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The Nun study is a well-known longitudinal epidemiology study of aging and dementia that recruited elderly nuns who were not yet diagnosed with dementia (i.e., incident cohort) and who had dementia prior to entry (i.e., prevalent cohort). In such a natural history of disease study, multistate modeling of the combined data from both incident and prevalent cohorts is desirable to improve the efficiency of inference. While important, the multistate modeling approaches for the combined data have been scarcely used in practice because prevalent samples do not provide the exact date of disease onset and do not represent the target population due to left-truncation. In this paper, we demonstrate how to adequately combine both incident and prevalent cohorts to examine risk factors for every possible transition in studying the natural history of dementia. We adapt a four-state nonhomogeneous Markov model to characterize all transitions between different clinical stages, including plausible reversible transitions. The estimating procedure using the combined data leads to efficiency gains for every transition compared to those from the incident cohort data only. [ABSTRACT FROM AUTHOR]

Summary: Non‐parametric estimation of the survival function using observed failure time data depends on the underlying data generating mechanism, including the ways in which the data may be censored and/or truncated. For data arising from a single source or collected from a single cohort, a wide range of estimators have been proposed and compared in the literature. Often, however, it may be possible, and indeed advantageous, to combine and then analyse survival data that have been collected under different study designs. We review non‐parametric survival analysis for data obtained by combining the most common types of cohort. We have two main goals: (i) to clarify the differences in the model assumptions and (ii) to provide a single lens through which some of the proposed estimators may be viewed. Our discussion is relevant to the meta‐analysis of survival data obtained from different types of study, and to the modern era of electronic health records. [ABSTRACT FROM AUTHOR]

Background: Glucosamine is a widely used supplement to treat joint pain and osteoarthritis despite inconclusive randomized trial results on its effectiveness. In contrast, observational studies associate glucosamine with significant reductions in mortality and cancer incidence. We evaluated the extent of bias, particularly selection bias, to explain these surprising beneficial effects. Methods: We searched the literature to identify all observational studies reporting on the effect of glucosamine use on major outcomes. Results: We identified 11 observational studies, reporting a mean 16% reduction in all‐cause mortality (hazard ratio [HR] 0.84, 95% CI: 0.81–0.87) with glucosamine use, as well as significant reductions in cancer incidence and other major diseases including cardiovascular, respiratory and diabetes. We show that these significant effects can result from selection bias due to collider stratification, as all studies used "prevalent" cohorts, where glucosamine use started before cohort entry, and where subjects agreed to join the cohorts. Our illustration of the bias using the UK Biobank publication involving a half‐million subjects shows how a true rate ratio of mortality of 1.0 in the population can result in a biased rate ratio of 0.82 in the prevalent cohort. Conclusions: The observational studies reporting significant reductions in mortality, cancer incidence and other outcomes with glucosamine were affected by selection bias from collider stratification. In the absence of properly conducted observational studies that circumvent this bias by considering "new users", the studies to date cannot support the prescription of this supplement as a preventive measure for mortality, cancer, and other chronic diseases. [ABSTRACT FROM AUTHOR]

What is the average lifespan in a stationary population viewed at a single moment in time? Even though periods and cohorts are identical in a stationary population, we show that the answer to this question is not life expectancy but a length-biased version of life expectancy. That is, the distribution of lifespans of the people alive at a single moment is a self-weighted distribution of cohort lifespans, such that longer lifespans have proportionally greater representation. One implication is that if death rates are unchanging, the average lifespan of the current population always exceeds period life expectancy. This result connects stationary population lifespan measures to a well-developed body of statistical results; provides new intuition for established demographic results; generates new insights into the relationship between periods, cohorts, and prevalent cohorts; and offers a framework for thinking about mortality selection more broadly than the concept of demographic frailty. [ABSTRACT FROM AUTHOR]

