Illness-death Model Research Articles

How blood pressure predicts frailty transitions in older adults in a population-based cohort study: a multi-state transition model.

BackgroundLow blood pressure (BP) is associated with frailty in older adults. Our aim was to explore how BP predicts transitions between frailty states.MethodsWe used data from the Lausanne cohort Lc65+, a population-based cohort of older adults randomly drawn from a population registry in Switzerland, in 2004, 2009 and 2014. BP was measured using a clinically validated oscillometric automated device and frailty was defined using Fried’s phenotype, every 3 years. We used an illness-death discrete multi-state Markov model to estimate hazard ratios of forward and backward transitions between frailty states (outcome) in relation to BP categories (predictor of interest) with adjustment for sex, age and antihypertensive medication (other predictors).ResultsAmong 4200 participants aged 65–70 years (58% female) at baseline, 70% were non-frail, 27% pre-frail and 2.0% frail. Over an average follow-up of 5.8 years, 2422 transitions were observed, with 1575 (65%) forward and 847 (35%) backward. Compared with systolic BP (SBP) <130 mmHg, the hazard ratio (95% confidence interval) of the transition from non-frail to pre-frail was 0.86 (0.74 to 1.00) for SBP 130–150 mmHg, and 0.89 (0.74 to 1.06) for SBP ≥150 mmHg. Compared with SBP <130 mmHg, the hazard ratio of the transition from pre-frail to frail was 0.71 (0.50 to 1.01) for SBP 130–150 mmHg, and 0.90 (0.62 to 1.32) for SBP ≥150 mmHg. Diastolic BP was a weaker predictor of forward transitions.ConclusionsBP categories had no strong relationship with either forward transitions or backward transitions in frailty states. If our findings are confirmed with greater precision and assuming a causal relationship, they would suggest that there is no well-defined optimal BP level to prevent frailty among older adults.

Diabetes free life expectancy and years of life lost associated with type 2 diabetes: projected trends in Germany between 2015 and 2040

BackgroundType 2 diabetes (T2D) causes substantial disease burden and is projected to affect an increasing number of people in coming decades. This study provides projected estimates of life years free of type 2 diabetes (T2D) and years of life lost ({mathrm{YLL}}) associated with T2D for Germany in the years 2015 and 2040.MethodsBased on an illness-death model and the associated mathematical relation between prevalence, incidence and mortality, we projected the prevalence of diagnosed T2D using currently available data on the incidence rate of diagnosed T2D and mortality rates of people with and without diagnosed T2D. Projection of prevalence was achieved by integration of a partial differential equation, which governs the illness-death model. These projected parameters were used as input values to calculate life years free of T2D and {mathrm{YLL}} associated with T2D for the German population aged 40 to 100 years in the years 2015 and 2040, while accounting for different assumptions on future trends in T2D incidence and mortality.ResultsAssuming a constant incidence rate, women and men at age 40 years in 2015 will live approximately 38 years and 33 years free of T2D, respectively. Up to the year 2040, these numbers are projected to increase by 1.0 years and 1.3 years. Assuming a decrease in T2D-associated excess mortality of 2% per year, women and men aged 40 years with T2D in 2015 will be expected to lose 1.6 and 2.7 years of life, respectively, compared to a same aged person without T2D. In 2040, these numbers would reduce by approximately 0.9 years and 1.6 years. This translates to 10.8 million and 6.4 million {mathrm{YLL}} in the German population aged 40–100 years with prevalent T2D in 2015 and 2040, respectively.ConclusionsGiven expected trends in mortality and no increase in T2D incidence, the burden due to premature mortality associated with T2D will decrease on the individual as well as on the population level. In addition, the expected lifetime without T2D is likely to increase. However, these trends strongly depend on future improvements of excess mortality associated with T2D and future incidence of T2D, which should motivate increased efforts of primary and tertiary prevention.

A SUGGESTED ILLNESS-DEATH MODEL TO STUDY COVID-19 DISEASE IN SAUDI ARABIA

In this work, we introduce an illness-death model to study COVID-19 disease. First, a multistate model was constructed to describe the possible transitions in COVID-19 states. The parameters of the suggested model were then estimated. The model was applied later to a sample of COVID-19 patients who were treated in Al-Ahmadi Hospital in Medina, Kingdom of Saudi Arabia. We used some transitions between disease states to obtain estimates of model parameters from the patient sample data. Both survival curves and hazard curves for the patients sample were obtained, in addition to distinguishing between patients based on some of their demographic characteristics.

