Parametric Survival Research Articles

Statins, Risk of Death and Ischemic Stroke in Patients with Dementia: A Registry-Based Observational Cohort Study.

Survival in patients with dementia is variable. Statins are a cornerstone of cardiovascular prevention. However, the effect of statins on mortality and risk for stroke in patients with dementia is not clear. The aim of the study was to analyse the association between the use of statins and the risk of all-cause death and first ischemic stroke in patients diagnosed with dementia. A longitudinal cohort study of 48 771 patients based on combined Swedish registries was conducted. The association between the incident use of statins one year prior to dementia diagnosis, allcause mortality and first ischemic stroke was examined using propensity score-matched flexible parametric or Cox hazard survival models and is presented with hazard ratios and corresponding 95% confidence intervals. After propensity score matching, incident users of statins (n=1412) had a lower risk of all-cause death (HR 0.82, 95% CI 0.74-0.91) and ischemic stroke (HR 0.62, 95% CI 0.43-0.89) compared to matched non-users (n=4482). In stratified analysis, the protective association between incident statin use and survival was observed in men, patients older than 75 years, with Alzheimer's disease and vascular dementia. Furthermore, we observed a protective association between incident use of statins and first ischemic stroke risk in men, patients older than 75 years and with mixed dementia. There was a graded association between cumulative doses of statins and mortality. The use of statins might be beneficial for the survival and ischemic stroke risk in patients with dementia in a dose-dependent manner.

Development of chest deflection injury risk curve in oblique frontal small female PMHS sled tests

Objective The study's aim is to examine which sternum deflection measure best represents injury in oblique frontal impacts with small female surrogates. Methods Data from sixteen PMHS sled tests were used to calculate sternum deflection using displacements in the A-P (x) direction, transverse (xy) plane, sagittal (xz) plane, and triplanar (xyz). Peak deflections were the response variable and were combined with injury outcomes to generate injury risk curves (IRCs) using parametric statistical survival modeling. The IRC with the lowest Brier Score Metric was considered the best deflection measure representing injury. Results The triplanar (xyz) deflection metric was the best indicator of injury. At the 10 and 50% probability levels, the magnitudes of this metric were 33 mm and 55 mm, respectively. The quality of the risk curve was fair for 10% and good for 50%, based on the ISO recommendations. Conclusions The current study reports on the injury risk to small females in an upright seated position in oblique frontal impacts. The triplanar and transverse plane deflection metrics were similar for this posture; however, occupants in reclined configurations may demonstrate a different response, and further investigations are necessary.

Length of stay for inpatient incompetent to stand trial patients: importance of clinical and demographic variables

We investigated clinical and demographic variables to better understand their relationship to hospital length of stay for patients involuntarily committed to California state psychiatric hospitals under the state's incompetent to stand trial (IST) statutes. Additionally, we determined the most important variables in the model that influenced patient length of stay. We retrospectively studied all patients admitted as IST to California state psychiatric hospitals during the period January 1, 2010 through June 30, 2018 (N = 20 041). Primary diagnosis, total number of violent acts while hospitalized, age at admission, treating hospital, level of functioning at admission, ethnicity, sex, and having had a previous state hospital admission were evaluated using a parametric survival model. The analysis showed that the most important variables related to length of stay were (1) diagnosis, (2) number of violent acts while hospitalized, and (3) age of admission. Specifically, longer length of stay was associated with (1) having a diagnosis of schizophrenia or neurocognitive disorder, (2) one or more violent acts, and (3) older age at admission. The other variables studied were also statistically significant, but not as influential in the model. We found significant relations between length of stay and the variables studied, with the most important variables being (1) diagnosis, (2) number of physically violent acts, and (3) age at admission. These findings emphasize the need for treatments to target cognitive issues in the seriously mentally ill as well as treatment of violence and early identification of violence risk factors.

