Overweight in classical phenylketonuria children: A retrospective cohort study.

Purpose: This cohort study aimed to determine the frequency of overweight and obesity in classical phenylketonuria children and to identify the possible influence of metabolic control on the BMI of the studied patients.

Patients And Methods: The study group included 63 classical phenylketonuria patients (40 girls and 23 boys; aged 5-16 years). Their z-score BMI, metabolic control, educational level of parents and socioeconomic status were determined.

Fecal Fat Concentration and Excretion in the First 2 Years of Life: a Cross-Sectional Study.

Objectives: Data on pancreatic exocrine secretion in the youngest children are scarce. The aim of the study was to determine the range of normal values for fecal fat concentration (FFC) and fecal fat excretion (FFE) in infants and toddlers up to 2 years of age.

Methods: A total of 160 subjects aged 1 to 24 months (8 groups of 20: aged 1-3, 4-6 months, etc) were included in the study.

Fecal elastase-1 in healthy children up to 2 years of age: a cross-sectional study.

Background: Fecal elastase-1 (E-1) levels in infants and young children may be expected to differ from those in adults and older children because of the immaturity of the gastrointestinal tract and the specificity of their diet. Despite the availability of data describing E-1 levels in the stools of preterm infants, older children, adults and subjects with malabsorption, there is still a lack of data regarding E-1 in healthy infants and toddlers. The aim of this cross-sectional study was to evaluate fecal E-1 concentrations in infants and children from 1 up to 24 months of age.

Determinants of Serum Glycerophospholipid Fatty Acids in Cystic Fibrosis.

The etiology of altered blood fatty acid (FA) composition in cystic fibrosis (CF) is understood only partially. We aimed to investigate the determinants of serum glycerophospholipids' FAs in CF with regard to the highest number of FAs and in the largest cohort to date. The study comprised 172 CF patients and 30 healthy subjects (HS).

[Detection and considering of nonlinear effects in regression modeling].

One of the basic problems solved in the research work is the search for causal relationships between the variables analyzed. Very important, but not the only requirement for the existence of causality is to demonstrate the occurrence of a statistically significant correlation and determine the shape of the present relationship (regression). In the vast majority of experimenters limited to the study of linear associations.

[The estimation of causal effect in observational studies].

One of the frequently encountered problems in the statistical analysis of the data is the correct interpretation of the effects occurring under the influence of some kind of treatment used by the researcher or appearing without its share of action. In the first case we are dealing with an experimental study, in the second with the observational study. In the experimental study, the researcher has full control over the procedure of random allocation of cases to a group subjected to a predetermined factor and a group not exposed to the action (randomization).

[Do we always correctly interpret the results of statistical nonparametric tests].

Mann-Whitney, Wilcoxon, Kruskal-Wallis and Friedman tests create a group of commonly used tests to analyze the results of clinical and laboratory data. These tests are considered to be extremely flexible and their asymptotic relative efficiency exceeds 95 percent. Compared with the corresponding parametric tests they do not require checking the fulfillment of the conditions such as the normality of data distribution, homogeneity of variance, the lack of correlation means and standard deviations, etc.

[Statistical inference in the presence of missing data].

In experimental studies and particularly in medical research the missing data frequently ap-pear. Their existence can strongly affect the conclusions drawn. Therefore, it is important to treat them in appropriate way.

[Case-control models in experimental designs].

Case-control studies are one of the quickest and frequently least expensive studies both to design and carry out. Unfortunately, they are also the most vulnerable to possible biases. A crucial point is the selection of an appropriate control group.

[Binary hypothesis testing with ROC curves].

In medical research we frequently need to delimit some areas of the measured parameters to use them in diagnosing existence of some kind of abnormality. In the article the usage of ROC curves and comparison of obtained results to the outcomes of logistic analysis, decision trees and reference intervals is described.

[Confounding variables in observational studies and their influence on obtained results].

In estimating the influence of one variable on another one we must ensure that observed effect is not caused by factors other than these under investigation. These last, called confounders of analyzed variables, influencing simultaneously both analyzed quantities may generate spurious non existing in reality relationships or change direction of these really existing.

[Methods of exact nonparametric inference for non-typical distributions of experimental data].

In medical research we frequently find data sets with specific structure such as small data sets, unbalanced, sparse or heavily tied. The peculiar properties of those sets influence the p-value which quantity is used in decision making process. Four examples of experimental data, for which estimation of asymptotic p-value leads to erroneous decision, are presented.

[Common errors in analysis of the relationship between variables].

One of the key research problems is the analysis of relationships between variables. Mathematical modeling is commonly used technique in quantification of these associations. This paper describes errors most frequently occurring in simple and complex models constructions such as assumption violation, influence of outliers, data clustering, influence of confounding variables, limited scope of model application.

[Application of virtual instrumentation technique in toxicological studies].

Research investigations require frequently direct connection of measuring equipment to the computer. Virtual instrumentation technique considerably facilitates programming of sophisticated acquisition-and-analysis procedures. In standard approach these two steps are performed subsequently with separate software tools.

[Use of rough sets theory in analysis of qualitative data in toxicology studies].

In analysis of medical data a problem of simplified and not effective analysis of qualitative data is frequently encountered. This situation takes place particularly when data are measured in the weakest measuring scale--nominal scale. However this scale contains less detailed information than interval or ordinal scale, there exist a lot of different mathematical approaches to examine existing dependencies, among other causality.