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.     

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.     

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.     

Genome‐Wide Analysis of Gene‐Gene and Gene‐Environment Interactions Using Closed‐Form Wald Tests

Despite the successful discovery of hundreds of variants for complex human traits using genome‐wide association studies, the degree to which genes and environmental risk factors jointly affect disease risk is largely unknown. One obstacle toward this goal is that the computational effort required for testing gene‐gene and gene‐environment interactions is enormous. As a result, numerous computationally efficient tests were recently proposed. However, the validity of these methods often relies on unrealistic assumptions such as additive main effects, main effects at only one variable, no linkage disequilibrium between the two single‐nucleotide polymorphisms (SNPs) in a pair or gene‐environment independence. Here, we derive closed‐form and consistent estimates for interaction parameters and propose to use Wald tests for testing interactions. The Wald tests are asymptotically equivalent to the likelihood ratio tests (LRTs), largely considered to be the gold standard tests but generally too computationally demanding for genome‐wide interaction analysis. Simulation studies show that the proposed Wald tests have very similar performances with the LRTs but are much more computationally efficient. Applying the proposed tests to a genome‐wide study of multiple sclerosis, we identify interactions within the major histocompatibility complex region. In this application, we find that (1) focusing on pairs where both SNPs are marginally significant leads to more significant interactions when compared to focusing on pairs where at least one SNP is marginally significant; and (2) parsimonious parameterization of interaction effects might decrease, rather than increase, statistical power.     

Age‐Period‐Cohort approaches to back‐calculation of cancer incidence rate

A compartment model for cancer incidence and mortality is developed in which healthy subjects may develop cancer and subsequently die of cancer or another cause. In order to adequately represent the experience of a defined population, it is also necessary to allow for subjects who are diagnosed at death, as well as subjects who migrate and are subsequently lost to follow‐up. Expressions are derived for the number of cancer deaths as a function of the number of incidence cases and vice versa, which allows for the use of mortality statistics to obtain estimates of incidence using survival information. In addition, the model can be used to obtain estimates of cancer prevalence, which is useful for health care planning. The method is illustrated using data on lung cancer among males in Connecticut. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

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.     

Evaluation of a Two‐Stage Approach in Trans‐Ethnic Meta‐Analysis in Genome‐Wide Association Studies

Meta‐analysis of genome‐wide association studies (GWAS) has achieved great success in detecting loci underlying human diseases. Incorporating GWAS results from diverse ethnic populations for meta‐analysis, however, remains challenging because of the possible heterogeneity across studies. Conventional fixed‐effects (FE) or random‐effects (RE) methods may not be most suitable to aggregate multiethnic GWAS results because of violation of the homogeneous effect assumption across studies (FE) or low power to detect signals (RE). Three recently proposed methods, modified RE (RE‐HE) model, binary‐effects (BE) model and a Bayesian approach (Meta‐analysis of Transethnic Association [MANTRA]), show increased power over FE and RE methods while incorporating heterogeneity of effects when meta‐analyzing trans‐ethnic GWAS results. We propose a two‐stage approach to account for heterogeneity in trans‐ethnic meta‐analysis in which we clustered studies with cohort‐specific ancestry information prior to meta‐analysis. We compare this to a no‐prior‐clustering (crude) approach, evaluating type I error and power of these two strategies, in an extensive simulation study to investigate whether the two‐stage approach offers any improvements over the crude approach. We find that the two‐stage approach and the crude approach for all five methods (FE, RE, RE‐HE, BE, MANTRA) provide well‐controlled type I error. However, the two‐stage approach shows increased power for BE and RE‐HE, and similar power for MANTRA and FE compared to their corresponding crude approach, especially when there is heterogeneity across the multiethnic GWAS results. These results suggest that prior clustering in the two‐stage approach can be an effective and efficient intermediate step in meta‐analysis to account for the multiethnic heterogeneity.     

