Two‐step estimation in ratio‐of‐mediator‐probability weighted causal mediation analysis

This study investigates appropriate estimation of estimator variability in the context of causal mediation analysis that employs propensity score‐based weighting. Such an analysis decomposes the total effect of a treatment on the outcome into an indirect effect transmitted through a focal mediator and a direct effect bypassing the mediator. Ratio‐of‐mediator‐probability weighting estimates these causal effects by adjusting for the confounding impact of a large number of pretreatment covariates through propensity score‐based weighting. In step 1, a propensity score model is estimated. In step 2, the causal effects of interest are estimated using weights derived from the prior step's regression coefficient estimates. Statistical inferences obtained from this 2‐step estimation procedure are potentially problematic if the estimated standard errors of the causal effect estimates do not reflect the sampling uncertainty in the estimation of the weights. This study extends to ratio‐of‐mediator‐probability weighting analysis a solution to the 2‐step estimation problem by stacking the score functions from both steps. We derive the asymptotic variance‐covariance matrix for the indirect effect and direct effect 2‐step estimators, provide simulation results, and illustrate with an application study. Our simulation results indicate that the sampling uncertainty in the estimated weights should not be ignored. The standard error estimation using the stacking procedure offers a viable alternative to bootstrap standard error estimation. We discuss broad implications of this approach for causal analysis involving propensity score‐based weighting.     

A Multi‐Locus Likelihood Method for Assessing Parent‐of‐Origin Effects Using Case‐Control Mother‐Child Pairs

Parent‐of‐origin effects have been pointed out to be one plausible source of the heritability that was unexplained by genome‐wide association studies. Here, we consider a case‐control mother‐child pair design for studying parent‐of‐origin effects of offspring genes on neonatal/early‐life disorders or pregnancy‐related conditions. In contrast to the standard case‐control design, the case‐control mother‐child pair design contains valuable parental information and therefore permits powerful assessment of parent‐of‐origin effects. Suppose the region under study is in Hardy‐Weinberg equilibrium, inheritance is Mendelian at the diallelic locus under study, there is random mating in the source population, and the SNP under study is not related to risk for the phenotype under study because of linkage disequilibrium (LD) with other SNPs. Using a maximum likelihood method that simultaneously assesses likely parental sources and estimates effect sizes of the two offspring genotypes, we investigate the extent of power increase for testing parent‐of‐origin effects through the incorporation of genotype data for adjacent markers that are in LD with the test locus. Our method does not need to assume the outcome is rare because it exploits supplementary information on phenotype prevalence. Analysis with simulated SNP data indicates that incorporating genotype data for adjacent markers greatly help recover the parent‐of‐origin information. This recovery can sometimes substantially improve statistical power for detecting parent‐of‐origin effects. We demonstrate our method by examining parent‐of‐origin effects of the gene PPARGC1A on low birth weight using data from 636 mother‐child pairs in the Jerusalem Perinatal Study.     

Impacts of care‐giving and sources of support: a comparison of end‐of‐life and non‐end‐of‐life caregivers in Canada

This is the second in a series of papers that deal with care‐giving in Canada, as based on data available from the Canadian General Social Survey (2007). Building on the first paper, which reviewed the differences between short‐term, long‐term and end‐of‐life (EOL) caregivers, this paper uniquely examines the caregiver supports employed by EOL caregivers when compared to non‐EOL caregivers (short‐term and long‐term caregivers combined). Both papers employ data from Statistics Canada's General Social Survey (GSS Cycle 21: 2007). The GSS includes three modules, where respondents were asked questions about the unpaid home care assistance that they had provided in the last 12 months to someone at EOL or with either a long‐term health condition or a physical limitation. The objective of this research paper was to investigate the link between the impact of the care‐giving experience and the caregiver supports received, while also examining the differences in these across EOL and non‐EOL caregivers. By way of factor analysis and regression modelling, we examine differences between two types of caregivers: (i) EOL and (ii) non‐EOL caregivers. The study revealed that with respect to socio‐demographic characteristics, health outcomes and caregiver supports, EOL caregivers were consistently worse off. This suggests that although all non‐EOL caregivers are experiencing negative impacts from their care‐giving role, comparatively greater supports are needed for EOL caregivers.     

