Epidemiology, biostatistics, and public health 

About: Epidemiology, biostatistics, and public health is an academic journal published by Prex S.r.l.. The journal publishes majorly in the area(s): Medicine & Population. It has an ISSN identifier of 2282-2305. Over the lifetime, 362 publications have been published receiving 2201 citations.

Standardised regression coefficient as an effect size index in summarising findings in epidemiological studies

Abstract: Background: a major problem in evaluating and reviewing the published findings of studies on the association between a quantitative explanatory variable and a quantitative dependent variable is that the results are analysed and reported in many different ways. To achieve an effective review of different studies, a consistent presentation of the results is necessary. This paper aims to exemplify the main topics related to summarising and pooling research findings from multivariable models with a quantitative response variable. Methods: we outline the complexities involved in synthesising associations. We describe a method by which it is possible to transform the findings into a common effect size index which is based on standardised regression coefficients. To describe the approach we searched original research articles published before January 2012 for findings of the relationship between polychlorinated biphenyls (PCBs) and birth weight of new-borns. Studies with maternal PCB measurements and birth weight as a continuous variable were included. Results: the evaluation of 24 included articles reveled that there was variation in variable measurement methods, transformations, descriptive statistics and inference methods. Research syntheses were performed summarizing regression coefficients to estimate the effect of PCBs on birth weight. A birth weight decline related to increase in PCB level was found. Conclusions: the proposed method can be useful in quantitatively reviewing published studies when different exposure measurement methods are used or differential control of potential confounding factors is not an issue.

Essentials of Epidemiology in Public Health

Abstract: Review of: Essentials of Epidemiology in Public Health. Ann Aschengrau and George R. Seage III. Burlington, MA: Jones and Bartlett Learning, 2014, 534 pp., $111.95. ISBN: 978-1-284-02891-1

Sensitivity analysis for direct and indirect effects in the presence of exposure-induced mediator-outcome confounders.

Abstract: Questions of mediation are often of interest in reasoning about mechanisms, and methods have been developed to address these questions. However, these methods make strong assumptions about the absence of confounding. Even if exposure is randomized, there may be mediator-outcome confounding variables. Inference about direct and indirect effects is particularly challenging if these mediator-outcome confounders are affected by the exposure because in this case these effects are not identified irrespective of whether data is available on these exposure-induced mediator-outcome confounders. In this paper, we provide a sensitivity analysis technique for natural direct and indirect effects that is applicable even if there are mediator-outcome confounders affected by the exposure. We give techniques for both the difference and risk ratio scales and compare the technique to other possible approaches.

Determination of Minimum Sample Size Requirement for Multiple Linear Regression and Analysis of Covariance Based on Experimental and Non-experimental Studies

Abstract: Background: MLR and ANCOVA are common statistical techniques and are used for both experimental and non-experimental studies. However, both types of study designs may require different basis of sample size requirement. Therefore, this study aims to proposed sample size guidelines for MLR and ANCOVA for both experimental and non-experimental studies. Methods: We estimated the minimum sample sizes required for MLR and ANCOVA by using Power and Sample Size software (PASS) based on the pre-specified values of alpha, power and effect size (R 2 ). In addition, we also performed validation of the estimates using a real clinical data to evaluate how close the approximations of selected statistics which were derived from the samples were to the actual parameters in the targeted populations. All the coefficients, effect sizes and r-squared obtained from the sample were then compared with their respective parameters in the population. Results: Small minimum sample sizes required for performing both MLR and ANCOVA when r-squared is used as the effect size. However, the validation results based on an evaluation from a real-life dataset suggest that a minimum sample size of 300 or more is necessary to generate a close approximation of estimates with the parameters in the population. Conclusions: We proposed sample size calculation when r-squared is used as an effect size is more suitable for experimental studies. However, taking a larger sample size such as 300 or more is necessary for clinical survey that is conducted in a non-experimental manner.

Guidelines of the minimum sample size requirements for Kappa agreement test

Abstract: Background and aims: To estimate sample size for Cohen’s kappa agreement test can be challenging especially when it is expected that the true marginal rating frequencies are not the same. This study aims to present the tables that display a minimum sample size determination for an agreement test when certain assumptions are hold. Method: We adopted the sample size formula provided by Flack and colleagues (1988) to calculate the minimum sample sizes required using PASS software. The power is pre-specified to be at least 80% and the alpha to be less than 0.05. The effect sizes were derived from several pre-specified estimates such as the pattern of the true marginal rating frequencies and the difference between the two kappa coefficients in the hypothesis testing. Results: When the true marginal rating frequencies are the same, the minimum sample size determination can range from 2 to 698 depending on the actual value of the effect size. When the true marginal rating frequencies are not the same, then the majority of the minimum sample size required for this condition is more than double than that required sample size when the true marginal rating frequencies are the same. Conclusion: Concerning that the sample size formula could produce a very extreme small sample size, therefore, the determination of K 1 and K 2 should be based on reasonable estimates. We recommend for all sample size determinations for Cohen’s kappa agreement test, the true marginal rating frequencies can be assumed the same. Otherwise, it will be necessary to multiple the estimated minimum sample size by two to accommodate if the true marginal rating frequencies is not the same.