Estimation in multi‐arm two‐stage trials with treatment selection and time‐to‐event endpoint

We consider estimation of treatment effects in two‐stage adaptive multi‐arm trials with a common control. The best treatment is selected at interim, and the primary endpoint is modeled via a Cox proportional hazards model. The maximum partial‐likelihood estimator of the log hazard ratio of the selected treatment will overestimate the true treatment effect in this case. Several methods for reducing the selection bias have been proposed for normal endpoints, including an iterative method based on the estimated conditional selection biases and a shrinkage approach based on empirical Bayes theory. We adapt these methods to time‐to‐event data and compare the bias and mean squared error of all methods in an extensive simulation study and apply the proposed methods to reconstructed data from the FOCUS trial. We find that all methods tend to overcorrect the bias, and only the shrinkage methods can reduce the mean squared error. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.     

Adjusting for time‐dependent sensitivity in an illness‐death model,with application to mother‐to‐child transmission of HIV

In mother‐to‐child transmission of HIV, identifying infected infants relies on a diagnostic test with imperfect sensitivity that is administered at scheduled visits. Under this scenario, a participant's true state may be unknown at the start and end times of the study, and the detection of transitions into illness may be delayed or missed altogether. This could lead to biased estimates of the risk of transmission and covariate associations. When a test has imperfect sensitivity, but perfect specificity, the additional uncertainty can be captured as a random variable measuring delay in detection. The cumulative distribution then defines a time‐dependent sensitivity function that increases over time. We present a maximum likelihood based illness‐death model that accounts for imperfect sensitivity by including the delay as an exponential distribution. We specify transition rates as penalized B‐splines to allow for nonhomogeneity of risk and discuss the model under Markov and semi‐Markov assumptions. We apply this method to our motivating data set, a study of 1499 mother and infant pairs at three sites in Africa. Copyright © 2014 John Wiley & Sons, Ltd.     

Study design for non‐recurring,time‐to‐event outcomes in the presence of error‐prone diagnostic tests or self‐reports

Sequentially administered, laboratory‐based diagnostic tests or self‐reported questionnaires are often used to determine the occurrence of a silent event. In this paper, we consider issues relevant in design of studies aimed at estimating the association of one or more covariates with a non‐recurring, time‐to‐event outcome that is observed using a repeatedly administered, error‐prone diagnostic procedure. The problem is motivated by the Women's Health Initiative, in which diabetes incidence among the approximately 160,000 women is obtained from annually collected self‐reported data. For settings of imperfect diagnostic tests or self‐reports with known sensitivity and specificity, we evaluate the effects of various factors on resulting power and sample size calculations and compare the relative efficiency of different study designs. The methods illustrated in this paper are readily implemented using our freely available R software package icensmis, which is available at the Comprehensive R Archive Network website. An important special case is that when diagnostic procedures are perfect, they result in interval‐censored, time‐to‐event outcomes. The proposed methods are applicable for the design of studies in which a time‐to‐event outcome is interval censored. Copyright © 2016 John Wiley & Sons, Ltd.     

Multivariate meta‐analysis for non‐linear and other multi‐parameter associations

In this paper, we formalize the application of multivariate meta‐analysis and meta‐regression to synthesize estimates of multi‐parameter associations obtained from different studies. This modelling approach extends the standard two‐stage analysis used to combine results across different sub‐groups or populations. The most straightforward application is for the meta‐analysis of non‐linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta‐analysis is implemented in the package mvmeta within the statistical environment R . As an illustrative example, we propose a two‐stage analysis for investigating the non‐linear exposure–response relationship between temperature and non‐accidental mortality using time‐series data from multiple cities. Multivariate meta‐analysis represents a useful analytical tool for studying complex associations through a two‐stage procedure. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

Daughters‐in‐Law and Mothers‐in‐Law Seeking Their Place Within the Family: A Qualitative Study of Differing Viewpoints

