Poverty and Wellbeing at the ‘Grassroots’—How Much is Visible to Researchers?

This paper discusses the grassroots level understanding of poverty and wellbeing. There is rich debate and ever expanding literature on the meaning of wellbeing and poverty and their relationship in developing countries. In recent times wellbeing and poverty have been scrutinised within the discourse on multidimensionality of poverty. Most research outputs though are grounded in quantitative data. Investigations that focus on the perceptions and understandings of poor people about their situations remain sparse in the literature. The current study is an attempt to address this gap. The paper explores the common grounds and the points of departure between the researchers’ views of poverty and wellbeing and the perception at the grassroots. The paper presents findings of primary research conducted by the author in Dhar district of Madhya Pradesh and Madhubani district of Bihar in India. Semi-structured survey instruments were deployed to interview a selection of poor, marginal and non-poor households. In addition to identifying the commonalities in grassroots and researchers’ understandings of poverty, the paper draws attention to factors that may be outside the radar of the researchers. It is envisaged that mapping a more holistic understanding of poverty and wellbeing will have important long-term policy implications for poverty reduction.

Propensity‐score‐based priors for Bayesian augmented control design

Drug developers are required to demonstrate substantial evidence of effectiveness through the conduct of adequate and well‐controlled (A&WC) studies to obtain marketing approval of their medicine. What constitutes A&WC is interpreted as the conduct of randomized controlled trials (RCTs). However, these trials are sometimes unfeasible because of their size, duration, and cost. One way to reduce sample size is to leverage information on the control through a prior. One consideration when forming data‐driven prior is the consistency of the external and the current data. It is essential to make this process less susceptible to choosing information that only helps improve the chances toward making an effectiveness claim. For this purpose, propensity score methods are employed for two reasons: (1) it gives the probability of a patient to be in the trial, and (2) it minimizes selection bias by pairing together treatment and control within the trial and control subjects in the external data that are similar in terms of their pretreatment characteristics. Two matching schemes based on propensity scores, estimated through generalized boosted methods, are applied to a real example with the objective of using external data to perform Bayesian augmented control in a trial where the allocation is disproportionate. The simulation results show that the data augmentation process prevents prior and data conflict and improves the precision of the estimator of the average treatment effect.     

Habitat patch size and tree species richness shape the bird community in urban green spaces of rapidly urbanizing Himalayan foothill region of India

Urban Ecosystems - Rapid urbanization is emerging as one of the leading threats to the biodiversity globally. But is especially a cause of concern for tropical countries which are urbanizing much...     

Adaptive designs for best treatment identification with top-two Thompson sampling and acceleration

We consider outcome adaptive phase II or phase II/III trials to identify the best treatment for further development. Different from many other multi-arm multi-stage designs, we borrow approaches for the best arm identification in multi-armed bandit (MAB) approaches developed for machine learning and adapt them for clinical trial purposes. The best arm identification in MAB focuses on the error rate of identification at the end of the trial, but we are also interested in the cumulative benefit of trial patients, for example, the frequency of patients treated with the best treatment. In particular, we consider Top-Two Thompson Sampling (TTTS) and propose an acceleration approach for better performance in drug development scenarios in which the sample size is much smaller than that considered in machine learning applications. We also propose a variant of TTTS (TTTS2) which is simpler, easier for implementation, and has comparable performance in small sample settings. An extensive simulation study was conducted to evaluate the performance of the proposed approach in multiple typical scenarios in drug development.