Estimating Prison Stays Among Current Prison Populations

Background: Reporting estimates of length of stay in prison populations is a common objective in corrections research. Researchers and prison administrators use these estimates for many different purposes. These include projecting future prison operational and capacity needs, describing levels of punitiveness among states, and explaining the drivers of prison growth or decline. Because of their critical importance to so many dimensions of corrections and criminal justice, researchers have compared the merits of various methods to estimate prison length of stay. Objective: This article revisits a survival-based approach for estimating length of stay originally described in Patterson and Preston and uses historical prison data from the Bureau of Justice Statistics National Corrections Reporting Program to compare this method to alternatives. It also describes and tests the merits of extending this method to parametric frameworks. Method: Using 20 years of data in nine states, we model estimates of (1) average length of stay for the 1995 prison admission cohort and (2) length of stay distributions for the 1995 prison stock and compare estimates to true values for these samples over a 20-year period. We compare results derived from adjusted and unadjusted stock-flow calculations, release cohorts, and nonparametric and parametric survival models. Results: We demonstrate that estimates of length of stay using survival-based estimators consistently perform much better than other estimators and that there are advantages to using parametric estimation techniques over nonparametric ones. Parametric-based estimates are less variable and more reliable on average. Conclusion: We conclude that in the future, stay length estimates should be estimated using survival models like the ones we describe and that data exist which provide the means to do so effectively.

AFT survival model to capture the rate of aging and age-specific mortality trajectories among first-allogeneic hematopoietic stem cells transplant patients.

Accelerated failure time (AFT) model is commonly applied in engineering studies to address the failure rate of a machine. In humans, survival profile of transplant patients is among the rare scenarios whereby AFT is applicable. To date, it is uncertain whether reliable risk estimates and age-specific mortality trajectories have been published using conventional statistics approach. By investigating mortality trajectory, the rate of aging d(log(μ(x)))/dx of Hematopoietic Stem Cells Transplants (HSCTs) patients who had underwent first-allogeneic transplants can be obtained, and to unveil the possibility of elasticity of human aging rate in HSCTs. A modified parametric frailty survival model was introduced to the survival profiles of 11,160 patients who had underwent first-allogeneic HSCTs in the United States between 1995 and 2006; data was shared by Center for International Bone and Marrow Transplant Research. In comparison to stratification, the modification permits two entities in relation to time to be presented; age and calendar time. To consider its application in empirical studies, the data contains arbitrary right-censoring, a statistical condition which is preferred by choice in many transplant studies. The finalized multivariate AFT model was adjusted for clinical and demographic covariates, and age-specific mortality trajectories were presented by donor source and post-transplant time-lapse intervals. Two unexpected findings are presented: i) an inverse J-shaped hazard in unrelated donor-source t≤100-day; ii) convergence of unrelated-related hazard lines in 100-day<t ≤ 365-day suggests maximum manifestation of senescence among survivors. Analyses of long-term survivors (t>365-day) must consider for periodic medical improvements, and transplant year as a standalone time-variable is not sufficient for statistical adjustment in the finalized multivariate model. In relevance to clinical studies, biennial event-history analysis and age-specific mortality trajectories of long-term survivors provide a more relevant intervention audit report for transplant protocols than the popular statistical presentation; i.e. survival probabilities among donor-source.

A comparison of parametric and semi-parametric survival models with artificial neural networks

ABSTRACTSurvival models are used to examine data in the event of an occurrence. These are discussed in various types including parametric, non-parametric and semi-parametric models. Parametric models require a clear distribution of survival time, and semi-parametric models assume proportional hazards. Among these models, the non-parametric model of artificial neural network has the fewest assumptions and can be often replaced by other models. Given the importance of distribution Weibull survival models in this study of simulation shape parameter of the Weibull distribution have been assumed as 1, 2 and 3, and also the average rate at levels of 0%–75% have been censored. The values predicted by the neural network forecasting model with parametric survival and Cox regression models were compared. This comparison considering levels of complexity due to the hazard model using the ROC curve and the corresponding tests have been carried out.

Persistence with biologic agents for the treatment of rheumatoid arthritis in Japan.

