1. Limitations of proposed Study Design. Discuss potential bias and limitations.
The proposed study design that is selected for the study is a case control design. This design will be very effective in exploring the predictors of severe infection due to H1N1. However an article by Geneletti et al (2009) argues that retrospective case–control studies are more susceptible to selection bias than other epidemiologic studies as by design they require that both cases and controls are representative of the same population. However, as cases and control recruitment processes are often different, it is not always obvious that the necessary exchangeability conditions hold. Selection bias typically arises when the selection criteria are associated with the risk factor under investigation.
2. Limitations of proposed Sampling Method. Discuss potential bias and limitations.
In this study there will be some limitations and possibly bias of the propose sampling method. Since there will be limited data available for the H1N1 virus, the sample size will not be very large. There could be a selection bias since the charts that are reviewed will not represent the population. The two hundred charts that will be reviewed will not give a precise result. It is best to have a larger sample size and follow the cohorts over a longer period of time. Hackshaw (2008) reveals that the main problem with small studies is interpretation of results, in particular confidence intervals and P-values. Hackshaw also argues that when comparing characteristics between two or more groups of subjects (e.g. examining risk factors or treatments for disease), the size of the study depends on the magnitude of the expected effect size, which is usually quantified by a relative risk, odds ratio, absolute risk difference, hazard ratio, or difference between two means or medians. The smaller the true-effect size, the larger the study needs to be. This is because it is more difficult to distinguish between a real effect and random variation.
- Quote paper
- Carol Benjamin (Author), 2010, Bias and Confounding in Research, Munich, GRIN Verlag, https://www.grin.com/document/265473