Bortezomib, C1-inhibitor and plasma exchange do not prolong the survival of multi-transgenic GalT-KO pig kidney xenografts in baboons.

Galactosyl-transferase KO (GalT-KO) pigs represent a potential solution to xenograft rejection, particularly in the context of additional genetic modifications. We have performed life supporting kidney xenotransplantation into baboons utilizing GalT-KO pigs transgenic for human CD55/CD59/CD39/HT. Baboons received tacrolimus, mycophenolate mofetil, corticosteroids and recombinant human C1 inhibitor combined with cyclophosphamide or bortezomib with or without 2-3 plasma exchanges. One baboon received a control GalT-KO xenograft with the latter immunosuppression. All immunosuppressed baboons rejected the xenografts between days 9 and 15 with signs of acute humoral rejection, in contrast to untreated controls (n = 2) that lost their grafts on days 3 and 4. Immunofluorescence analyses showed deposition of IgM, C3, C5b-9 in rejected grafts, without C4d staining, indicating classical complement pathway blockade but alternate pathway activation. Moreover, rejected organs exhibited predominantly monocyte/macrophage infiltration with minimal lymphocyte representation. None of the recipients showed any signs of porcine endogenous retrovirus transmission but some showed evidence of porcine cytomegalovirus (PCMV) replication within the xenografts. Our work indicates that the addition of bortezomib and plasma exchange to the immunosuppressive regimen did not significantly prolong the survival of multi-transgenic GalT-KO renal xenografts. Non-Gal antibodies, the alternative complement pathway, innate mechanisms with monocyte activation and PCMV replication may have contributed to rejection.

Predicting costs of stem-cell transplantation.

PURPOSE: Few studies have formally evaluated the relationship between costs, baseline patient characteristics, and major complications of stem-cell transplantation. We sought (1) to determine whether obtaining baseline information enabled identification of patients whose treatments would be the most costly and (2) to estimate inpatient costs for managing specific transplantation complications. PATIENTS AND METHODS: We collected inpatient costs and clinical information for 236 consecutive patients undergoing transplantation at a single institution between July 1, 1994, and February 20, 1997. Multivariable linear regression was used to evaluate the associations between baseline patient characteristics and costs of hospitalization for initial transplantation and between clinical events and such costs. RESULTS: The median initial inpatient cost in 1997 dollars was $55,500 for autologous transplantation (range, $28,200 to $148,200) and $105,300 for allogeneic transplantation (range, $32,500 to $338,000). When only baseline variables were considered, use of a mismatched allogeneic donor and year of transplantation were significant predictors of costs. No characteristics predicted which patients would incur the highest 10% of costs. When clinical events were considered, infection and in-hospital death were associated with higher costs in autologous transplant recipients ($18,400 and $20,500, respectively), whereas infection, veno-occlusive disease, acute graft-versus-host disease, and death were predicted to add between $15,300 and $28,100 each to allogeneic transplantation costs. CONCLUSION: We were not able to identify before transplantation the patients whose treatments would be the most costly. However, the association between clinical complications and higher costs suggests that prevention may have significant economic benefits. Interventions that decrease these complications may have favorable cost-benefit ratios even if they do not affect overall survival.

Nipple fluid carcinoembryonic antigen and prostate-specific antigen in cancer-bearing and tumor-free breasts.

PURPOSE: Mammograms and breast examinations are established methods for early breast cancer detection. Routine mammography screening reduces breast cancer mortality among women ages > or = 50 years, but additional screening methods are needed. We and others have found high levels of carcinoembryonic antigen (CEA) and prostate-specific antigen (PSA) in nipple aspirate fluids (NAFs), but the usefulness for these bio-markers for early breast cancer detection is unknown. PATIENTS AND METHODS: NAFs from one or both breasts of 388 women were analyzed for CEA, PSA, and albumin levels. The study included 44 women with newly diagnosed invasive breast cancers, 67 women with proliferative breast lesions (ductal and lobular carcinoma in situ and atypical ductal hyperplasia), and 277 controls without these breast lesions. Analyses were conducted using the log(10)-transformed CEA and PSA levels to normalize the distributions of these tumor markers. RESULTS: Nipple fluid CEAs are significantly higher for cancerous breasts than tumor-free breasts (median 1,830 and 1,400 ng/mL, respectively; P <.01). However, at 90% specificity of the assay (CEA = 11,750 ng/mL), the corresponding sensitivity for cancer detection is 32%. CEA levels are not significantly different for breasts with proliferative lesions compared with tumor-free breasts. Nipple fluid PSAs do not differ by tumor status. Analyses of NAF albumin-standardized CEAs and PSAs yield similar results. Nipple fluid CEA and PSA titers are correlated in the affected and unaffected breast of women with unilateral lesions. CONCLUSION: Nipple fluid CEAs are higher for breasts with untreated invasive cancers, but the test sensitivity is low. Nipple fluid PSA titers do not seem to be useful for breast cancer detection.