BackgroundTo assess persistence rates of biologic agents for the treatment of rheumatoid arthritis in Japan.MethodsBased on Japanese claims data of 16,214 patients between 2012 and 2014, 6-, 12-, and 18-month persistence rates of different biologic agents were calculated. Determinants of persistence were assessed by means of a multivariate Cox proportional hazard model controlling for age, sex, and comorbidities. A sensitivity analysis was performed with different definitions of persistence and parametric survival analysis.ResultsOverall persistence rates in Japan are high and reach 86% after 1 year in the entire sample. The persistence rate for the biologic-naïve subpopulation is above 95%. Persistence is higher for older patients (hazard ratio 0.60 [95% confidence interval 0.40–0.91] for >75 years compared to ≤60 years) and lower for patients with a high comorbidity score (hazard ratio 1.33; 95% confidence interval 1.03–1.70 for Charlson Comorbidity Index score 3–5 compared to ≤2). We found a high variation of persistence between different drugs.ConclusionJapanese rheumatoid arthritis patients have a high persistence rate of biologic treatments. However, multiple factors affect the persistence rate of Japanese patients, including age, comorbidities, and patient type. Naïve patients tend to have a higher persistence rate than continuing biologic patients.

General partially linear additive transformation model with right-censored data

We propose a class of general partially linear additive transformation models (GPLATM) with right-censored survival data in this work. The class of models are flexible enough to cover many commonly used parametric and nonparametric survival analysis models as its special cases. Based on the B spline interpolation technique, we estimate the unknown regression parameters and functions by the maximum marginal likelihood estimation method. One important feature of the estimation procedure is that it does not need the baseline and censoring cumulative density distributions. Some numerical studies illustrate that this procedure can work very well for the moderate sample size.

Editorial: Journal of Statistical Distributions and Applications

© A m Introduction Statistical distributions are the foundations of statistical methodology in both theory and applications. They are the back bone of every parametric statistical method, including inference, modeling, survival, reliability, and others. In recent years, partly due to the advanced computing technology, there have been a series of developments of new methodology for generating new families of statistical distributions, which have greatly enhanced parametric statistical methods for handling real world scenarios that could not be modeled using existing distributions. One main reason for the need of generalized families is that each of the useful basic statistical distributions has its own weakness in real-world applications. The real-world phenomena are often much more complex for these commonly known basic statistical distributions to provide adequate fit. For example, Johnson et al. (2005) presented various modifications and generalizations of the Poisson distribution. Some of these distributions were developed in an attempt to explain the unequal mean and variance in the numerical data observed in different fields of applications. The history of statistical distributions started in the 18 century. The first major gathering on statistical distributions and their applications was in 1963 at McGill, Canada where experts participated in the International Symposium on Classical and Contagious Discrete Distributions. Another major gathering, the NATO Advanced Study Institute on Statistical Distributions in Scientific Work, was held at the University of Calgary, Canada from July 29 to August 10, 1974. Patil et al. (1974) in their preface to the Proceedings of the NATO Advanced Study Institute, referred to the McGill symposium as “a milestone in the recognition and development of the theory and application of statistical distributions.” According to Professor Kotz in his Foreword to the book by Consul and Famoye (2006) “The main impetus for the development of an orderly investigation of statistical distributions and their applications was the International Symposium on Classical and Contagious Discrete Distributions, organized by G.P. Patil in August of 1969 (1963) ...” Since then, various conferences and meetings have been organized on statistical distributions. However, no dedicated journal on statistical distributions was ever launched. In one of the Lukacs Symposia in the early 1990s held at Bowling Green State University, Bowling Green, Ohio, Dr. Adrienne Kemp (University of St. Andrews in Scotland) in her address stated that all research articles on statistical distributions are scattered in various journals and there is no dedicated journal for statistical distributions. To

Comparative effectiveness of treatments for recurrent ovarian cancer.

