Biomedical subjects

Douglas E Schaubel

Publications and source records attributed to Douglas E Schaubel.

At least 19 recordsLinked to original sources

PubMed2006

A semiparametric additive rates model for recurrent event data.

Recurrent event data often arise in biomedical studies, with examples including hospitalizations, infections, and treatment failures. In observational studies, it is often of interest to estimate the effects of covariates on the marginal recurrent event rate. The majority of existing rate regression methods assume multiplicative covariate effects. We propose a semiparametric model for the marginal recurrent event rate, wherein the covariates are assumed to add to the unspecified baseline rate. Covariate effects are summarized by rate differences, meaning that the absolute effect on the rate function can be determined from the regression coefficient alone. We describe modifications of the proposed method to accommodate a terminating event (e.g., death). Proposed estimators of the regression parameters and baseline rate are shown to be consistent and asymptotically Gaussian. Simulation studies demonstrate that the asymptotic approximations are accurate in finite samples. The proposed methods are applied to a state-wide kidney transplant data set.

Biometry↗

PubMed2006

Evaluating the survival benefit of kidney retransplantation.

BACKGROUND: The magnitude of the survival benefit associated with kidney retransplantation has not been well studied. METHODS: Using data from the Canadian Organ Replacement Register (CORR), we studied patients (n=3,067) initiating renal replacement therapy during 1981-1998 who had received a transplant and experienced graft failure (GF). Such patients were followed until death, loss to follow-up or the end of the observation period (December 31, 1998). Using Cox regression, we estimated the post-GF covariate-adjusted hazard ratio (HR) for retransplant versus dialysis, and determined whether the contrast differed across patient subgroups. Through nonproportional hazards models, we also examine patterns in the retransplant/dialysis HR with time following retransplant. RESULTS: Overall, retransplantation is associated with a covariate-adjusted 50% reduction in mortality, relative to remaining on dialysis (HR=0.50; P<0.0001). This benefit is most pronounced in the 18- to 59-year age group. Retransplanted patients were at significantly higher risk of death relative to patients on dialysis only during the first month posttransplant (HR=1.66; P=0.047), and experienced significantly reduced mortality thereafter. CONCLUSIONS: Following primary graft failure, retransplantation is associated with significantly reduced mortality rates among Canadian end-stage renal disease patients. Further study should be undertaken to assess the applicability of our findings to other patient populations.

Adolescent↗

PubMed2006

Weight gain in children with hypertonia of cerebral origin receiving intrathecal baclofen therapy.

OBJECTIVE: To identify the pattern of weight change in children receiving intrathecal baclofen (ITB) therapy. DESIGN: A retrospective medical chart review was conducted to identify weight status before and after ITB pump implantation. SETTING: Tertiary care children's hospital and academic medical center. PARTICIPANTS: All children and adolescents with hypertonia of cerebral origin who were younger than 19 years of age at the time of pump placement and followed in our pediatric baclofen pump program. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: A linear mixed model was used to examine the rate of change in weight (weight-gain velocity) before and after surgery. Weight change was adjusted for age, sex, functional level determined by the Gross Motor Functional Classification System, tube feeding, dystonia, and other comorbidities. RESULTS: The average weight-gain velocity was 2.32 kg/y presurgery and 2.93 kg/y postsurgery, adjusted for potential confounders. The 0.61 kg/y increase in weight-gain velocity attained statistical significance (P = .028). CONCLUSIONS: Although excessive weight gain is not a common problem in children with cerebral origin spasticity, increased weight-gain velocity is prevalent in children receiving ITB therapy. Health care providers may anticipate a welcome weight gain in the underweight child. This consequence should be considered when managing children receiving ITB therapy, and health care providers must appropriately intervene to prevent excessive weight gain. Further studies exploring the reasons for the increased weight-gain velocity are warranted.

Adolescent↗

PubMed2006

A sequential stratification method for estimating the effect of a time-dependent experimental treatment in observational studies.

