baseline and eligible increased beta coefficients for the middle temporal gyrus surface area, as well as fusiform gyrus cortical thickness. Alternatively, the beta coefficient for the rostral anterior cingulate decreased. Removing one group of variables used for estimating weights resulted in the weighted regression coefficient moving closer to the unweighted regression coefficient. In addition, we found considerably different beta coefficients for most surface area regions and all thickness measures when we did not impute missing covariate data. Our findings highlight the importance of using inverse probability weighting (IPW) in the neuroimaging field, especially in the context of mental health‐related research. We found that including all variables related to exposure‐outcome in the IPW model and combining IPW with multiple imputations can help reduce bias. We encourage future psychiatric neuroimaging studies to define their target population, collect information on eligible but not included participants and use inverse probability of censoring weighting (IPCW) to reduce selection bias. [ABSTRACT FROM AUTHOR]

Youth alcohol use (AU) is a major global health problem. Tomando Buenas Decisiones (TBD), an adapted family-based prevention program based on the existing Guiding Good Choices program, was piloted and tested in Zacatecas, Mexico. We examined the effects of the TBD program on reducing family risk for youth AU. We used a quasi-experimental design comprising a sample of 268 parents with children 8 to 16 years. All participants were working actively in one of five Zacatecan private companies. Inverse probability weighting based on intervention status, along with censoring propensity scores, was used to estimate the difference in risk levels between intervention participants and wait-listed controls. Latent growth curve modeling was used to analyze the intervention impact on three first-order risk factors (i.e., positive attitudes for drug use, family conflict, poor family management) and a combined measure of general family risk (i.e., a second-order factor using the three first-order risk factors) at baseline, post-intervention, and 3-month follow-up. Relative to wait-listed controls, the TBD program was efficacious in reducing positive attitudes for drug use, family conflict, poor family management, and general family risk levels. However, at the 3-month follow-up, effects on positive attitudes toward drug use were no longer significant. Findings from this study address a gap in establishing efficacious preventive interventions for youth AU in Latin America. We anticipate that our study's findings could help inform prevention efforts to reduce leading youth alcohol-related harms such as drunk driving, and domestic violence among Mexican adolescents and their families. Highlights: Tomando Buenas Decisiones has the potential to reduce family risks associated with alcohol use among Mexican youth. Inverse probability weighting improves the effect-estimation of preventive interventions in non-experimental conditions. Testing the proximal effects of preventive programs is an appropriate strategy to validate its adaptability in other contexts. [ABSTRACT FROM AUTHOR]

Summary: We study approaches for adjusting machine learning methods when the training sample differs from the prediction sample on unobserved dimensions. The machine learning literature predominately assumes selection only on observed dimensions. Common approaches are to weight or include variables that influence selection as solutions to selection on observables. Simulation results show that selection on unobservables increases mean squared prediction error using popular machine‐learning algorithms. Common machine learning practices such as weighting or including variables that influence selection into the training or prediction sample often worsen sample selection bias. We propose two control function approaches that remove the effects of selection bias before training and find that they reduce mean‐squared prediction error in simulations. We apply these approaches to predicting the vote share of the incumbent in gubernatorial elections using previously observed re‐election bids. We find that ignoring selection on unobservables leads to substantially higher predicted vote shares for the incumbent than when the control function approach is used. [ABSTRACT FROM AUTHOR]

Generalized estimating equations (GEEs) provide a useful framework for estimating marginal regression parameters based on data from cluster randomized trials (CRTs), but they can result in inaccurate parameter estimates when some outcomes are informatively missing. Existing techniques to handle missing outcomes in CRTs rely on correct specification of a propensity score model, a covariate‐conditional mean outcome model, or require at least one of these two models to be correct, which can be challenging in practice. In this article, we develop new weighted GEEs to simultaneously estimate the marginal mean, scale, and correlation parameters in CRTs with missing outcomes, allowing for multiple propensity score models and multiple covariate‐conditional mean models to be specified. The resulting estimators are consistent provided that any one of these models is correct. An iterative algorithm is provided for implementing this more robust estimator and practical considerations for specifying multiple models are discussed. We evaluate the performance of the proposed method through Monte Carlo simulations and apply the proposed multiply robust estimator to analyze the Botswana Combination Prevention Project, a large HIV prevention CRT designed to evaluate whether a combination of HIV‐prevention measures can reduce HIV incidence. [ABSTRACT FROM AUTHOR]