e16551 Background: Traditionally, recurrent ovarian cancer has been treated with chemotherapy. However, increasingly, secondary cytoreductive surgery (SCS) is being performed. We undertook this study to compare survival between SCS vs chemotherapy vs both or neither to treat recurrent ovarian cancer. Methods: Using SEER-Medicare 1997-2007, we identified women age 66+ years with primary late stage epithelial ovarian cancer with fee-for-service Medicare treated with surgery and chemotherapy for their primary cancer who experienced a recurrence. We defined recurrent cancer using claims for chemotherapy, hospice or SCS following a 6 month treatment free window after primary treatment. Survival time is calculated from time of first recurrence. We conducted parametric and semi-parametric multivariate survival analyses to assess comparative treatment survival rates and factors affecting survival. We use propensity score weighting methods to obtain robust estimates of comparative effectiveness. To address the risk that a fraction of patients classified by our algorithms as non-recurrent are, indeed, recurrent, we use finite mixture models. Results: Of the 1,623 women with recurrent cancer, 72% received secondary chemotherapy while 15% receive both chemotherapy and surgery. &lt;2% receive secondary surgery and 12% received hospice with no active treatment. Univariate analyses show that the mean survival time after first recurrence of ovarian cancer is 1,357 days. Multivariate survival models controlling for patient demographics, cancer type and comorbidities show that, compared to patients who received both surgery and chemotherapy, those who received only SCS (-569 days) as well as those who received only chemotherapy (-381 days) had significantly lower survival times. Survival time is negatively associated with black race and age at recurrence and positively associated with date of reoccurrence. Patients with greater comorbidities have significantly shorter survival times. Conclusions: Patients with recurrent ovarian cancer treated with both secondary surgery and chemotherapy survive longer than patients treated with either chemotherapy or surgery. Women who are Black, or older at time of recurrence have worse survival.

Long-Term Survival in HIV Positive Patients with up to 15 Years of Antiretroviral Therapy

BackgroundLife expectancy has increased for newly diagnosed HIV patients since the inception of combination antiretroviral treatment (cART), but there remains a need to better understand the characteristics of long-term survival in HIV-positive patients. We examined long-term survival in HIV-positive patients receiving cART in the Australian HIV Observational Database (AHOD), to describe changes in mortality compared to the general population and to develop longer-term survival models.MethodsData were examined from 2,675 HIV-positive participants in AHOD who started cART. Standardised mortality ratios (SMR) were calculated by age, sex and calendar year across prognostic characteristics using Australian Bureau of Statistics national data as reference. SMRs were examined by years of duration of cART by CD4 and similarly by viral load. Survival was analysed using Cox-proportional hazards and parametric survival models.ResultsThe overall SMR for all-cause mortality was 3.5 (95% CI: 3.0–4.0). SMRs by CD4 count were 8.6 (95% CI: 7.2–10.2) for CD4<350 cells/µl; 2.1 (95% CI: 1.5–2.9) for CD4 = 350–499 cells/µl; and 1.5 (95% CI: 1.1–2.0) for CD4≥500 cells/µl. SMRs for patients with CD4 counts <350 cells/µL were much higher than for patients with higher CD4 counts across all durations of cART. SMRs for patients with viral loads greater than 400 copies/ml were much higher across all durations of cART. Multivariate models demonstrated improved survival associated with increased recent CD4, reduced recent viral load, younger patients, absence of HBVsAg-positive ever, year of HIV diagnosis and incidence of ADI. Parametric models showed a fairly constant mortality risk by year of cART up to 15 years of treatment.ConclusionObserved mortality remained fairly constant by duration of cART and was modelled accurately by accepted prognostic factors. These rates did not vary much by duration of treatment. Changes in mortality with age were similar to those in the Australian general population.