Survival analysis is often used to compare experimental and conventional treatments. In observational studies, the therapy may change during follow-up and such crossovers can be summarized by time-dependent covariates. Given the ever-increasing donor organ shortage, higher-risk kidneys from expanded criterion donors (ECD) are being transplanted. Transplant candidates can choose whether to accept an ECD organ (experimental therapy), or to remain on dialysis and wait for a possible non-ECD transplant later (conventional therapy). A three-group time-dependent analysis of such data involves estimating parameters corresponding to two time-dependent indicator covariates representing ECD transplant and non-ECD transplant, each compared to remaining on dialysis on the waitlist. However, the ECD hazard ratio estimated by this time-dependent analysis fails to account for the fact that patients who forego an ECD transplant are not destined to remain on dialysis forever, but could subsequently receive a non-ECD transplant. We propose a novel method of estimating the survival benefit of ECD transplantation relative to conventional therapy (waitlist with possible subsequent non-ECD transplant). Compared to the time-dependent analysis, the proposed method more accurately characterizes the data structure and yields a more direct estimate of the relative outcome with an ECD transplant.

Biometry↗

PubMed2005

Variance estimation for clustered recurrent event data with a small number of clusters.

Often in biomedical studies, the event of interest is recurrent and within-subject events cannot usually be assumed independent. In semi-parametric estimation of the proportional rates model, a working independence assumption leads to an estimating equation for the regression parameter vector, with within-subject correlation accounted for through a robust (sandwich) variance estimator; these methods have been extended to the case of clustered subjects. We consider variance estimation in the setting where subjects are clustered and the study consists of a small number of moderate-to-large-sized clusters. We demonstrate through simulation that the robust estimator is quite inaccurate in this setting. We propose a corrected version of the robust variance estimator, as well as jackknife and bootstrap estimators. Simulation studies reveal that the corrected variance is considerably more accurate than the robust estimator, and slightly more accurate than the jackknife and bootstrap variance. The proposed methods are used to compare hospitalization rates between Canada and the U.S. in a multi-centre dialysis study.

Canada↗

PubMed2005

Analysis of clustered recurrent event data with application to hospitalization rates among renal failure patients.

End-stage renal disease (commonly referred to as renal failure) is of increasing concern in the United States and many countries worldwide. Incidence rates have increased, while the supply of donor organs has not kept pace with the demand. Although renal transplantation has generally been shown to be superior to dialysis with respect to mortality, very little research has been directed towards comparing transplant and wait-list patients with respect to morbidity. Using national data from the Scientific Registry of Transplant Recipients, we compare transplant and wait-list hospitalization rates. Hospitalizations are subject to two levels of dependence. In addition to the dependence among within-patient events, patients are also clustered by listing center. We propose two marginal methods to analyze such clustered recurrent event data; the first model postulates a common baseline event rate, while the second features cluster-specific baseline rates. Our results indicate that kidney transplantation offers a significant decrease in hospitalization, but that the effect is negated by a waiting time (until transplant) of more than 2 years. Moreover, graft failure (GF) results in a significant increase in the hospitalization rate which is greatest in the first month post-GF, but remains significantly elevated up to 4 years later. We also compare results from the proposed models to those based on a frailty model, with the various methods compared and contrasted.

Biometry↗

PubMed2005

Hepatitis C is a risk factor for death after liver retransplantation.

Retransplantation for liver allograft failure associated with hepatitis C virus (HCV) has been increasing due to nearly universal posttransplant HCV recurrence and has been demonstrated to be associated with poor outcomes. We report on the risk factors for death after retransplantation among liver recipients with HCV. A retrospective cohort of liver transplant recipients who underwent retransplantation between January 1997 and December 2002 was identified in the Scientific Registry of Transplant Recipients database. Cox regression was used to assess the relative effect of HCV diagnosis on mortality risk after retransplantation and was adjusted for multiple covariates. Of 1,718 liver retransplantations during the study period, 464 (27%) were associated with a diagnosis of HCV infection. Based on Cox regression, retransplant recipients with HCV had a 30% higher covariate-adjusted mortality risk than those without HCV diagnosis (hazard ratio [HR], 1.30; 95% confidence interval [CI], 1.10-1.54; P = 0.002). Other covariates associated with significant relative risk of death after retransplantation included older recipient age, presence in an intensive care unit (ICU), serum creatinine, and donor age. Additional regression analysis revealed that the increase in mortality risk associated with HCV was concentrated between 3 and 24 months postretransplantation, among patients age 18 to 39 at retransplant, and in patients retransplanted during the years 2000 to 2002. In conclusion, HCV liver recipients account for a considerable proportion of all retransplantations performed. Surprisingly, younger age predicted a higher mortality for recipients with HCV undergoing liver retransplantation. This may reflect a willingness to retransplant younger patients with an increased severity of illness or a more virulent HCV infection in this population. Although HCV was predictive of an increased risk of death, consideration of other characteristics of HCV patients, including donor and recipient age and need for preoperative ICU care may identify those at significantly higher risk.

