Journal of Preventive Medicine and Public Health

Brief Report

Citation Discovery Tools for Conducting Adaptive Meta-analyses to Update Systematic Reviews: Jong-Myon Bae, Eun Hee Kim; J Prev Med Public Health. 2016;49(2):129-133. Published online March 14, 2016; DOI: https://doi.org/10.3961/jpmph.15.074

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Objectives
The systematic review (SR) is a research methodology that aims to synthesize related evidence. Updating previously conducted SRs is necessary when new evidence has been produced, but no consensus has yet emerged on the appropriate update methodology. The authors have developed a new SR update method called ‘adaptive meta-analysis’ (AMA) using the ‘cited by’, ‘similar articles’, and ‘related articles’ citation discovery tools in the PubMed and Scopus databases. This study evaluates the usefulness of these citation discovery tools for updating SRs.
Methods
Lists were constructed by applying the citation discovery tools in the two databases to the articles analyzed by a published SR. The degree of overlap between the lists and distribution of excluded results were evaluated.
Results
The articles ultimately selected for the SR update meta-analysis were found in the lists obtained from the ‘cited by’ and ‘similar’ tools in PubMed. Most of the selected articles appeared in both the ‘cited by’ lists in Scopus and PubMed. The Scopus ‘related’ tool did not identify the appropriate articles.
Conclusions
The AMA, which involves using both citation discovery tools in PubMed, and optionally, the ‘related’ tool in Scopus, was found to be useful for updating an SR.

Summary

Citations

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English Abstracts

Power Estimation and Follow-Up Period Evaluation in Korea Radiation Effect and Epidemiology Cohort Study.: In Seong Cho, Minkyo Song, Yunhee Choi, Zhong Min Li, Yoon Ok Ahn; J Prev Med Public Health. 2010;43(6):543-548.; DOI: https://doi.org/10.3961/jpmph.2010.43.6.543

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Abstract PDF

OBJECTIVES
The objective of this study was to calculate sample size and power in an ongoing cohort, Korea radiation effect and epidemiology cohort (KREEC). METHOD: Sample size calculation was performed using PASS 2002 based on Cox regression and Poisson regression models. Person-year was calculated by using data from '1993-1997 Total cancer incidence by sex and age, Seoul' and Korean statistical informative service. RESULTS: With the assumption of relative risk=1.3, exposure:non-exposure=1:2 and power=0.8, sample size calculation was 405 events based on a Cox regression model. When the relative risk was assumed to be 1.5 then number of events was 170. Based on a Poisson regression model, relative risk=1.3, exposure:non-exposure=1:2 and power=0.8 rendered 385 events. Relative risk of 1.5 resulted in a total of 157 events. We calculated person-years (PY) with event numbers and cancer incidence rate in the non-exposure group. Based on a Cox regression model, with relative risk=1.3, exposure:non-exposure=1:2 and power=0.8, 136 245PY was needed to secure the power. In a Poisson regression model, with relative risk=1.3, exposure:non-exposure=1:2 and power=0.8, person-year needed was 129517PY. A total of 1939 cases were identified in KREEC until December 2007. CONCLUSIONS: A retrospective power calculation in an ongoing study might be biased by the data. Prospective power calculation should be carried out based on various assumptions prior to the study.

Summary

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Cancer Risk in Adult Residents near Nuclear Power Plants in Korea - A Cohort Study of 1992-2010
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Journal of Korean Medical Science.2012; 27(9): 999. CrossRef

Strengthening Causal Inference in Studies using Non-experimental Data: An Application of Propensity Score and Instrumental Variable Methods.: Myoung Hee Kim, Young Kyung Do; J Prev Med Public Health. 2007;40(6):495-504.; DOI: https://doi.org/10.3961/jpmph.2007.40.6.495

5,328 View
91 Download
8 Crossref

Abstract PDF

OBJECTIVES
This study attempts to show how studies using non-experimental data can strengthen causal inferences by applying propensity score and instrumental variable methods based on the counterfactual framework. For illustrative purposes, we examine the effect of having private health insurance on the probability of experiencing at least one hospital admission in the previous year. METHODS: Using data from the 4th wave of the Korea Labor and Income Panel Study, we compared the results obtained using propensity score and instrumental variable methods with those from conventional logistic and linear regression models, respectively. RESULTS: While conventional multiple regression analyses fail to identify the effect, the results estimated using propensity score and instrumental variable methods suggest that having private health insurance has positive and statistically significant effects on hospital admission. CONCLUSIONS: This study demonstrates that propensity score and instrumental variable methods provide potentially useful alternatives to conventional regression approaches in making causal inferences using non-experimental data.