Abstract: Within the framework of individual developmental theory and intergenerational ambivalence, we examined the complex dynamics of the mother‐in‐law and daughter‐in‐law relationship. Using focus group and semistructured interview data, we explored mothers‐in‐law’s and daughters‐in‐law’s perceptions of “being part of the family.” The diversity among the perceptions of the unrelated 23 daughters‐in‐law and 19 mothers‐in‐law countered the validity of stereotypes often held about the women in these roles. The women described the ambivalence many of them felt as they searched for their place in the family. Findings suggest that women of all ages may benefit from education about this critical relationship and need to develop skills to express themselves to their mothers‐in‐law/daughters‐in‐law to facilitate adequate management of the ambivalence prevalent in this relationship.     

The k‐in‐a‐row up‐and‐down design,revisited

The percentile‐finding experimental design known variously as ‘forced‐choice fixed‐staircase’, ‘geometric up‐and‐down’ or ‘k‐in‐a‐row’ (KR) was introduced by Wetherill four decades ago. To date, KR has been by far the most widely used up‐and‐down (U&D) design for estimating non‐median percentiles; it is implemented most commonly in sensory studies. However, its statistical properties have not been fully documented, and the existence of a unique mode in its asymptotic treatment distribution has been recently disputed. Here we revisit the KR design and its basic properties. We find that KR does generate a unique stationary mode near its target percentile, and also displays better operational characteristics than two other U&D designs that have been studied more extensively. Supporting proofs and numerical calculations are presented. A recent experimental example from anesthesiology serves to highlight some of the ‘up‐and‐down’ design family's properties and advantages. Copyright © 2009 John Wiley & Sons, Ltd.     

Empirical Bayes estimation of the selected treatment mean for two‐stage drop‐the‐loser trials: a meta‐analytic approach

Point estimation for the selected treatment in a two‐stage drop‐the‐loser trial is not straightforward because a substantial bias can be induced in the standard maximum likelihood estimate (MLE) through the first stage selection process. Research has generally focused on alternative estimation strategies that apply a bias correction to the MLE; however, such estimators can have a large mean squared error. Carreras and Brannath (Stat. Med. 32:1677‐90) have recently proposed using a special form of shrinkage estimation in this context. Given certain assumptions, their estimator is shown to dominate the MLE in terms of mean squared error loss, which provides a very powerful argument for its use in practice. In this paper, we suggest the use of a more general form of shrinkage estimation in drop‐the‐loser trials that has parallels with model fitting in the area of meta‐analysis. Several estimators are identified and are shown to perform favourably to Carreras and Brannath's original estimator and the MLE. However, they necessitate either explicit estimation of an additional parameter measuring the heterogeneity between treatment effects or a quite unnatural prior distribution for the treatment effects that can only be specified after the first stage data has been observed. Shrinkage methods are a powerful tool for accurately quantifying treatment effects in multi‐arm clinical trials, and further research is needed to understand how to maximise their utility. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.     

Two‐phase designs for joint quantitative‐trait‐dependent and genotype‐dependent sampling in post‐GWAS regional sequencing

We evaluate two‐phase designs to follow‐up findings from genome‐wide association study (GWAS) when the cost of regional sequencing in the entire cohort is prohibitive. We develop novel expectation‐maximization‐based inference under a semiparametric maximum likelihood formulation tailored for post‐GWAS inference. A GWAS‐SNP (where SNP is single nucleotide polymorphism) serves as a surrogate covariate in inferring association between a sequence variant and a normally distributed quantitative trait (QT). We assess test validity and quantify efficiency and power of joint QT‐SNP‐dependent sampling and analysis under alternative sample allocations by simulations. Joint allocation balanced on SNP genotype and extreme‐QT strata yields significant power improvements compared to marginal QT‐ or SNP‐based allocations. We illustrate the proposed method and evaluate the sensitivity of sample allocation to sampling variation using data from a sequencing study of systolic blood pressure.