Unexpected abnormal coagulation test results in a 2-year-old child: A case report

Rejection of the sample with repeated blood withdrawal is always an unwanted consequence of sample nonconformity and preanalytical errors, especially in the most vulnerable population – children. Here is presented a case with unexpected abnormal coagulation test results in a 2-yearold child with no previously documented coagulation disorder. Child is planned for tympanostomy tubes removal under the anaesthesia driven procedure, and preoperative coagulation tests revealed prolonged prothrombin time, activated partial thromboplastin time and thrombin time, with fibrinogen and antithrombin within reference intervals. From the anamnestic and clinical data, congenital coagulation disorder was excluded, and with further investigation, sample mismatch, clot presence and accidental ingestion of oral anticoagulant, heparin contamination or vitamin K deficiency were excluded too. Due to suspected EDTA carryover during blood sampling another sample was taken the same day and all tests were performed again. The results for all tests were within reference intervals confirming EDTA effect on falsely prolongation of the coagulation times in the first sample. This case can serve as alert to avoid unnecessary loss in terms of blood withdrawal repetitions and discomfort of the patients and their relatives, tests repeating, prolonging medical procedures, and probably delaying diagnosis or proper medical treatment. It is the responsibility of the laboratory specialists to continuously educate laboratory staff and other phlebotomists on the correct blood collection as well as on its importance for the patient’s safety

Simultaneous critical values for tt-tests in very high dimensions

This article considers the problem of multiple hypothesis testing using $t$-tests. The observed data are assumed to be independently generated conditional on an underlying and unknown two-state hidden model. We propose an asymptotically valid data-driven procedure to find critical values for rejection regions controlling the $k$-familywise error rate ($k$-FWER), false discovery rate (FDR) and the tail probability of false discovery proportion (FDTP) by using one-sample and two-sample $t$-statistics. We only require a finite fourth moment plus some very general conditions on the mean and variance of the population by virtue of the moderate deviations properties of $t$-statistics. A new consistent estimator for the proportion of alternative hypotheses is developed. Simulation studies support our theoretical results and demonstrate that the power of a multiple testing procedure can be substantially improved by using critical values directly, as opposed to the conventional $p$-value approach. Our method is applied in an analysis of the microarray data from a leukemia cancer study that involves testing a large number of hypotheses simultaneously.Comment: Published in at http://dx.doi.org/10.3150/10-BEJ272 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Data driven rank tests for classes of tail alternatives

Tail alternatives describe the frequent occurrence of a non-constant shift in the two-sample problem with a shift function increasing in the tail. The classes of shift functions can be built up using Legendre polynomials. It is important to rightly choose the number of polynomials involved. Here this choice is based on the data, using a modification of Schwarz's selection rule. Given the data driven choice of the model, appropriate rank tests are applied. Simulations show that the new data driven rank tests work very well. While other tests for detecting shift alternatives as Wilcoxon's test may completely break down for important classes of tail alternatives, the new tests have high and stable power. The new tests have also higher power than data driven rank tests for the unconstrained two-sample problem. Theoretical support is obtained by proving consistency of the new tests against very large classes of alternatives, including all common tail alternatives. A simple but accurate approximation of the null distribution makes application of the new tests easy

The penalty in data driven Neyman's tests

Data driven Neyman's tests are based on two elements: Neyman's smooth tests in finite dimensional submodels and a selection rule to choose the "right'' submodel. As selection rule usually (a modification of) Schwarz's rule is applied. In this paper we consider data driven Neyman's tests with selection rules allowing also other penalties than the one in Schwarz's rule. It is shown that the nice properties of consistency against very large classes of alternatives and the more deep result of asymptotic optimality in the sense of vanishing shortcoming continue to hold for other penalties as well, including the one corresponding to Akaike's selection rule

Data-driven rate-optimal specification testing in regression models

We propose new data-driven smooth tests for a parametric regression function. The smoothing parameter is selected through a new criterion that favors a large smoothing parameter under the null hypothesis. The resulting test is adaptive rate-optimal and consistent against Pitman local alternatives approaching the parametric model at a rate arbitrarily close to 1/\sqrtn. Asymptotic critical values come from the standard normal distribution and the bootstrap can be used in small samples. A general formalization allows one to consider a large class of linear smoothing methods, which can be tailored for detection of additive alternatives.Comment: Published at http://dx.doi.org/10.1214/009053604000001200 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

The penalty in data driven Neyman's tests

Data driven Neyman's tests are based on two elements: Neyman's smooth tests in finite dimensional submodels and a selection rule to choose the ``right'' submodel. As selection rule usually (a modification of) Schwarz's rule is applied. In this paper we consider data driven Neyman's tests with selection rules allowing also other penalties than the one in Schwarz's rule. It is shown that the nice properties of consistency against very large classes of alternatives and the more deep result of asymptotic optimality in the sense of vanishing shortcoming continue to hold for other penalties as well, including the one corresponding to Akaike's selection rule

Risk management for traffic safety control

This paper offers a range of modelling ideas and techniques from mathematical statistics appropriate for analysing traffic accident data for the East region operation of CLP Power Hong Kong Limited and for the Hong Kong population in general. We further make proposals for alternative ways to record and collect data, and discuss ways to identify the major contributing factors behind accidents. We hope that our findings will enable the design of effective accident prevention strategies for CLP