The aim of this work is to develop methods for studying the determinants of marriage incidence using marriage histories collected under two different types of retrospective cross-sectional study designs. These designs are: sampling of ever married women, that is, women who have been married at least once before the cross-section, a prevalent cohort, and sampling of women irrespective of marital status, a general crosssectional cohort. While retrospective histories from a prevalent cohort do not identify incidence rates without parametric modelling assumptions, the rates can be identified when combined with data from a general cohort. Moreover, education, a strong endogenous covariate, and marriage processes are correlated. Hence, they need to be modelled jointly in order to estimate the marriage incidence. For this purpose we specify a multi-state model and propose a likelihood-based estimation method. We outline the assumptions under which a likelihood expression involving only marriage incidence parameters can be derived. This is of particular interest when either retrospective education histories are not available or related parameters are not of interest. Our simulation results confirm the gain in efficiency by combining data from the two designs, while demonstrating how the parameter estimates are affected by violations of the assumptions used in deriving the simplified likelihood expressions. Two Indian National Family Health Surveys are used as motivation for the methodological development and to demonstrate the application of the methods. [ABSTRACT FROM AUTHOR]

Variable selection with censored survival data is of great practical importance, and several methods have been proposed for variable selection based on different models. However, the impacts of biased samples caused by left-truncation and covariate measurement error to variable selection are not fully explored. In this paper, we mainly focus on the additive hazards model and analyze variable selection and estimation for survival data subject to left-truncation and measurement error in covariates. We develop the three-stage procedure to correct for error effects, select informative variables, and estimate the parameters of interest simultaneously. Numerical studies are reported to assess the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to analyse the phenotype of systemic lupus erythematosus (SLE) at first presentation and during follow-up in a newly established SLE cohort based at 'Attikon' University Hospital. The hospital combines primary, secondary and tertiary care for the region of Western Attica, Greece. Methods: This study comprised a mixed prevalent and incident cohort of 555 Caucasian patients diagnosed with SLE according to American College of Rheumatology 1997 criteria and/or the Systemic Lupus Erythematosus International Collaborating Clinics (SLICC) 2012 criteria. Demographic and clinical characteristics, patterns of severity, treatments and SLICC damage index were recorded for each patient at the time of diagnosis and at last evaluation. Results: The mean age at lupus diagnosis was 38.3 years (standard deviation = 15.6 years), with a median disease duration at last follow-up of two years (interquartile range 1-11). At initial presentation, the most common 'classification' manifestations were arthritis (73.3%), acute cutaneous lupus (65%) and unexplained fever (25%), while among symptoms not included in any criteria set, Raynaud's phenomenon (33%) was the most common. Kidney and neuropsychiatric involvement as presenting manifestations were present in 10.3% and 11.5% cases, respectively. Irreversible damage accrual was present in 17.8% within six months of disease diagnosis, attributed mainly to thrombotic and neuropsychiatric disease. At last evaluation, 202 (36.4%) patients had developed severe disease, of whom more than half were treated with pulse cyclophosphamide. Conclusion: In this cohort of Caucasian patients, lupus nephritis is not as common as in older cohorts, while neuropsychiatric disease is emerging as a major frontier in lupus prevention and care. These data may help to document changes in the natural history and treatment of SLE over time and may have implications for its early recognition and management. [ABSTRACT FROM AUTHOR]

Analysis of survival data with biased samples caused by left-truncation or length-biased sampling has received extensive interest. Many inference methods have been developed for various survival models. These methods with ignorance of mismeasurement, however, may produce different estimations and yield misleading conclusions when survival data are typically error-contaminated. Although error-prone survival data commonly arise in practice, little work has been available in the literature for handling length-biased data with measurement error. In survival analysis, methods of analyzing the transformation model with those complex features have not been fully explored. In this paper, we develop a valid inference method under the transformation model. We establish asymptotic results for the proposed estimators. The proposed method enjoys appealing features in that there is no need to specify the distribution of the covariates and the increasing function in the transformation model. Numerical studies are reported to assess the performance of the proposed method. [ABSTRACT FROM AUTHOR]

