Statistical characterization and development of summary measures of non-normal distributions of nuclear morphometry data from prostate cancer cells for use as prognostic indicators

Nuclear morphometry (NM) uses image analysis to measure features of the cell nucleus which are classified as: bulk properties, shape or form, and DNA distribution. Studies have used these measurements as diagnostic and prognostic indicators of disease with inconclusive results. The distributional properties of these variables have not been systematically investigated although much of the medical data exhibit nonnormal distributions. Measurements are done on several hundred cells per patient so summary measurements reflecting the underlying distribution are needed.^ Distributional characteristics of 34 NM variables from prostate cancer cells were investigated using graphical and analytical techniques. Cells per sample ranged from 52 to 458. A small sample of patients with benign prostatic hyperplasia (BPH), representing non-cancer cells, was used for general comparison with the cancer cells.^ Data transformations such as log, square root and 1/x did not yield normality as measured by the Shapiro-Wilks test for normality. A modulus transformation, used for distributions having abnormal kurtosis values, also did not produce normality.^ Kernel density histograms of the 34 variables exhibited non-normality and 18 variables also exhibited bimodality. A bimodality coefficient was calculated and 3 variables: DNA concentration, shape and elongation, showed the strongest evidence of bimodality and were studied further.^ Two analytical approaches were used to obtain a summary measure for each variable for each patient: cluster analysis to determine significant clusters and a mixture model analysis using a two component model having a Gaussian distribution with equal variances. The mixture component parameters were used to bootstrap the log likelihood ratio to determine the significant number of components, 1 or 2. These summary measures were used as predictors of disease severity in several proportional odds logistic regression models. The disease severity scale had 5 levels and was constructed of 3 components: extracapsulary penetration (ECP), lymph node involvement (LN+) and seminal vesicle involvement (SV+) which represent surrogate measures of prognosis. The summary measures were not strong predictors of disease severity. There was some indication from the mixture model results that there were changes in mean levels and proportions of the components in the lower severity levels. ^

Multinomial goodness-of-fit: large sample tests with survey design correction and exact tests for small samples

I introduce the new mgof command to compute distributional tests for discrete (categorical, multinomial) variables. The command supports largesample tests for complex survey designs and exact tests for small samples as well as classic large-sample x2-approximation tests based on Pearson’s X2, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read, 1984, Journal of the Royal Statistical Society, Series B (Methodological) 46: 440–464). The complex survey correction is based on the approach by Rao and Scott (1981, Journal of the American Statistical Association 76: 221–230) and parallels the survey design correction used for independence tests in svy: tabulate. mgof computes the exact tests by using Monte Carlo methods or exhaustive enumeration. mgof also provides an exact one-sample Kolmogorov–Smirnov test for discrete data.

TEST

Der Müller und die fünf Räuber, Überfall²³

Hypothesis testing and earthquake prediction.

Requirements for testing include advance specification of the conditional rate density (probability per unit time, area, and magnitude) or, alternatively, probabilities for specified intervals of time, space, and magnitude. Here I consider testing fully specified hypotheses, with no parameter adjustments or arbitrary decisions allowed during the test period. Because it may take decades to validate prediction methods, it is worthwhile to formulate testable hypotheses carefully in advance. Earthquake prediction generally implies that the probability will be temporarily higher than normal. Such a statement requires knowledge of "normal behavior"--that is, it requires a null hypothesis. Hypotheses can be tested in three ways: (i) by comparing the number of actual earth-quakes to the number predicted, (ii) by comparing the likelihood score of actual earthquakes to the predicted distribution, and (iii) by comparing the likelihood ratio to that of a null hypothesis. The first two tests are purely self-consistency tests, while the third is a direct comparison of two hypotheses. Predictions made without a statement of probability are very difficult to test, and any test must be based on the ratio of earthquakes in and out of the forecast regions.

