3%) and lung cancers (15.3%). Among the first-line drugs recommended in NeuPSIG for CRNP, pregabalin (78.7%) was the most common drug prescribed followed by amitriptyline (67%). The most common co-prescribed drugs were acid suppressants drugs (50.7%). Tapentadol, which is not part of the NeuPSIG guidelines, was prescribed on 51 occasions for neuropathic pain. Underdosing was observed in 272 prescriptions. Only 12 prescriptions completely adhered, while 275 had partial, and 13 prescriptions had poor adherence to NeuPSIG guidelines. Conclusion The most commonly used drugs in the treatment of CRNP were pregabalin and amitriptyline. Most physician partially or did not adhere to the NeuPSIG guideline in the management of CRNP. Copyright © 2020 Indian Journal of Palliative Care.Background Insomnia and poor sleep quality are common problems in patients with cancer. It interferes with the coping ability, symptoms, and treatment outcomes. The Pittsburgh Sleep Quality Index (PSQI) is a reliable, valid instrument to assess the quality of sleep in patients with cancer. Patients and Methods The study was conducted at the department of medical oncology of a tertiary cancer care center. Consecutive eligible participants were recruited and evaluated for sleep quality using PSQI questionnaire. The questionnaire was administered only once with the questions evaluating to the quality of sleep over the last 1 month. A PSQI total score of ≤5 was suggestive of good quality of sleep and a score of >5 was indicative of poor quality of sleep. Results Ninety-two consecutive consenting cancer patients admitted for chemotherapy participated in the study. Thirty-one (33.7%) patients had early cancer and 35 (38%) patients had Stage IV metastatic disease. Thirty-six (39.1%) patients reported sleep of less then 6 h and 30 (32.6%) patients had impaired functioning during day due to sleepiness. Fifty-three (57.6%) patients had poor total PSQI score, of which 39 (73.5%) were female and 14 (26.5%) were male. The study showed no correlation of the PSQI scores with the stage of the disease, and the prior treatment received. Conclusions The study showed that Indian cancer patients have short sleep duration and poor quality of sleep. A higher prevalence of sleep disturbances was seen among female cancer patients. PSQI questionnaire can be a cost-effective way of screening cancer patients for poor quality of sleep. Copyright © 2020 Indian Journal of Palliative Care.Injection calcitonin is a natural hormone inhibiting osteoclastic bone resorption have been used as an analgesic to control bone metastasis pain or pain due to osteoporosis or fracture. This randomized double blind placebo controlled trial was undertaken to determine the role of injection Salmon Calcitonin therapy to control refractory pain caused due to bone metastasis arising from cancer breast, lung, prostate or kidney. All patients had received palliative radiotherapy and were suffering unsatisfactory pain relief on NSAIDs and tab morphine. Fourteen days inj. calcitonin or placebo injections were administered in 23 patients initially as high dose induction dose (800 IU per day SC) followed 200 IU subcutaneous (SC) once a day. Patients were assessed for pain intensity and quality of life on EORTC QLQ-30 questionnaire 6 hourly for 2 days and on 7th and 30th day. Any incidence of hypercalcemia, bone fracture, nerve root and bone marrow compression were also noted. https://www.selleckchem.com/products/GDC-0941.html This study found a significant reduction in pain after SC calcitonin injection therapy at 14 and 30 days' assessment. No patients in the study group required rescue analgesia after 18 hrs. There was a statistically significant difference in rescue analgesics required between the groups during two days hospitalization. Global health as well as physical and social wellbeing was better at 30 and 90 days in the study group as compared to control group, however it could not reach a statistical significance which may be attributed to the small sample size of the study. Copyright © 2020 Indian Journal of Palliative Care.Penalized likelihood approaches are widely used for high-dimensional regression. Although many methods have been proposed and the associated theory is now well developed, the relative efficacy of different approaches in finite-sample settings, as encountered in practice, remains incompletely understood. There is therefore a need for empirical investigations in this area that can offer practical insight and guidance to users. In this paper, we present a large-scale comparison of penalized regression methods. We distinguish between three related goals prediction, variable selection and variable ranking. Our results span more than 2300 data-generating scenarios, including both synthetic and semisynthetic data (real covariates and simulated responses), allowing us to systematically consider the influence of various factors (sample size, dimensionality, sparsity, signal strength and multicollinearity). We consider several widely used approaches (Lasso, Adaptive Lasso, Elastic Net, Ridge Regression, SCAD, the Dantzig Selector and Stability Selection). We find considerable variation in performance between methods. Our results support a "no panacea" view, with no unambiguous winner across all scenarios or goals, even in this restricted setting where all data align well with the assumptions underlying the methods. The study allows us to make some recommendations as to which approaches may be most (or least) suitable given the goal and some data characteristics. Our empirical results complement existing theory and provide a resource to compare methods across a range of scenarios and metrics. © The Author(s) 2019.Approximate Bayesian computation (ABC) has become one of the major tools of likelihood-free statistical inference in complex mathematical models. Simultaneously, stochastic differential equations (SDEs) have developed to an established tool for modelling time-dependent, real-world phenomena with underlying random effects. When applying ABC to stochastic models, two major difficulties arise First, the derivation of effective summary statistics and proper distances is particularly challenging, since simulations from the stochastic process under the same parameter configuration result in different trajectories. Second, exact simulation schemes to generate trajectories from the stochastic model are rarely available, requiring the derivation of suitable numerical methods for the synthetic data generation. To obtain summaries that are less sensitive to the intrinsic stochasticity of the model, we propose to build up the statistical method (e.g. the choice of the summary statistics) on the underlying structural properties of the model.