It is frequently of interest to estimate the time that individuals survive with a disease, that is, to estimate the time between disease onset and occurrence of a clinical endpoint such as death. Epidemiologic survival data are commonly collected from either an incident cohort, whose members' disease onset occurs after the study baseline date, or from a cohort with prevalent disease that is followed forward in time. Incident cohort survival data are limited by study termination, while prevalent cohort data provide biased (left-truncated) survival data. In this article, we investigate the advantages of a study design featuring simultaneous follow-up of prevalent and incident cohorts to the estimation of the survivor function. Our analyses are supported by simulations and illustrated using data on survival after myotonic dystrophy diagnosis from the United Kingdom Clinical Practice Research Datalink (CPRD). We demonstrate that the NPMLE using combined incident and prevalent cohort data estimates the true survivor function very well, even for moderate sample sizes, and ameliorates the disadvantages of using a purely incident or prevalent cohort. [ABSTRACT FROM AUTHOR]

The Nun Study, a longitudinal study to examine risk factors for the progression of dementia, consists of subjects who were already diagnosed with dementia (ie, prevalent cohort) and those who do not have dementia (ie, incident cohort) at study enrollment. When assessing the risk factors' effects on the survival time from dementia diagnosis until death, utilizing data from both cohorts supports more efficient statistical inference because the two cohorts provide valuable complementary information. A major challenge in analyzing the combined cohort data is that the prevalent cases are not representative of the target population. Moreover, the dates of dementia diagnosis are not ascertained for the prevalent cohort in the Nun Study. Hence, the survival time for the prevalent cohort is only partially observed from study enrollment until death or censoring, with the time from dementia diagnosis to study enrollment missing. In this paper, we propose an efficient estimation method that uses both incident and prevalent cohorts under the proportional mean residual life model. By assuming proportionality of the mean residual life time with covariates in the incident cohort, we can utilize the natural relationship between the mean residual life function and the hazard function of the survival time measured from enrollment until death for the prevalent cohort. We evaluate the efficiency gain from using the combined cohort data through simulations and demonstrate that the proposed method is valid and efficient. [ABSTRACT FROM AUTHOR]

Summary: To understand better the relationship between patient characteristics and their residual survival after an intermediate event such as the local recurrence of cancer, it is of interest to identify patients with the intermediate event and then to analyse their residual survival data. One challenge in analysing such data is that the observed residual survival times tend to be longer than those in the target population, since patients who die before experiencing the intermediate event are excluded from the cohort identified. We propose to model jointly the ordered bivariate survival data by using a copula model and appropriately adjusting for the sampling bias. We develop an estimating procedure to estimate simultaneously the parameters for the marginal survival functions and the association parameter in the copula model, and we use a two‐stage expectation–maximization algorithm. Using empirical process theory, we prove that the estimators have strong consistency and asymptotic normality. We conduct simulation studies to evaluate the finite sample performance of the method proposed. We apply the method to two cohort studies to evaluate the association between patient characteristics and residual survival. [ABSTRACT FROM AUTHOR]

Background: Several epidemiologic designs allow studying fecundability, the monthly probability of pregnancy occurrence in noncontracepting couples in the general population. These designs may, to varying extents, suffer from attenuation bias and other biases. We aimed to compare the main designs: incident and prevalent cohorts, pregnancy-based, and current duration approaches.Methods: A realistic simulation model produced individual reproductive lives of a fictitious population. We drew random population samples according to each study design, from which the cumulative probability of pregnancy was estimated. We compared the abilities of the designs to highlight the impact of an environmental factor influencing fecundability, relying on the Cox model with censoring after 12 or 6 months.Results: Regarding the estimation of the cumulative probability of pregnancy, the pregnancy-based approach was the most prone to bias. When we considered a hypothetical factor associated with a hazard ratio (HR) of pregnancy of 0.7, the estimated HR was in the 0.78-0.85 range, according to designs. This attenuation bias was largest for the prevalent cohort and smallest for the current duration approach, which had the largest variance. The bias could be limited in all designs by censoring durations at 6 months.Conclusion: Attenuation bias in HRs cannot be ignored in fecundability studies. Focusing on the effect of exposures during the first 6 months of unprotected intercourse through censoring removes part of this bias. For risk factors that can accurately be assessed retrospectively, retrospective fecundity designs, although biased, are not much more strongly so than logistically more intensive designs entailing follow-up. [ABSTRACT FROM AUTHOR]

