Warning: fopen(/home/virtual/epih/journal/upload/ip_log/ip_log_2023-12.txt): failed to open stream: Permission denied in /home/virtual/lib/view_data.php on line 83 Warning: fwrite() expects parameter 1 to be resource, boolean given in /home/virtual/lib/view_data.php on line 84 Comparison of methods of extracting information for meta-analysis of observational studies in nutritional epidemiology
Skip Navigation
Skip to contents

Epidemiol Health : Epidemiology and Health



Page Path
HOME > Epidemiol Health > Volume 38; 2016 > Article
Comparison of methods of extracting information for meta-analysis of observational studies in nutritional epidemiology
Jong-Myon Baeorcid
Epidemiol Health 2016;38:e2016003.
DOI: https://doi.org/10.4178/epih.e2016003
Published online: January 11, 2016
  • 247 Download
  • 7 Crossref

Department of Preventive Medicine, Jeju National University School of Medicine, Jeju, Korea

Correspondence: Jong-Myon Bae  Department of Preventive Medicine, Jeju National University School of Medicine, 102 Jejudaehak-ro, Jeju 63243, Korea  Tel: +82-64-755-5567, Fax: +82-64-725-2593, E-mail: jmbae@cheju.ac.kr
• Received: December 2, 2015   • Accepted: January 11, 2016

