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The MAX Statistic is Less Powerful for Genome Wide Association Studies Under Most Alternative Hypotheses  
Pages 144-151
Benjamin Shifflett, Rong Huang and Steven D. Edland
DOI:
https://doi.org/10.6000/1929-6029.2017.06.04.2
Published: 8 December 2017


Abstract: Genotypic association studies are prone to inflated type I error rates if multiple hypothesis testing is performed, e.g., sequentially testing for recessive, multiplicative, and dominant risk. Alternatives to multiple hypothesis testing include the model independent genotypic c2 test, the efficiency robust MAX statistic, which corrects for multiple comparisons but with some loss of power, or a single Armitage test for multiplicative trend, which has optimal power when the multiplicative model holds but with some loss of power when dominant or recessive models underlie the genetic association. We used Monte Carlo simulations to describe the relative performance of these three approaches under a range of scenarios. All three approaches maintained their nominal type I error rates. The genotypic c2 and MAX statistics were more powerful when testing a strictly recessive genetic effect or when testing a dominant effect when the allele frequency was high. The Armitage test for multiplicative trend was most powerful for the broad range of scenarios where heterozygote risk is intermediate between recessive and dominant risk. Moreover, all tests had limited power to detect recessive genetic risk unless the sample size was large, and conversely all tests were relatively well powered to detect dominant risk. Taken together, these results suggest the general utility of the multiplicative trend test when the underlying genetic model is unknown.

Keywords: Armitage test, case-control study, efficiency robust statistics, MAX statistic, multiple comparisons;, Type I error.

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ijsmr logo-pdf 1349088093

Sample Size Calculation in Clinical Studies: Some Common Scenarios  
Pages 152-161
Mohammad Z. I. Chowdhury, Khokan C. Sikdar and Tanvir C. Turin
DOI:
https://doi.org/10.6000/1929-6029.2017.06.04.3
Published: 8 December 2017


Abstract: Determining the optimal sample size is crucial for any scientific investigation. An optimal sample size provides adequate power to detect statistical significant difference between the comparison groups in a study and allows the researcher to control for the risk of reporting a false-negative finding (Type II error). A study with too large a sample is harder to conduct, expensive, time consuming and may expose an unnecessarily large number of subjects to potentially harmful or futile interventions. On the other hand, if the sample size is too small, a best conducted study may fail to answer a research question due to lack of sufficient power. To draw a valid and accurate conclusion, an appropriate sample size must be determined prior to start of any study. This paper covers the essentials in calculating sample size for some common study designs. Formulae along with some worked examples were demonstrated for potential applied health researchers. Although maximum power is desirable, this is not always possible given the resources available for a study. Researchers often needs to choose a sample size that makes a balance between what is desirable and what is feasible.

Keywords: Sample Size Calculation, Power, Hypothesis Test, Level of Significance, Mean, Proportion.

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ijsmr logo-pdf 1349088093

Effects of some Biological Covariates on the Probability of First Recurrence of Malaria following Treatment with Artemisinin Combination Therapy -Pages 1-9

Cletus Kwa Kum, Daniel Thorburn, Gebrenegus Ghilagaber, Anders Björkman and
José Pedro Gil

DOI: https://doi.org/10.6000/1929-6029.2018.07.01.1

Published: 6 February 2018


Abstract: Many investigations have shown that artemisinin-based combination therapies are effective in the treatment of uncomplicated malaria and that they do not increase parasite resistance to treatment as much as treatment with single substance. We study the relation between some biological covariates and the time to first recurrence of malaria for children treated for malaria in a clinical trial. One group received artesunate plus sulfadoxine-pyrimethamine and the other only sulfadoxine-pyrimethamine. We consider the event malaria-free for the first 42 (and 84) days. We use logistic regression models for the analyses. The main results show that the probability of no recurrence is higher if the parasite density in the blood is high. The results are inconclusive for other explanatory biological variables. The infecting parasites having genes that indicate resistance, gave different results at the two different treatment centres. There was no appreciable difference in the effects of treatment over the two follow-up periods and these treatments do not have any effect on the probability of a recurrence.

Keywords: Logistic model, clinical covariates, malaria treatment, parasite density, drug resistance, genotype, incomplete data.

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ijsmr logo-pdf 1349088093

Lindley Approximation Technique for the Parameters of Lomax Distribution  
Pages 162-168
Afaq Ahmad, Kawsar Fatima and S.P. Ahmad
DOI:
https://doi.org/10.6000/1929-6029.2017.06.04.4
Published: 8 December 2017


Abstract: The present study is concerned with the estimation of shape and scale parameter of Lomax distribution using Bayesian approximation techniques (Lindley’s Approximation). Different priors viz gamma, exponential and Levy priors are used to obtain the Bayes estimates of parameters of Lomax distributions under Lindley approximation technique. For comparing the efficiency of the obtained results a simulation study is carried out using R-software.

Keywords: Lomax distribution, Bayesian Estimation, Prior, Loss functions, Lindley’s Approximation.

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ijsmr logo-pdf 1349088093

The Trend of the Bibliographical Output from Libyan Engineering Schools: A 30-Year Review From 1984-2013Pages 10-17

Awad Bodalal and Salah Mashite


DOI: https://doi.org/10.6000/1929-6029.2018.07.01.2

Published: 6 February 2018


Abstract:  In this study, the research output from the major Libyan engineering schools was gathered and compared for the period of thirty years (from 1984 to 2013). The Elsevier database, Science Direct, was used to gather these publications and only engineering articles were included. A comparative analysis was performed on three levels; first a local comparison between the different faculties of engineering across Libya and secondly, a broader comparison between Libya and the neighboring METAL (Morocco, Egypt, Tunisia, Algeria and Libya) countries and finally, the third comparison was performed between Turkey and the METAL countries. In the local comparison, the output was normalized by the number of teaching staff while in the broader regional comparison, gross domestic product and population were used as standardization factors. When analyzing the research output of the Libyan engineering schools, it was observed that most publications came from Tripoli (47.1%, n=131) followed by Benghazi (25.9%, n=72), Misurata (4.1%, n=12) and Omar Al-Mukhtar (4.0%, n=11). However, when the number of staff members was taken into consideration, Benghazi University and Omar Al-Mukhtar University had higher research productivity levels than Tripoli University and Misurata University respectively. The regional comparison showed a clear difference between Libya and its neighbors, having the lowest output among them. Finally, it was found that across the three decades under study, Turkey produced more research than all the METAL countries combined. More attention needs to be paid to research and publications in Libyan engineering schools. A number of recommendations were made to help improve the publication rate in Libyan engineering faculties.

Keywords: Education, Statistics, Bibliography, Conflict studies.

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