An omnibus test for several hazard alternatives in prevention randomized controlled clinical trials.

The logrank test is optimal for testing the equality of survival distributions against a proportional hazards alternative. Under a late effects alternative, it is no longer appropriate, and one may turn to Fleming-Harrington's class of weighted logrank tests instead. In some settings, such as in preventive clinical trials where the statistical analysis has to be designed before the trial begins, it can be difficult to choose a priori between the logrank and Fleming-Harrington tests. A solution to this issue is provided. A decision rule is constructed for the problem of testing the equality of two survival distributions when the expected alternative may be one of the proportional hazards and late effects. A formula for computing the necessary sample size is obtained for this decision rule. A comprehensive simulation study is conducted to assess finite sample properties of the proposed test statistic. The proposed test improves both the logrank test and Fleming-Harrington's test for late effects. Finally, the methodology is illustrated on a data set in the field of prevention of Alzheimer's disease.

Influence of exposure assessment methods on associations between long-term exposure to outdoor fine particulate matter and risk of cancer in the French cohort Gazel.

BACKGROUND: Many studies investigated the relationship between outdoor fine particulate matter (PM2.5) and cancer. While they generally indicated positive associations, results have not been fully consistent, possibly because of the diversity of methods used to assess exposure. OBJECTIVES: To investigate how using different PM2.5 exposure assessment methods influences risk estimates in the large French general population-based Gazel cohort (20,625 participants at enrollment) with a 26-year follow-up with complete residential histories. METHODS: We focused on two cancer incidence outcomes: all-sites combined and lung. We used two distinct exposure assessment methods: a western European land use regression (LUR), and a chemistry-dispersion model (Gazel-Air) for France, each with a time series ≥20-years annual concentrations. Spearman correlation coefficient between the two estimates of PM2.5 was 0.71 across all person-years; the LUR tended to provide higher exposures. We used extended Cox models with attained age as time-scale and time-dependent cumulative exposures, adjusting for a set of confounders including sex and smoking, to derive hazard ratios (HRs) and their 95% confidence interval, implementing a 10-year lag between exposure and incidence/censoring. RESULTS: We obtained similar two-piece linear associations for all-sites cancer (3711 cases), with a first slope of HRs of 1.53 (1.24-1.88) and 1.43 (1.19-1.73) for one IQR increase of cumulative PM2.5 exposure for the LUR and the Gazel-Air models respectively, followed by a plateau at around 1.5 for both exposure assessments. For lung cancer (349 cases), the HRs from the two exposure models were less similar, with largely overlapping confidence limits. CONCLUSION: Our findings using long-term exposure estimates from two distinct exposure assessment methods corroborate the association between air pollution and cancer risk.

Contribution of Long-Term Exposure to Outdoor Black Carbon to the Carcinogenicity of Air Pollution: Evidence regarding Risk of Cancer in the Gazel Cohort.

BACKGROUND: Black carbon (BC), a component of fine particulate matter [particles with an aerodynamic diameter ≤2.5 µm (PM2.5)], may contribute to carcinogenic effects of air pollution. Until recently however, there has been little evidence to evaluate this hypothesis. OBJECTIVE: This study aimed to estimate the associations between long-term exposure to BC and risk of cancer. This study was conducted within the French Gazel cohort of 20,625 subjects. METHODS: We assessed exposure to BC by linking subjects' histories of residential addresses to a map of European black carbon levels in 2010 with back- and forward-extrapolation between 1989 and 2015. We used extended Cox models, with attained age as time-scale and time-varying cumulative exposure to BC, adjusted for relevant sociodemographic and lifestyle variables. To consider latency between exposure and cancer diagnosis, we implemented a 10-y lag, and as a sensitivity analysis, a lag of 2 y. To isolate the effect of BC from that of total PM2.5, we regressed BC on PM2.5 and used the residuals as the exposure variable. RESULTS: During the 26-y follow-up period, there were 3,711 incident cancer cases (all sites combined) and 349 incident lung cancers. Median baseline exposure in 1989 was 2.65 10-5/m [interquartile range (IQR): 2.23-3.33], which generally slightly decreased over time. Using 10 y as a lag-time in our models, the adjusted hazard ratio per each IQR increase of the natural log-transformed cumulative BC was 1.17 (95% confidence interval: 1.06, 1.29) for all-sites cancer combined and 1.31 (0.93, 1.83) for lung cancer. Associations with BC residuals were also positive for both outcomes. Using 2 y as a lag-time, the results were similar. DISCUSSION: Our findings for a cohort of French adults suggest that BC may partly explain the association between PM2.5 and lung cancer. Additional studies are needed to confirm our results and further disentangle the effects of BC, total PM2.5, and other constituents. https://doi.org/10.1289/EHP8719.