Exposure–response relationship of AMG 386 in combination with weekly paclitaxel in recurrent ovarian cancer and its implication for dose selection

PurposeTo characterize exposure–response relationships of AMG 386 in a phase 2 study in advanced ovarian cancer for the facilitation of dose selection in future studies.MethodsA population pharmacokinetic model of AMG 386 (N = 141) was developed and applied in an exposure–response analysis using data from patients (N = 160) with recurrent ovarian cancer who received paclitaxel plus AMG 386 (3 or 10 mg/kg once weekly) or placebo. Reduction in the risk of progression or death with increasing exposure (steady-state area under the concentration-versus-time curve [AUCss]) was assessed using Cox regression analyses. Confounding factors were tested in multivariate analysis. Alternative AMG 386 doses were explored with Monte Carlo simulations using population pharmacokinetic and parametric survival models.ResultsThere was a trend toward increased PFS with increased AUCss (hazard ratio [HR] for each one-unit increment in AUCss, 0.97; P = 0.097), suggesting that the maximum effect on prolonging PFS was not achieved at the highest dose tested (10 mg/kg). Among patients with AUCss ≥ 9.6 mg h/mL, PFS was 8.1 months versus 5.7 months for AUCss < 9.6 mg h/mL and 4.6 months for placebo. No relationship between AUCss and grade ≥3 adverse events was observed. Simulations predicted that AMG 386 15 mg/kg once weekly would result in an AUCss ≥ 9.6 mg h/mL in >90% of patients with median PFS of 8.2 months versus 5.0 months for placebo (HR [15 mg/kg vs. placebo], 0.56).ConclusionsIncreased exposure to AMG 386 was associated with improved clinical outcomes in recurrent ovarian cancer, supporting the evaluation of a higher dose in future studies.Electronic supplementary materialThe online version of this article (doi:10.1007/s00280-011-1787-5) contains supplementary material, which is available to authorized users.

Confirmation of R132H mutation of isocitrate dehydrogenase 1 as an independent prognostic factor in anaplastic astrocytoma

Identification of mutations affecting codon 132 of the enzyme cytosolic isocitrate dehydrogenase 1 (IDH1) in astrocytomas, oligodendrogliomas and secondary glioblastomas has influenced current concepts of diffuse gliomas. A recent study published in this journal demonstrated that in anaplastic astrocytomas and glioblastomas IDH1 mutation status better reflected patient prognosis than histological diagnosis according to the current WHO classification system [3]. Even more intriguingly, patients with IDH1 wild type anaplastic astrocytomas demonstrated not only shorter overall survival (OS) when compared to IDH1 mutant anaplastic astrocytoma patients, but even survived shorter than patients with IDH1 mutant glioblastoma. Results were based on data from the NOA-04 trial [7] and the German Glioma Network [6]. Here, we screened an independent tumor dataset of patients at the Neurooncology Center (Comprehensive Cancer Center Tubingen) consisting of 281 gliomas that were not enrolled in the aforementioned studies for IDH1 R132H mutation using a recently developed monoclonal antibody that has been successfully validated [1]. Patient age (mean: 51 years, range 18–81 years) and survival (Maximum follow-up: 12.5 years, mean 2.3 years) were retrieved from clinical records (Departments of Neurology and Neurosurgery). For all tumor samples, grading was performed by the first three authors (JS, MM, RM), resulting in 39 diffuse astrocytomas grade II, 44 anaplastic astrocytomas grade III and 198 glioblastomas grade IV. Mixed oligo-astrocytic tumors and cases on which the raters did not unanimously agree were not included. Sections were deparaffinized, rehydrated, and immunostained using the Benchmark immunohistochemistry system (Ventana Medical Systems, Tucson, AZ, USA). Slides were incubated with mIDH1R132H hybridoma supernatant for 32 min (clone H09, dilution 1:2, standard Cell Conditioner 2 pretreatment and signal amplification). Cases exhibiting cytoplasmic IDH1 immunoreactivity in tumor cells were considered positive, i.e. carrying the IDH1 R132H mutation (Fig. 1). Statistical testing (ANOVA/ student t for age, Kaplan–Meier survival/Log-rank determination, Parametric survival fit/Weibull distribution) was performed using JMP In 7.0 statistical software (SAS, Cary, NJ, USA). The R132H mutation was present in 18/39 (46%) grade II, 24/44 (55%) grade III and 10/198 grade IV (5%) neoplasms and thus was slightly lower than previously reported (63% for grade II, 58% for grade III [4]). In all WHO grades, patients with IDH1 mutated tumors (mean age: 36 years in grade II, 39 years in grade III and 45 years in grade IV) were significantly younger than patients with J. Schittenhelm (&) R. Meyermann Department of Neuropathology, Institute of Pathology and Neuropathology, University of Tubingen, Tubingen, Germany e-mail: jens.schittenhelm@med.uni-tuebingen.de