Adolescent↗

PubMed2005

Semiparametric methods for clustered recurrent event data.

In biomedical studies, the event of interest is often recurrent and within-subject events cannot usually be assumed independent. In addition, individuals within a cluster might not be independent; for example, in multi-center or familial studies, subjects from the same center or family might be correlated. We propose methods of estimating parameters in two semi-parametric proportional rates/means models for clustered recurrent event data. The first model contains a baseline rate function which is common across clusters, while the second model features cluster-specific baseline rates. Dependence structures for patients-within-cluster and events-within-patient are both unspecified. Estimating equations are derived for the regression parameters. For the common baseline model, an estimator of the baseline mean function is proposed. The asymptotic distributions of the model parameters are derived, while finite-sample properties are assessed through a simulation study. Using data from a national organ failure registry, the proposed methods are applied to the analysis of technique failures among Canadian dialysis patients.

Analysis of Variance↗

PubMed2005

Baseline comorbidity in kidney transplant recipients: a comparison of comorbidity indices.

BACKGROUND: An increasing number of patients starting renal replacement therapy are older and have complex comorbidity. In keeping with these demographics, an increased number of older patients undergo transplantation each year. To date, no study has reported baseline comorbidity characteristics of those who underwent transplantation, validated the use of comorbidity indices, or asked whether comorbidity predicts patient outcome after kidney transplantation. Our objective is to report baseline comorbidity and compare the use of different indices for recipients of kidneys from both deceased and living donors. METHODS: Using data from the Canadian Organ Replacement Registry, we tested the ability of 4 comorbidity indices to predict patient survival by using a Cox regression model. Model covariates included donor source, age, race, sex, treatment period, primary renal disease cause, months on dialysis therapy, and comorbidities. RESULTS: A total of 6,324 patients were included; 22% had > or =1 comorbid condition at baseline. After adjustment for age, sex, and cause of renal disease, increased comorbidity was associated strongly with reduced patient survival. Of all comorbidity indices examined, the model containing the Charlson Comorbidity Index (CCI) offered the best fit. The model containing log--CCI had an index of concordance of 74%. CONCLUSION: The CCI is a suitable tool for the measurement of comorbidity in renal transplant recipients.

Adult↗

PubMed2005

Predicting mortality after kidney transplantation: a clinical tool.

An increasing number of patients referred for transplantation are older and have complex comorbidity affecting outcome. Patient counseling is often empiric and time consuming. For the physician there are few clinical tools available to help quantify survival chances after transplantation. We used registry data to develop a series of tables that could be used in the clinical setting to predict survival probability. Using data from the Canadian Organ Replacement Registry, we generated clinical survival tables using Cox's regression model. Model covariates included age, race, gender, treatment period, primary renal disease cause, donor source, months on dialysis and comorbidities. A total of 6324 patients were included, 22% had > or =1 comorbid condition at baseline. After adjustment for age, gender and cause of renal disease, increased comorbidity was strongly associated with reduced patient-survival (P < 0.05). Age and comorbidity specific clinical survival tables showing the expected 1-, 3- and 5-year patient survival probabilities were generated. Separate tables were created for diabetics, nondiabetics, living-donor organs and deceased-donor transplantation. Patient-specific survival data can be estimated from registry data. We suggest annual or biannual tables generated by national registries across Europe and N. America, may be useful to those physicians faced with counseling patients and families.

Adult↗

PubMed2005

Analytical approaches for transplant research, 2004.

This article provides detailed explanations of the methods frequently employed in outcomes analyses performed by the Scientific Registry of Transplant Recipients (SRTR). All aspects of the analytical process are discussed, including cohort selection, post-transplant follow-up analysis, outcome definition, ascertainment of events, censoring, and adjustments. The methods employed for descriptive analyses are described, such as unadjusted mortality rates and survival probabilities, and the estimation of covariant effects through regression modeling. A section on transplant waiting time focuses on the kidney and liver waiting lists, pointing out the different considerations each list requires and the larger questions that such analyses raise. Additionally, this article describes specialized modeling strategies recently designed by the SRTR and aimed at specific organ allocation issues. The article concludes with a description of simulated allocation modeling (SAM), which has been developed by the SRTR for three organ systems: liver, thoracic organs, and kidney-pancreas. SAMs are particularly useful for comparing outcomes for proposed national allocation policies. The use of SAMs has already helped in the development and implementation of a new policy for liver candidates with high MELD scores to be offered organs regionally before the organs are offered to candidates with low MELD scores locally.