Summary

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A Review of Power and Sample Size Estimation in Genomewide Association Studies.: Ae Kyung Park, Ho Kim; J Prev Med Public Health. 2007;40(2):114-121.; DOI: https://doi.org/10.3961/jpmph.2007.40.2.114

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80 Download
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Abstract PDF

Power and sample size estimation is one of the crucially important steps in planning a genetic association study to achieve the ultimate goal, identifying candidate genes for disease susceptibility, by designing the study in such a way as to maximize the success possibility and minimize the cost. Here we review the optimal two-stage genotyping designs for genomewide association studies recently investigated by Wang et al(2006). We review two mathematical frameworks most commonly used to compute power in genetic association studies prior to the main study: Monte-Carlo and non-central chi-square estimates. Statistical powers are computed by these two approaches for case-control genotypic tests under one-stage direct association study design. Then we discuss how the linkagedisequilibrium strength affects power and sample size, and how to use empirically-derived distributions of important parameters for power calculations. We provide useful information on publicly available softwares developed to compute power and sample size for various study designs.

Summary

Citations

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Sample Size and Statistical Power Calculation in Genetic Association Studies
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Statistical Issues in Genomic Cohort Studies.: Sohee Park; J Prev Med Public Health. 2007;40(2):108-113.; DOI: https://doi.org/10.3961/jpmph.2007.40.2.108

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28 Download

Abstract PDF: When conducting large-scale cohort studies, numerous statistical issues arise from the range of study design, data collection, data analysis and interpretation. In genomic cohort studies, these statistical problems become more complicated, which need to be carefully dealt with. Rapid technical advances in genomic studies produce enormous amount of data to be analyzed and traditional statistical methods are no longer sufficient to handle these data. In this paper, we reviewed several important statistical issues that occur frequently in large-scale genomic cohort studies, including measurement error and its relevant correction methods, cost-efficient design strategy for main cohort and validation studies, inflated Type I error, gene-gene and gene-environment interaction and time-varying hazard ratios. It is very important to employ appropriate statistical methods in order to make the best use of valuable cohort data and produce valid and reliable study results.; Summary

Original Article

Analysis of the Abstracts of Cancer Related Articles Published from 1990 to 1996 in Korea.: Chang Yup Kim, Young Ho Khang, Young Sung Lee, Chul Whan Kang, Keun Young Yoo, Gilwon Kang, Beom Man Ha; Korean J Prev Med. 2001;34(3):200-210.

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Abstract PDF: OBJECTIVE
To explore the status of cancer research in the Republic of Korea. METHODS: Thirty-eight medical journals, published in Korea between 1990 and 1996, were reviewed for abstracts relating to cancer research. Of the 5,899 eligible abstracts related to cancer, 4,732 were collected and evaluated. RESULTS: Including first author and first two co-authors, a total of 7,427 authors were identified. Those who published an average of one or more article per one year were defined as cancer researchers for this study. This group, however, accounted for a small proportion of the total (3.1%). Analysis of the selected abstracts showed that the study goals in more than half focused on pathophysiologic mechanisms. Studies that were designed to use causal relationships such as cohort studies and randomized controlled trials were rare. A greater number of analytic and experimental studies were found in abstracts published by the cancer researcher group. More advanced study designs that explored causal relationships and analytic procedures were found in abstracts published later than those abstracts published from 1990 to 1992. CONCLUSION: Our findings show that researchers who published more articles adopted more advanced study designs. This study provides primary data that can be used to compare the status of cancer research in future studies.; Summary

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