MGOF: Stata module to perform goodness-of-fit tests for multinomial data

mgof computes goodness-of-fit tests for the distribution of a discrete (categorical, multinomial) variable. The default is to perform classical large sample chi-squared approximation tests based on Pearson's X2 statistic and the log likelihood ratio (G2) statistic or a statistic from the Cressie-Read family. Alternatively, mgof computes exact tests using Monte Carlo methods or exhaustive enumeration. A Kolmogorov-Smirnov test for discrete data is also provided. The moremata package, also available from SSC, is required.

Multinomial goodness-of-fit: large sample tests with survey design correction and exact tests for small samples

A new Stata command called -mgof- is introduced. The command is used to compute distributional tests for discrete (categorical, multinomial) variables. Apart from classic large sample $\chi^2$-approximation tests based on Pearson's $X^2$, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read 1984), large sample tests for complex survey designs and exact tests for small samples are supported. The complex survey correction is based on the approach by Rao and Scott (1981) and parallels the survey design correction used for independence tests in -svy:tabulate-. The exact tests are computed using Monte Carlo methods or exhaustive enumeration. An exact Kolmogorov-Smirnov test for discrete data is also provided.

Trade liberalisation and technical efficiency: Evidence from Bangladesh manufacturing industries

The paper investigates the effects of trade liberalisation on the technical efficiency of the Bangladesh manufacturing sector by estimating a combined stochastic frontier-inefficiency model using panel data for the period 197894 for 25 three-digit level industries. The results show that the overall technical efficiency of the manufacturing sector as well as the technical efficiencies of the majority of the individual industries has increased over time. The findings also clearly suggest that trade liberalisation, proxied by export orientation and capital deepening, has had significant impact on the reduction of the overall technical inefficiency. Similarly, the scale of operation and the proportion of non-production labour in total employment appear as important determinants of technical inefficiency. The evidence also indicates that both export-promoting and import-substituting industries have experienced rises in technical efficiencies over time. Besides, the results are suggestive of neutral technical change, although (at the 5 per cent level of significance) the empirical results indicate that there was no technical change in the manufacturing industries. Finally, the joint test based on the likelihood ratio (LR) test rejects the Cobb-Douglas production technology as description of the database given the specification of the translog production technology.

Association between polymorphisms in the progesterone receptor gene and endometriosis

The progesterone receptor (PR) is a candidate gene for the development of endometriosis, a complex disease with strong hormonal features, common in women of reproductive age. We typed the 306 base pair Alu insertion (AluIns) polymorphism in intron G of PR in 101 individuals, estimated linkage disequilibrium (LD) between five single-nucleotide polymorphisms (SNPs) across the PR locus in 980 Australian triads (endometriosis case and two parents) and used transmission disequilibrium testing (TDT) for association with endometriosis. The five SNPs showed strong pairwise LD, and the AluIns was highly correlated with proximal SNPs rs1042839 ({Delta}2 = 0.877, D9 = 1.00, P < 0.0001) and rs500760 ({Delta}2 = 0.438, D9 = 0.942, P < 0.0001). TDT showed weak evidence of allelic association between endometriosis and rs500760 (P = 0.027) but not in the expected direction. We identified a common susceptibility haplotype GGGCA across the five SNPs (P = 0.0167) in the whole sample, but likelihood ratio testing of haplotype transmission and non-transmission of the AluIns and flanking SNPs showed no significant pattern. Further, analysis of our results pooled with those from two previous studies suggested that neither the T2 allele of the AluIns nor the T1/T2 genotype was associated with endometriosis.

