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We assessed the impact of the coronavirus disease 2019 (COVID-19) pandemic on the statistical analysis of time-to-event outcomes in late-phase oncology trials. Using a simulated case study that mimics a Phase III ongoing trial during the pandemic, we evaluated the impact of COVID-19-related deaths, time off-treatment and missed clinical visits due to the pandemic, on overall survival and/or progression-free survival in terms of test size (also referred to as Type 1 error rate or alpha level), power, and hazard ratio (HR) estimates. We found that COVID-19-related deaths would impact both size and power, and lead to biased HR estimates; the impact would be more severe if there was an imbalance in COVID-19-related deaths between the study arms. Approaches censoring COVID-19-related deaths may mitigate the impact on power and HR estimation, especially if study data cut-off was extended to recover censoring-related event loss. The impact of COVID-19-related time off-treatment would be modest for power, and moderate for size and HR estimation. Different rules of censoring cancer progression times result in a slight difference in the power for the analysis of progression-free survival. The simulations provided valuable information for determining whether clinical-trial modifications should be required for ongoing trials during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Assessment of anti-drug antibodies (ADAs) for neutralizing activity is important for the clinical development of biopharmaceuticals. Two types of neutralizing antibody (NAb) assays (competitive ligand-binding assay [CLBA] and cell-based assay [CBA]) are commonly used to characterize neutralizing activities. To support the clinical development of benralizumab, a humanized, anti–interleukin-5 receptor α, anti-eosinophil monoclonal antibody, we developed and validated a CLBA and a CBA. The CLBA and CBA were compared for sensitivity, drug tolerance, and precision to detect NAbs in serum samples from clinical trials. The CLBA was more sensitive (27.1 and 37.5 ng/mL) than the CBA (1.02 and 1.10 μg/mL) in detecting NAbs to benralizumab for the polyclonal and monoclonal ADA controls, respectively. With the same polyclonal ADA control, the CLBA detected 250 ng/mL of ADA in the presence of 100 ng/mL of benralizumab, whereas the CBA detected 1.25 μg/mL of ADA in the presence of 780 ng/mL of benralizumab. In 195 ADA-positive samples from 5 studies, 63.59% (124/195) and 16.9% (33/195) were positive for NAb as measured by the CLBA and the CBA, respectively. ADA titers were strongly correlated (Pearson’s correlation coefficient r = 0.91; n = 195) with CLBA titers. Moreover, the CLBA titer correlated with CBA percentage inhibition in the CBA-positive samples (Spearman’s coefficient r = 0.50; n = 33). Our data demonstrated advantages of the CLBA in various aspects and supported the choice of the CLBA as a NAb assay for the phase III trials. [ABSTRACT FROM AUTHOR]

Quality control is an essential operation of the pharmaceutical industry. Tolerance intervals are commonly used in quality control to assure that a large proportion of critical quality attributes meet certain specification criteria with high confidence. Discrete measurements, such as microbial cell count, number of defective devices or discretized continuous responses due to limit of quantitation or rounding, are abundant in pharmaceutical manufacturing and quality control. Tolerance intervals for discrete variables are widely used in industrial appli- cations to set alert and action limits for critical quality attributes for the purpose of control and surveillance. A substantial amount of discrete data consist of excess number of zeros and often displays over-dispersion in variance. By far, there is no statistical method available [ABSTRACT FROM AUTHOR]

