Saumen Mandal

Bio: Saumen Mandal is an academic researcher from University of Manitoba. The author has contributed to research in topics: Estimator & Optimal design. The author has an hindex of 7, co-authored 30 publications receiving 169 citations.

Optimal Adaptive Designs in Phase III Clinical Trials for Continuous Responses with Covariates

Abstract: Some optimal adaptive allocation design was given by (2002) for phase III clinical trials involving two treatments having binary responses, but no covariate We extend that idea to introduce an optimal adaptive allocation design for two treatments having some continuous responses Moreover, we allow covariates in our model Exact and limiting proportion of allocation for the proposed design are numerically evaluated

Multi-treatment optimal response-adaptive designs for phase III clinical trials

Abstract: Response-adaptive designs are used in phase III clinical trials to allocate a larger number of patients to the better treatment. Optimal response-adaptive designs have become popular in recent days for this purpose, where the design is derived from some optimal viewpoints, mostly by optimizing some objective function subject to some constraint(s). However, most of the optimal designs are derived with two treatments and only a few works are available for several treatments. The present paper provides a generalized framework to derive multi-treatment optimal response-adaptive designs. A detailed performance study is provided for three treatment trials minimising failures. The applicability is also judged by redesigning some real clinical trials.

Probability and Statistical Inference

Abstract: The Foundations of Statistics By Prof. Leonard J. Savage. (Wiley Publications in Statistics.) Pp. xv + 294. (New York; John Wiley and Sons, Inc.; London: Chapman and Hall, Ltd., 1954.) 48s. net.

Extreme Value Theory

Abstract: Extreme Value Theory is the branch of statistics that is used to model extreme events. The topic is of interest to meteorologists because much of the recent literature on climate change has focussed on the possibility that extreme events (very high or low temperatures, high precipitation events, droughts, hurricanes etc.) may be changing in parallel with global warming. As a specific example, the paper by Stott, Stone and Allen (2004) used the generalized Pareto distribution (see Section 2) to estimate the probability of the European heatwave event of 2003 under two conditions, (a) based on climate model data without an anthropogenic signal, (b) including anthropogenic effects (greenhouse gases etc.). They estimated a probability of about 1/1000 under (a) but about 1/250 under (b). Although even the probability under (b) is low, the increase in probability compared with (a) led them to conclude that the fraction of attributable risk due to the anthropogenic influence is about 75%. Another example of the use of statistics to examine trends in probabilities of extreme events is the recent paper by Elsner et al. (2008), which is highly relevant to the question of whether there is an increasing trend in severe hurricanes that may possibly be associated with anthropogenic global warming.

How to measure diversity when you must.

Abstract: Racial/ethnic diversity has become an increasingly important variable in the social sciences. Research from multiple disciplines consistently demonstrates the tremendous impact of ethnic diversity on individuals and organizations. Investigators use a variety of measures, and their choices can affect the conclusions that can be drawn and limit the ability to compare and generalize results across studies effectively. The current article reviews 3 popular approaches to the measurement of diversity: the simplistic majority–minority approach and 2 multiple categories variants, the generalized variance and the lesser used entropy statistic. We discuss the properties of each approach and reject the majority– minority approach. We provide 5 examples using the generalized variance and entropy statistics and illustrate their versatility and flexibility. We urge investigators to adopt these multicategory measures and to use our discussion to determine which measure of diversity is most appropriate given the nature of one’s data set and research question.

Monotonic convergence of a general algorithm for computing optimal designs

Abstract: Monotonic convergence is established for a general class of multiplicative algorithms introduced by Silvey, Titterington and Torsney [Comm. Statist. Theory Methods 14 (1978) 1379--1389] for computing optimal designs. A conjecture of Titterington [Appl. Stat. 27 (1978) 227--234] is confirmed as a consequence. Optimal designs for logistic regression are used as an illustration.