Analysis of Multiply Matched Retrospective Studies When Risk Factor Under Study is Discrete,With Two or More Levels

The odds ratio is known to closely approximate the relative risk when the disease is rare. Logistic regression models are often used to estimate such odds ratios, but here a different model is used which avoids the assumptions implicit in logistic modelling; it also has the advantage of providing a test of homogeneity for odds rat os in situations where the logistic model cannot.     

Estimation of Linear Regression Model with Random Coefficients Ensuring Almost Non-Negativity of Variance Estimators

For a linear regression model with random coefficients, this paper considers the estimation of the mean of coefficient vector which, in turn, involves the estimation of variances of random coefficients. The conventional estimation methods for it sometimes provides negative estimates. In order to circumvent this kind of difficulty, a proposal is forwarded and is examined in the light of existing ones.     

Discovery of isoquinolinone and naphthyridinone-based inhibitors of poly(ADP-ribose) polymerase-1 (PARP1) as anticancer agents: Structure activity relationship and preclinical characterization

The exploitation of GLU988 and LYS903 residues in PARP1 as targets to design isoquinolinone (I & II) and naphthyridinone (III) analogues is described. Compounds of structure I have good biochemical and cellular potency but suffered from inferior PK. Constraining the linear propylene linker of structure I into a cyclopentene ring (II) offered improved PK parameters, while maintaining potency for PARP1. Finally, to avoid potential issues that may arise from the presence of an anilinic moiety, the nitrogen substituent on the isoquinolinone ring was incorporated as part of the bicyclic ring. This afforded a naphthyridinone scaffold, as shown in structure III. Further optimization of naphthyridinone series led to identification of a novel and highly potent PARP1 inhibitor 34, which was further characterized as preclinical candidate molecule. Compound 34 is orally bioavailable and displayed favorable pharmacokinetic (PK) properties. Compound 34 demonstrated remarkable antitumor efficacy both as a single-agent as well as in combination with chemotherapeutic agents in the BRCA1 mutant MDA-MB-436 breast cancer xenograft model. Additionally, compound 34 also potentiated the effect of agents such as temozolomide in breast cancer, pancreatic cancer and Ewing’s sarcoma models.     

The growth form of populations in some aphids,with special reference to the relation between population density and the movements

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National Institute of Agricultural Sciences     

Testing the Metabolic Scaling Theory of tree growth

1.  Metabolic Scaling Theory (MST) predicts a 'universal scaling law' of tree growth. Proponents claim that MST has strong empirical support: the size-dependent growth curves of 40 out of 45 species in a Costa Rican forest have scaling exponents indistinguishable from the MST prediction.
2.  Here, we show that the Costa Rican study has been misinterpreted. Using Standardized Major Axis (SMA) line-fitting to estimate scaling exponents, we find that four out of five species represented by more than 100 stems have scaling exponents that deviate significantly from the MST prediction. On the other hand, sample sizes were too small to make strong inferences in the cases of 33 species represented by fewer than 50 stems.
3.  Recently, it has been argued that MST is useful for predicting average scaling exponents, even if individual species do not conform to the theory. We find that the mean scaling exponent of the Costa Rican trees is greater than predicted (across-species mean  =  0.44), and hypothesize that scaling exponents in natural forests will generally be greater than predicted, because the theory fails to model asymmetric competition for light.
4.   Synthesis . We highlight shortcomings in the interpretation of data used in support of a key MST prediction. We recommend that future research into biological scaling should compare the merits of alternative models rather than focusing attention on tests of a single theory.     

The Treatment of Missing Values in Logistic Regression

The efficiencies of the estimators in the linear logistic regression model are examined using simulations under six missing value treatments. These treatments use either the maximum likelihood or the discriminant function approach in the estimation of the regression coefficients. Missing values are assumed to occur at random. The cases of multivariate normal and dichotomous independent variables are both considered. We found that in general, there is no uniformly best method. However, mean substitution and discriminant function estimation using existing pairs of values for correlations turn out to be favourable for the cases considered.     

Binary Responses and the Three-period Cross-over

Problems with carry-over effects in the simple two-period cross-over have lead to interest in more complex cross-over designs. A method for analysing the optimum two-treatment three-period design with binary response variables is given by making a simple extension to Gart's logistic model. The method gives independent tests for, and estimates of the difference in treatment and first-order carry-over effects. An example of the analysis is given, using the loglinear models facility in GLIM.     

Comparing Stochastically Restricted Linear Estimators in a Regression Model

Conditions for superiority of the minimum dispersion estimator over another with respect to the covariance matrix are derived when the vector parameter of a regression model is subject to competing stochastic restrictions. The restrictions may also consist both of a deterministic part and a stochastic part.