Summary: This article mainly focuses on analyzing covariate data from incident and prevalent cohort studies and a prevalent sample with only baseline covariates of interest and truncation times. Our major task in both research streams is to identify the effects of covariates on a failure time through very general single‐index survival regression models without observing survival outcomes. With a strict increase of the survival function in the linear predictor, the ratio of incident and prevalent covariate densities is shown to be a non‐degenerate and monotonic function of the linear predictor under covariate‐independent truncation. Without such a structural assumption, the conditional density of a truncation time in a prevalent cohort is ensured to be a non‐degenerate function of the linear predictor. In light of these features, some innovative approaches, which are based on the maximum rank correlation estimation or the pseudo least integrated squares estimation, are developed to estimate the coefficients of covariates up to a scale factor. Existing theoretical results are further used to establish the n‐consistency and asymptotic normality of the proposed estimators. Moreover, extensive simulations are conducted to assess and compare the finite‐sample performance of various estimators. To illustrate the methodological ideas, we also analyze data from the Worcester Heart Attack Study and the National Comorbidity Survey Replication. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Lower limb amputation is a serious complication of diabetes mellitus. Understanding how amputation risk differs by population subgroups is crucial in terms of directing preventive strategies. In this study, we describe those factors that impact amputation risk in the entire prevalent diabetic population of New Zealand.Methods: A national prevalent cohort of 217,207 individuals with diabetes in 2010 were followed up until the end of 2013 for lower limb amputations, and 2014 for mortality. Inpatient hospitalisation data were used to define lower limb amputation using ICD-10 codes. Cox proportional hazards models were used to describe relative hazard of amputation over the follow-up period.Results: A total of 784 individuals (3.6 cases/1000 individuals) underwent a major (above-ankle) lower limb amputation during follow-up, while 1217 (5.6/1000) underwent a minor (below ankle) amputation. The risk of major and minor amputation was 39% and 77% greater for men than women, respectively (adjusted HR: major amputation 1.39, 95% CI 1.20, 1.61; minor amputation 1.77, 95% CI 1.56, 2.00). Indigenous Māori were at 65% greater risk of above-knee amputation compared with the European/Other diabetic population (HR 1.65, 95% CI 1.37, 1.97). Amputation risk increased with increasing comorbidity burden, and peripheral vascular disease conferred the greatest independent risk of all comorbid conditions. Prior minor amputation increased the risk of subsequent major amputation by tenfold (HR 10.04, 95% CI 7.83, 12.87), and increased the risk of another minor amputation by 20-fold (HR 21.39, 95% CI 17.89, 25.57). Death was common among the total cohort, but particularly among those who underwent amputation, with more than half of those who underwent a major amputation dying within 3 years of their procedure (57%).Conclusions/interpretation: Using a large, well-defined, national prevalent cohort of people with diabetes, we found that being male, indigenous Māori, living in deprivation, having a high comorbidity burden and/or having a previous amputation were strongly associated with subsequent risk of lower limb amputation. The use of this prevalent cohort strengthens the value of our estimates in terms of applicability to the general population, and highlights the subgroups at greatest risk of lower limb amputation. [ABSTRACT FROM AUTHOR]

