Aim: The dynamics of coronavirus disease 2019 (COVID-19) pandemic has become of special concern to the rheumatology community. Rheumatic patients are required to engage in effective health management but their behavior is often influenced by intrinsic and extrinsic factors. This cross-sectional study aims to examine patients' experiences during the current pandemic and its implication on their health perception and behavior. Method: A patient-centered electronic survey was used, randomly sampling rheumatic patients in Saudi Arabia during March and April 2020. Questions included patients' socio-demographics, diseases, medications, COVID-19 knowledge, source of information, fear level, disease activity perception, health care utilization, medication accessibility, and therapeutic compliance (measured using a modified version of Medication Adherence Reporting Scale). Correlation and regression coefficients were used to evaluate associations among the aforementioned variables. Results: A total of 637 respondents were included. The majority were rheumatoid arthritis patients (42.7%). Patients' knowledge about COVID-19 was correlated with social media use (P = .012). Fear of COVID-19 infection correlated with healthcare facility for follow-up visits (P = .024) and fear of disease deterioration if contracting the infection correlated with patients' levels of knowledge (P = .035). Both types of fear did not correlate with patients' perceptions of disease activity. However, patients' perceptions of worsened disease activity were correlated with unplanned healthcare visits (P < .001), medication non-adherence, and difficulty accessing medication (P = .010 and .006, respectively). Conclusion: The COVID-19 pandemic and surrounding public health measures could affect rheumatic patients' health management which might contribute to disease flare-up and subsequently taxing healthcare systems even further.

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The purpose of this paper is to classify UIS data in order to identify their risk, reduce drug abuse, and to prevent high-risk in HIV behavior. A method for fitting proportional hazards models to censored survival data is described. Stratification is performed recursively. A tree-based method for censored survival data is developed, based on maximizing the difference in survival between groups of patients represented by nodes in a binary tree.

We proposed three methods to find an approximate confidence interval for the variance of the random effects for a one-way analysis of the variance model in completely randomized design. We compared the proposed methods with some other methods reported in the literature. Several criteria are used for the empirical comparisons: the mean width of the confidence interval, the variance of the width, and the coverage probability. We use Simulation and Monte-Carlo techniques to perform the comparison study. We use R language to facilitate the simulation procedures. We found that one of the proposed methods was in general superior to the others.

In any longitudinal study, a dropout before the final timepoint can rarely be avoided. The chosen dropout model is commonly one of these types: Missing Completely at Random (MCAR), Missing at Random (MAR), Missing Not at Random (MNAR), and Shared Parameter (SP). In this paper we estimate the parameters of the longitudinal model for simulated data and real data using the Linear Mixed Effect (LME) method. We investigate the consequences of misspecifying the missingness mechanism by deriving the so-called least false values. These are the values the parameter estimates converge to, when the assumptions may be wrong. The knowledge of the least false values allows us to conduct a sensitivity analysis, which is illustrated. This method provides an alternative to a local misspecification sensitivity procedure, which has been developed for likelihood-based analysis. We compare the results obtained by the method proposed with the results found by using the local misspecification method. We apply the local misspecification and least false methods to estimate the bias and sensitivity of parameter estimates for a clinical trial example.