Data Interpretation, Statistical↗

PubMed2005

The survival benefit of liver transplantation.

Demand for liver transplantation continues to exceed donor organ supply. Comparing recipient survival to that of comparable candidates without a transplant can improve understanding of transplant survival benefit. Waiting list and post-transplant mortality was studied among a cohort of 12 996 adult patients placed on the waiting list between 2001 and 2003. Time-dependent Cox regression models were fitted to determine relative mortality rates for candidates and recipients. Overall, deceased donor transplant recipients had a 79% lower mortality risk than candidates (HR = 0.21; p < 0.001). At Model for End-stage Liver Disease (MELD) 18-20, mortality risk was 38% lower (p < 0.01) among recipients compared to candidates. Survival benefit increased with increasing MELD score; at the maximum score of 40, recipient mortality risk was 96% lower than that for candidates (p < 0.001). In contrast, at lower MELD scores, recipient mortality risk during the first post-transplant year was much higher than for candidates (HR = 3.64 at MELD 6-11, HR = 2.35 at MELD 12-14; both p < 0.001). Liver transplant survival benefit at 1 year is concentrated among patients at higher risk of pre-transplant death. Futile transplants among severely ill patients are not identified under current practice. With 1 year post-transplant follow-up, patients at lower risk of pre-transplant death do not have a demonstrable survival benefit from liver transplant.

Adolescent↗

PubMed2004

Impact of graft failure on patient survival on dialysis: a comparison of transplant-naive and post-graft failure mortality rates.

BACKGROUND: While the number of patients returning to dialysis after graft failure (GF) is increasing steadily, the impact of a failed kidney transplant on mortality among dialysis patients has not been studied well. METHODS: Data from the Canadian Organ Replacement Register were utilized to examine the outcomes of an incident cohort of patients (n = 25,632) initiating renal replacement therapy (RRT) between 1990 and 1998. Cox regression was used to compare covariate-adjusted mortality among five RRT categories: transplant-naive dialysis, cadaveric primary renal transplant, living-donor primary renal transplant, post-GF dialysis and retransplant. RRT category-specific hazard ratios (HR) were estimated using Cox regression and adjusting for age, sex, race, calendar period, primary renal diagnosis and comorbid conditions. RESULTS: Mortality among post-GF dialysis patients was approximately equal to that of transplant-naive patients (HR = 0.90; P = 0.30) while the HR for retransplanted patients was significantly decreased, relative to the transplant-naive group (HR = 0.35; P<0.01). Diabetes was found to be a significantly (P<0.01) stronger mortality risk factor among post-GF dialysis patients (HR = 3.71) compared with the transplant-naive group (HR = 1.73). In the post-GF group, cardiovascular disease (HR = 1.66) and 'other serious illness' (HR = 2.07) were found to be much stronger risk factors for mortality than in the transplant-naive group (HR = 1.33 and 1.43, respectively), although the differences failed to reach statistical significance. CONCLUSIONS: These results suggest that transplant-naive and post-GF dialysis patients have equivalent mortality risk and that mortality is significantly reduced upon retransplantation. In addition, the results highlight the importance of diabetes and, possibly, comorbid conditions as potential modifiable risk factors in the management of post-GF dialysis patients.

Adult↗

PubMed2004

Non-parametric estimation of gap time survival functions for ordered multivariate failure time data.

Times between sequentially ordered events (gap times) are often of interest in biomedical studies. For example, in a cancer study, the gap times from incidence-to-remission and remission-to-recurrence may be examined. Such data are usually subject to right censoring, and within-subject failure times are generally not independent. Statistical challenges in the analysis of the second and subsequent gap times include induced dependent censoring and non-identifiability of the marginal distributions. We propose a non-parametric method for constructing one-sample estimators of conditional gap-time specific survival functions. The estimators are uniformly consistent and, upon standardization, converge weakly to a zero-mean Gaussian process, with a covariance function which can be consistently estimated. Simulation studies reveal that the asymptotic approximations are appropriate for finite samples. Methods for confidence bands are provided. The proposed methods are illustrated on a renal failure data set, where the probabilities of transplant wait-listing and kidney transplantation are of interest.

Acute Kidney Injury↗

PubMed2004

Centre-specific variation in renal transplant outcomes in Canada.