Confirmatory adaptive group sequential designs for single-arm phase II studies with multiple time-to-event endpoints.

Existing methods concerning the assessment of long-term survival outcomes in one-armed trials are commonly restricted to one primary endpoint. Corresponding adaptive designs suffer from limitations regarding the use of information from other endpoints in interim design changes. Here we provide adaptive group sequential one-sample tests for testing hypotheses on the multivariate survival distribution derived from multi-state models, while making provision for data-dependent design modifications based on all involved time-to-event endpoints. We explicitly elaborate application of the methodology to one-sample tests for the joint distribution of (i) progression-free survival (PFS) and overall survival (OS) in the context of an illness-death model, and (ii) time to toxicity and time to progression while accounting for death as a competing event. Large sample distributions are derived using a counting process approach. Small sample properties are studied by simulation. An already established multi-state model for non-small cell lung cancer is used to illustrate the adaptiveprocedure.

Lifetime risk of developing diabetes and years of life lost among those with diabetes in Brazil

BackgroundGiven the paucity of studies for low- or middle-income countries, we aim to provide the first ever estimations of lifetime risk of diabetes, years of life spent and lost among those with diabetes for Brazilians. Estimates of Brazil´s diabetes burden consist essentially of reports of diabetes prevalence from national surveys and mortality data. However, these additional metrics are at times more meaningful ways to characterize this burden.MethodsWe joined data on incidence of physician-diagnosed diabetes from the Brazilian risk factor surveillance system, all-cause mortality from national statistics, and diabetes mortality rate ratios from ELSA-Brasil, an ongoing cohort study. To calculate lifetime risk of developing diabetes, we applied an illness-death state model. To calculate years of life lost for those with diabetes and years lived with the disease, we additionally calculated the mortality rates for those with diabetes.ResultsA 35-year-old white adult had a 23.4% (95% CI = 22.5%-25.5%) lifetime risk of developing diabetes by age 80 while a same-aged black/brown adult had a 30.8% risk (95% confidence interval (CI) = 29.6%-33.2%). Men diagnosed with diabetes at age 35 would live 32.9 (95% CI = 32.4-33.2) years with diabetes and lose 5.5 (95% CI = 5.1-6.1) years of life. Similarly-aged women would live 38.8 (95% CI = 38.3-38.9) years with diabetes and lose 2.1 (95% CI = 1.9-2.6) years of life.ConclusionsAssuming maintenance of current rates, one-quarter of young Brazilians will develop diabetes over their lifetimes, with this number reaching almost one-third among young, black/brown women. Those developing diabetes will suffer a decrease in life expectancy and will generate a considerable cost in terms of medical care.

A copula-based approach for dynamic prediction of survival with a binary time-dependent covariate.

Dynamic prediction methods incorporate longitudinal biomarker information to produce updated, more accurate predictions of conditional survival probability. There are two approaches for obtaining dynamic predictions: (1) a joint model of the longitudinal marker and survival process, and (2) an approximate approach that specifies a model for a specific component of the joint distribution. In the case of a binary marker, an illness-death model is an example of a joint modeling approach that is unified and produces consistent predictions. However, previous literature has shown that approximate approaches, such as landmarking, with additional flexibility can have good predictive performance. One such approach proposes using a Gaussian copula to model the joint distribution of conditional continuous marker and survival distributions. It has the advantage of specifying established, flexible models for the marginals for which goodness-of-fit can be assessed, and has easy estimation that can be implemented in standard software. In this article, we provide a Gaussian copula approach for dynamic prediction to accommodate a binary marker using a continuous latent variable formulation. We compare the predictive performance of this approach to joint modeling and landmarking using simulations and demonstrate its use for obtaining dynamic predictions in an application to a prostate cancer study.

Nonparametric estimation in an illness-death model with component-wise censoring.