Registry databases are increasingly being used for comparative effectiveness research in cancer. Such databases reflect the real-world patient population and physician practice, and thus are natural sources for comparing multiple treatment scenarios and the associated long-term clinical outcomes. Registry databases usually include both incident and prevalent cohorts, which provide valuable complementary information for patients with more recent diagnoses in the incident cohort as well as patients with long-term follow-up data in the prevalent cohort. However, utilizing such data to derive valid inference poses two major challenges: the data from a prevalent cohort are not random samples of the target population, and there may be substantial differences in the baseline characteristics of patients between treatment arms, which influences the decisions about treatment selection in both cohorts. In this article we extend propensity score methodology to observational studies that involve both prevalent and incident cohorts, and assess the effectiveness of radiation therapy (RT) in SEER-Medicare patients diagnosed with stage IV breast cancer. Specifically we utilize the incident cohort to estimate the propensity for receiving RT, and then combine data from both the incident and prevalent cohorts to estimate the effect of RT by adjusting for the propensity scores in the model. We evaluate the proposed method with simulations. We demonstrate that the proposed propensity score method simultaneously removes sampling bias and selection bias under several assumptions. The Canadian Journal of Statistics 45: 202-219; 2017 © 2017 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

Length-biased data are encountered frequently due to prevalent cohort sampling in follow-up studies. Quantile regression provides great flexibility for assessing covariate effects on survival time, and is a useful alternative to Cox's proportional hazards model and the accelerated failure time (AFT) model for survival analysis. In this paper, we develop a Buckley-James-type estimator for right-censored length-biased data under a quantile regression model. The problem of informative right-censoring of length-biased data induced by prevalent cohort sampling must be handled. Following on from the generalization of the Buckley-James-type estimator under the AFT model proposed by Ning et al. (Biometrics 67:1369-1378, ), we propose a Buckley-James-type estimating equation for regression coefficients in the quantile regression model and develop an iterative algorithm to obtain the estimates. The resulting estimator is consistent and asymptotically normal. We evaluate the performance of the proposed estimator on finite samples using extensive simulation studies. Analysis of real data is presented to illustrate our proposed methodology. [ABSTRACT FROM AUTHOR]

Registries of pulmonary arterial hypertension (PAH) are important means by which to characterize the presentation and outcome of patients and to provide a basis for predicting the course of the disease. This article summarizes the published conclusions of the World Symposium of Pulmonary Hypertension task force that addressed registries and epidemiology of PAH. [ABSTRACT FROM AUTHOR]

We show that relative mean survival parameters of a semiparametric log-linear model can be estimated using covariate data from an incident sample and a prevalent sample, even when there is no prospective follow-up to collect any survival data. Estimation is based on an induced semiparametric density ratio model for covariates from the two samples, and it shares the same structure as for a logistic regression model for case-control data. Likelihood inference coincides with well-established methods for case-control data. We show two further related results. First, estimation of interaction parameters in a survival model can be performed using covariate information only from a prevalent sample, analogous to a case-only analysis. Furthermore, propensity score and conditional exposure effect parameters on survival can be estimated using only covariate data collected from incident and prevalent samples. [ABSTRACT FROM AUTHOR]

In prospective cohort studies, individuals are usually recruited according to a certain cross-sectional sampling criterion. The prevalent cohort is defined as a group of individuals who are alive but possibly with disease at the beginning of the study. It is appealing to incorporate the prevalent cases to estimate the incidence rate of disease before the enrollment. The method of back calculation of incidence rate has been used to estimate the incubation time from human immunodeficiency virus (HIV) infection to AIDS. The time origin is defined as the time of HIV infection. In aging cohort studies, the primary time scale is age of disease onset, subjects have to survive certain years to be enrolled into the study, thus creating left truncation (delay entry). The current methods usually assume that either the disease incidence is rare or the excess mortality due to disease is small compared with the healthy subjects. So far the validity of the results based on these assumptions has not been examined. In this paper, a simple alternative method is proposed to estimate dementia incidence rate before enrollment using prevalent cohort data with left truncation. Furthermore, simulations are used to examine the performance of the estimation of disease incidence under different assumptions of disease incidence rates and excess mortality hazards due to disease. As application, the method is applied to the prevalent cases of dementia from the Honolulu-Asia Aging Study to estimate the dementia incidence rate and to assess the effect of hypertension, Apoe 4 and education on dementia onset. [ABSTRACT FROM AUTHOR]

