Barış Sürücü

An Open, Randomized, Controlled, Comparative Study of the Combined Effect of Intralesional Triamcinolone Acetonide and Onion Extract Gel and Intralesional Triamcinolone Acetonide Alone in the Treatment of Hypertrophic Scars and Keloids

BACKGROUND Various treatment regimens have been used in scars. The literature offers little consensus about appropriate therapy. OBJECTIVE To compare intralesional triamcinolone acetonide (TAC) alone or combined with onion extract in keloidal and hypertrophic scars. MATERIALS AND METHODS Fourteen patients were treated with intralesional TAC and onion extract gel, and 13 patients were treated with intralesional TAC alone. Findings were recorded and graded at each visit (weeks 0, 4, 12, and 20). The scores before treatment and at week 20 were compared. RESULTS Twenty-seven patients [17 men (63%) and 10 women (37%) aged 15 to 73 (average age 28.1±,11.7)] were enrolled in the study. At baseline, the difference in the two treatment groups was not statistically significant (p>.05). At week 20, there was statistically significant improvement in both treatment groups (p<.05). TAC with onion extract was more effective than TAC alone in terms of pain-sensitiveness, itching, and elevation but not in erythema and induration. Treatment was well tolerated, without any adverse effect. CONCLUSION Treatment with intralesional TAC and onion extract and TAC alone were effective. Combined with onion extract gel, intralesional TAC appears to be superior to TAC alone in the treatment of keloids and hypertrophic scars.

Monitoring reliability for a three-parameter Weibull distribution

Control charts are widely used to monitor production processes in the manufacturing industry and are also useful for monitoring reliability. A method to monitor reliability has recently been proposed when the distributions of inter-failure times are exponential and Weibull with known parameters. This method has also been extended to monitor the cumulative time elapsed between a fixed number of failures for the exponential distribution. In this paper, we consider a three-parameter Weibull distribution to model inter-failure times, use a robust estimation technique to estimate the unknown parameters, and extend the proposed method to monitor the cumulative time elapsed between r failures using the three-parameter Weibull distribution. Since the distribution of the sum of independent Weibull random variates is not known (except in specific cases with known parameters), we give two useful moment approximations to be able to apply their scheme. We show how effective the approximations are and the usefulness of the method in detecting a possible instability during production.

Goodness-of-Fit Tests for Multivariate Distributions

We propose three new statistics, Z p , C p , and R p for testing a p-variate (p ≥ 2) normal distribution and compare them with the prominent test statistics. We show that C p is overall most powerful and is effective against skew, long-tailed as well as short-tailed symmetric alternatives. We show that Z p and R p are most powerful against skew and long-tailed alternatives, respectively. The Z p and R p statistics can also be used for testing an assumed p-variate nonnormal distribution.

Goodness-of-fit tests based on Kullback-Leibler information

We evaluate the power of the sample entropy goodness-of-fit tests for s-normal, exponential, and uniform distributions. We compare them with the mainstream statistical tests, the W test based on the best linear unbiased estimator (BLUE) of the location parameter, the Z test based on the sample spacings, and the R test based on the correlation coefficient between the order statistics of the sample & the corresponding population quantiles. We show that the latter are more powerful overall. The mainstream statistical tests, particularly the Z test, readily extend to censored samples and to multi-sample situations.

A power comparison and simulation study of goodness-of-fit tests

We give the results of a comprehensive simulation study of the power properties of prominent goodness-of-fit tests. For testing the normal N(@m,@s^2), we propose a new omnibus goodness-of-fit statistic C which is a combination of the Shapiro-Wilk statistic W and the correlation statistic R. We show that the test of normality based on C is overall more powerful than other prominent goodness-of-fit tests and is effective against both symmetric as well as skew alternatives. We also show that the null distribution of C can be approximated by a four-moment F. For the exponential E(@q,@s), Tiku statistic Z (using sample spacings) and modified Anderson-Darling A are the most powerful. For testing other distributions, the statistics based on generalized sample spacings and the modified Anderson-Darling statistic provide the most powerful tests.

Assessing the validity of a statistical distribution : some illustrative examples from dermatological research

Background.  Assuming a statistical distribution is one of the key points before conducting a statistical analysis. Goodness‐of‐fit tests are used to assess the validity of an assumed statistical distribution. In dermatological research, the goodness‐of‐fit tests used are less powerful.

Testing for censored bivariate distributions with applications to environmental data

Analysis of censored environmental data has been of special interest to many scientists and practitioners for the recent years. Numerous works have been published on modeling bivariate environmental data when variables of interest are below some detection limits. Depending on the problem, one of the variables or both variables may be unobserved. These situations especially arise in modeling the joint distributions of environmental variables such as flood, drought and epidemiological. Some of these variables cannot be observed as they are too small to be detected below certain threshold points. Because of this censored structure, it is difficult to assess the validity of proposed bivariate distributions. Moreover, there is a wide need for a simple goodness-of-fit test for researchers working on practical environmental problems. This motivates us to propose a goodness-of-fit test for location-scale type bivariate distributions with censored data. The asymptotic distribution of the proposed test is shown to have a Chi-square distribution. A simulation study is carried out to show the power performances of the test. A real environmental data from the literature is analyzed to illustrate the efficacy of our proposed test.