In disease settings where study participants are at risk for death and a serious nonfatal event, composite endpoints defined as the time until the earliest of death or the nonfatal event are often used as the primary endpoint in clinical trials. In practice, if the nonfatal event can only be detected at clinic visits and the death time is known exactly, the resulting composite endpoint exhibits "component-wise censoring." The standard method used to estimate event-free survival in this setting fails to account for component-wise censoring. We apply a kernel smoothing method previously proposed for a marker process in a novel way to produce a nonparametric estimator for event-free survival that accounts for component-wise censoring. The key insight that allows us to apply this kernel method is thinking of nonfatal event status as an intermittently observed binary time-dependent variable rather than thinking of time to the nonfatal event as interval-censored. We also propose estimators for the probability in state and restricted mean time in state for reversible or irreversible illness-death models, under component-wise censoring, and derive their large-sample properties. We perform a simulation study to compare our method to existing multistate survival methods and apply the methods on data from a large randomized trial studying a multifactor intervention for reducing morbidity and mortality among men at above average risk of coronary heartdisease.

Estimation of covariate effects on net survivals in the relative survival progressive illness-death model.

Recently, there has been a lot of development in relative survival field. In the absence of data on the cause of death, the research has tended to focus on the estimation of survival probability of a cancer (as a disease of interest). In many cancers, one nonfatal event that decreases the survival probability can occur. There are a few methods that assess the role of prognostic factors for multiple types of clinical events while dealing with uncertainty about the cause of death. However, these methods require proportional hazard or Markov assumptions. In practice, one or both of these assumptions might be violated. Violation of the proportional hazard assumption can lead to estimates that are biased, and difficult to interpret and violation of Markov assumption results in inconsistent estimators. In this work, we propose a semi-parametric approach to estimate the possibly time-varying regression coefficients in the likely non-Markov relative survival progressive illness-death model. The performance of the proposed estimator is investigated through simulations. We illustrate our approach using data from a study on rectal cancer resected for cure conducted in two French population-based digestive cancer registries.

Survival Analysis of Gastric Cancer Patients Using Illness-Death Model In Kerman City, Southeast Iran during 2001-2016.

BACKGROUND Gastric cancer (GC) is the fifth most common cancer and the third most common cause of cancer death worldwide. The aim of this study was to investigate the factors affecting the survival of patients with GC using the illness-death model (metastasis as an intermediate event). METHODS In this retrospective cohort study, 339 patients with GC who were referred to Shahid Bahonar and Afzalipour Hospitals in Kerman, Southeast Iran during 2001-2016 were included. Demographic, therapeutic, and clinical data were collected from the patients' medical records. To evaluate the factors affecting patients' survival and the relationship between the factors, the illness-death model (metastasis as an intermediate event) was used. RESULTS One, three, and five-year survival rates in patients with GC were estimated to be 63%, 40%, and 30%, respectively. The results of analysis of illness-death model showed that age (HR = 0.98, 95% CI: 0.97- 0.99; p = 0.007) and histological grade (HR = 1.77; 95% CI: 1.68- 2.67; p =0.007) affected metastasis whereas history of cigarette smoking (HR = 1.89; 95% CI: 1.08- 3.3; p = 0.02) and chemotherapy (HR = 0.63; 95% CI: 0.4-0.93; p = 0.02) affected death hazard without metastasis. History of opium use (HR=2.11; 95% CI: 1.17- 3.8; p = 0.002), family history of GC (HR = 2.48; 95% CI: 1.2-5.15; p = 0.01) and histological grade (HR = 1.85; 95% CI: 1.11- 3.08; p = 0.02) were identified as factors affecting death hazard in patients with metastasis. CONCLUSION According to the results, patients' age at the time of diagnosis and histological grade have a significant effect on the occurrence of metastasis. In addition, using the disease-death model, a complete understanding of the behavior and effects of the studied variables on different disease states can be realized.

The emerging epidemic of inflammatory bowel disease in Asia and Iran by 2035: A modeling study

BackgroundThe projection studies are imperative to satisfy demands for health care systems and proper response to the public health problems such as inflammatory bowel disease (IBD).MethodsTo accomplish this, we established an illness-death model based on available data to project the future prevalence of IBD in Asia, Iran in particular, separately from 2017 to 2035. We applied two deterministic and stochastic approaches.ResultsIn 2035, as compared to 2020, we expected a 2.5-fold rise in prevalence for Iran with 69 thousand cases, a 2.3-fold increment for North Africa and the Middle East with 220 thousand cases, quadrupling of the prevalence for India with 2.2 million cases, a 1.5-fold increase for East Asia region with 4.5 million cases, and a 1.6-fold elevation in prevalence for high‐income Asia‐Pacific and Southeast Asia regions with 183 and 199 thousand cases respectively.ConclusionsOur results showed an emerging epidemic for the prevalence of IBD in Asia regions and/or countries. Hence, we suggest the need for immediate action to control this increasing trend in Asia and Iran. However, we were virtually unable to use information about age groups, gender, and other factors influencing the evolution of IBD in our model due to lack of access to reliable data.