Background: Low-grade systemic inflammation, particularly elevated IL-6, predicts mortality in chronic obstructive pulmonary disease (COPD). Although altered body composition, especially increased visceral fat (VF) mass, could be a significant contributor to low-grade systemic inflammation, this remains unexplored in COPD. Objective: The objective was to investigate COPD-specific effects on VF and plasma adipocytokines and their predictive value for mortality. Design: Within the Health, Aging, and Body Composition (Health ABC) Study, an observational study in community-dwelling older persons, we used propensity scores to match n = 729 persons with normal lung function to n = 243 persons with obstructive lung disease (OLD; defined as the ratio of forced expiratory volume in 1 s to forced vital capacity < lower limit of normal). Matching was based on age, sex, race, clinic site, BMI, and smoking status. Within this well-balanced match, we compared computed tomography- acquired visceral fat area (VEA) and plasma adipocytokines, analyzed independent associations of VEA and OLD status on plasma adipocytokines, and studied their predictive value for 9.4-y mortality. Results: Whereas whole-body fat mass was comparable between groups, persons with OLD had increased VFA and higher plasma IL-6, adiponectin, and plasminogen activator inhibitor 1 (PAI-1). Both OLD status and VFA were independently positively associated with IL-6. Adiponectin was positively associated with OLD status but negatively associated with VFA. PAI-1 was no longer associated with OLD status after VFA was accounted for. Participants with OLD had increased risk of all-cause, respiratory, and cardiovascular mortality, of which IL-6 was identified as an independent predictor. Conclusion: Our data suggest that excessive abdominal visceral fat contributes to increased plasma IL-6, which, in turn, is strongly associated with all-cause and cause-specific mortality in older persons with OLD. [ABSTRACT FROM AUTHOR]

Background and aims Cross-sectional studies suggest that obstructive lung disease (OLD) and smoking affect lean mass and mobility. A study was undertaken to investigate whether OLD and smoking accelerate the ageing-related decline in lean mass and physical functioning. Methods 260 patients with OLD (mean6SD forced expiratory volume in 1 s (FEV1) 63618% predicted), 157 smoking controls (FEV1 95616% predicted), 866 formersmoking controls (FEV1 100616% predicted) and 891 never-smoking controls (FEV1 104617% predicted) participating in the Health, Aging and Body Composition (ABC) Study were studied. At baseline the mean age was 7463 years and participants reported no functional limitations. Baseline and 7-year longitudinal data of body composition (by dual-energy x-ray absorptiometry), muscle strength (by hand and leg dynamometry) and Short Physical Performance Battery (SPPB) were investigated. Results Compared with never-smoking controls, patients with OLD and smoking controls had a significantly lower weight, fat mass, lean mass and bone mineral content (BMC) at baseline (p<0.05). While the loss of weight, fat mass, lean mass and strength was comparable between patients with OLD and neversmoking controls, the SPPB declined 0.12 points/year faster in men with OLD (p¼0.01) and BMC declined 4 g/ year faster in women with OLD (p¼0.02). In smoking controls only lean mass declined 0.1 kg/year faster in women (p¼0.03) and BMC 8 g/year faster in men (p¼0.02) compared with never-smoking controls. Conclusions Initially well-functioning older adults with mild-to-moderate OLD and smokers without OLD have a comparable compromised baseline profile of body composition and physical functioning, while 7-year longitudinal trajectories are to a large extent comparable to those observed in never-smokers without OLD. This suggests a common insult earlier in life related to smoking. [ABSTRACT FROM AUTHOR]

Cancer patients are subject to multiple competing risks of death and may die from causes other than the cancer diagnosed. The probability of not dying from the cancer diagnosed, which is one of the patients’ main concerns, is sometimes called the ‘personal cure’ rate. Two approaches of modelling competing-risk survival data, namely the cause-specific hazards approach and the mixture model approach, have been used to model competing-risk survival data. In this article, we first show the connection and differences between crude cause-specific survival in the presence of other causes and net survival in the absence of other causes. The mixture survival model is extended to population-based grouped survival data to estimate the personal cure rate. Using the colorectal cancer survival data from the Surveillance, Epidemiology and End Results Programme, we estimate the probabilities of dying from colorectal cancer, heart disease, and other causes by age at diagnosis, race and American Joint Committee on Cancer stage. [ABSTRACT FROM AUTHOR]

Statistical analysis can be used as circumstantial evidence indicating that race, gender, age or other legally prohibited factor (exposure) was a significant cause of an unfavourable employment decision (response). Statistical methods, e.g. Cornfield's inequality, have been used to examine the employer's reason (omitted factor), e.g. requirement of having a professional certificate is able to explain the disparity. Based on a range of plausible values of the associations among omitted variable, exposure and response, the usual sensitivity analysis methods recalculate the p-values of the test of association between exposure and response. However, the current methods treat the sensitivity parameters as fixed and do not account for sampling error. A new method of modelling an omitted factor is proposed for assessing the causal relationship between exposure and response. The association parameters between the omitted factor and the exposure are estimated using extra available data. The proposed method is flexible and can incorporate a wide range of statistical models. The method is illustrated using data from an age discrimination case. [ABSTRACT FROM PUBLISHER]