BACKGROUND: The 'centre effect' has accounted for significant variation in renal allograft outcomes in the United States and Europe. To determine whether similar variation exists in Canada, we analysed mortality and graft failure (GF) rates among Canadian end-stage renal disease patients who received a renal allograft from 1988 to 1997 (n = 5082) across 20 transplant centres. METHODS: Patients were followed from the date of transplantation to the time of GF and/or death. A Cox proportional hazards model was used to estimate mortality and GF hazard ratios (HRs) adjusted for relevant covariates, including centre volume. Centre-specific HRs were derived by comparing each centre's outcome rates against all others. RESULTS: Twenty centres were included in the analysis. There was significant centre-specific variation in recipient and transplant characteristics (e.g. age, diabetes mellitus, donor source and centre volume) as well as covariate-adjusted facility-specific outcome rates. Facility-specific HRs for GF (including death with a functioning graft) ranged from 0.51 to 1.77, while mortality HRs (including death beyond GF) showed a similar spread (0.44-1.84). These HRs represent a 3- to 4-fold difference in transplant outcomes among the 20 centres studied. Centres performing less than 200 transplants over the study period were associated with lower graft and patient survival. CONCLUSIONS: These findings demonstrate significant centre-specific variation in the success of renal transplantation in Canada. Further studies are needed to elucidate the causes of this variation, with the goal of developing strategies to minimize the centre effect and ensure the best possible outcomes for all renal transplant recipients.

Adult↗

PubMed2004

Marginal means/rates models for multiple type recurrent event data.

Recurrent events are frequently observed in biomedical studies, and often more than one type of event is of interest. Follow-up time may be censored due to loss to follow-up or administrative censoring. We propose a class of semi-parametric marginal means/rates models, with a general relative risk form, for assessing the effect of covariates on the censored event processes of interest. We formulate estimating equations for the model parameters, and examine asymptotic properties of the parameter estimators. Finite sample properties of the regression coefficients are examined through simulations. The proposed methods are applied to a retrospective cohort study of risk factors for preschool asthma.

Asthma↗

PubMed2004

Access to renal transplantation for minority patients with ESRD in Canada.

BACKGROUND: The incidence of end-stage renal disease is increasing worldwide. Renal transplantation generally is the preferred modality of renal replacement therapy; however, in the United States, it is known that minority groups experience decreased access to renal transplantation. It is unknown whether similar differences exist in Canada. METHODS: Using the Canadian Organ Replacement Register, we identified 25,632 Canadian patients 18 years or older initiating renal replacement therapy during 1990 to 1998. We used Cox regression models to examine adjusted renal transplantation rates among whites, aboriginals, blacks, South Asians, and East Indians during an 8-year period. RESULTS: Adjusted overall transplantation rates were decreased in comparison to whites for aboriginals (rate ratio [RR], 0.54; 95% confidence interval, 0.45 to 0.63), blacks (RR, 0.54; 95% confidence interval, 0.46 to 0.66), South Asians (RR, 0.69; 95% confidence interval, 0.61 to 0.79), and East Indians (RR, 0.66; 95% confidence interval, 0.56 to 0.78). Race was at least as strong a predictor of transplantation as other known predictors, including age, sex, primary renal diagnosis, and comorbidities. Disparities in renal transplantation rates increased over time from 1990 to 1998 for all racial groups in comparison to whites. CONCLUSION: Renal transplantation rates differ substantially by race in Canada, and these differences appear to be worsening over time. Future work should focus on identifying the specific barriers responsible for these differences in care.

Adolescent↗

PubMed2004

Analytical approaches for transplant research.

It is highly desirable to base decisions designed to improve medical practice or organ allocation policies on the analyses of the most recent data available. Yet there is often a need to balance this desire with the added value of evaluating long-term outcomes (e.g. 5-year mortality rates), which requires the use of data from earlier years. This article explains the methods used by the Scientific Registry of Transplant Recipients in order to achieve these goals simultaneously. The analysis of waiting list and transplant outcomes depends strongly on statistical methods that can combine data from different cohorts of patients that have been followed for different lengths of time. A variety of statistical methods have been designed to address these goals, including the Kaplan-Meier estimator, Cox regression models, and Poisson regression. An in-depth description of the statistical methods used for calculating waiting times associated with the various types of organ transplants is provided. Risk of mortality and graft failure, adjusted analyses, cohort selection, and the many complicating factors surrounding the calculation of follow-up time for various outcomes analyses are also examined.

Cohort Studies↗