Unmarried or less-educated patients with mantle cell lymphoma are less likely to undergo a transplant, leading to lower survival

It is unknown how many mantle cell lymphoma (MCL) patients undergo consolidation with autologous hematopoietic cell transplantation (AHCT), and the reasons governing the decision, are also unknown. The prognostic impact of omitting AHCT is also understudied. We identified all MCL patients diagnosed from 2000 to 2014, aged 18 to 65 years, in the Swedish Lymphoma Register. Odds ratios (ORs) and 95% confidence intervals (CIs) from logistic regression models were used to compare the likelihood of AHCT within 18 months of diagnosis. All-cause mortality was compared between patients treated with/without AHCT using hazard ratios (HRs) and 95% CIs estimated from Cox regression models. Probabilities of being in each of the following states: alive without AHCT, alive with AHCT, dead before AHCT, and dead after AHCT, were estimated over time from an illness-death model. Among 369 patients, 148 (40%) were not treated with AHCT within 18 months. Compared with married patients, never married and divorced patients had lower likelihood of undergoing AHCT, as had patients with lower educational level, and comorbid patients. Receiving AHCT was associated with reduced all-cause mortality (HR = 0.58, 95% CI: 0.40-0.85). Transplantation-related mortality was low (2%). MCL patients not receiving an AHCT had an increased mortality rate, and furthermore, an undue concern about performing an AHCT in certain societal groups was seen. Improvements in supportive functions potentially increasing the likelihood of tolerating an AHCT and introduction of more tolerable treatments for these groups are needed.

Investigating the association between cancer and the risk of dementia: Results from the Memento cohort.

IntroductionStudies on the association of cancer and risk of dementia are inconclusive due to result heterogeneity and concerns of survivor bias and unmeasured confounding.MethodsThis study uses data from the Memento cohort, a French multicenter cohort following persons with either mild or isolated cognitive complaints for a median of 5 years. Illness‐death models (IDMs) were used to estimate transition‐specific hazard ratios (HRs) and 95% confidence intervals (CIs) for incident cancer in relation to dementia from time since study entry.ResultsThe analytical sample (N = 2258) excluded 65 individuals without follow‐up information. At the end of follow‐up, 286 individuals were diagnosed with dementia, 166 with incident cancer, and 95 died. Incident cancer was associated with a reduced risk of dementia (HR = 0.58, 95% CI = 0.35‐0.97), with a corresponding E‐value of 2.84 (lower CI = 1.21).DiscussionThis study supports a protective relationship between incident cancer and dementia, encouraging further investigations to understand potential underlying mechanisms.

Prediction Model for Disease Progression and Relevant Prognostic Factors in Hodgkin Patients: An Application of Frailty Multistate Model

Background: In clinical cancer studies, there has been a high tendency of searching for more specific and new prognostic factors in cancers in the last few years. This multistate study aimed to model the progression of Hodgkin's disease by accounting for individual effect (heterogeneity) using the joint and independent frailty models. Method: After the utilization of the illness-death model, joint-modeling accounted for the dependency between relapse and death by considering the individual characteristics as a frailty term. Therefore, the effect of influential prognostic factors was evaluated on disease progression by frailty and joint-frailty multistate models. Results: The individual predictions were determined using the frameworks of the both models. The model was applied to 389 Hodgkin lymphoma patients. Gender (male), age (over 55 years), and low level of hemoglobin (less than 10.5) were associated with an increased risk of death and relapse in patients. The likelihood cross-validation criterion was proposed to choose the joint frailty model as a better fitting model. Conclusion: Multistate models were appropriate tools to study the whole event history of the subjects, which provided a deep insight into the dynamics of the disease. The problem of events-subjects dependency in the survival data was clarified using the multistate model. Therefore, the heterogeneity and dependency between the states led to more accurate estimations of the effects of the prognostic factors, thereby improving the predictions.

