A general class of improved population variance estimators under non-sampling errors using calibrated weights in stratified sampling

This paper proposes a new calibration estimator for population variance within a stratified two-phase sampling design. It takes into account random non-response and measurement errors, specifically applying this method to estimate the variance in Gas turbine exhaust pressure data. The study integrates additional information from two highly positively correlated auxiliary variables to develop a general class of estimators tailored for the stratified two-phase sampling scheme. The properties of these estimators, in terms of their biases and mean square errors, have been thoroughly examined and extensively analyzed through numerical and simulation studies. Furthermore, the calibrated weights of the strata are derived. The proposed estimators outperform the natural estimator of population variance. Finally, suitable recommendations have been made for survey statisticians intending to apply these findings to real-life problems.

Estimating the growth rate of infection during the early phase of a pandemic like COVID-19

At the very outbreak of a pandemic, it is very important to be able to assess the spreading rate of the disease i.e., the rate of increase of infected people in a specific locality. Combating the pandemic situation critically depends on an early and correct prediction of, to what extent the disease may possibly grow within a short period of time. This paper attempts to estimate the spreading rate by counting the total number of infected persons at times. Adaptive clustering is especially suitable for forming clusters of infected persons distributed spatially in a locality and successive sampling is used to measure the growth in number of infected persons. We have formulated a 'chain ratio to regression type estimator of population total' in two occasions adaptive cluster successive sampling and studied the properties of the estimator. The efficacy of the proposed strategy is demonstrated through simulation technique as well as real life population which is followed by suitable recommendation.

Global Versus Indian Perspective of Pioglitazone-induced Adverse Drug Reactions Including Bladder Cancer: A Comparative Retrospective Pharmacovigilance Analysis

PURPOSE

In 2011, France and Germany banned pioglitazone due to a concomitant risk for bladder cancer. There has been continued debate about this topic. Therefore, we present a detailed analysis of data from individual case safety reports of pioglitazone use (PG-ICSRs) associated with bladder cancer reported worldwide and in India.

METHODS

Data from PG-ICSRs reported by the National Coordination Centre's Pharmacovigilance Programme of India, as well as over 131 World Health Organization member countries in the Uppsala Monitoring Centre's VigiLyze pharmacovigilance database system, from January 1, 1967, to March 4, 2018, were collected. Comparisons between data from global and Indian PG-ICSRs were made by applying filters such as country, bladder cancer, age group, gender, time period, information component, and data mining.

FINDINGS

Among the adverse drug reactions (ADRs) reported with pioglitazone use worldwide, bladder cancer and related terms were the most highly reported (43%). The most frequently co-reported concurrently used drug was metformin, which was included in 25% and 40% of overall and bladder cancer-specific PG-ICSRs, respectively. Suspected bladder cancer-specific pioglitazone-related reactions were reported in 27 countries, with 8548 serious and 1858 fatal cases and an information components value of 9.15. The Americas had the highest relative percentage of suspected bladder cancer in PG-ICSRs (53%), while the prevalence was much lower in India (2%). In both cohorts, men over the age of 45 years constituted the most highly reported population.

IMPLICATIONS

India has a very low prevalence of reported overall and bladder cancer-specific pioglitazone-related ADRs compared to Europe and the Americas. Possible explanations for the difference in reporting rates include variance in genetic makeup, low BC risk factor, pioglitazone prescription at a lower therapeutic dose, greater use of chemopreventive spices in the diet, higher frequency of metformin as a concurrent drug, and under-reporting of ADRs.

Efficient Class of Estimators for Finite Population Mean using Auxiliary Information in Two-Occasion Successive Sampling

In the case of sampling on two occasions, a class of estimators is considered which uses information on the first occasion as well as the second occasion in order to estimate the population means on the current (second) occasion. The usefulness of auxiliary information in enhancing the efficiency of this estimation is examined through the class of proposed estimators. Some properties of the class of estimators and a strategy of optimum replacement are discussed. The proposed class of estimators were empirically compared with the sample mean estimator in the case of no matching. The established optimum estimator, which is a linear combination of the means of the matched and unmatched portions of the sample at the current occasion, was empirically compared with the proposed class of estimators. Mutual comparisons of the proposed estimator were carried out. Suitable recommendations are made to the survey statistician for practical applications.

Veterinary pharmacovigilance in India: A need of hour

Veterinary pharmacovigilance (PV) is important for the Medicine which are used for treating disease in animals. It becomes more important when these animals are further used for producing food. Adverse drug reactions (ADRs) have a direct impact on animals and indirect impact on human beings, for example, through milk products, other animal producing food products. Currently, PV program of India is playing a vital role in assessing the safety of medicines in Indian Population. The safety of medicine in animals can be assessed by veterinary PV. The research institutes involved in animal research and veterinary hospitals can be considered as ADR monitoring centers to assess the safety of medicines on animals.

Best Practices for Improving the Quality of Individual Case Safety Reports in Pharmacovigilance

BACKGROUND

The quality of individual case safety reports (ICSRs) plays a vital role in identifying new safety signals in pharmacovigilance. This article focuses on establishing a method for ensuring the quality data.

OBJECTIVE

To develop an in-house method for assessing the documentation grading and completeness score of ICSRs.

METHODS

In the proposed method, 16 parameters, from report title to case narrative, are adopted to assess the quality of ICSRs. The in-house method ensures the completeness of the data and enhances the quality of ICSRs.

RESULTS

The in-house method was found effective in calculating the completeness score of ICSR ranges from 0.05 to 1. Indian ICSR completeness scores significantly improved after the implementation of the proposed method in the third quarter of 2013. In 2014 and until the third quarter of 2015, the score was found to be 0.91 and 0.93 out of 1, respectively.

CONCLUSION

The higher quality ICSRs aids in more effective identification of new drug safety alerts and provides evidence-based information for regulating the drug safety.

Status of documentation grading and completeness score for Indian individual case safety reports

The quality of individual case safety reports (ICSRs) generated under Pharmacovigilance Programme of India (PvPI) plays a pivotal role in detecting a signal from Indian drug safety data. Currently, more than hundred thousand ICSRs were generated under PvPI and reported to Uppsala Monitoring Centre. The documentation grading and completeness score of Indian ICSRs were rapidly increasing, and the current score was 0.94 out of 1.0. Periodical training on emphasizing the quality ICSRs is need of the hour.