Behavioral risk factors among women presenting for genetic testing.

Considerable research attention has been given to the impact of genetic testing on psychological outcomes. Participation in genetic testing also may impact on health behaviors that increase the risk of cancer and other chronic diseases. The purpose of this study is to describe behavioral cancer risk factors of women who requested genetic testing for breast and ovarian cancer susceptibility (BRCA1, BRCA2). Before participation in a genetic testing program, 119 women completed a series of questionnaires designed to assess their health behaviors, perception of risk, and depressive symptomatology. Eight percent of participants were current smokers, 27% did not engage in at least moderate exercise, 46% did not regularly protect themselves from the sun, 39% did not consume at least five servings of fruits and vegetables per day, and 9% drank at least one alcoholic beverage per day. Poisson regression analysis revealed that age was the only predictor of behavioral risk profiles, with older women having fewer cancer risk behaviors. These patients who presented for genetic testing generally had better health behaviors than the general population. However, given their possible high-risk status, these patients should consider further improving their preventable cancer risk factors and, in particular, their diet, sun protection, and physical activity levels. Inclusion of behavioral risk factor counseling in the context of the genetic testing process may be an important opportunity to reach this at-risk population.

Disclosure of familial genetic information: perceptions of the duty to inform.

BACKGROUND: The familial implications of genetic information can lead to a conflict between a physician's duties to maintain patient confidentiality and to inform at-risk relatives about susceptibility to genetic diseases. As genes are discovered that can identify patients at risk of adverse outcomes, this conflict has become the subject of discussion and debate. METHODS: We performed a one-time telephone survey of a population-based sample of 200 Jewish women to assess knowledge and attitudes about genetic testing. Attitudes toward sharing genetic test results with family members were evaluated using three hypothetical scenarios that described an easily preventable disease, a disease (breast cancer) in which the only option for prevention was prophylactic mastectomies, and a nonpreventable disease. RESULTS: Nearly all respondents believed that a patient should inform at-risk family members when the disease was preventable (100% and 97% in the relevant scenarios), compared with only 85% who felt a duty to inform at-risk family members about a nonpreventable disease (P <0.001). The proportions of respondents who believed that physicians should seek out and inform at-risk family members against a patient's wishes was much lower: only 18% of respondents to the easily preventable disease scenario, 22% of respondents to the breast cancer scenario, and 16% of respondents to the nonpreventable disease scenario. CONCLUSIONS: Most women surveyed believed that genetic information should be shared within families, unless it violated a patient's wishes. These sorts of opinions should be considered in the debate over the confidentiality of genetic information.

An evaluation of utility measurement in Crohn's disease.

SUMMARY: : We evaluated the use of utility measurements to assess the quality of life of patients with Crohn's disease. Utility scores were obtained using the Time Trade-Off (TTO), Standard Gamble, and Visual Analog Scale (VAS) methods in 180 consecutive patients with Crohn's disease. The mean utility scores of patients with a spectrum of disease severity were compared with other measures of disease activity to assess the operating properties of these instruments. All methods of utility estimation yielded lower mean scores in patients with more severe disease. (Remission versus chronically active, therapy resistant disease: TTO 0.96 versus 0.88; Standard Gamble 0.88 versus 0.74; VAS 0.84 versus 0.61). TTO scores were consistently higher than those derived by the other methods (p = 0.001). The utility scores were reliable in patients who were stable (intraclass correlation coefficient 0.55-0.84), but were less responsive than the Crohn's Disease Activity Index (responsiveness ratio 0.97-1.3 versus 2.10) to changes in disease severity. Patients with active Crohn's disease have decreased quality of life as measured by utility scores. Although utilities are valid and reliable quality of life assessments, they are less responsive than other measures of outcome used for clinical trials.

Statistical considerations in the design and analysis of community intervention trials.

Community intervention trials are often characterized by the allocation of intact social units to different intervention groups. The assessment of adequate sample size for such trials must take into account the statistical dependencies among responses observed within an allocated unit. However, the small numbers of units typically involved in such trials imply that many methods of analysis that have been proposed for analyzing correlated data, particularly in the case of a dichotomous outcome variable, are not applicable to such designs. In this article we investigate this issue and determine the minimum number of units required per group, for the case of both a dichotomous and a continuous outcome variable, needed to provide adequate statistical power for detecting various levels of treatment effect. The use of significance testing as a method of detecting intracluster correlation is also investigated, and, in general, discouraged.

The statistical analysis of kappa statistics in multiple samples.

