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Background: The global burden of cancer has exponentially increased over the last few years. In 2018 alone, approximately more than half of the 18.1 million individuals who had cancer succumbed to it. Lung cancer cases and fatalities are particularly on the rise. Therefore, exploring the factors surrounding lung cancer mortality is of utmost importance. Aims: We investigate the clinicopathological and epidemiological characteristics of patients with lung cancer undergoing treatments, and their 5-year survival rates from a case series study in Qatar. Methods and Results: All patients' data (between January 2010 and December 2014) in this case series study were retrieved from Al-Amal Hospital database. Kaplan-Meier survival plots, life tables and Cox regression were utilized for the statistical analysis. A total of 229 lung cancer patients were included in this study; of which 23.6% are Qatari (40 males and 14 females) and 76.4% non-Qatari (133 males and 42 females). Approximately 57.6% of our patients received at least one type of treatment. We observe a 5-year survival rate of 9.4% in our patient cohort. We also observe other predictive factors, such as distant metastasis (adjusted hazards ratio, HR = 2.414, 95% CI: 1.035-5.632), smoking status (adjusted HR = 3.909, 95% CI: 1.664-9.180) and the treatment history (adjusted HR = 0.432, 95% CI: 0.270-0.691), to be significant. Conclusion: Lung cancer is a prevalent health condition in Qatar, particularly owing to the rising use of tobacco in the country. The survival rate for lung cancer patients in this country is lower, compared to the global average. Moreover, several factors such as distant metastasis, smoking status, and treatment history are associated with lung cancer survival in Qatar. Nih-NCI, Grant/Award Number: NIH-NCI Cancer Center Support Grant P30 CA016059; Qatar University, Grant/Award Number: QUST-1-CAS-2019-1 Funding information Scopus

Background Exploring spatial patterns in the context of cancer disease mapping (DM) is a decisive approach to bring evidence of geographical tendencies in assessing disease status and progression. However, this framework is not insulated from spatial confounding, a topic of significant interest in cancer epidemiology, where the latent correlation between the spatial random effects and fixed effects (such as covariates), often lead to misleading interpretation. Aims To introduce three popular approaches (RHZ, HH and SPOCK; details in paper) often employed to tackle spatial confounding, and illustrate their implementation in cancer research via the popular statistical software R. Methods As a solution to alleviate spatial confounding, restricted spatial regressions are constructed by either projecting the latent effect onto the orthogonal space of covariates, or by displacing the spatial locations. Popular parametric count data models, such as the Poisson, generalized Poisson and negative binomial, were considered for the areal count responses, while the spatial association is quantified via the conditional autoregressive (CAR) model. Our method of inference in Bayesian, sometimes aided by the integrated nested Laplace approximation (INLA) to accelerate computing. The methods are implemented in the R package RASCO available from the first author's GitHub page. Results The results reveal that all three methods perform well in alleviating the bias and variance inflation present in the spatial models. The effects of spatial confounding were also explored, which, if ignored in practice, may lead to wrong conclusions. Conclusion Spatial confounding continues to remain a critical bottleneck in deriving precise inference from spatial DM models. Hence, its effects must be investigated, and mitigated. Several approaches are available in the literature, and they produce trustworthy results. The central contribution of this paper is providing the practitioners the R package RASCO, capable of fitting a large number of spatial models, as well as their restricted versions.

In standard analyses of data well-modeled by a nonlinear mixed model, an aberrant observation, either within a cluster, or an entire cluster itself, can greatly distort parameter estimates and subsequent standard errors. Consequently, inferences about the parameters are misleading. This paper proposes an outlier robust method based on linearization to estimate fixed effects parameters and variance components in the nonlinear mixed model. An example is given using the four-parameter logistic model and bioassay data, comparing the robust parameter estimates with the nonrobust estimates given by SAS®. Copyright © 2015 John Wiley & Sons, Ltd. Wiley Online library

Profile monitoring is an approach in quality control best used where the process data follow a profile (or curve). The majority of previous studies in profile monitoring focused on the parametric (P) modeling of either linear or nonlinear profiles, with both fixed and random effects, under the assumption of correct model specification. More recently, in the absence of an obvious P model, nonparametric (NP) methods have been employed in the profile monitoring context. For situations where a P model is adequate over part of the data but inadequate of other parts, we propose a semiparametric procedure that combines both P and NP profile fits. We refer to our semiparametric procedure as mixed model robust profile monitoring (MMRPM). These three methods (P, NP and MMRPM) can account for the autocorrelation within profiles and treat the collection of profiles as a random sample from a common population. For each approach, we propose a version of Hotelling's T2 statistic for use in Phase I analysis to determine unusual profiles based on the estimated random effects and obtain the corresponding control limits. Simulation results show that our MMRPM method performs well in making decisions regarding outlying profiles when compared to methods based on a misspecified P model or based on NP regression. In addition, however, the MMRPM method is robust to model misspecification because it also performs well when compared to a correctly specified P model. The proposed chart is able to detect changes in Phase I data and has easily calculated control limits. We apply all three methods to the automobile engine data of Amiri et al.5 and find that the NP and the MMRPM methods indicate signals that did not occur in a P approach. Copyright © 2012 John Wiley & Sons, Ltd.

The adaptive exponentially weighted moving average (AEWMA) control chart has the advantage of detecting balance mixed range of mean shifts. Its performance has been studied under the assumption that the process parameters are known. Under this assumption, previous studies have shown AEWMA to provide superior statistical performance when compared with other different types of control charts. In practice, however, the process parameters are usually unknown and are required to be estimated. Using a Markov Chain approach, we show that the performance of the AEWMA control chart is affected when parameters are estimated compared with the known-parameter case. In addition, we show the effect of different standard deviation estimators on the chart performance. Finally, a performance comparison is conducted between the exponentially weighted moving average (EWMA) chart and the AEWMA chart when the process parameters are unknown. We recommend the use of the AEWMA chart over the ordinary EWMA chart especially when a small number of Phase I samples is available to estimate the unknown parameters. Copyright © 2012 John Wiley & Sons, Ltd.