Complex Survival System Modeling for Risk Assessment of Infant Mortality Using a Parametric Approach.

Pakistan is still one of the five countries contributing to half of the child deaths worldwide and holds a low ratio of infant survival. A high rate of poverty, low level of education, limited health facilities, rural-urban inequalities, and political uncertainty are the main reasons for this condition. Survival models that evaluate the performance of models over simulated and real data set may serve as an effective technique to determine accurate complex systems. The present study proposed an efficient extension of the recent parametric technique for risk assessment of infant mortality to address complex survival systems in the presence of extreme observations. This extended method integrated four distributions with the basic algorithm using a real data set of infant survival without extreme observations. The proposed models are compared with the standard partial least squares-Cox regression (PLS-CoxR), and higher efficiency of these proposed algorithms is observed for handling complex survival time systems for risk assessment. The algorithm is also used to analyze simulated data set for further verification of results. The optimal model revealed that the mother's age, type of residence, wealth index, permission to go to a medical facility, distance to a health facility, and awareness about tuberculosis significantly affected the survival time of infants. The flexibility and continuity of extended parametric methods support the implementation of public health surveillance data effectively for data-oriented evaluation. The findings may support projecting targeted interventions, producing awareness, and implementing policies planned to reduce infant mortality.

Modeling the Ranked Antenatal Care Visits Using Optimized Partial Least Square Regression.

The frequency and timing of antenatal care visits are observed to be the significant factors of infant and maternal morbidity and mortality. The present research is conducted to determine the risk factors of reduced antenatal care visits using an optimized partial least square regression model. A data set collected during 2017-2018 by Pakistan Demographic and Health Surveys is used for modeling purposes. The partial least square regression model coupled with rank correlation measures are introduced for improved performance to address ranked response. The proposed models included PLSρ s , PLSτ A , PLSτ B , PLSτ C , PLS D , PLSτ GK , PLS G , and PLS U . Three filter-based factor selection methods are executed, and leave-one-out cross-validation by linear discriminant analysis is measured on predicted scores of all models. Finally, the Monte Carlo simulation method with 10 iterations of repeated sampling for optimization of validation performance is applied to select the optimum model. The standard and proposed models are executed over simulated and real data sets for efficiency comparison. The PLSρ s is found to be the most appropriate proposed method to model the observed ranked data set of antenatal care visits based on validation performance. The optimal model selected 29 influential factors of inadequate use of antenatal care. The important factors of reduced antenatal care visits included women's educational status, wealth index, total children ever born, husband's education level, domestic violence, and history of cesarean section. The findings recommended that partial least square regression algorithms coupled with rank correlation coefficients provide more efficient estimates of ranked data in the presence of multicollinearity.

The Partial Least Squares Spline Model for Public Health Surveillance Data.

Factor discovery of public health surveillance data is a crucial problem and extremely challenging from a scientific viewpoint with enormous applications in research studies. In this study, the main focus is to introduce the improved survival regression technique in the presence of multicollinearity, and hence, the partial least squares spline modeling approach is proposed. The proposed method is compared with the benchmark partial least squares Cox regression model in terms of accuracy based on the Akaike information criterion. Further, the optimal model is practiced on a real data set of infant mortality obtained from the Pakistan Demographic and Health Survey. This model is implemented to assess the significant risk factors of infant mortality. The recommended features contain key information about infant survival and could be useful in public health surveillance-related research.

Janus Kinase Inhibitors and Risk of Venous Thromboembolism: A Systematic Review and Meta-analysis.

OBJECTIVE: To assess the risk of venous thromboembolism (VTE) in patients treated with Janus kinase (JAK) inhibitors in clinical trials. PATIENTS AND METHODS: We performed a literature search of Ovid MEDLINE and ePub Ahead of Print, In-Process & Other Non-Indexed Citations, and Daily; Ovid EMBASE; Ovid Cochrane Central Register of Controlled Trials; Ovid Cochrane Database of Systematic Reviews; and Scopus, from inception to December 4, 2019, for randomized, placebo-controlled trials with JAK inhibitors as an intervention and reported adverse events. Odds ratio with 95% CI was calculated to estimate the VTE risk using a random effects model. Two independent reviewers screened and extracted data. The GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach was used to assess certainty in estimated VTE risk. RESULTS: We included 29 trials (13,910 patients). No statistically significant association was found between use of JAK inhibitors and risk of VTE (odds ratio, 0.91; 95% CI, 0.57 to 1.47; P=.70; I2=0; low certainty because of serious imprecision). Results using Bayesian analysis were consistent with those of the primary analysis. Results of stratified and meta-regression analyses suggested no interaction by dose of drug, indication for treatment, or length of follow-up. CONCLUSION: We found insufficient evidence to support an increased risk of JAK inhibitor-associated VTE based on currently available data.

Identifying the factors associated with cesarean section modeled with categorical correlation coefficients in partial least squares.

Cesarean section (CS) is associated with maternal morbidity and mortality in developing countries. This study is conducted to assess factors associated with CS in Pakistan using partial least squares (PLS) algorithm, where categorical factors are modeled. Nationally representative maternal data from Pakistan Demographic and Health Surveys (PDHS) conducted during 2012-2013 is used in this study. Among correlation coefficient based PLS regression proposed algorithms for categorical factors, Pearson's Contingency Coefficient (CC) PLS coupled with loading weight (LW) appeared to be the most efficient method in terms of model performance and influential factor selection. Region of residence, type of place of residence, mother's and her partner's level of education, wealth index, year of birth, previous terminated pregnancy, use of contraception, prenatal care provided by a doctor and nurse/midwife/LHV (lady health visitor), assistance provided by a nurse/midwife/LHV,number of antenatal visits, size of child, antenatal care provided by government hospital, transport facility for medical care, baby birth status, mother's age at first birth, preceding birth interval and vaccination of hepatitis B-1 and B2 are found to be significantly affecting the CS delivery method. Correlation coefficient based PLS regression algorithms may serve more efficiently as a multivariate technique to treat high-dimensional categorical data.

Impact of age, tumor size, lymph node metastasis, stage, receptor status and menopausal status on overall survival of breast cancer patients in Pakistan.

BACKGROUND: Survival of breast cancer patients depends on a number of factors which are not only prognostic but are also predictive. A number of studies have been carried out worldwide to find out prognostic and predictive significance of different clinicopathological and molecular variables in breast cancer. This study was carried out at Nuclear Medicine, Oncology and Radiotherapy Institute (NORI), Islamabad, to find out the impact of different factors on overall survival of breast cancer patients coming from Northern Pakistan. MATERIALS AND METHODS: This observational retrospective study was carried out in the Oncology Department of NORI Hospital. A total of 2,666 patients were included. Data were entered into SPSS 20. Multinomial logistic regression analysis was performed to determine associations of different variables with overall survival. P values <0.05 were considered significant. RESULTS: The mean age of the patients was 47.6 years, 49.5% being postmenopausal. Some 1,708 were ER positive and 1,615 were PR positive, while Her 2 neu oncogene positivity was found in 683. A total of 1,237 presented with skin involvement and 426 had chest wall involvement. Some 1,663 had > 5cm tumors. Lymph node involvement was detected in 2,131. Overall survival was less than 5 years in 669 patients, only 324 surviving for more than 10 years, and in the remainder overall survival was in the range of 5-10 years. CONCLUSIONS: Tumor size, lymph node metastases, receptor status, her 2 neu positivity, skin involvement, and chest wall involvement have significant effects whereas age and menopausal status have no significant effect on overall survival of breast cancer patients in Pakistan.