The impact of transitional programmes on post‐transition outcomes for youth leaving out‐of‐home care: a meta‐analysis

Youth residing in out‐of‐home care settings have often been exposed to childhood trauma, and commonly report experiencing adverse outcomes after transitioning from care. This meta‐analysis appraised internationally published literature investigating the impact of transitional programme participation (among youth with a baseline age of 15–24 years) on post‐transition outcomes of housing, education, employment, mental health and substance use. A comprehensive search of sociology (e.g. ProQuest Sociology), psychology (e.g. PsycInfo) and health (e.g. ProQuest Family Health) electronic abstraction databases was conducted for the period 1990–2014. Search terms included ‘out‐of‐home care’, ‘transition’, ‘housing’, ‘education’, ‘employment’, ‘mental health’ and ‘substance use’. Nineteen studies, all from the United States, met the inclusion criteria and were included in the meta‐analysis. Living independently and homelessness were the most commonly described housing outcomes. Rates of post‐transition employment varied, while rates of post‐secondary education were low. Depression and alcohol use were commonly reported among transitioning youth. Findings of the meta‐analysis showed that attention should be given to the potential benefit of transitional programme participation on outcomes such as housing, employment and education. Moderator analyses showed that these benefits may differ based on study design, sample size and sampling unit, but not for mean age or gender. Detailed and rigorous research is needed internationally to examine the characteristics of transitional programmes resulting in more successful outcomes for youth, and whether these outcomes are sustained longitudinally.     

Tests of calibration and goodness‐of‐fit in the survival setting

To access the calibration of a predictive model in a survival analysis setting, several authors have extended the Hosmer–Lemeshow goodness‐of‐fit test to survival data. Grønnesby and Borgan developed a test under the proportional hazards assumption, and Nam and D'Agostino developed a nonparametric test that is applicable in a more general survival setting for data with limited censoring. We analyze the performance of the two tests and show that the Grønnesby–Borgan test attains appropriate size in a variety of settings, whereas the Nam‐D'Agostino method has a higher than nominal Type 1 error when there is more than trivial censoring. Both tests are sensitive to small cell sizes. We develop a modification of the Nam‐D'Agostino test to allow for higher censoring rates. We show that this modified Nam‐D'Agostino test has appropriate control of Type 1 error and comparable power to the Grønnesby–Borgan test and is applicable to settings other than proportional hazards. We also discuss the application to small cell sizes. Copyright © 2015 John Wiley & Sons, Ltd.     

A Bayesian mixture of semiparametric mixed‐effects joint models for skewed‐longitudinal and time‐to‐event data

In longitudinal studies, it is of interest to investigate how repeatedly measured markers in time are associated with a time to an event of interest, and in the mean time, the repeated measurements are often observed with the features of a heterogeneous population, non‐normality, and covariate measured with error because of longitudinal nature. Statistical analysis may complicate dramatically when one analyzes longitudinal–survival data with these features together. Recently, a mixture of skewed distributions has received increasing attention in the treatment of heterogeneous data involving asymmetric behaviors across subclasses, but there are relatively few studies accommodating heterogeneity, non‐normality, and measurement error in covariate simultaneously arose in longitudinal–survival data setting. Under the umbrella of Bayesian inference, this article explores a finite mixture of semiparametric mixed‐effects joint models with skewed distributions for longitudinal measures with an attempt to mediate homogeneous characteristics, adjust departures from normality, and tailor accuracy from measurement error in covariate as well as overcome shortages of confidence in specifying a time‐to‐event model. The Bayesian mixture of joint modeling offers an appropriate avenue to estimate not only all parameters of mixture joint models but also probabilities of class membership. Simulation studies are conducted to assess the performance of the proposed method, and a real example is analyzed to demonstrate the methodology. The results are reported by comparing potential models with various scenarios. Copyright © 2015 John Wiley & Sons, Ltd.     

