Biomedical subjects

A Gerylovová

Publications and source records attributed to A Gerylovová.

At least 19 recordsLinked to original sources

PubMed1997

[Assessment of health needs, their geographic distribution and association of health care indicators in Districts within the Czech Republic].

BACKGROUND: The objective of the present study was to use available data provided by the programme Health Services Indicators (HSI) for partial analyses focused on the estimated health services needs in different districts of the Czech Republic (CR) and on evaluation of some intentions of the health policy. METHODS AND RESULTS: After evaluation of the standardized and gross mortality rates in different districts of the CR the authors evaluated relationships of selected indicators recorded in 1995. The correlation of the gross mortality as an orientational estimate of health services needs in different districts of the CR and expenditures of health insurance companies for health care per one insured subject was low and was not statistically significant (r = -0.062). A close correlation with expenditure of health insurance companies was found with the number of doctors (r = 0.894), the number of nurses (r = 0.842) and the number of hospital beds (r = 0.679). The number of population per general practitioner correlated only weakly with the expenditure of health insurance companies (r = 0.012). A marked correlation (r = -0.676) was found between the percentage of general practitioners from the total number of doctors in the district and expenditures of the health insurance companies. CONCLUSIONS: The authors recommend that the role of general practitioners in the system of health care should be appreciated and that primary care should be conceived as the basis of effective, economical and high standard health care. It is desirable that HSI data should become the baseline of systematic operational research into health services.

Czech Republic↗

PubMed1991

[Use of nonparametric methods in medicine. VII. Kendall's coefficient of concordance in several sequences].

The authors describe the procedure, suggested by Kendall for evaluation of the concordance of several sequences. Kendall's coefficient, called also coefficient of concordance, can be used when it is possible to assess the sequence of investigated variables. The calculation procedure and interpretation of the coefficient is explained on a simple example. For evaluation of significance tables of critical values are presented for 1% and 5% levels of significance.

Statistics as Topic↗

PubMed1991

[Use of nonparametric methods in medicine. VIII. Conclusion].

The submitted paper terminates the series of eight articles on the application of non-parametric methods in medicine, which were published in this journal in 1990-1991. Attention is drawn, on the one hand, to the urgent need of knowledge of mathematical and statistical methods, on the other hand, to the fact that proper processing of experimental data is not guaranteed by the use of computer technique alone, the complexity and numerical correctness of calculations but by interpretation of results and their plausibility which depends above all on the correctness of the reflections of the research worker. On a general level attention is paid to the description, comparison and evaluation of correlations, in particular the possibility to conclude from statistical correlation that a causal relationship exists. In the present as well as in previous articles the authors emphasize repeatedly that statistics are only one of the important tools of the doctor, but they cannot replace his responsibility, nor his professional experience.

Statistics as Topic↗

PubMed1990

[Use of nonparametric methods in medicine. III. Comparison of levels in 2 independent samples].

The authors present two nonparametric tests which can be used to compare the level of a random variable based on two independent random samplings. Wilcoxon's (Mann-Whitney's) test is based on the sum of sequences which are appropriate for individual elements in a common group. In large samples the tested criterium is based on the normal distribution, for small samples a table of critical values is enclosed which makes rapid decision possible. The median test is based on the idea that if two independent random samples are from the same population both samples contain equal numbers of elements above and below the median for the common group of all elements of both sub-groups. The tested criterium to which the median test leads is the chi 2-test or Fisher's test of accurate probabilities.

Statistics as Topic↗

PubMed1990

[Use of nonparametric methods in medicine. I. Introduction].

This is the introductory article of a series of eight where the authors discuss non-parametric methods. They compare the advantages and disadvantages of non-parametric methods with parametric ones. Non-parametric methods can be used in particular where the investigated variables do not have a normal distribution, where the small size of the investigation and the type of classification do not permit to test, when information on the investigated variables are presented by means of rank or normal scales etc. Their disadvantage is a smaller ability to refute the null hypothesis, when it is not correct, as compared with parametric tests.

Statistics as Topic↗

PubMed1990

[Use of nonparametric methods in medicine. II. Comparison of levels in 2 related samples].

The authors present two non-parametric tests which are suitable for comparison of the level of a random variable on the basis of results obtained from two dependent samples. The sign test is discussed which is based on the evaluation of the statistical significance of the difference in the number of positive and negative deviations. The application of this test in practice is facilitated by the enclosed table which gives critical frequencies which still lead to the refusal of the zero hypothesis. The authors discuss also Wilcoxon's test for paired values. This test takes into account not only the trend of deviations but also their magnitude and thus uses more information than the sign test and has thus a greater scope. For this test an auxiliary table of critical values is also presented.

Statistics as Topic↗

PubMed1990

[Use of nonparametric methods in medicine. IV. Comparison of levels in more than 2 samples].

The authors mention two tests for comparison of the level of random variables in more than two random samples. It is thus a question of generalization of methods presented in parts II and III. Friedman's test resolves the position for dependent samples when data assembled during different experimental situations in the same group are to be evaluated. This test calls for a complete series of measurements from every statistical unit, in mathematical terminology this means that the samples are equally extensive. In case of large samples Friedman's test is based on chi 2 distribution; for a small number of samples special tables of critical values are given. Kruskal-Wallis's test resolves the position for independent samples. It is also based on chi 2 distribution.

Statistics as Topic↗

PubMed1990

[Use of nonparametric methods in medicine. V. A probability test using iteration].

The authors give an account of the so-called Wald-Wolfowitz test of iteration of two types of elements by means of which it is possible to test the probability of the pattern of two types of elements. To facilitate the application of the test five percent critical values are given for the number of iterations for left-sided, right-sided and bilateral alternative hypotheses. The authors present also tables of critical values for up and down iterations which are obtained when we replace the originally assessed sequence of observations by a sequence +1 and -1, depending on the sign of the consecutive differences. The application of the above tests is illustrated on examples.

Probability↗

PubMed1990

[Use of nonparametric methods in medicine. VI. Evaluation using dependence by rank].

The authors deal with the evaluation of dependence between two quantitative variables. They analyze Spearman's and Kendall's rank correlation coefficients. The procedure is explained on examples. Decision taking between the zero and alternative hypothesis is made possible by the enclosed tables of critical values. When the number of data is higher than 30, the significance of the two coefficients can be evaluated by means of the testing characteristic with a normal distribution.

Statistics as Topic↗

PubMed1984