A LT H 1 7 ( 2 0 1 4 ) A 7 1 9 -A 8 1 3 regression model was used to determine difference of the number of hospitalization among groups of severity and health insurance. Costs were converted to $US using 30.59 Thai-baht per $1US. Results: Among 1,982 patients included, the average age was 40.3±24.0 years with 60.7% male. A total of 1,936 patients were non-high risk patients, while 46 patients were high-risk patients. There were 1,293 patients under universal coverage schemes (UCS), 264 patients under social security schemes, and 626 patients under civil servant medical benefit schemes (CSMBS). The average annual cost/patient was $598±871. In adjusted analyses, the health care cost of highrisk patients was $67 higher than that of non-high risk patients (95% confidence interval (CI); $64-$69). The cost of patients under CSMBS was $109 (95%CI; $105-$113) higher than that of patients under UCS. ConClusions: The health care costs in a cohort of patients with asthma were substantial and were higher in high-risk patients and patients under CSMBS.objeCtives: This study aims to propose an appropriate statistical method to analyse the longitudinal health-related quality of life (HRQoL) data. Methods: This was a longitudinal HRQoL study conducted among new smear positive pulmonary tuberculosis (PTB) patients diagnosed at the chest clinic of Penang General Hospital between March 2010 and February 2011. Eligible patients (i.e., literate and 18 years and above) were asked to self-complete the SF-36v2 questionnaire (either in Malay, Mandarin, Tamil or English) at the start of the treatment, after the intensive phase and at the end of the treatment. The mean physical component summary (PCS) and mental component summary (MCS) scores, ranging from 47-53, were considered equivalent to the general population norms. Repeated measures ANOVA (with single imputations) and linear mixed model were used to analyse the data. Results: A total of 216 patients completed the questionnaire at the start of their treatment. Out of these, 177 and 153 completed the questionnaire at the second and third follow-ups, respectively. Throughout the treatment, the mean PCS and MCS scores for the patients were less than 47. In repeated measures ANOVA analysis, level of education, diabetes, being alcoholic and cough with sputum were the significant predictors of PCS, whereas none of the covariates explained a significant variance in the MCS scores. In linear mixed model, ethnicity, marital status, being a smoker, productive cough and ≥ 3 TB-related symptoms were the significant predictors of PCS. Similarly, covariates such as ethnicity, hypertension, being a smoker, monthly income ≥ 1000 MYR and ≥ 3 TB-related symptoms significantly explained variance in the MCS scores. ConClusions: The study's findings indicated compromised health among the study participants even at the end of treatment. According to different findings obtained from both methods and the limited assumption in applying repeated measures ANOVA, linear mixed model was preferred to analyse this...
Large-scale short hairpin RNA (shRNA) screens on well-characterized human cancer cell lines have been widely used to identify novel cancer dependencies. However, the off-target effects of shRNA reagents pose a significant challenge in the analysis of these screens. To mitigate these off-target effects, various approaches have been proposed that aggregate different shRNA viability scores targeting a gene into a single gene-level viability score. Most computational methods for discovering cancer dependencies rely on these gene-level scores. In this paper, we propose a computational method, named NBDep, to find cancer self-dependencies by directly analyzing shRNA-level dependency scores instead of gene-level scores. The NBDep algorithm begins by removing known batch effects of the shRNAs and selecting a subset of concordant shRNAs for each gene. It then uses negative binomial random effects models to statistically assess the dependency between genetic alterations and the viabilities of cell lines by incorporating all shRNA dependency scores of each gene into the model. We applied NBDep to the shRNA dependency scores available at Project DRIVE, which covers 26 different types of cancer. The proposed method identified more well-known and putative cancer genes compared to alternative gene-level approaches in pan-cancer and cancer-specific analyses. Additionally, we demonstrated that NBDep controls type-I error and outperforms statistical tests based on gene-level scores in simulation studies.
One of the most important issues that confront statisticians in longitudinal studies is dropouts. A variety of reasons may lead to withdrawal from a study and produce two different missingness mechanisms, namely, missing at random and non-ignorable dropouts. Nevertheless, none of these mechanisms is tenable in most studies. In addition, it may be that not all of dropouts are nonignorable. Many dropout handling methods have been employed by assuming only one of these dropout mechanisms. In this study, the dropout indicator is improved to take into account both dropout mechanisms. In this two-stage approach, a selection model is combined with an imputation method for dropout process in a longitudinal study with two time points. Simulation studies in a variety of situations are conducted to evaluate this approach in estimating the mean of the response variable at the second time point. This parameter is estimated by using maximum likelihood method. The results of the simulation studies indicate the superiority of the proposed method to the existing ones in estimating the mean of the variable with dropouts. In addition, this method is performed on a methadone dataset of 161 patients admitted to an Iranian clinic to estimate the final methadone dose.
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