Regression models for interval censored data using parametric pseudo-observations

BackgroundTime-to-event data that is subject to interval censoring is common in the practice of medical research and versatile statistical methods for estimating associations in such settings have been limited. For right censored data, non-parametric pseudo-observations have been proposed as a basis for regression modeling with the possibility to use different association measures. In this article, we propose a method for calculating pseudo-observations for interval censored data.MethodsWe develop an extension of a recently developed set of parametric pseudo-observations based on a spline-based flexible parametric estimator. The inherent competing risk issue with an interval censored event of interest necessitates the use of an illness-death model, and we formulate our method within this framework. To evaluate the empirical properties of the proposed method, we perform a simulation study and calculate pseudo-observations based on our method as well as alternative approaches. We also present an analysis of a real dataset on patients with implantable cardioverter-defibrillators who are monitored for the occurrence of a particular type of device failures by routine follow-up examinations. In this dataset, we have information on exact event times as well as the interval censored data, so we can compare analyses of pseudo-observations based on the interval censored data to those obtained using the non-parametric pseudo-observations for right censored data.ResultsOur simulations show that the proposed method for calculating pseudo-observations provides unbiased estimates of the cumulative incidence function as well as associations with exposure variables with appropriate coverage probabilities. The analysis of the real dataset also suggests that our method provides estimates which are in agreement with estimates obtained from the right censored data.ConclusionsThe proposed method for calculating pseudo-observations based on the flexible parametric approach provides a versatile solution to the specific challenges that arise with interval censored data. This solution allows regression modeling using a range of different association measures.

Fitting a shared frailty illness-death model to left-truncated semi-competing risks data to examine the impact of education level on incident dementia

BackgroundSemi-competing risks arise when interest lies in the time-to-event for some non-terminal event, the observation of which is subject to some terminal event. One approach to assessing the impact of covariates on semi-competing risks data is through the illness-death model with shared frailty, where hazard regression models are used to model the effect of covariates on the endpoints. The shared frailty term, which can be viewed as an individual-specific random effect, acknowledges dependence between the events that is not accounted for by covariates. Although methods exist for fitting such a model to right-censored semi-competing risks data, there is currently a gap in the literature for fitting such models when a flexible baseline hazard specification is desired and the data are left-truncated, for example when time is on the age scale. We provide a modeling framework and openly available code for implementation.MethodsWe specified the model and the likelihood function that accounts for left-truncated data, and provided an approach to estimation and inference via maximum likelihood. Our model was fully parametric, specifying baseline hazards via Weibull or B-splines. Using simulated data we examined the operating characteristics of the implementation in terms of bias and coverage. We applied our methods to a dataset of 33,117 Kaiser Permanente Northern California members aged 65 or older examining the relationship between educational level (categorized as: high school or less; trade school, some college or college graduate; post-graduate) and incident dementia and death.ResultsA simulation study showed that our implementation provided regression parameter estimates with negligible bias and good coverage. In our data application, we found higher levels of education are associated with a lower risk of incident dementia, after adjusting for sex and race/ethnicity.ConclusionsAs illustrated by our analysis of Kaiser data, our proposed modeling framework allows the analyst to assess the impact of covariates on semi-competing risks data, such as incident dementia and death, while accounting for dependence between the outcomes when data are left-truncated, as is common in studies of aging and dementia.

Healing, surviving, or dying? \u2013 projecting the German future disease burden using a Markov illness-death model

BackgroundIn view of the upcoming demographic transition, there is still no clear evidence on how increasing life expectancy will affect future disease burden, especially regarding specific diseases. In our study, we project the future development of Germany’s ten most common non-infectious diseases (arthrosis, coronary heart disease, pulmonary, bronchial and tracheal cancer, chronic obstructive pulmonary disease, cerebrovascular diseases, dementia, depression, diabetes, dorsal pain and heart failure) in a Markov illness-death model with recovery until 2060.MethodsThe disease-specific input data stem from a consistent data set of a major sickness fund covering about four million people, the demographic components from official population statistics. Using six different scenarios concerning an expansion and a compression of morbidity as well as increasing recovery and effective prevention, we can show the possible future range of disease burden and, by disentangling the effects, reveal the significant differences between the various diseases in interaction with the demographic components.ResultsOur results indicate that, although strongly age-related diseases like dementia or heart failure show the highest relative increase rates, diseases of the musculoskeletal system, such as dorsal pain and arthrosis, still will be responsible for the majority of the German population’s future disease burden in 2060, with about 25–27 and 13–15 million patients, respectively. Most importantly, for almost all considered diseases a significant increase in burden of disease can be expected even in case of a compression of morbidity.ConclusionA massive case-load is emerging on the German health care system, which can only be alleviated by more effective prevention. Immediate action by policy makers and health care managers is needed, as otherwise the prevalence of widespread diseases will become unsustainable from a capacity point-of-view.

