We examined the epidemiology of chronic beryllium disease among a stratified, random sample (n = 895) of nuclear weapons workers using the blood beryllium lymphocyte transformation (BeLT) test and chest radiograph for case identification. Of 18 new cases of beryllium sensitization, 12 had beryllium disease, and three more developed pulmonary granulomas on lung biopsy over the succeeding 2 yr. Beryllium-sensitized cases did not differ from noncases in age, gender, race, ethnicity, smoking, most respiratory symptoms, spirometric or radiographic abnormalities, or job tenure. The six sensitized cases without initial disease differed from beryllium disease cases in having greater pack-years of smoking. Sensitization occurred among workers with inadvertent or bystander exposure, such as a secretary and security guard. However, beryllium sensitization risk was higher for machinists (4.7%) and for persons reporting measured overexposure (7.4%, odds ratio 5.1); exposure beginning before 1970 (3.6%, odds ratio 2.7); consistent beryllium exposure (3.4%); and sawing (4.7%) or band sawing (6.0%) of beryllium metal. We conclude that both individual susceptibility to sensitization and exposure circumstances are important in developing disease.

We examined the prevalence of beryllium sensitization in relation to work process and beryllium exposure measurements in a beryllia ceramics plant that had operated since 1980. We interviewed 136 employees (97.8% of the workforce), ascertained beryllium sensitization with the beryllium lymphocyte proliferation blood test, and reviewed historical industrial hygiene measurements. Of eight beryllium‐sensitized employees (5.9%), six (4.4% of participating employees) had granulomatous disease on transbronchial lung biopsy. Machinists had a sensitization rate of 14.3% compared to a rate of 1.2% among other employees. Machining had significantly higher general area and breathing zone measurements than did other processes in the time period in which most beryllium‐sensitized cases had started machining work. Daily weighted average (DWA) estimates of exposure for machining processes also exceeded estimates for other work processes in that time period, with a median DWA of 0.9 μ/m3. Machining process DWAs accounted for the majority of DWAs exceeding the 2.0 μg/m3 OSHA standard, with 8.1% of machining DWAs above the standard. We conclude that lowering machining process‐related exposures may be important to lowering risk of beryllium disease. © 1996 Wiley‐Liss, Inc.

Objectives-To describe relative hazards in sectors of the beryllium industry, risk factors ofberyllium disease and sensitisation related to work process were sought in a beryllium manufacturing plant producing pure metal, oxide, alloys, and ceramics. Methods-All 646 active employees were interviewed; beryllium sensitisation was ascertained with the beryllium lymphocyte proliferation blood test on 627 employees; clinical evaluation and bronchoscopy were offered to people with abnormal test results; and industrial hygiene measurements related to work processes taken in 1984-93 were reviewed. Results-59 employees (9.4%) had abnormal blood tests, 47 of whom underwent bronchoscopy. 24 Beryllium exposure leads to cell mediated immunological sensitisation in a small percentage of workers exposed to beryllium aerosols, dusts, or fumes; of the sensitised workers, many have granulomatous lung disease.' Prevention of beryllium disease depends on knowledge of risk factors which can be modified. Although inborn genetic factors are associated with risk of disease in those exposed to beryllium,4 these cannot be changed in an existing workforce exposed to beryllium. In contrast, work related risk factors offer the opportunity to lower risk of beryllium disease and to understand the exposure characteristics associated with high disease rates. In our previous studies of plant workforces exposed to beryllium, we found risks of beryllium sensitisation or disease related to work processes in three plants representing single sectors of the beryllium industry. These include machining of beryllium metal,' grinding, dicing, and drilling of beryllia ceramics,' dry pressing, and research and development in a plant which manufactured beryllia ceramics historically.' We report here the results of epidemiological and exposure surveillance in a plant which encompasses most sectors of the beryllium industry in production of beryllium metal, alloys, and beryllium oxide from which ceramics were made historically. We sought to describe risks of beryllium disease related to work processes which could provide opportunities for future study of exposure variables conferring excess risk. Understanding of qualitative and quantitative exposure-response relations is critical to prevention of disease in the many sectors of the beryllium industry.The plant opened in 1953 to produce beryllium-copper alloy, which is cast and fabricated into tubes, wire, sheet, plates, and metal parts before shipment to other factories to become finished products. Beryllium metal operations were developed in about 1957 in buildings and under management which were largely separate from alloy operations. Beryllium metal is produced from beryllium hydroxide through a chemical process. The two component areas involved in beryllium metal production are the pebble plant, which contains fluoride and reduction furnaces, and vacuum melting.' As the crystalline structure of cast beryllium metal is unsuitable for many applications, the metal is partitioned into differing grade...

Arming the immune system against cancer has emerged as a powerful tool in oncology during recent years. Instead of poisoning a tumor or destroying it with radiation, therapeutic cancer vaccine, a type of cancer immunotherapy, unleashes the immune system to combat cancer. This indirect mechanism-of-action of vaccines poses the possibility of a delayed onset of clinical effect, which results in a delayed separation of survival curves between the experimental and control groups in therapeutic cancer vaccine trials with time-to-event endpoints. This violates the proportional hazard assumption. As a result, the conventional study design based on the regular log-rank test ignoring the delayed effect would lead to a loss of power. In this paper, we propose two innovative approaches for sample size and power calculation using the piecewise weighted log-rank test to properly and efficiently incorporate the delayed effect into the study design. Both theoretical derivations and empirical studies demonstrate that the proposed methods, accounting for the delayed effect, can reduce sample size dramatically while achieving the target power relative to a standard practice.