A comparison of the results of intent‐to‐treat,per‐protocol,and g‐estimation in the presence of non‐random treatment changes in a time‐to‐event non‐inferiority trial

While intent‐to‐treat (ITT) analysis is widely accepted for superiority trials, there remains debate about its role in non‐inferiority trials. It has often been said that ITT analysis tends to be anti‐conservative in demonstrating non‐inferiority, suggesting that per‐protocol (PP) analysis may be preferable for non‐inferiority trials, despite the inherent bias of such analyses. We propose using randomization‐based g‐estimation analyses that more effectively preserve the integrity of randomization than do the more widely used PP analyses. Simulation studies were conducted to investigate the impacts of different types of treatment changes on the conservatism or anti‐conservatism of analyses using the ITT, PP, and g‐estimation methods in a time‐to‐event outcome. The ITT results were anti‐conservative for all simulations. Anti‐conservativeness increased with the percentage of treatment change and was more pronounced for outcome‐dependent treatment changes. PP analysis, in which treatment‐switching cases were censored at the time of treatment change, maintained type I error near the nominal level for independent treatment changes, whereas for outcome‐dependent cases, PP analysis was either conservative or anti‐conservative depending on the mechanism underlying the percentage of treatment changes. G‐estimation analysis maintained type I error near the nominal level even for outcome‐dependent treatment changes, although information on unmeasured covariates is not used in the analysis. Thus, randomization‐based g‐estimation analyses should be used to supplement the more conventional ITT and PP analyses, especially for non‐inferiority trials. Copyright © 2010 John Wiley & Sons, Ltd.     

Differential impacts of care‐giving across three caregiver groups in Canada: end‐of‐life care,long‐term care and short‐term care

Using data from Statistic Canada's General Social Survey Cycle 21 (GSS 2007), this study explores whether differences exist in the impacts of care‐giving among three groups of caregivers providing informal care either in the caregiver's or recipient's home, or in other locations within the community: (i) those providing end‐of‐life (EOL) care (n = 471); (ii) those providing long‐term care (more than 2 years) for someone with a chronic condition or long‐term illness (n = 2722); and (iii) those providing short‐term care (less than 2 years) for someone with a chronic condition or long‐term illness (n = 2381). This study lays out the variation in sociodemographic characteristics across the three caregiver groups while also building on our understanding of the differential impacts of care‐giving through an analysis of determinants. All three groups of caregivers shared a number of sociodemographic characteristics, including being female, married, employed and living in a Census Metropolitan Area (CMA). With respect to health, EOL caregivers were found to have significantly higher levels of ‘fair or poor’ self‐assessed health than the other two groups. Overall, the findings suggest that EOL caregivers are negatively impacted by the often additional role of care‐giving, more so than both short‐term and long‐term caregivers. EOL caregivers experienced a higher proportion of negative impacts on their social and activity patterns. Furthermore, EOL caregivers incurred greater financial costs than the other two types of informal caregivers. The impacts of EOL care‐giving also negatively influence employment for caregivers when compared with the other caregiver groups. Consequently, EOL caregivers, overall, experienced greater negative impacts, including negative health outcomes, than did long‐term or short‐term caregivers. This provides the evidence for the assertion that EOL care‐giving is the most intense type of care‐giving, potentially causing the greatest caregiver burden; this is shown through the greater negative impacts experienced by the EOL caregivers when compared with the short‐term and long‐term caregivers.     

3‐carboxy‐4‐methyl‐5‐propyl‐2‐furanpropanoic acid (CMPF): A metabolite identified after consumption of fish oil and fish

3‐carboxy‐4‐methyl‐5‐propyl‐2‐furanpropanoic acid (CMPF) is a known metabolite of furan fatty acids and was first referred to as a urofuran fatty acid, as it was found in urine of humans and other species after consumption of furan fatty acids or foods containing furan fatty acids. More recently, CMPF has been identified as a highly prominent metabolite following the consumption of fish oil, fish oil fractions and diets rich in fish, and can be regarded as biomarker of oil‐rich fish or fish oil intakes. As furan fatty acids are known to occur in fish and fish oil (at a low level), it is possible that the CMPF in plasma arises from these furan fatty acids. On a structural basis, this is a likely explanation rather than the CMPF being an actual metabolite of long‐chain marine omega‐3 fatty acids. Recent studies in high fat‐fed mice given purified CMPF suggest that CMPF might contribute to the improved metabolic effects observed following consumption of long‐chain marine omega‐3 fatty acids but much is still to be known about the relationships between CMPF and health.     

