The Malaysian journal of medical sciences 

About: The Malaysian journal of medical sciences is an academic journal. The journal publishes majorly in the area(s): Population & Health care. Over the lifetime, 1149 publications have been published receiving 10558 citations.

Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data.

Abstract: Background Different study designs and population size may require different sample size for logistic regression This study aims to propose sample size guidelines for logistic regression based on observational studies with large population Methods We estimated the minimum sample size required based on evaluation from real clinical data to evaluate the accuracy between statistics derived and the actual parameters Nagelkerke r-squared and coefficients derived were compared with their respective parameters Results With a minimum sample size of 500, results showed that the differences between the sample estimates and the population was sufficiently small Based on an audit from a medium size of population, the differences were within ± 05 for coefficients and ± 002 for Nagelkerke r-squared Meanwhile for large population, the differences are within ± 10 for coefficients and ± 002 for Nagelkerke r-squared Conclusions For observational studies with large population size that involve logistic regression in the analysis, taking a minimum sample size of 500 is necessary to derive the statistics that represent the parameters The other recommended rules of thumb are EPV of 50 and formula; n = 100 + 50i where i refers to number of independent variables in the final model

Sample Size Calculation in Animal Studies Using Resource Equation Approach.

Abstract: Animal research plays an important role in the pre-clinical phase of clinical trials. In animal studies, the power analysis approach to sample size calculation is recommended. Whenever it is not possible to assume the standard deviation and the effect size, an alternative to the power analysis approach is the 'resource equation' approach, which sets the acceptable range of the error degrees of freedom (DF) in an analysis of variance (ANOVA). The aim of this article is to guide researchers in calculating the minimum and maximum numbers of animals required in animal research by reformulating the error DF formulas.

Life Event, Stress and Illness

Abstract: The relationship between stress and illness is complex. The susceptibility to stress varies from person to person. Among the factors that influenced the susceptibility to stress are genetic vulnerability, coping style, type of personality and social support. Not all stress has negative effect. Studies have shown that short-term stress boosted the immune system, but chronic stress has a significant effect on the immune system that ultimately manifest an illness. It raises catecholamine and suppressor T cells levels, which suppress the immune system. This suppression, in turn raises the risk of viral infection. Stress also leads to the release of histamine, which can trigger severe broncho-constriction in asthmatics. Stress increases the risk for diabetes mellitus, especially in overweight individuals, since psychological stress alters insulin needs. Stress also alters the acid concentration in the stomach, which can lead to peptic ulcers, stress ulcers or ulcerative colitis. Chronic stress can also lead to plaque buildup in the arteries (atherosclerosis), especially if combined with a high-fat diet and sedentary living. The correlation between stressful life events and psychiatric illness is stronger than the correlation with medical or physical illness. The relationship of stress with psychiatric illness is strongest in neuroses, which is followed by depression and schizophrenia. There is no scientific evidence of a direct cause-and-effect relationship between the immune system changes and the development of cancer. However, recent studies found a link between stress, tumour development and suppression of natural killer (NK) cells, which is actively involved in preventing metastasis and destroying small metastases.

Prevalence and Sources of Stress among Universiti Sains Malaysia Medical Students.

Abstract: Background: Being in medical school has always been regarded as highly stressful. Excessive stress causes physical and mental health problems. Persistent stress can impair students’ academic achievement and personal or professional development. The aim of this study is to explore the nature of stress among medical students by determining the prevalence, sources and pattern of stress and the factors affecting it. Methods: We chose a cross-sectional study design utilizing validated questionnaires, the 12 items General Health Questionnaire (GHQ-12) and Medical Student Stressor Questionnaire (MSSQ), to evaluate stress levels and stressors. School and ethical committee clearance were obtained prior to the study. Data were analysed using SPSS version 12. Results: Of the medical students who were administered the questionnaire, 761 (72%) respondents participated in this study. The prevalence of stress among the medical students was 29.6%. The top 10 stressors were academic-related. Prevalence of stress for the first, second, third, fourth and fifth year students was 26.3%, 36.5 %, 31.4%, 35.3% and 21.9%, respectively. Year of study was the only significant factor affecting stress among medical students (P-value < 0.05). Conclusions: The prevalence of stress among medical students in USM is high. Academic related problems were the major stressor among medical students. Year of study was the factor most significantly associated with medical students’ stress. There was a bimodal pattern of the stress level throughout the year of study.

A Review on Sample Size Determination for Cronbach's Alpha Test: A Simple Guide for Researchers.

Abstract: Background Reliability studies are commonly used in questionnaire development studies and questionnaire validation studies. This study reviews the sample size guideline for Cronbach's alpha test. Methods Manual sample size calculation using Microsoft Excel software and sample size tables were tabulated based on a single coefficient alpha and the comparison of two coefficients alpha. Results For a single coefficient alpha test, the approach by assuming the Cronbach's alpha coefficient equals to zero in the null hypothesis will yield a smaller sample size of less than 30 to achieve a minimum desired effect size of 0.7. However, setting the coefficient of Cronbach's alpha larger than zero in the null hypothesis could be necessary and this will yield larger sample size. For comparison of two coefficients of Cronbach's alpha, a larger sample size is needed when testing for smaller effect sizes. Conclusions In the assessment of the internal consistency of an instrument, the present study proposed the Cronbach's alpha's coefficient to be set at 0.5 in the null hypothesis and hence larger sample size is needed. For comparison of two coefficients' of Cronbach's alpha, justification is needed whether testing for extremely low and extremely large effect sizes are scientifically necessary.