Prevalence of berylliosis, a lung disorder driven by the activation of beryllium-specific T cells, is associated with a major histocompatibility complex (MHC) class II marker (HLADPB1Glu69) and with the type of industrial exposure. We evaluated the interaction between marker and exposure in a beryllium-exposed population in which the prevalence of berylliosis was associated with machining beryllium. The presence of the marker was associated with higher prevalence INTRODUCTIONAvailable models of genetic susceptibility to environmental agents suggest that susceptibility genes may increase the carcinogenic risk of aromatic compounds either at lower [Nakachi et al., 1993] or higher [Cartwright et al., 1992;Kihara et al., 1994] levels of exposure to the agent. In the former case, the environmental factor would predominate in the determination of disease risk at high exposure levels. In this context, genetic screening might not prevent the majority of disease cases; on the contrary, reliance upon genetic screening for disease prevention may lead to relaxed exposure control and to an increased number of disease cases [Van Damme et al., 1995]. In berylliosis, an occupational lung disease caused by exposure to beryllium, both a genetic susceptibility factor, i.e., being a carrier of allelic variants of the HLA-DP gene coding for a glutamate in position 69 of the HLA-DP b 1 chain (HLA-DPB1Glu69) [Richeldi et al., 1993], and industrial process-related risk factors, such as machining beryllium (e.g., drilling, dicing, grinding) [Kreiss et al., 1993], have been identified. Thus, berylliosis represents an ideal model with which to assess the interaction between genetic and environmental factors. Recently, the evaluation of a beryllium worker cohort showed that jobs with higher berylliosis risk were associated with higher beryllium exposures. In this cohort, berylliosis prevalence was 10.6% in machinists, with a median daily weighted average (DWA) exposure of 0.9 µg/m 3 , and 1.2% in nonmachinists, with a median DWA exposure of 0.3 µg/m 3 , suggesting a dose-response relationship between exposure and disease [Kreiss et al., 1996]. This cohort was therefore chosen to assess the role of genetic and exposure factors in INDUSTRIAL MEDICINE 32:337-340 (1997) r 1997 Wiley-Liss, Inc.beryllium disease susceptibility and was evaluated for the carrier status of the HLA-DPB1Glu69 genetic marker. MATERIALS AND METHODSA population of 136 subjects from the workforce (139 workers) of a beryllium ceramics plant in the southwestern United States was enrolled in a genetic marker study after granting informed consent. These workers had been enrolled in a berylliosis surveillance program including interview, blood beryllium-stimulated lymphocyte proliferation (BeLP) test and, for those with abnormal blood BeLP test, bronchoalveolar lavage (BAL) with lung BeLP test and transbronchial biopsy. Six of the 136 individuals were diagnosed with berylliosis based on an abnormal blood BeLP test and an abnormal lung biopsy showing noncaseating granulo...

These results show that ETE is associated with decreased survival and increased recurrence rates regardless of the extent of the radiation therapy field. Also, ETE does not necessarily indicate a significantly increased incidence of axillary recurrence. Therefore, axillary irradiation based on this pathologic finding may not be indicated.

In some clinical settings such as the cancer immunotherapy trials, a treatment time-lag effect may be present and the lag duration possibly vary from subject to subject. An efficient study design and analysis procedure should not only take into account the time-lag effect but also consider the individual heterogeneity in the lag duration. In this paper, we present a Generalized Piecewise Weighted Logrank (GPW-Logrank) test, designed to account for the random time-lag effect while maximizing the study power with respect to the weights. Based on the proposed test, both analytic and numeric approaches are developed for the sample size and power calculation. Asymptotic properties are derived and finite sample efficiency is evaluated in simulations. Compared with the standard practice ignoring the delayed effect, the proposed design and analysis procedures are substantially more efficient when a random lag is expected; further, compared with the existing methods by Xu et al considering the fixed time-lag effect, the proposed approaches are significantly more robust when the lag model is misspecified. An R package (DelayedEffect.Design) is developed for implementation.

There is considerable interest among pharmaceutical and other medical product developers in adaptive clinical trials, in which knowledge learned during the course of a trial affects ongoing conduct or analysis of the trial. When the FDA released a draft Guidance document on adaptive design clinical trials in early 2010, expectations were high that it would lead to an increase in regulatory submissions involving adaptive design features, particularly for confirmatory trials. A 6-year (2008-2013) retrospective survey was performed within the Center for Biologics Evaluation and Research (CBER) at the FDA to gather information regarding the submission and evaluation of adaptive design trial proposals. We present an up-to-date summary of adaptive design proposals seen in CBER and provide an overview of our experiences. We share our concerns regarding the statistical issues and operational challenges raised during the review process for adaptive design trials. We also provide general recommendations for developing proposals for such trials. Our motivation in writing this paper was to encourage the best study design proposals to be submitted to CBER. Sometimes these can be adaptive, and sometimes a simpler design is most efficient.