Planning for Uncertainty and Fallbacks Can Increase the Number of Transplants in a Kidney-Paired Donation Program.

A kidney-paired donation (KPD) pool consists of transplant candidates and their incompatible donors, along with nondirected donors (NDDs). In a match run, exchanges are arranged among pairs in the pool via cycles, as well as chains created from NDDs. A problem of importance is how to arrange cycles and chains to optimize the number of transplants. We outline and examine, through example and by simulation, four schemes for selecting potential matches in a realistic model of a KPD system; proposed schemes take account of probabilities that chosen transplants may not be completed as well as allowing for contingency plans when the optimal solution fails. Using data on candidate/donor pairs and NDDs from the Alliance for Paired Donation, the simulations extend over 8 match runs, with 30 pairs and 1 NDD added between each run. Schemes that incorporate uncertainties and fallbacks into the selection process yield substantially more transplants on average, increasing the number of transplants by as much as 40% compared to a standard selection scheme. The gain depends on the degree of uncertainty in the system. The proposed approaches can be easily implemented and provide substantial advantages over current KPD matching algorithms.

Access and outcomes among minority transplant patients, 1999-2008, with a focus on determinants of kidney graft survival.

Coincident with an increasing national interest in equitable health care, a number of studies have described disparities in access to solid organ transplantation for minority patients. In contrast, relatively little is known about differences in posttransplant outcomes between patients of specific racial and ethnic populations. In this paper, we review trends in access to solid organ transplantation and posttransplant outcomes by organ type, race and ethnicity. In addition, we present an analysis of categories of factors that contribute to the racial/ethnic variation seen in kidney transplant outcomes. Disparities in minority access to transplantation among wait-listed candidates are improving, but persist for those awaiting kidney, simultaneous kidney and pancreas and intestine transplantation. In general, graft and patient survival among recipients of solid organ transplants is highest for Asians and Hispanic/Latinos, intermediate for whites and lowest for African Americans. Although much of the difference in outcomes between racial/ethnic groups can be accounted for by adjusting for patient characteristics, important observed differences remain. Age and duration of pretransplant dialysis exposure emerge as the most important determinants of survival in an investigation of the relative impact of center-related versus patient-related variables on kidney graft outcomes.

Innovations in the assessment of transplant center performance: implications for quality improvement.

Continuous quality improvement efforts have become a central focus of leading health care organizations. The transplant community has been a pioneer in periodic review of clinical outcomes to ensure the optimal use of limited donor organs. Through data collected from the Organ Procurement and Transplantation Network (OPTN) and analyzed by the Scientific Registry of Transplant Recipients (SRTR), transplantation professionals have intermittent access to specific, accurate and clinically relevant data that provides information to improve transplantation. Statistical process control techniques, including cumulative sum charts (CUSUM), are designed to provide continuous, real-time assessment of clinical outcomes. Through the use of currently collected data, CUSUMs can be constructed that provide risk-adjusted program-specific data to inform quality improvement programs. When retrospectively compared to currently available data reporting, the CUSUM method was found to detect clinically significant changes in center performance more rapidly, which has the potential to inform center leadership and enhance quality improvement efforts.

Survival benefit-based deceased-donor liver allocation.

Currently, patients awaiting deceased-donor liver transplantation are prioritized by medical urgency. Specifically, wait-listed chronic liver failure patients are sequenced in decreasing order of Model for End-stage Liver Disease (MELD) score. To maximize lifetime gained through liver transplantation, posttransplant survival should be considered in prioritizing liver waiting list candidates. We evaluate a survival benefit based system for allocating deceased-donor livers to chronic liver failure patients. Under the proposed system, at the time of offer, the transplant survival benefit score would be computed for each patient active on the waiting list. The proposed score is based on the difference in 5-year mean lifetime (with vs. without a liver transplant) and accounts for patient and donor characteristics. The rank correlation between benefit score and MELD score is 0.67. There is great overlap in the distribution of benefit scores across MELD categories, since waiting list mortality is significantly affected by several factors. Simulation results indicate that over 2000 life-years would be saved per year if benefit-based allocation was implemented. The shortage of donor livers increases the need to maximize the life-saving capacity of procured livers. Allocation of deceased-donor livers to chronic liver failure patients would be improved by prioritizing patients by transplant survival benefit.