The creation of new firms is an important incentive for the economic growth of a country, since it generates employment, it encourages the competition, and promotes innovation. In this work, we investigate the survival of Spanish firms which were created since 2001 and closed down between 2004 and 2012. The information was gathered from Technological Innovation Panel (PITEC), a survey with a focus the technological innovation in Spanish firms. In particular, a Cox regression model with time-dependent covariates was used in order to identify and quantify the determinants of the risk of exit for the firm. The selection bias due to the interval sampling for the firms was corrected by using methods for doubly truncated lifetimes. Interestingly, it is seen how the correction for the selection bias changes both the size and the statistical significance of the effects provided by standard Cox regression. [ABSTRACT FROM AUTHOR]

When estimating the causal effects of time-varying treatments on survival in nested case-control (NCC) studies, marginal structural Cox models (Cox-MSMs) with inverse probability weights (IPWs) are a natural approach. However, calculating IPWs from the cases and controls is difficult because they are not random samples from the full cohort, and the number of subjects may be insufficient for calculation. To overcome these difficulties, we propose a method for calculating IPWs to fit Cox-MSMs to NCC sampling data. We estimate the IPWs using a pseudo-likelihood estimation method with an inverse probability of sampling weight using NCC samples, and additional samples of subjects who experience treatment changes and subjects whose follow-up is censored are required to calculate the weights. Our method only requires covariate histories for the samples. The confidence intervals are calculated from the robust variance estimator for the NCC sampling data. We also derive the asymptotic properties of the estimator of Cox-MSM under NCC sampling. The proposed methods will allow researchers to apply several case-control matching methods to improve statistical efficiency. A simulation study was conducted to evaluate the finite sample performance of the proposed method. We also applied our method to a motivating pharmacoepidemiological study examining the effect of statins on the incidence of coronary heart disease. The proposed method may be useful for estimating the causal effects of time-varying treatments in NCC studies. [ABSTRACT FROM AUTHOR]

This article clarifies how the biostatistical literature on time-varying treatments (TVT) can provide tools for dealing with time-varying confounding in difference-in-differences (DiD) studies. I use a simulation study to compare the bias and standard error of inverse probability weighting estimators from the TVT framework, a DiD framework, and hybrid approaches that combine ideas from both frameworks. I simulated longitudinal data with treatment effect heterogeneity over multiple time points using linear and logistic models. Simulation settings looked at both time-invariant confounders and time-varying confounders affected by prior treatment. Estimators that combined ideas from both frameworks had lower bias than standard TVT and DiD estimators when assumptions were unmet. The TVT framework provides estimation tools that can complement DiD tools in a wide range of applied settings. It also provides alternate estimands for consideration in policy settings. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, many institutions such as universities and workplaces implemented testing regimens with every member of some population tested longitudinally, and those testing positive isolated for some time. Although the primary purpose of such regimens was to suppress disease spread by identifying and isolating infectious individuals, testing results were often also used to obtain prevalence and incidence estimates. Such estimates are helpful in risk assessment and institutional planning and various estimation procedures have been implemented, ranging from simple test-positive rates to complex dynamical modeling. Unfortunately, the popular test-positive rate is a biased estimator of prevalence under many seemingly innocuous longitudinal testing regimens with isolation. We illustrate how such bias arises and identify conditions under which the test-positive rate is unbiased. Further, we identify weaker conditions under which prevalence is identifiable and propose a new estimator of prevalence under longitudinal testing. We evaluate the proposed estimation procedure via simulation study and illustrate its use on a dataset derived by anonymizing testing data from The Ohio State University. for this article are available online. [ABSTRACT FROM AUTHOR]

Immunization is the most effective intervention for reducing morbidity and mortality rates associated with vaccine-preventable diseases. Despite the Indonesian government's inclusion of several childhood vaccinations in the national immunization program (NIP), the number of unvaccinated or partially vaccinated children remains high. This observational study aimed to determine the completeness of childhood immunization and the factors influencing it in Indonesia. Data were extracted from the fifth wave of the Indonesia Family Life Survey (IFLS). The sample (n = 16,236) consists of children residing in 13 provinces, representing over 80% of Indonesia's population. The difference between groups was tested using the chi-square test. Logistic regression was performed to identify the variables associated with the completeness of basic immunization. Immunization is categorized as complete when children have received all the mandatory vaccines recommended by the Ministry of Health. We examined and compared the results using complete case analysis, inverse probability weighting, and multiple imputations. The highest percentages of complete vaccinations were polio, tuberculosis, and DPT. Children who live in Sumatera and Kalimantan were more likely to be fully immunized, with ORs of 0.6 (95%CI 0.48–0.74) and 0.54 (0.37–0.80), respectively. Children who live in urban areas, have mothers who received the tetanus vaccine during pregnancy, have mothers with a higher level of education and health insurance, have fathers aged 41–50, and live with a large number of family members were more likely to be fully immunized (p < 0.05). Socioeconomic determinants were strongly correlated with the completeness of childhood vaccination in Indonesia. [ABSTRACT FROM AUTHOR]