Methods are presented for assessing and comparing the results of k > or = 2 independent samples of measured agreement or concordance, where in each sample a given member of a pair of observations is classified according to the presence or absence of a binary trait. Examples include the assessment of interobserver agreement across different groups of patients in a clinical study, investigations of sibling concordance across different genetic groups, and meta-analyses of observer agreement across different studies. The methodology described is based on application of goodness-of-fit theory to testing hypotheses concerning kappa statistics. Partitioning methods allow a variety of hypotheses to be tested, including an assessment of the degree of agreement within each sample, a testing procedure based on the pooled data, and a test of heterogeneity that may be used to assess the validity of pooling across samples. Three examples are given.

Confidence interval construction for effect measures arising from cluster randomization trials.

Methods of confidence interval construction are provided for summary measures of treatment effect arising from designs randomizing clusters to one of two treatment groups. Three basic designs are considered for the case of continuous and dichotomous variables: completely randomized, pair-matched and stratified.

Cluster randomization trials in tropical medicine: a case study.

Field trials in tropical medicine are often designed so that intact social units (e.g., families, schools, communities) rather than independent individuals are randomized to an intervention group. Reasons are diverse, but include administrative convenience, a desire to reduce the effect of treatment contamination, and the need to avoid ethical issues that might otherwise arise. Dependencies among cluster members typical of such designs must be considered when determining sample size and analysing the resulting data. Failure to do so can result in false conclusions that the treatment is effective. The purpose of this paper is to compare different methods which can be used to construct tests of the effect of treatment when outcomes are binary (e.g., infected/uninfected). The discussion will be illustrated using data from a trial which randomly assigned families to either a control group or a screening and treatment programme for imported intestinal parasites.

Breast screening outcomes in women with and without a family history of breast and/or ovarian cancer.

OBJECTIVES: To compare breast screening outcomes between women with a moderate or strong family history of breast and/or ovarian cancer with those without such a history. SETTING: The Ontario Breast Screening Programme (OBSP) is a population-based programme offering mammography and clinical breast examination to Ontario women of 50 and older. METHODS: Data from a cohort of 143,574 women screened by the OBSP from 1996 to 1997 were included. Referral rates, cancer detection rates, positive predictive values and the histological features of screen-detected cancers were examined within family history groups, age groups and screening modalities. Logistic regression analysis of cancer detection was conducted to adjust for potential confounding variables; subgroup analysis by hormone replacement therapy (HRT) use was also undertaken. RESULTS: Compared with women with no family history, women with a moderate or strong family history of breast and/or ovarian cancer were more likely to have their cancer detected (odds ratio [OR]=1.44, 95% confidence interval [CI] 1.20-1.74 and OR=1.42, 95% CI 1.10-1.83, respectively). Among women using HRT, however, there was no association observed between family history and cancer detection (moderate: OR=0.98, 95% CI 0.65-1.48; strong: OR=1.17, 95% CI 0.68-2.02) with history. The histological features of invasive tumours were similar among family history groups. CONCLUSIONS: Greater cancer detection rates and high proportions of invasive tumours with good prognosis indicate that women aged 50 and over with a family history may have the potential to benefit from regular breast cancer screening. Further studies are required to identify optimal screening guidelines and to examine whether HRT reduces the ability to detect cancer in these women.

Analysis of dichotomous outcome data for community intervention studies.

Community intervention trials are becoming increasingly popular as a tool for evaluating the effectiveness of health education and intervention strategies. Typically, units such as households, schools, towns, counties, are randomized to receive either intervention or control, then outcomes are measured on individuals within each of the units of randomization. It is well recognized that the design and analysis of such studies must account for the clustering of subjects within the units of randomization. Furthermore, there are usually both subject level and cluster level covariates that must be considered in the modelling process. While suitable methods are available for continuous outcomes, data analysis is more complicated when dichotomous outcomes are measured on each subject. This paper will compare and contrast several of the available methods that can be applied in such settings, including random effects models, generalized estimating equations and methods based on the calculation of 'design effects', as implemented in the computer package SUDAAN. For completeness, the paper will also compare these methods of analysis with more simplistic approaches based on the summary statistics. All the methods will be applied to a case study based on an adolescent anti-smoking intervention in Australia. The paper concludes with some general discussion and recommendations for routine design and analysis.

Developments in cluster randomized trials and Statistics in Medicine.

The design and analysis of cluster randomized trials has been a recurrent theme in Statistics in Medicine since the early volumes. In celebration of 25 years of Statistics in Medicine, this paper reviews recent developments, particularly those that featured in the journal. Issues in design such as sample size calculations, matched paired designs, cohort versus cross-sectional designs, and practical design problems are covered. Developments in analysis include modification of robust methods to cope with small numbers of clusters, generalized estimation equations, population averaged and cluster specific models. Finally, issues on presenting data, some other clustering issues and the general problem of evaluating complex interventions are briefly mentioned.