Effectiveness of adaptive designs for phase II cancer trials.

BACKGROUND: Evaluation of new therapies for cancer has suffered a paradigm shift in the last years. The use of innovative and more efficient designs is a priority for the scientific community; nevertheless, the use of this kind of design is not yet wide spread. PURPOSE: In this paper will examine the effectiveness of adaptive designs compared with traditional designs in phase II clinical trials. METHODS: We reviewed a group of abstracts records between 1980 and 2008 and extracted data regarding statistical design, year of publication, kind of evaluated product, localization, sample size and results of the trials. RESULTS: Nine hundred and eighty-nine clinical trials were identified and from them 333 traditional designs and 19 adaptive designs were included in the review. Two hundred statistical papers were located and 16 were included in the review. The most frequent designs were Standard up and down designs, continual reassessment methods and its variation and designs with Bayesian approaches. More than 80% of the studies evaluated different schemes of chemotherapy. Adaptive designs evaluated only drugs and not any kind of treatment combination and the most often localizations evaluated in both designs were lung, haematology malignancies, and colon cancers. CONCLUSIONS: Adaptive designs are more efficient from the statistical point of view but they are not yet widely used because of complex and computationally intensive methods needed, substantial effort for planning the trials and lack of regulatory guidance.

Assessing the impact of bycatch on dolphin populations: the case of the common dolphin in the eastern North Atlantic.

Fisheries interactions have been implicated in the decline of many marine vertebrates worldwide. In the eastern North Atlantic, at least 1000 common dolphins (Delphinus delphis) are bycaught each year, particularly in pelagic pair-trawls. We have assessed the resulting impact of bycatch on this population using a demographic modeling approach. We relied on a sample of females stranded along the French Atlantic and western Channel coasts. Strandings represent an extensive source of demographic information to monitor our study population. Necropsy analysis provided an estimate of individual age and reproductive state. Then we estimated effective survivorship (including natural and human-induced mortality), age at first reproduction and pregnancy rates. Reproductive parameters were consistent with literature, but effective survivorship was unexpectedly low. Demographic parameters were then used as inputs in two models. A constant parameter matrix proposed an effective growth rate of -5.5±0.5%, corresponding to the current situation (including bycatch mortality). Subsequently, deterministic projections suggested that the population would be reduced to 20% of its current size in 30 years and would be extinct in 100 years. The demographic invariant model suggested a maximum growth rate of +4.5±0.09%, corresponding to the optimal demographic situation. Then, a risk analysis incorporating Potential Biological Removal (PBR), based on two plausible scenarii for stock structure suggested that bycatch level was unsustainable for the neritic population of the Bay of Biscay under a two-stock scenario. In depth assessment of stock structure and improved observer programs to provide scientifically robust bycatch estimates are needed. Effective conservation measures would be reducing bycatch to less than 50% of the current level in the neritic stock to reach PBR. Our approach provided indicators of the status and trajectory of the common dolphin population in the eastern North Atlantic and therefore proved to be a valuable tool for management, applicable to other dolphin populations.

Joint modeling of event time and nonignorable missing longitudinal data.

Survival studies usually collect on each participant, both duration until some terminal event and repeated measures of a time-dependent covariate. Such a covariate is referred to as an internal time-dependent covariate. Usually, some subjects drop out of the study before occurrence of the terminal event of interest. One may then wish to evaluate the relationship between time to dropout and the internal covariate. The Cox model is a standard framework for that purpose. Here, we address this problem in situations where the value of the covariate at dropout is unobserved. We suggest a joint model which combines a first-order Markov model for the longitudinally measured covariate with a time-dependent Cox model for the dropout process. We consider maximum likelihood estimation in this model and show how estimation can be carried out via the EM-algorithm. We state that the suggested joint model may have applications in the context of longitudinal data with nonignorable dropout. Indeed, it can be viewed as generalizing Diggle and Kenward's model (1994) to situations where dropout may occur at any point in time and may be censored. Hence we apply both models and compare their results on a data set concerning longitudinal measurements among patients in a cancer clinical trial.