©2016, Korean Society of Epidemiology

This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

    A common method for conducting a quantitative systematic review (QSR) for observational studies related to nutritional epidemiology is the “highest versus lowest intake” method (HLM), in which only the information concerning the effect size (ES) of the highest category of a food item is collected on the basis of its lowest category. However, in the interval collapsing method (ICM), a method suggested to enable a maximum utilization of all available information, the ES information is collected by collapsing all categories into a single category. This study aimed to compare the ES and summary effect size (SES) between the HLM and ICM.
    A QSR for evaluating the citrus fruit intake and risk of pancreatic cancer and calculating the SES by using the HLM was selected. The ES and SES were estimated by performing a meta-analysis using the fixed-effect model. The directionality and statistical significance of the ES and SES were used as criteria for determining the concordance between the HLM and ICM outcomes.
    No significant differences were observed in the directionality of SES extracted by using the HLM or ICM. The application of the ICM, which uses a broader information base, yielded more-consistent ES and SES, and narrower confidence intervals than the HLM.
    The ICM is advantageous over the HLM owing to its higher statistical accuracy in extracting information for QSR on nutritional epidemiology. The application of the ICM should hence be recommended for future studies.
A quantitative systematic reviews involving meta-analysis may be applied as an efficient solution to inconsistencies in the outcomes of epidemiological studies [1,2]. However, nutritional epidemiological studies that investigate disease outbreaks caused by food items in regular diet are prone to errors in the course of the meta-analysis of the findings of observational studies such as cohort and case-control studies [3]. Given the problems intrinsic to nutritional epidemiology, such as different research methods, validity of the food frequency questionnaire used, and interregional differences in dietary patterns [4,5], heterogeneity is a factor that should be considered when conducting a meta-analysis of nutritional epidemiological studies [6].
In observational studies related to nutritional epidemiology, dietary intake levels are grouped into 3 to 5 quantiles depending on the predefined categorization, and the effect size (ES) is presented accordingly. As this methodology inevitably poses the problem of inter-study discrepancies in reference points and interval units, only the ES of the highest intake quantile is used for meta-analyses [7]. This ES extraction method, termed the “highest versus lowest” method (HLM), has the following limitations: First, information on the quantiles between the lowest and highest ones are ignored [8]. Second, no clear distinction is made between non-intake and low-intake cases in the lowest-intake quantile [9]. Third, no clear cutoff intake level is set for the highest intake quantile [10].
To overcome these limitations of the HLM, Islami’s collegues presented the interval collapsing method (ICM) [9,11], in which all intervals are taken into account for size calculation. Herein, a meta-analysis is performed by using a fixed-effect model (FEM) to calculate the ES values of all the intervals, which are then collapsed into one ES for the calculation of the summary effect size (SES). This concept is consistent with the method used to calculate the SES after obtaining the collapsed ES through an FEM meta-analysis, with the ES presented according to sex or cancer tissue [12,13]. To investigate the efficiency of applying the ICM to the data of specific food items depending on the exposure source, it is necessary to find out how the ICM outcomes differ from those of the traditional HLM. Therefore, the purpose of this study was to compare the outcomes of the HLM and ICM applied to the same food item in order to determine the advantages and disadvantages of these two methods.
The meta-analysis performed by Bae et al. [13] was selected for the outcome comparison between HLM and ICM applications. This article was considered suitable for the purpose of the present study because all 9 observational studies selected for the meta-analysis presented the values in 3 to 5 quantiles of citrus fruit intake levels, and the meta-analysis was performed by extracting the ES of the highest intake group and 95% confidence interval (CI) with respect to the lowest intake group.
Let the reference group be the lowest intake group (i=1) and assume that each k interval has an odds ratio (ORi) and 95% CI, and the ES value obtained by using the HLM is the k-layered OR (ORk). On the other hand, the ICM was applied after obtaining the ORi and its standard error (SEi). The ES values of the respective studies were calculated by using the generic inverse-variance weighted-average method [14]. For example, Stolzenberg-Solomon et al. [15] presented the results on citrus fruit intake of a study in quintiles (Table 1). While the ES of the highest intake quintile (Q5) extracted by using the HLM was 0.79 (95% CI, 0.47 to 1.31), the ES extracted by ICM from the same data was 0.96 (95% CI, 0.75 to 1.22), which was calculated in the FEM meta-analysis based on the ES values of four quintiles (Q2 to Q5).
An FEM meta-analysis was performed on the extraction values obtained from each paper to estimate the SES values and their respective 95% CI obtained when the HLM and ICM were applied. The differences in heterogeneity patterns were tracked by calculating the I2 values. The concordance between the two methods was considered excellent if the SES values maintain the directionality toward null (=1) and no fluctuations in statistical significance occurred based on 95% CI. In addition, the differing patterns were examined by calculating the standard error of log effect size (SElogES) by using the 95% CI of the SES.
Table 2 lists the ES, 95% CI, and SElogES of the papers selected for the meta-analysis [15-23], arranged to compare the values extracted by using the HLM and ICM. In all 9 articles, no inter-method differences were observed in the directionality of ES and statistical significance, except that the ICM showed a narrower CI and smaller SElogES.
Table 3 was compiled to compare the outcomes of the FEM meta-analysis by using the ES and SElogES values estimated by using the HLM and ICM. The SES and 95% CI showed no inter-method differences in directionality and statistical significance, with SElogSES being smaller in ICM as well. The I-squared values, which are an indicator of heterogeneity, were inconsistent.
Taking these results together, the ICM is advantageous over the HLM in that its outcome values have lower standard errors, hence narrower CIs, while maintaining the directionality and statistical significance of ES and SES.
As a limitation of this study, it should be pointed out that the two methods were comparatively analyzed based on a single meta-analysis. To improve the validity of the conclusions drawn in this study, more validation tests and application examples are required. In particular, as shown in Table 3, while no noticeable differences are observed in the average SES value between HLM and ICM in the 5 cohort studies, the 4 case-control studies show considerable differences between HLM and ICM (0.66 vs. 0.87). Although statistical significance could not be established due to the overlapping 95% CIs, given the remarkable differences in the I-squared values (20.7% vs. 59.6%), further clinical epidemiological research is necessary to determine the magnitude of SES changes depending on the degree of heterogeneity.
Another limitation of this study is the difficulty in interpreting the results obtained by using the ICM, as is the case with the HLM. Islami’s collegues interpreted ICM-estimated ES values as a dietary risk factor for prevalence in comparison with non-intake [9,11], but the reliability of this interpretation should be examined in terms of the methodological aspect. Specifically, dose-response meta-analysis (DRMA) should be performed additionally [24].
In nutritional epidemiology, the application of study results to concrete measures for disease prevention and health promotion projects, and its implementation for the general public can be achieved only when a clear answer can be given to the question of how much of a certain food item should be taken to increase the risk of prevalence. DRMA is currently used, whereby the intake level is converted into a portion size such as daily intake (g/d) [25]. However, DRMA cannot be applied if the related data are presented dichotomously in the selected articles or if intake level cannot be quantified [26]. Keeping in mind a report that 71% of the articles selected for meta-analysis do not lend themselves to DRMA [3], findings from nutritional epidemiological studies should be presented in a manner that would facilitate future meta-analyses. Under the current circumstances, DRMA should be considered as a method to be applied concurrently with the HLM or ICM, instead of replacing them [27-29].
In conclusion, of the methodologies of extracting information for meta-analysis on nutritional epidemiology, the ICM is advantageous over the HLM owing to its higher capacity for statistical accuracy based on a broader information base and should hence be recommended for future research.