Is pulse palpation helpful in detecting atrial fibrillation? A systematic review

Background Atrial fibrillation in the elderly is common and potentially life threatening. The classical sign of atrial fibrillation is an irregularly irregular pulse. Objective The objective of this research was to determine the accuracy of pulse palpation to detect atrial fibrillation. Methods We searched Medline, EMBASE, and the reference lists of review articles for studies that compared pulse palpation with the electrocardiogram (ECG) diagnosis of atrial fibrillation. Two reviewers independently assessed the search results to determine the eligibility of studies, extracted data, and assessed the quality of the studies. Results We identified 3 studies (2385 patients) that compared pulse palpation with ECG. The estimated sensitivity of pulse palpation ranged from 91% to 100%, while specificity ranged from 70% to 77%. Pooled sensitivity was 94% (95% confidence interval [CI], 84%-97%) and pooled specificity was 72% (95% CI 69%-75%). The pooled positive likelihood ratio was 3.39, while the pooled negative likelihood ratio was 0.10. Conclusions Pulse palpation has a high sensitivity but relatively low specificity for atrial fibrillation. It is therefore useful for ruling out atrial fibrillation. It may also be a useful screen to apply opportunistically for previously undetected atrial fibrillation. Assuming a prevalence of 3% for undetected atrial fibrillation in patients older than 65 years, and given the test's sensitivity and specificity, opportunistic pulse palpation in this age group would detect an irregular pulse in 30% of screened patients, requiring further testing with ECG. Among screened patients, 0.2% would have atrial fibrillation undetected with pulse palpation.

Conditional Confidence Interval for the Scale Parameter of a Weibull Distribution

2000 Mathematics Subject Classification: 62F25, 62F03.

TEST BINARY IMPORT

Der Müller und die fünf Räuber, Überfall²³

Finite Population Inference for Causal Parameters

Thesis (Ph.D.)--University of Washington, 2016-08

Cross-sectoral differences in the drivers of innovation: evidence from the Irish Community Innovation Survey

Purpose: The purpose of this paper is to analyse differences in the drivers of firm innovation performance across sectors. The literature often makes the assumption that firms in different sectors differ in their propensity to innovate but not in the drivers of innovation. The authors empirically assess whether this assumption is accurate through a series of econometric estimations and tests. Design/methodology/approach: The data used are derived from the Irish Community Innovation Survey 2004-2006. A series of multivariate probit models are estimated and the resulting coefficients are tested for parameter stability across sectors using likelihood ratio tests. Findings: The results indicate that there is a strong degree of heterogeneity in the drivers of innovation across sectors. The determinants of process, organisational, new to firm and new to market innovation varies across sectors suggesting that the pooling of sectors in an innovation production function may lead to biased inferences. Research limitations/implications: The implications of the results are that innovation policies targeted at stimulating innovation need to be tailored to particular industries. One size fits all policies would seem inappropriate given the large degree of heterogeneity observed across the drivers of innovation in different sectors. Originality/value: The value of this paper is that it provides an empirical test as to whether it is suitable to group sectoral data when estimating innovation production functions. Most papers simply include sectoral dummies, implying that only the propensity to innovate differs across sectors and that the slope of the coefficient estimates are in fact consistent across sectors.

Modelling the growth of Northern Cod

We develop a body size growth model of Northern cod (Gadus morhua) in Northwest Atlantic Fisheries Organization (NAFO) Divisions 2J3KL during 2009-2013. We use individual length-at-age data from the bottom trawl survey in these divisions during 2009–2013. We use the Von Bertalanffy (VonB) model extended to account for between-individual variations in growth, and variations that may be caused by the methods which fish are caught and sampled for length and age measurements. We assume between-individual variation in growth appears because individuals grow at a different rate (k), and they achieve different maximum sizes (l∞). We also included measurement error in length and age in our model since ignoring these errors can lead to biased estimates of the growth parameters. We use the structural errors-invariables (SEV) approach to estimate individual variation in growth, ageing error variation, and the true age distribution of the fish. Our results shows the existence of individual variation in growth and ME in age. According to the negative log likelihood ratio (NLLR) test, the best model indicated: 1) different growth patterns across divisions and years. 2) Between individual variation in growth is the same for the same division across years. 3) The ME in age and true age distribution are different for each year and division.

Testing the random walk hypothesis with variance ratio statistics

Mestrado em Finanças