Use of statins and incidence of dementia and cognitive impairment without dementia in a cohort study.

OBJECTIVE: Evidence of a relation between use of lipid lowering drugs and cognitive outcomes is mixed. This study aimed to test the association between use of statins and incidence of dementia and cognitive impairment without dementia (CIND) over 5 years of follow-up. METHODS: Data were from a population-based cohort study comprising 1,789 older Mexican Americans. All participants had cognitive and clinical evaluations performed every 12 to 15 months. Participants who fell below specified cutpoints on cognitive tests were then evaluated clinically. Dementia diagnoses were finalized by an adjudication team. A total of 1,674 participants free of dementia/CIND at baseline were included in these analyses. Statin use was verified at each participant's home by medicine cabinet inspection. Cox proportional hazards models were used to evaluate the association between statin use and incidence of dementia/CIND. RESULTS: Overall, 452 of 1,674 participants (27%) took statins at any time during the study. Over the 5-year follow-up period, 130 participants developed dementia/CIND. In Cox proportional hazards models adjusted for education, smoking status, presence of at least one APOE epsilon4 allele, and history of stroke or diabetes at baseline, persons who had used statins were about half as likely as those who did not use statins to develop dementia/CIND (HR = 0.52; 95% CI 0.34, 0.80). CONCLUSION: Statin users were less likely to have incident dementia/cognitive impairment without dementia during a 5-year follow-up. These results add to the emerging evidence suggesting a protective effect of statin use on cognitive outcomes.

Calculating life years from transplant (LYFT): methods for kidney and kidney-pancreas candidates.

The Organ Procurement and Transplantation Network (OPTN) Kidney Committee is considering a proposal for a new deceased donor kidney allocation system. Among the components under consideration is a strategy to rank candidates in part by the estimated incremental years of life that are expected to be achieved with a transplant from a specific available deceased donor, computed as the difference in expected median lifespan with that transplant compared with remaining on dialysis. This concept has been termed life years from transplant or LYFT. Median lifespans could be calculated, based on objective medical criteria, for each candidate when a deceased donor kidney becomes available, based on Cox regression models using current candidate and donor medical information. The distribution of the calculated LYFT scores for an average nonexpanded criteria donor kidney is similar across candidate sex, race/ethnicity, insurance status and, with the exception of diabetes, diagnosis. LYFT scores tend to be higher for younger candidates and lower for diabetics receiving a kidney-alone rather than a simultaneous kidney-pancreas transplant. Prioritizing candidates with higher LYFT scores for each available kidney could substantially increase total years of life among both transplant candidates and recipients. LYFT is also a powerful metric for assessing trends in allocation outcomes and for comparing alternative allocation systems.

Geographic variability in access to primary kidney transplantation in the United States, 1996-2005.

This article focuses on geographic variability in patient access to kidney transplantation in the United States. It examines geographic differences and trends in access rates to kidney transplantation, in the component rates of wait-listing, and of living and deceased donor transplantation. Using data from Centers for Medicare and Medicaid Services and the Organ Procurement and Transplantation Network/Scientific Registry of Transplant Recipients, we studied 700,000+ patients under 75, who began chronic dialysis treatment, received their first living donor kidney transplant, or were placed on the waiting list pre-emptively. Relative rates of wait-listing and transplantation by State were calculated using Cox regression models, adjusted for patient demographics. There were geographic differences in access to the kidney waiting list and to a kidney transplant. Adjusted wait-list rates ranged from 37% lower to 64% higher than the national average. The living donor rate ranged from 57% lower to 166% higher, while the deceased donor transplant rate ranged from 60% lower to 150% higher than the national average. In general, States with higher wait-listing rates tended to have lower transplantation rates and States with lower wait-listing rates had higher transplant rates. Six States demonstrated both high wait-listing and deceased donor transplantation rates while six others, plus D.C. and Puerto Rico, were below the national average for both parameters.

Between- and within-cluster covariate effects in the analysis of clustered data.