The author has no conflict of interest to declare for this study.

Supplementary material (Korean version) is available at http://www.e-epih.org/.
Table 1.
An example of information extraction using the “highest versus lowest” method (HLM) and interval collapsing method (ICM) in the paper by Stolzenberg-Solomon et al. [15]
Citrus fruit intake Age-adjusted HR (95% Cl) Extraction
by HLM by ICM
Q5 0.79 (0.47, 1.31) 0.79 (0.47, 1.31) 0.96 (0.75, 1.22)
Q4 1.14 (0.72, 1.81)
Q3 0.74 (0.44, 1.23)
Q2 1.15 (0.73, 1.82)
Q1 1.00 (reference)

HR, hazard ratio; CI, confidence interval; Q, quintile of intake.

Table 2.
The effect size (ES) and 95% confidence intervals (CI) obtained by using the two extracting methods: “highest versus lowest intake” method (HLM) and interval collapsing method (ICM)
First author [reference] Design HLM
Extracted ES (95% CI) SE of logES Estimated ES (95% CI) SE of logES
Stolzenberg-Solomon [15] CO 0.79 (0.47, 1.31) 0.2615 0.96 (0.75, 1.22) 0.1236
Coughlin [16] CO 0.95 (0.82, 1.11) 0.0769 0.94 (0.89, 1.00) 0.0314
Lin [17] CO 0.95 (0.62, 1.45) 0.2174 0.95 (0.71, 1.27) 0.1470
Larsson [18] CO 1.12 (0.68, 1.83) 0.2525 1.10 (0.83, 1.45) 0.1439
Nothlings [19] CO 1.08 (0.82, 1.43) 0.1419 1.04 (0.89, 1.22) 0.0814
Olsen [20] CC 0.60 (0.30, 1.10) 0.3314 0.89 (0.61, 1.31) 0.1948
Norell [21] CC 0.44 (0.25, 0.76) 0.2850 0.62 (0.44, 0.89) 0.1835
Ji [22] CC 0.62 (0.45, 0.87) 0.1704 0.77 (0.64, 0.93) 0.0947
Chan [23] CC 0.78 (0.58, 1.00) 0.1390 0.85 (0.72, 1.00) 0.0825

SE of logES, standard error of logarithm effect size; CO, cohort studies; CC, case-control studies.

Table 3.
The summary effect size (SES) and 95% confidence intervals (CI) obtained by using two extracting methods: “highest versus lowest intake” method (HLM) and interval collapsing method (ICM)
Categories HLM
SES (95% Cl) SE of logSES l2 SES (95% Cl) SE of logSES l2
Overall 0.83 (0.70, 0.98) 0.0858 49.9 0.93 (0.88, 0.97) 0.0248 39.5
5 Cohort studies 0.97 (0.86, 1.10) 0.0608 0.0 0.96 (0.91, 1.01) 0.0277 0.0
4 Case-control studies 0.66 (0.55, 0.80) 0.0967 20.7 0.87 (0.80, 0.96) 0.0464 59.6

SE of logES, standard error of logarithm effect size.

