Bruce W. Turnbull

Effects of selenium supplementation for cancer prevention in patients with carcinoma of the skin. A randomized controlled trial. Nutritional Prevention of Cancer Study Group.

OBJECTIVE To determine whether a nutritional supplement of selenium will decrease the incidence of cancer. DESIGN A multicenter, double-blind, randomized, placebo-controlled cancer prevention trial. SETTING Seven dermatology clinics in the eastern United States. PATIENTS A total of 1312 patients (mean age, 63 years; range, 18-80 years) with a history of basal cell or squamous cell carcinomas of the skin were randomized from 1983 through 1991. Patients were treated for a mean (SD) of 4.5 (2.8) years and had a total follow-up of 6.4 (2.0) years. INTERVENTIONS Oral administration of 200 microg of selenium per day or placebo. MAIN OUTCOME MEASURES The primary end points for the trial were the incidences of basal and squamous cell carcinomas of the skin. The secondary end points, established in 1990, were all-cause mortality and total cancer mortality, total cancer incidence, and the incidences of lung, prostate, and colorectal cancers. RESULTS After a total follow-up of 8271 person-years, selenium treatment did not significantly affect the incidence of basal cell or squamous cell skin cancer. There were 377 new cases of basal cell skin cancer among patients in the selenium group and 350 cases among the control group (relative risk [RR], 1.10; 95% confidence interval [CI], 0.95-1.28), and 218 new squamous cell skin cancers in the selenium group and 190 cases among the controls (RR, 1.14; 95% CI, 0.93-1.39). Analysis of secondary end points revealed that, compared with controls, patients treated with selenium had a nonsignificant reduction in all-cause mortality (108 deaths in the selenium group and 129 deaths in the control group [RR; 0.83; 95% CI, 0.63-1.08]) and significant reductions in total cancer mortality (29 deaths in the selenium treatment group and 57 deaths in controls [RR, 0.50; 95% CI, 0.31-0.80]), total cancer incidence (77 cancers in the selenium group and 119 in controls [RR, 0.63; 95% CI, 0.47-0.85]), and incidences of lung, colorectal, and prostate cancers. Primarily because of the apparent reductions in total cancer mortality and total cancer incidence in the selenium group, the blinded phase of the trial was stopped early. No cases of selenium toxicity occurred. CONCLUSIONS Selenium treatment did not protect against development of basal or squamous cell carcinomas of the skin. However, results from secondary end-point analyses support the hypothesis that supplemental selenium may reduce the incidence of, and mortality from, carcinomas of several sites. These effects of selenium require confirmation in an independent trial of appropriate design before new public health recommendations regarding selenium supplementation can be made

Selenium supplementation, baseline plasma selenium status and incidence of prostate cancer: an analysis of the complete treatment period of the Nutritional Prevention of Cancer Trial

To present the results (to January 1996, the end of blinded treatment) of the Nutritional Prevention of Cancer (NPC) Trial, a randomized trial of selenium (200 µg daily) designed to test the hypothesis that selenium supplementation (SS) could reduce the risk of recurrent nonmelanoma skin cancer among 1312 residents of the Eastern USA.

Nonparametric Estimation of a Survivorship Function with Doubly Censored Data

Abstract A simple iterative procedure is proposed for obtaining estimates of a response time distribution when some of the data are censored on the left and some on the right. The procedure is based on the product-limit method of Kaplan and Meier [15], and it also uses the idea of self-consistency due to Efron [8]. Under fairly general assumptions, the method is shown to yield unique consistent maximum likelihood estimators. Asymptotic expressions for their variances and covariances are derived and an extension to the case of arbitrary censoring is suggested.

Latent Class Models for Joint Analysis of Longitudinal Biomarker and Event Process Data: Application to Longitudinal Prostate-Specific Antigen Readings and Prostate Cancer