Abstract Background Treatment switching in randomised controlled trials (RCTs) is a problem for health technology assessment when substantial proportions of patients switch onto effective treatments that would not be available in standard clinical practice. Often statistical methods are used to adjust for switching: these can be applied in different ways, and performance has been assessed in simulation studies, but not in real-world case studies. We assessed the performance of adjustment methods described in National Institute for Health and Care Excellence Decision Support Unit Technical Support Document 16, applying them to an RCT comparing panitumumab to best supportive care (BSC) in colorectal cancer, in which 76% of patients randomised to BSC switched onto panitumumab. The RCT resulted in intention-to-treat hazard ratios (HR) for overall survival (OS) of 1.00 (95% confidence interval [CI] 0.82–1.22) for all patients, and 0.99 (95% CI 0.75–1.29) for patients with wild-type KRAS (Kirsten rat sarcoma virus). Methods We tested several applications of inverse probability of censoring weights (IPCW), rank preserving structural failure time models (RPSFTM) and simple and complex two-stage estimation (TSE) to estimate treatment effects that would have been observed if BSC patients had not switched onto panitumumab. To assess the performance of these analyses we ascertained the true effectiveness of panitumumab based on: (i) subsequent RCTs of panitumumab that disallowed treatment switching; (ii) studies of cetuximab that disallowed treatment switching, (iii) analyses demonstrating that only patients with wild-type KRAS benefit from panitumumab. These sources suggest the true OS HR for panitumumab is 0.76–0.77 (95% CI 0.60–0.98) for all patients, and 0.55–0.73 (95% CI 0.41–0.93) for patients with wild-type KRAS. Results Some applications of IPCW and TSE provided treatment effect estimates that closely matched the point-estimates and CIs of the expected truths. However, other applications produced estimates towards the boundaries of the expected truths, with some TSE applications producing estimates that lay outside the expected true confidence intervals. The RPSFTM performed relatively poorly, with all applications providing treatment effect estimates close to 1, often with extremely wide confidence intervals. Conclusions Adjustment analyses may provide unreliable results. How each method is applied must be scrutinised to assess reliability.

Abstract Background Case-cohort studies are conducted within cohort studies, with the defining feature that collection of exposure data is limited to a subset of the cohort, leading to a large proportion of missing data by design. Standard analysis uses inverse probability weighting (IPW) to address this intended missing data, but little research has been conducted into how best to perform analysis when there is also unintended missingness. Multiple imputation (MI) has become a default standard for handling unintended missingness and is typically used in combination with IPW to handle the intended missingness due to the case-control sampling. Alternatively, MI could be used to handle both the intended and unintended missingness. While the performance of an MI-only approach has been investigated in the context of a case-cohort study with a time-to-event outcome, it is unclear how this approach performs with a binary outcome. Methods We conducted a simulation study to assess and compare the performance of approaches using only MI, only IPW, and a combination of MI and IPW, for handling intended and unintended missingness in the case-cohort setting. We also applied the approaches to a case study. Results Our results show that the combined approach is approximately unbiased for estimation of the exposure effect when the sample size is large, and was the least biased with small sample sizes, while MI-only and IPW-only exhibited larger biases in both sample size settings. Conclusions These findings suggest that a combined MI/IPW approach should be preferred to handle intended and unintended missing data in case-cohort studies with binary outcomes.

One-shot devices are products that can only be used once. Typical one-shot devices include airbags, fire extinguishers, inflatable life vests, ammo, and handheld flares. Most of them are life-saving products and should be highly reliable in an emergency. Quality control of those productions and predicting their reliabilities over time is critically important. To assess the reliability of the products, manufacturers usually test them in controlled conditions rather than user conditions. We may rely on public datasets that reflect their reliability in actual use, but the datasets often come with missing observations. The experimenter may lose information on covariate readings due to human errors. Traditional missing-data-handling methods may not work well in handling one-shot device data as they only contain their survival statuses. In this research, we propose Multiple Imputation with Unsupervised Learning (MIUL) to impute the missing data using Hierarchical Clustering, k-prototype, and density-based spatial clustering of applications with noise (DBSCAN). Our simulation study shows that MIUL algorithms have superior performance. We also illustrate the method using datasets from the Crash Report Sampling System (CRSS) of the National Highway Traffic Safety Administration (NHTSA).