Life Expectancy Following Diagnosis of a Vertebral Compression Fracture

The life expectancy of vertebral compression fracture (VCF) patients was evaluated as a function of their treatment. Compared to non-operated patients, the kyphoplasty and vertebroplasty patient cohort had 115% and 44% greater adjusted life expectancy, respectively. Kyphoplasty patients had a 34% greater adjusted life expectancy than vertebroplasty patients. Balloon kyphoplasty and vertebroplasty are minimally invasive procedures for the treatment of painful VCFs. This comparative effectiveness study characterized the life expectancy of VCF patients as a function of their treatment. Life expectancy of VCF patients in the 100% U.S. Medicare dataset (2005–2008) was estimated using a parametric Weibull survival model (adjusted for comorbidities), and compared between operated and non-operated patients as well as between kyphoplasty and vertebroplasty patients. A total of 858,978 patients with a newly diagnosed VCF were identified, including 119,253 kyphoplasty patients (13.9%) and 63,693 vertebroplasty patients (7.4%). Adjusted life expectancy was 85% greater for operated than non-operated patients (p < 0.001; 95% confidence interval: 82–89%). Compared to non-operated patients, the kyphoplasty and vertebroplasty patient cohort had 115% (p < 0.001; 95% confidence interval: 111–119%) and 44% (p < 0.001; 95% confidence interval: 42–47%) greater adjusted life expectancy, respectively. Kyphoplasty patients had a 34% greater adjusted life expectancy than vertebroplasty patients (p < 0.001; 95% confidence interval: 31–36%). Across all gender–age groups, the median life expectancy predicted by the parametric Weibull model was 2.2–7.3 years greater for operated than non-operated patients. Statistically significant and substantial differences in life expectancy were observed between the treated and non-treated cohorts in the Medicare population. Among the treated cohorts, patients in the vertebroplasty group experienced less of a survival benefit than those who received kyphoplasty. The results will be a useful basis for future cost effectiveness studies of VCF treatments for the Medicare population.

Use of parametric and non-parametric survival analysis techniques to evaluate the effectiveness of two acaricides in controlling tsetse flies. (&lt;i&gt;Diptera Glossinidae&lt;/i&gt;).

This paper presents parametric and non-parametric survival analysis procedures that can be used to compare acaricides. The effectiveness of Delta Tick Pour On and Delta Tick Spray in knocking down tsetse flies were determined. The two formulations were supplied by Chemplex. The comparison was based on data collected from trials carried out in Zimbabwe at Rukomichi Research Station in 2003. Rukomichi Research Station is owned by the Department of Veterinary Services and is mainly used for conducting research on tsetse flies. The Kaplan Meier estimate of survivor and hazard function was used as the non-parametric procedure for comparing survival times of tsetse exposed to Delta Tick Spray and Delta Tick Pour On. In addition the log rank statistic was also used to explore the differences between the two formulations. The parametric procedure assumed that the survival times follow a lognormal distribution after checking the suitability of the distribution and estimating the hazard, survivor function and quartiles assuming the distribution. A proportional hazard model was also fitted to compare the two formulations. The non-parametric procedure revealed that the median survival times of tsetse exposed to Delta Tick Pour On was 10 minutes compared to 12 minutes for those exposed to Delta Tick Spray. The parametric procedure showed that the median survival time of tsetse exposed to Delta Tick Pour On was 10.59 minutes compared to 13.70 for those exposed to Delta Tick Spray. The proportional hazards model indicated that the hazard of being knocked down is 1.335 more for tsetse exposed to Delta Tick Pour On than those on Delta Tick Spray (p