Standard methods for the regression analysis of clustered data postulate models relating covariates to the response without regard to between- and within-cluster covariate effects. Implicit in these analyses is the assumption that these effects are identical. Example data show that this is frequently not the case and that analyses that ignore differential between- and within-cluster covariate effects can be misleading. Consideration of between- and within-cluster effects also helps to explain observed and theoretical differences between mixture model analyses and those based on conditional likelihood methods. In particular, we show that conditional likelihood methods estimate purely within-cluster covariate effects, whereas mixture model approaches estimate a weighted average of between- and within-cluster covariate effects.

Nonparametric tests of tumor prevalence data.

This note discusses the statistical analysis of tumor prevalence data arising from tumorgenicity experiments with focus on the comparison of different treatments. In this situation, the commonly used tests can be classified into two types: interval-based tests and model-based tests (Hoel, D. G. and Walburg, H. E., 1972, Journal of the National Cancer Institute 49, 361-372; Dinse, G. E. and Lagakos, S. W. 1983, Applied Statistics 32, 236-248). It is known that the results obtained from the interval-based tests may vary according to the choice of intervals and, for the model-based tests, it may be difficult to justify the assumed model. A computationally simple alternative to these tests is proposed; this alternative is not interval-based and makes no strong model assumption. The results of a simulation study comparing the proposed test with other tests are presented and suggest that the proposed approach is quite satisfactory.

Marker processes in survival analysis.

In the development of many diseases there are often associated variables which continuously measure the progress of an individual towards the final expression of the disease (failure). Such variables are stochastic processes, here called marker processes, and, at a given point in time, they may provide information about the current hazard and subsequently on the remaining time to failure. Here we consider a simple additive model for the relationship between the hazard function at time t and the history of the marker process up until time t. We develop some basic calculations based on this model. Interest is focused on statistical applications for markers related to estimation of the survival distribution of time to failure, including (i) the use of markers as surrogate responses for failure with censored data, and (ii) the use of markers as predictors of the time elapsed since onset of a survival process in prevalent individuals. Particular attention is directed to potential gains in efficiency incurred by using marker process information.

A consequence of omitted covariates when estimating odds ratios.

In the epidemiologic literature, one finds three criteria for confounding, which we will call the classical (marginal), operational (change-in-estimate) and conditional criteria. We define mavericks to be covariates that satisfy the operational criterion, but not the classical criterion. We present what is known about the problems of mavericks for estimating odds ratios and clarify the interpretation of odds ratios. Key results are: (1) omitting mavericks biases odds ratios towards 1; (2) omitting mavericks cannot artificially introduce an effect in contrast to omitting classical confounders; (3) the operational criterion for confounding corresponds to the conditional criterion when estimating odds ratios, but for relative risks, there are no mavericks (i.e. the classical and operational criterion correspond); and (4) the interpretation of odds ratios obtained from standard methods is that of comparing proportions, not of individual risk.

Estimating the incubation time distribution and expected number of cases of transfusion-associated acquired immune deficiency syndrome.

The number of cases of transfusion-associated acquired immune deficiency syndrome (TA-AIDS) that will be seen over the next few years is difficult to estimate, because of the uncertainty about the number of persons infected with the human immunodeficiency virus (HIV) via blood transfusion and about the duration of the incubation period from HIV infection via transfusion to diagnosis of AIDS. Presented here are a mathematical model and nonparametric and parametric statistical analyses of recent data on TA-AIDS that indicate clearly the existing estimability problems. The methods provide short-term projections of new TA-AIDS cases to be reported; the results suggest about 1100 new cases to be reported in the United States between July 1988 and June 1989 and about 1500 more between July 1989 and June 1990. Estimates of the number of eventual TA-AIDS cases to be seen are considerably more uncertain and require additional assumptions about the incubation distribution. Under the assumption that the probability of an infected person developing AIDS within 8 years of infection is 0.40 (an estimation derived from cohort studies in homosexual men and hemophiliacs), parametric and nonparametric analyses give, respectively, point estimates of 14,300 and 15,000 for the number of eventual cases of AIDS (in the age group 13-69) attributable to infection by blood transfusion prior to July 1985. The parametric analysis gives a corresponding 95 percent confidence interval.

Likelihood analysis of multi-state models for disease incidence and mortality.