Background The General Population Nutrition Intervention Trial was a randomized primary esophageal and gastric cancer prevention trial conducted from 1985 to 1991, in which 29584 adult participants in Linxian, China, were given daily vitamin and mineral supplements. Treatment with "factor D," a combination of 50 pg selenium, 30 mg vitamin E, and 15 mg beta-carotene, led to decreased mortality from all causes, cancer overall, and gastric cancer. Here, we present 10-year follow-up after the end of active intervention. Methods Participants were assessed by periodic data collection, monthly visits by village health workers, and quarterly review of the Linxian Cancer Registry. Hazard ratios (HRs) and 95% confidence intervals (Cls) for the cumulative effects of four vitamin and mineral supplementation regimens were calculated using adjusted proportional hazards models. Results Through May 31, 2001, 276 participants were lost to follow-up; 9727 died, including 3242 from cancer (1515 from esophageal cancer and 1199 from gastric cancer). Participants who received factor D had lower overall mortality (HR = 0.95, 95% Cl = 0.91 to 0.99; P= .009; reduction in cumulative mortality from 33.62% to 32.19%) and gastric cancer mortality (HR = 0.89, 95% Cl = 0.79 to 1.00; P= .043; reduction in cumulative gastric cancer mortality from 4.28% to 3.84%) than subjects who did not receive factor D. Reductions were mostly attributable to benefits to subjects younger than 55 years. Esophageal cancer deaths between those who did and did not receive factor D were not different overall; however, decreased 17% among participants younger than 55(HR = 0.83, 95% Cl = 0.71 to 0.98; P= .025) but increased 14% among those aged 55 years or older (HR = 1.14, 95% Cl = 1.00 to 1.30; P= .47). Vitamin A and zinc supplementation was associated with increased total and stroke mortality; vitamin C and molybdenum supplementation, with decreased stroke mortality. Conclusion The beneficial effects of selenium, vitamin E, and beta-carotene on mortality were still evident up to 10 years after the cessation of supplementation and were consistently greater in younger participants. Late effects of other supplementation regimens were also observed. [ABSTRACT FROM AUTHOR]

A segmented line regression model has been used to describe changes in cancer incidence and mortality trends [Kim, H.-J., Fay, M.P., Feuer, E.J. and Midthune, D.N., 2000, Permutation tests for joinpoint regression with applications to cancer rates. Statistics in Medicine, 19, 335-351. Kim, H.-J., Fay, M.P., Yu, B., Barrett., M.J. and Feuer, E.J., 2004, Comparability of segmented line regression models. Biometrics, 60, 1005-1014.]. The least squares fit can be obtained by using either the grid search method proposed by Lerman [Lerman, P.M., 1980, Fitting segmented regression models by grid search. Applied Statistics, 29, 77-84.] which is implemented in Joinpoint 3.0 available at http://srab.cancer.gov/joinpoint/index.html, or by using the continuous fitting algorithm proposed by Hudson [Hudson, D.J., 1966, Fitting segmented curves whose join points have to be estimated. Journal of the American Statistical Association, 61, 1097-1129.] which will be implemented in the next version of Joinpoint software. Following the least squares fitting of the model, inference on the parameters can be pursued by using the asymptotic results of Hinkley [Hinkley, D.V., 1971, Inference in two-phase regression. Journal of the American Statistical Association, 66, 736-743.] and Feder [Feder, P.I., 1975a, On asymptotic distribution theory in segmented regression Problems-Identified Case. The Annals of Statistics, 3, 49-83.] Feder [Feder, P.I., 1975b, The log likelihood ratio in segmented regression. The Annals of Statistics, 3, 84-97.] Via simulations, this paper empirically examines small sample behavior of these asymptotic results, studies how the two fitting methods, the grid search and the Hudson's algorithm affect these inferential procedures, and also assesses the robustness of the asymptotic inferential procedures. [ABSTRACT FROM AUTHOR]