Patient Experiences with Involuntary Out‐of‐Network Charges

Background

Approximately 40 percent of individuals using out-of-network physicians experience involuntary out-of-network care, leading to unexpected and sometimes burdensome financial charges. Despite its prevalence, research on patient experiences with involuntary out-of-network care is limited. Greater understanding of patient experiences may inform policy solutions to address this issue.

Objective

To characterize the experiences of patients who encountered involuntary out-of-network physician charges.

Methods

Qualitative study using 26 in-depth telephone interviews with a semi-structured interview guide. Participants were a purposeful sample of privately insured adults from across the United States who experienced involuntary out-of-network care. They were diverse with regard to income level, education, and health status. Recurrent themes were generated using the constant comparison method of data analysis by a multidisciplinary team.

Results

Four themes characterize the perspective of individuals who experienced involuntary out-of-network physician charges: (1) responsibilities and mechanisms for determining network participation are not transparent; (2) physician procedures for billing and disclosure of physician out-of-network status are inconsistent; (3) serious illness requiring emergency care or hospitalization precludes ability to choose a physician or confirm network participation; and (4) resources for mediation of involuntary charges once they occur are not available.

Conclusions

Our data reveal that patient education may not be sufficient to reduce the prevalence and financial burden of involuntary out-of-network care. Participants described experiencing involuntary out-of-network health care charges due to system-level failures. As policy makers seek solutions, our findings suggest several potential areas of further consideration such as standardization of processes to disclose that a physician is out-of-network, holding patients harmless not only for out-of-network emergency room care but also for non-elective hospitalization, and designation of a mediator for involuntary charges.     

Spline‐based self‐controlled case series method

The self‐controlled case series (SCCS) method is an alternative to study designs such as cohort and case control methods and is used to investigate potential associations between the timing of vaccine or other drug exposures and adverse events. It requires information only on cases, individuals who have experienced the adverse event at least once, and automatically controls all fixed confounding variables that could modify the true association between exposure and adverse event. Time‐varying confounders such as age, on the other hand, are not automatically controlled and must be allowed for explicitly. The original SCCS method used step functions to represent risk periods (windows of exposed time) and age effects. Hence, exposure risk periods and/or age groups have to be prespecified a priori, but a poor choice of group boundaries may lead to biased estimates. In this paper, we propose a nonparametric SCCS method in which both age and exposure effects are represented by spline functions at the same time. To avoid a numerical integration of the product of these two spline functions in the likelihood function of the SCCS method, we defined the first, second, and third integrals of I‐splines based on the definition of integrals of M‐splines. Simulation studies showed that the new method performs well. This new method is applied to data on pediatric vaccines. Copyright © 2017 John Wiley & Sons, Ltd.     

Incorporation of the time aspect into the liability‐threshold model for case‐control‐family data

Familial aggregation and the role of genetic and environmental factors can be investigated through family studies analysed using the liability‐threshold model. The liability‐threshold model ignores the timing of events including the age of disease onset and right censoring, which can lead to estimates that are difficult to interpret and are potentially biased. We incorporate the time aspect into the liability‐threshold model for case‐control‐family data following the same approach that has been applied in the twin setting. Thus, the data are considered as arising from a competing risks setting and inverse probability of censoring weights are used to adjust for right censoring. In the case‐control‐family setting, recognising the existence of competing events is highly relevant to the sampling of control probands. Because of the presence of multiple family members who may be censored at different ages, the estimation of inverse probability of censoring weights is not as straightforward as in the twin setting but requires consideration. We propose to employ a composite likelihood conditioning on proband status that markedly simplifies adjustment for right censoring. We assess the proposed approach using simulation studies and apply it in the analysis of two Danish register‐based case‐control‐family studies: one on cancer diagnosed in childhood and adolescence, and one on early‐onset breast cancer. Copyright © 2017 John Wiley & Sons, Ltd.     

Goodness‐of‐fit test for proportional subdistribution hazards model

This paper concerns using modified weighted Schoenfeld residuals to test the proportionality of subdistribution hazards for the Fine–Gray model, similar to the tests proposed by Grambsch and Therneau for independently censored data. We develop a score test for the time‐varying coefficients based on the modified Schoenfeld residuals derived assuming a certain form of non‐proportionality. The methods perform well in simulations and a real data analysis of breast cancer data, where the treatment effect exhibits non‐proportional hazards. Copyright © 2013 John Wiley & Sons, Ltd.