Gaurangadeb Chattopadhyay

Comparative Efficacies of Artemisinin Combination Therapies in Plasmodium falciparum Malaria and Polymorphism of pfATPase6, pfcrt, pfdhfr, and pfdhps Genes in Tea Gardens of Jalpaiguri District, India

ABSTRACT In India, chloroquine has been replaced by a combination of artesunate and sulfadoxine-pyrimethamine (AS-SP) for uncomplicated P. falciparum malaria. Other available combinations, artemether-lumefantrine (AM-LF) and artesunate-mefloquine (AS-MQ), not included in the national program, are widely used by private practitioners. Little is known about the therapeutic efficacy of these artemisinin combinations and the prevalence of molecular markers associated with antimalarial drug resistance. A total of 157 patients with P. falciparum monoinfection were recruited and randomized into three study groups (AS-SP, AM-LF, and AS-MQ). All patients were followed up for 42 days to study the clinical and parasitological responses according to the WHO protocol (2009). We assessed the polymorphism of the pfATPase6, pfcrt, pfdhfr, and pfdhps genes by the DNA-sequencing method. The PCR-corrected therapeutic efficacies of AS-SP, AM-LF, and AS-MQ were 90.6% (95% confidence interval [CI], 0.793 to 0.969), 95.9% (95% CI, 0.860 to 0.995), and 100% (95% CI, 0.927 to 1.00), respectively. No specific mutational pattern was observed in the pfATPase6 gene. All isolates had a K76T mutation in the pfcrt gene. In the pfdhfr-pfdhps genotype, quadruple mutation was frequent, and quintuple mutation was documented in 6.3% of P. falciparum isolates. The significant failure rate of AS-SP (9.5%), although within the limit (10%) for drug policy change, was due to SP failure because of prevailing mutations in pfdhfr, I51R59N108, with pfdhps, G437 and/or E540. The efficacy of this ACT needs periodic monitoring. Artemether-lumefantrine and artesunate-mefloquine are effective alternatives to the artesunate-sulfadoxine-pyrimethamine combination.

Simultaneous identification of growth law and estimation of its rate parameter for biological growth data: a new approach

Scientific formalizations of the notion of growth and measurement of the rate of growth in living organisms are age-old problems. The most frequently used metric, “Average Relative Growth Rate” is invariant under the choice of the underlying growth model. Theoretically, the estimated rate parameter and relative growth rate remain constant for all mutually exclusive and exhaustive time intervals if the underlying law is exponential but not for other common growth laws (e.g., logistic, Gompertz, power, general logistic). We propose a new growth metric specific to a particular growth law and show that it is capable of identifying the underlying growth model. The metric remains constant over different time intervals if the underlying law is true, while the extent of its variation reflects the departure of the assumed model from the true one. We propose a new estimator of the relative growth rate, which is more sensitive to the true underlying model than the existing one. The advantage of using this is that it can detect crucial intervals where the growth process is erratic and unusual. It may help experimental scientists to study more closely the effect of the parameters responsible for the growth of the organism/population under study.

Detailed Statistical Inference-multiple Decision Problem

The procedure for detailed statistical inference developed by the authors in an earlier paper (1992) for the two-decision case, is extended here to the case of several decisions. Alongwith the rule for choosing the decision, the problem of stating data dependent measures of confidence in terms of betting odds, is considered. The extension involves generalization oftlie coneept of legitimacy of betting odds introduced in the earlier paper and the choice of a suitable utility function for bets. The actual solution is worked out in the case of logarithmic utility. A rather intricate mathematical result requires to be established to prove the existence of an optimum rule in this case. Application of the procedure is illustrated through some numerical examples.

Finite Population Distribution Function Estimation Using P-Splines

Abtsrcat Estimating finite population distribution function (hereafter FPDF) is an important problem to the survey samplers since it summarizes almost all the relevant information of interest about the finite population. Chambers and Dunstan (1986) (henceforth CD) in their seminal paper propose a predictive estimator of FPDF incorporating the unit level auxiliary information available for the entire population. CD estimator assumes a linear regression model in the superpopulation. However, even for moderate deviation from linearity assumption, significant performance deterioration of CD estimator is observed. Here we propose a predictive estimator of FPDF based on a semiparametric regression model. For this we use recently developed penalized splines (P-splines) regression. The proposed estimator performs better than CD estimator if the linearity assumption fails. We find its asymptotic bias and variance. Finally, we compare the performance of the proposed estimator with other alternative estimators through simulation studies and illustrate its use with a real data set.

A Test for Equality of Marginals of Fgm Family of Bivariate Distributions Using Unordered Pairs

Abtsrcat A test for equality of marginal distributions of Farlie-Gumbel-Morgenstern (FGM) family of bivariate distributions is proposed on the basis of a paired sample of size n, when the labels of the observations are not known. The test has desirable power properties and is an improvement over the test prescribed by Davies and Phillips [1988]. The test is illustrated with some interesting real-life applications.

Paired Sample Tests with Unordered Observations

The problem of testing equality of means of a bivariate normal distribution on the basis of a sample of size n has been considered when the labels of the observations are not known. Two tests based on the sample moments of absolute values of the differences of the observations in each pair are suggested. Their power properties are investigated. It is interesting to observe that for small values of the parameter one is more powerful than the other while for larger values it is the other way around. AMS (2000) Subject Classification : 62F03, 62P10.

Coverage probability and exact inference

AbstractWith reference to “point estimation” of a real-valued parameter θ involved in the distribution of a real-valued random variable X, we consider a sample size n and an underlying unbiased estimator

On Strong and Weak Confounding in a Complex System: Characterization and Examples

{\hat \theta _n}