Current and future challenges in the design and analysis of cluster randomization trials.

Randomized trials in which the unit of randomization is a community, worksite, school or family are becoming widely used in the evaluation of life-style interventions for the prevention of disease. The increasing interest in adopting a cluster randomization design is being matched by rapid methodological developments. In this paper we describe several of these developments. Brief mention is also made of issues related to economic analysis and to the planning and conduct of meta-analyses for cluster randomization trials. Recommendations for reporting are also discussed.

Methods for comparing event rates in intervention studies when the unit of allocation is a cluster.

The aim of many research investigations is to compare the proportion of individuals in each of several groups that have a certain characteristic. The unit of allocation for such investigations is often an intact social unit, as in randomizing families, medical practices, schools, or entire communities, to different intervention groups. Standard statistical methods are not appropriate for these designs, since they do not take into account the dependencies among individuals within the same cluster. The authors review the strengths and weaknesses of several approaches for dealing with this problem, using data from a school-based smoking cessation trial. A principal conclusion is that the choice of method should depend on whether or not random allocation is used in the assignment of interventions.

The merits of matching in community intervention trials: a cautionary tale.

Concern about potential imbalance on risk factors in community intervention trials often prompts researchers to adopt a pair-matched design in which similar clusters of individuals are paired and one member of each matched pair is then randomly assigned to the intervention group. It is known that if there are few clusters in trial, it becomes increasingly difficult to obtain close matches on all potential risk factors. One may thus offset any gain in precision with loss in degrees of freedom due to matching. We shown in this paper that there are also several analytic limitations with pair-matched designs. These include: the restriction of prediction models to cluster-level baseline risk factors (for example, cluster size), the inability to test for homogeneity of odds ratios, and difficulties in estimating the intracluster correlation coefficient. These limitations lead us to present arguments that favour stratified designs in which there are more than two clusters in each stratum.

Testing the homogeneity of kappa statistics.

Procedures are compared for testing the homogeneity of k > or = 2 independent kappa statistics in the case of two raters and a dichotomous outcome. One of the procedures is based on the estimated large sample variance derived under a model frequently adopted for inferences concerning interobserver agreement. The other is based on a goodness-of-fit approach to this model. The results of a Monte Carlo simulation show that the two approaches have similar properties if the number of subjects in each sample is large (> 100), and the prevalence of the underlying trait of interest is not extreme, while the goodness-of-fit approach is recommended for comparisons involving smaller numbers of subjects or in which the prevalence of the underlying trait is small (< 0.3).

Statistical methodology for estimating twin similarity with respect to a dichotomous trait.

Statistical methodology is presented for the estimation of twin similarity with respect to a dichotomous trait. The methodology focuses on the intraclass correlation as the parameter of interest and is analogous to methodology commonly applied to continuous outcome data. For inference problems involving a single sample, confidence interval construction and significance testing are discussed. For two sample problems, test procedures are provided that are an alternative to an approach recently presented by Ramakrishnan et al. [(1992) Genet Epidemiol 9:273-287 (1)]. Two examples based on published data sets are given to illustrate the proposed techniques.

A comparison of methods for testing homogeneity of proportions in teratologic studies.

We consider teratologic studies in which the aim is to compare the survival rate of animals in a treatment group to the corresponding rate in a control group. The design of such studies often involves the allocation of intact litters of animals to treatment, invalidating the application of standard statistical methods. We review the strengths and weaknesses of several approaches for dealing with this problem including methodology recently developed for the analysis of clustered binary data. A simulation study is conducted in which litter sizes are generated from a distribution having specified mean and degree of imbalance. It is recommended on the basis of this study and on theoretical considerations that the choice of method should depend on whether the comparison of interest is experimental or observational. For experimental comparisons, involving the random assignment of litters to different treatment groups, methods based on the adjustment of standard chi-square statistics are recommended unless the number of litters in each group is very large.

Inference procedures for assessing interobserver agreement among multiple raters.

We propose a new procedure for constructing inferences about a measure of interobserver agreement in studies involving a binary outcome and multiple raters. The proposed procedure, based on a chi-square goodness-of-fit test as applied to the correlated binomial model (Bahadur, 1961, in Studies in Item Analysis and Prediction, 158-176), is an extension of the goodness-of-fit procedure developed by Donner and Eliasziw (1992, Statistics in Medicine 11, 1511-1519) for the case of two raters. The new procedure is shown to provide confidence-interval coverage levels that are close to nominal over a wide range of parameter combinations. The procedure also provides a sample-size formula that may be used to determine the required number of subjects and raters for such studies.