Second INTERMACS annual report: More than 1,000 primary left ventricular assist device implants

The Interagency Registry For Mechanical Circulatory Support (INTERMACS)1 an NHLBI-sponsored collaboration between the National Heart, Lung, and Blood Institute (NHLBI), the Food and Drug Administration (FDA), the Center for Medicaid and Medicare Services (CMS), and the advanced heart failure/mechanical circulatory support professional community, began prospective patient enrollment and data collection on June 23, 2006. On 3/27/09, CMS mandated that all United States hospitals approved for mechanical circulatory support as Destination Therapy (DT) must enter mechanical circulatory support patient data into a national database, INTERMACS. The power of INTERMACS data stems from the mandatory data submission on all durable mechanical circulatory devices, a formal process for adverse event adjudication, dedicated innovative electronic data submission, data element design to create a template for comparison with medical therapy, rigorous data monitoring, hospital auditing through the United Network of Organ Sharing, and a formal process for data access and publications. Since the inception of INTERMACS, an ongoing evolution of both strategies for device application and the types of available devices has continued to refine the landscape of mechanical circulatory support. Throughout this experience, the only device approved in the United States for permanent “destination” therapy was the HeartMate XVE2, a pulsatile ventricular assist device which is now known to frequently develop bearing wear and require device replacement within 2 years of implantation. Yet, in many countries outside the United States, newer axial flow and centrifugal flow rotary pumps provide chronic circulatory support. INTERMACS only collects data on devices which are FDA-approved for clinical use, and no adult rotary pump was approved in the United States for the first several years of the INTERMACS experience. The spectrum of devices entered into INTERMACS must also be viewed in the context of multiple concurrent U.S. clinical trials of continuous flow pumps implanted as bridge-to-transplant therapy as well as permanent support. Thus, for the first 2 years, despite the rigorous requirements for data completeness and accuracy, INTERMACS suffered from its inability to collect data on newer, more promising rotary pumps which were not yet FDA approved. The INTERMACS playing field changed dramatically in April of 2008, when the HeartMate II axial flow pump received FDA approval for clinical use as bridge-to-transplant therapy in the United States. A portion of this report will examine the changing practice patterns in the application of device type (continuous flow vs. pulsatile) and device strategies over the past three years. In fact, the genesis of INTERMACS, partly by chance and partly by design, uniquely positioned this database to observe, record, and analyze this historical transition (at least for the immediate future) from larger, powerful pulsatile pumps to the world of continuous flow technology, with the unproven promise of greater durability while retaining long-term patient functionality. This report begins the process of long-term evaluation of continuous flow technology against the background of a large registry of detailed patient and device data based on pulsatile pump technology.

Survival and efficiency curves for capital and the time-age-profile of vintage prices

The decline in the service flow from a stock of capital goods with age, due to the physical retirement of capital units and to the gradual loss of efficiency of each unit remaining, can be described by survival curves and efficiency curves. The paper reconsiders the relationship between these curves and the vintage prices of capital under neo-classical assumptions about perfect second hand markets and malleability, using duality between quantites and prices. Three examples with parametric survival and efficiency curves are presented. Concavity of the survival and efficiency curves may be compatible with convexity of the age-price profile. This approach is contrasted with the frequently cited Hulten-Wykoff approach. Empirical illustrations, focusing on the cenvexity/concavity issue, are given.

Application of Adjusted Survival Curves to Renal Transplant Data

An important means in the analysis of survival time data is the estimation and graphical representation of survival probabilities. In this paper unifactorial parametric and non-parametric survival curve estimators and two types of adjusted survival curves based on a parametric multifactorial approach are applied to renal transplant data. It is shown that the resulting survival curves can differ substantially. The unifactorial survival curves yield biased results in case of serious disequilibrium in the data. This drawback of the unifactorial methods has been overcome by the use of adjusted survival curves which take possible distortions in the data set into account. The benefits of adjusted survival curves in assessing potentially prognostic factors are elucidated by the application to data from renal transplantation.