Background Primary liver cancer is a common malignancy with a dismal prognosis. New primary prevention strategies are needed to reduce mortality from this disease. We examined the effects of supplementation with four different combinations of vitamins and minerals on primary liver cancer mortality among 29450 initially healthy adults from Linxian, China. Methods Participants were randomly assigned to take either a vitamin-mineral combination (‘factor’) or a placebo daily for 5.25 years (March 1986-May 1991). Four factors (at doses one to two times the US Recommended Daily Allowance)-retinol and zinc (factor A); riboflavin and niacin (factor B); ascorbic acid and molybde- num (factor C); and beta-carotene, alpha-tocopherol, and selenium (factor D)-were tested in a partial factorial design. The study outcome was primary liver cancer death occurring from 1986 through 2001. Adjusted Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) of liver cancer death with and without each factor. All P values are two-sided. Results A total of 151 liver cancer deaths occurred during the analysis period. No statistically significant differences in liver cancer mortality were found comparing the presence and absence of any of the four intervention factors. However, both factor A and factor B reduced liver cancer mortality in individuals younger than 55 years at randomization (HR = 0.59, 95% Cl = 0.34 to 1.00, and HR = 0.54, 95% Cl = 0.31 to 0.93, respectively) but not in older individuals (HR = 1.06, 95% Cl = 0.71 to 1.59, and HR = 1.12, 95% Cl = 0.75 to 1.68, respectively). Factor C reduced liver cancer death, albeit with only borderline statistical significance in males (HR = 0.70, 95% Cl = 0.47 to 1.02) but not in females (HR = 1.30, 95% Cl = 0.72 to 2.37). Cumulative risks of liver cancer death were 6.0 per 1000 in the placebo arm, 5.4 per 1000 in the arms with two factors, and 2.4 per 1000 in the arm with all four factors. Conclusion None of the factors tested reduced overall liver cancer mortality. However, three factors reduced liver cancer mortality in certain subgroups. [ABSTRACT FROM AUTHOR]

Abstract Objective: This paper presents a methodology for piecing together disparate data sources to obtain a comprehensive model for the use of mammography screening in the US population for the years 1975 – 2000. Methods: Two aspects of mammography usage, the age that a woman receives her first mammography and the interval between subsequent mammograms, are modeled separately. The initial dissemination of mammography is based on cross-sectional self report data from national surveys and the interval length between screening exams is fit using longitudinal mammography registry data. Results: The two aspects of mammography usage are combined to simulate screening histories for individual women that are representative of the US population. Simulated mammography patterns for the years 1994 – 2000 were found to be similar to observed screening patterns from the state level mammography registry for Vermont. Conclusions: The model presented gives insight into screening practices over time and provides an alternative public health measure for screening usage in the US population. The comprehensive description of mammography use from its introduction represents an important first step to understanding the impact of mammography on breast cancer incidence and mortality. [ABSTRACT FROM AUTHOR]

Segmented line regression models, which are composed of continuous linear phases, have been applied to describe changes in rate trend patterns. In this article, we propose a procedure to compare two segmented line regression functions, specifically to test (i) whether the two segmented line regression functions are identical or (ii) whether the two mean functions are parallel allowing different intercepts. A general form of the test statistic is described and then the permutation procedure is proposed to estimate the p-value of the test. The permutation test is compared to an approximateF-test in terms of the p-value estimation and the performance of the permutation test is studied via simulations. The tests are applied to compare female lung cancer mortality rates between two registry areas and also to compare female breast cancer mortality rates between two states. [ABSTRACT FROM AUTHOR]

Unlike randomized experimental studies, investigators do not have control over the treatment assignment in observational studies. Hence, the treated and control (non-treated) groups may have widely different distributions of unobserved covariates. Thus, if observational data are analysed as if they had arisen from a controlled study, the analyses are subject to potential bias. Sensitivity analysis is a technique for assessing whether the inference drawn from a study could be altered by a moderate ‘imbalance’, between the distribution of the covariates in different groups. In this paper, we examine the sensitivity analysis of the test of proportions in 2 × 2 tables from a new perspective: ‘could a non-significant result have occurred because the treated group has a higher prevalence of an unobserved risk factor?’. The study was motivated by an analysis of the studies concerning with the possible effect of spermicide use on birth defects that were cited in a legal decision. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]