Survidm: An R package for Inference and Prediction in an Illness-Death Model

survidm: An R package for Inference and Prediction in an Illness-Death Model

Lifetime risk of developing diabetes and years of life lost among those with diabetes in Brazil.

Given the paucity of studies for low- or middle-income countries, we aim to provide the first ever estimations of lifetime risk of diabetes, years of life spent and lost among those with diabetes for Brazilians. Estimates of Brazil´s diabetes burden consist essentially of reports of diabetes prevalence from national surveys and mortality data. However, these additional metrics are at times more meaningful ways to characterize this burden. We joined data on incidence of physician-diagnosed diabetes from the Brazilian risk factor surveillance system, all-cause mortality from national statistics, and diabetes mortality rate ratios from ELSA-Brasil, an ongoing cohort study. To calculate lifetime risk of developing diabetes, we applied an illness-death state model. To calculate years of life lost for those with diabetes and years lived with the disease, we additionally calculated the mortality rates for those with diabetes. A 35-year-old white adult had a 23.4% (95% CI = 22.5%-25.5%) lifetime risk of developing diabetes by age 80 while a same-aged black/brown adult had a 30.8% risk (95% confidence interval (CI) = 29.6%-33.2%). Men diagnosed with diabetes at age 35 would live 32.9 (95% CI = 32.4-33.2) years with diabetes and lose 5.5 (95% CI = 5.1-6.1) years of life. Similarly-aged women would live 38.8 (95% CI = 38.3-38.9) years with diabetes and lose 2.1 (95% CI = 1.9-2.6) years of life. Assuming maintenance of current rates, one-quarter of young Brazilians will develop diabetes over their lifetimes, with this number reaching almost one-third among young, black/brown women. Those developing diabetes will suffer a decrease in life expectancy and will generate a considerable cost in terms of medical care.

Estimating the impact of tax policy interventions on the projected number and prevalence of adults with type 2 diabetes in Germany between 2020 and 2040

IntroductionAs a population-wide intervention, it has been proposed to raise taxes on unhealthy products to prevent diseases such as type 2 diabetes. In this study, we aimed to estimate the...

Incidence of Subsequent Hip Fracture and Mortality in Elderly Patients: A Multistate Population-Based Cohort Study in Eastern Spain

ABSTRACTOsteoporotic hip fractures in older people may confer an increased risk of subsequent hip fractures and death. The aim of this study was to estimate the cumulative incidence of both recurrent hip fracture and death in the Valencia region. We followed a cohort of 34,491 patients aged ≥65 years who were discharged alive from Valencia Health System hospitals after an osteoporotic hip fracture between 2008 and 2015, until death or end of study (December 31, 2016). Two Bayesian illness‐death models were applied to estimate the cumulative incidences of recurrent hip fracture and death by sex, age, and year of discharge. We estimated 1‐year cumulative incidences of recurrent hip fracture at 2.5% in women and 2.3% in men, and 8.3% and 6.6%, respectively, at 5 years. Cumulative incidences of total death were 18.3% in women and 28.6% in men at 1 year, and 51.2% and 69.8% at 5 years. One‐year probabilities of death after recurrent hip fracture were estimated at 26.8% and 43.8%, respectively, and at 57.3% and 79.2% at 5 years. Our analysis showed an increasing trend in the 1‐year cumulative incidence of recurrent hip fracture from 2008 to 2015, but a decreasing trend in 1‐year mortality. Male sex and age at discharge were associated with increased risk of death. Women showed higher incidence of subsequent hip fracture than men although they were at the same risk of recurrent hip fracture. Probabilities of death after recurrent hip fracture were higher than those observed in the general population. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).