Data related to life histories of individuals can be obtained in many different ways, and the usefulness of multi-state models for statistical analysis is generally highly dependent on the type and nature of the data. In this paper, we focus on this, and present an approach to estimation for certain 'difficult' situations associated with retrospective or incomplete prospective observation. The paper begins with the identification of some problem areas in the analysis of data on life history processes. We discuss maximum likelihood estimation in some simple contexts and introduce a pseudo-likelihood which enables the simple analysis of some sampling procedures. This approach is illustrated on standard retrospective and case-cohort designs.

Nonparametric methods for survival/sacrifice experiments.

In many carcinogenicity studies, the time to disease occurrence is not clinically observable; a survival/sacrifice experiment is considered for nonparametric inference about the rate of disease occurrence. A multistate model for disease development and death is considered and an algorithm of the EM type for maximum likelihood estimation is obtained. Questions of identifiability and estimability are addressed. Under the model, interval hazards for disease occurrence are identifiable for intervals defined by the sacrifice times. A score test is developed appropriate for the comparison of two groups with respect to disease development without need of any assumption concerning lethality of the disease concerned.

On the use of case series to identify disease risk factors.

Methods to identify disease risk factors from a series of cases are considered. These include methods that compare risk factor levels among diagnostic categories and methods that relate risk factor levels to age at diagnosis, with a single diagnostic category. Statistical aspects considered include modelling assumptions, parameter identifiability, hypothesis-testing efficiency, assumptions concerning unsampled diagnostic categories and requirements for risk factor data and confounding factor data. It is argued that methods to identify risk factors using data on a single diagnostic category involve such strong assumptions that they have limited usefulness. Analyses that compare risk factor levels among diagnostic categories, on the other hand, should continue to play an important role in epidemiologic research, though there are important limitations in relation to analyses involving disease-free controls.

Estimation in Markov models from aggregate data.

In this paper, situations in which individuals move through a finite set of states according to a continuous-time Markov process are considered. Only aggregate data are available: these consist of the number of individuals in each state at specified observation times. We develop conditional least squares and approximate maximum-likelihood-estimation procedures for time-homogeneous models, and extend the methods so that they can handle immigration of individuals into the system during observation. Asymptotic covariance estimates are presented, and some problems for future study are noted.

Hazard rate models with covariates.

Many problems, particularly in medical research, concern the relationship between certain covariates and the time to occurrence of an event. The hazard or failure rate function provides a conceptually simple representation of time to occurrence data that readily adapts to include such generalizations as competing risks and covariates that vary with time. Two partially parametric models for the hazard function are considered. These are the proportional hazards model of Cox (1972) and the class of log-linear or accelerated failure time models. A synthesis of the literature on estimation from these models under prospective sampling indicates that, although important advances have occurred during the past decade, further effort is warranted on such topics as distribution theory, tests of fit, robustness, and the full utilization of a methodology that permits non-standard features. It is further argued that a good deal of fruitful research could be done on applying the same models under a variety of other sampling schemes. A discussion of estimation from case-control studies illustrates this point.

The analysis of failure times in the presence of competing risks.

Distinct problems in the analysis of failure times with competing causes of failure include the estimation of treatment or exposure effects on specific failure types, the study of interrelations among failure types, and the estimation of failure rates for some causes given the removal of certain other failure types. The usual formation of these problems is in terms of conceptual or latent failure times for each failure type. This approach is criticized on the basis of unwarranted assumptions, lack of physical interpretation and identifiability problems. An alternative approach utilizing cause-specific hazard functions for observable quantities, including time-dependent covariates, is proposed. Cause-specific hazard functions are shown to be the basic estimable quantities in the competing risks framework. A method, involving the estimation of parameters that relate time-dependent risk indicators for some causes to cause-specific hazard functions for other causes, is proposed for the study of interrelations among failure types. Further, it is argued that the problem of estimation of failure rates under the removal of certain causes is not well posed until a mechanism for cause removal is specified. Following such a specification, one will sometimes be in a position to make sensible extrapolations from available data to situations involving cause removal. A clinical program in bone marrow transplantation for leukemia provides a setting for discussion and illustration of each of these ideas. Failure due to censoring in a survivorship study leads to further discussion.