A retrospective substudy of the nutritional prevention of cancer (NPC) trials investigated the utility of longitudinally measured prostate-specific antigen (PSA) as a biomarker for subsequent onset of prostate cancer (PCa). Serial PSA levels were determined retrospectively from frozen blood samples that had been collected from all patients at successive clinic visits with the timing and the number of these visits highly variable. Diagnosis dates of all incident cases of PCa were recorded. Heterogeneity in PSA trajectories was observed that could not be fully explained by the usual linear mixed-effects model and measured covariates. Latent class models that incorporate both a longitudinal biomarker process and an event process offer a way to handle additional heterogeneity, to uncover distinct subpopulations, to incorporate correlated nonnormally distributed outcomes, and to classify individuals into risk classes. Our latent class joint model can aid the prediction of PCa probability given the longitudinal biomarker information available on an individual up to any date. The proposed model easily accommodates highly unbalanced longitudinal data and recurrent events. There are two levels of structure in the latent class joint model. First, the uncertainty of latent class membership is specified through a multinomial logistic model. Second, the class-specific marker trajectory and event process are specified parametrically and semiparametrically, under the assumption of conditional independence given the latent class membership. We use a likelihood approach to obtain parameter estimates via the EM algorithm. We fit the latent class joint model to the data from the NPC trials; four distinct subpopulations are identified that differ with regard to their PSA trajectories and risk for prostate cancer. Higher PSA level is significantly associated with increased risk of PCa, but appears to be conditionally independent once the latent classes are taken into account. Among the covariates, selenium supplementation and age at entry are statistically significant for various parts of the model. Assumptions—in particular the conditional independence between the longitudinal PSA biomarker and time to PCa diagnosis—are assessed.

Group sequential tests for bivariate response: interim analyses of clinical trials with both efficacy and safety endpoints

We describe group sequential tests for a bivariate response. The tests are defined in terms of the two response components jointly, rather than through a single summary statistic. Such methods are appropriate when the two responses concern different aspects of a treatment; for example, one might wish to show that a new treatment is both as effective and as safe as the current standard. We present a formulation of the bivariate testing problem, introduce group sequential tests that satisfy Type I error conditions, and show how to find the sample size guaranteeing a specified power. We describe how properties of group sequential tests for bivariate normal observations can be computed by numerical integration.

Repeated confidence intervals for group sequential clinical trials

We describe methods for the construction of valid confidence intervals for parameters of interest at repeated times during the course of a clinical trial with two treatments. This approach gives the investigator a way to monitor the trial and allows greater flexibility than methods that have rigid statistical stopping rules. Two situations are considered. In the first, responses are available immediately and normally distributed, the parameter of interest being the difference in means. In the second, observations are survival times and a proportional hazards model is assumed. Here the parameter of interest is the hazard ratio. Group sequential tests can be based on repeated confidence interval methods and these are compared to other multiple testing procedures that have been proposed.

The Nutritional Prevention of Cancer: 400 Mcg Per Day Selenium Treatment

Nonexperimental studies suggest that individuals with higher selenium (Se) status are at decreased risk of cancer. The Nutritional Prevention of Cancer (NPC) study randomized 1,312 high-risk dermatology patients to 200-mcg/day of Se in selenized yeast or a matched placebo; selenium supplementation decreased the risk of lung, colon, prostate, and total cancers but increased the risk of nonmelanoma skin cancer. In this article, we report on a small substudy in Macon, GA, which began in 1989 and randomized 424 patients to 400-mcg/day of Se or to matched placebo. The subjects from both arms had similar baseline Se levels to those treated by 200 mcg, and those treated with 400-mcg attained plasma Se levels much higher than subjects treated with 200 mcg. The 200-mcg/day Se treatment decreased total cancer incidence by a statistically significant 25%; however, 400-mcg/day of Se had no effect on total cancer incidence.

Group Sequential Analysis Incorporating Covariate Information

Abstract In this article we survey existing results concerning the joint distribution of the sequence of estimates of the parameter vector when a model is fitted to accumulating data and provide a unified theory that explains the “independent increments” structure commonly seen in group-sequential test statistics. Our theory covers normal linear models, including the case of correlated observations, and asymptotic results extend to generalized linear models and the proportional hazards regression model for survival data. The asymptotic results are derived using standard methods for the nonsequential case, and they hold as long as these nonsequential techniques are applicable at each individual analysis. In all cases, the joint distribution of the sequence of parameter estimates has the same form, exactly or asymptotically, as that of the sequence of means of an increasing number of independent, identically distributed normal variables. Thus our results provide the formal basis for extending the scope of s...

Confidence Intervals for a Binomial Parameter Following a Multistage Test With Application to MIL-STD 105D and Medical Trials

This paper describes a method for constructing confidence intervals for a binomial parameter upon termination of a sequential or multistage test. Tables are presented for use with the MIL-STD 105D multiple sampling plans for acceptance sampling. Also given are tables for use with some three-stage schemes that have been proposed in connection with biomedical trials. The results are compared with confidence intervals calculated as if the sampling plan had been one with a fixed sample size.

Survivorship Analysis of Heart Transplant Data

Abstract This article presents a number of parametric and nonparametric approaches to the analysis of nonexperimental heart transplant data from the Stanford Heart Transplantation Program, and attempts to come to some conclusion regarding whether heart transplantation is life-extending.