Testing the equality of two survival distributions can be difficult in a prevalent cohort study when non-random sampling of subjects is involved. Owing to the biased sampling scheme, the independent censoring assumption is often violated. Although the issues about biased inference caused by length-biased sampling have been widely recognized in the statistical, epidemiological and economical literature, there is no satisfactory solution for efficient two-sample testing. We propose an asymptotic most efficient non-parametric test by properly adjusting for length-biased sampling. The test statistic is derived from a full likelihood function and can be generalized from the two-sample test to a k-sample test. The asymptotic properties of the test statistic under the null hypothesis are derived by using its asymptotic independent and identically distributed representation. We conduct extensive Monte Carlo simulations to evaluate the performance of the test statistics proposed and compare them with the conditional test and the standard log-rank test for various biased sampling schemes and right-censoring mechanisms. For length-biased data, empirical studies demonstrated that the test proposed is substantially more powerful than the existing methods. For general left-truncated data, the test proposed is robust, still maintains accurate control of the type I error rate and is also more powerful than the existing methods, if the truncation patterns and right censoring patterns are the same between the groups. We illustrate the methods by using two real data examples. [ABSTRACT FROM AUTHOR]

Right-censored time-to-event data are often observed from a cohort of prevalent cases that are subject to length-biased sampling, Informative right censoring of data from the prevalent cohort within the population often makes it difficult to model risk factors on the unbiased failure times for the general population, because the observed failure times are length biased. In this paper, we consider two classes of flexible semiparametric models: the transformation models and the accelerated failure time models, to assess covariate effects on the population failure times by modeling the length-biased times. We develop unbiased estimating equation approaches to obtain the consistent estimators of the regression coefficients. Large sample properties for the estimators are derived. The methods are confirmed through simulations and illustrated by application to data from a study of a prevalent cohort of dementia patients. [ABSTRACT FROM AUTHOR]

Although many authors have proposed different approaches to the analysis of length-biased survival data, a number of issues have not been fully addressed. The most important among these issues is perhaps that regarding inclusion of covariates into the analysis of length-biased lifetime data collected through cross-sectional sampling of a population. One aspect of this problem, which appears to have been neglected in the literature, concerns the effect of length bias on the sampling distribution of the covariates. In most regression analyses, it is conventional to condition on the observed covariate values; however, certain covariate values could be preferentially selected into the sample, being linked to the long-term survivors, who themselves are favored by the sampling mechanism. This observation raises two questions: (1) Does the conditional analysis of covariates lead to biased estimators of regression coefficients?; and (2) does inference through the joint I likelihood of covariates and failure times yield more efficient estimators of the regression parameters? We present a joint likelihood approach and study the large-sample behavior of the resulting maximum likelihood estimators (MLEs). We find that these MLEs are more efficient than their conditional counterparts even though the two MLEs are asymptotically equal. Our results are illustrated using data on survival with dementia, collected as part of the Canadian Study of Health and Aging. [ABSTRACT FROM AUTHOR]

When survival data are colleted as part of a prevalent cohort study, the recruited cases have already experienced their initiating event. These prevalent cases are then followed for a fixed period of time at the end of which the subjects will either have failed or have been censored. When interests lies in estimating the survival distribution, from onset, of subjects with the disease, one must take into account that the survival times of the cases in a prevalent cohort study are left truncated. When it is possible to assume that there has not been any epidemic of the disease over the past period of time that covers the onset times of the subjects, one may assume that the underlying incidence process that generates the initiating event times is a stationary Poisson process. Under such assumption, the survival times of the recruited subjects are called “lengthbiased”. I discuss the challenges one is faced with in analyzing these type of data. To address the theoretical aspects of the work, I present asymptotic results for the NPMLE of the length-biased as well as the unbiased survival distribution. I also discuss estimating the unbiased survival function using only the follow-up time. This addresses the case that the onset times are either unknown or known with uncertainty. Some of our most recent work and open questions will be presented. These include some aspects of analysis of covariates, strong approximation, functional LIL and density estimation under length-biased sampling with right censoring. The results will be illustrated with survival data from patients with dementia, collected as part of the Canadian Study of Health and Aging (CSHA).