  • 1. Morris RD. Meta-analysis in cancer epidemiology. Environ Health Perspect 1994;102 Suppl 8:61-66.Article
  • 2. Ahn HS, Kim HJ. An introduction to systematic review. J Korean Med Assoc 2014;57:49-59 (Korean).Article
  • 3. Bekkering GE, Harris RJ, Thomas S, Mayer AM, Beynon R, Ness AR, et al. How much of the data published in observational studies of the association between diet and prostate or bladder cancer is usable for meta-analysis? Am J Epidemiol 2008;167:1017-1026.ArticlePubMed
  • 4. Gandini S, Merzenich H, Robertson C, Boyle P. Meta-analysis of studies on breast cancer risk and diet: the role of fruit and vegetable consumption and the intake of associated micronutrients. Eur J Cancer 2000;36:636-646.ArticlePubMed
  • 5. Xu X, Yu E, Liu L, Zhang W, Wei X, Gao X, et al. Dietary intake of vitamins A, C, and E and the risk of colorectal adenoma: a meta-analysis of observational studies. Eur J Cancer Prev 2013;22:529-539.ArticlePubMed
  • 6. Berlin JA. Invited commentary: benefits of heterogeneity in meta-analysis of data from epidemiologic studies. Am J Epidemiol 1995;142:383-387.ArticlePubMed
  • 7. Liu XO, Huang YB, Gao Y, Chen C, Yan Y, Dai HJ, et al. Association between dietary factors and breast cancer risk among Chinese females: systematic review and meta-analysis. Asian Pac J Cancer Prev 2014;15:1291-1298.ArticlePubMed
  • 8. Wu SH, Liu Z. Soy food consumption and lung cancer risk: a meta-analysis using a common measure across studies. Nutr Cancer 2013;65:625-632.ArticlePubMedPMC
  • 9. Ren JS, Kamangar F, Forman D, Islami F. Pickled food and risk of gastric cancer: a systematic review and meta-analysis of English and Chinese literature. Cancer Epidemiol Biomarkers Prev 2012;21:905-915.ArticlePubMed
  • 10. Wu W, Kang S, Zhang D. Association of vitamin B6, vitamin B12 and methionine with risk of breast cancer: a dose-response meta-analysis. Br J Cancer 2013;109:1926-1944.ArticlePubMedPMC
  • 11. Islami F, Ren JS, Taylor PR, Kamangar F. Pickled vegetables and the risk of oesophageal cancer: a meta-analysis. Br J Cancer 2009;101:1641-1647.ArticlePubMedPMC
  • 12. Bae JM, Lee EJ, Guyatt G. Citrus fruit intake and stomach cancer risk: a quantitative systematic review. Gastric Cancer 2008;11:23-32.ArticlePubMed
  • 13. Bae JM, Lee EJ, Guyatt G. Citrus fruit intake and pancreatic cancer risk: a quantitative systematic review. Pancreas 2009;38:168-174.ArticlePubMed
  • 14. Palmer TM, Sterne JA. Meta-analysis in Stata: an updated collection from the Stata Journal. 2nd ed. Texas: Stata Press Publication; 2016. p 25.
  • 15. Stolzenberg-Solomon RZ, Pietinen P, Taylor PR, Virtamo J, Albanes D. Prospective study of diet and pancreatic cancer in male smokers. Am J Epidemiol 2002;155:783-792.ArticlePubMed
  • 16. Coughlin SS, Calle EE, Patel AV, Thun MJ. Predictors of pancreatic cancer mortality among a large cohort of United States adults. Cancer Causes Control 2000;11:915-923.ArticlePubMed
  • 17. Lin Y, Kikuchi S, Tamakoshi A, Yagyu K, Obata Y, Inaba Y, et al. Dietary habits and pancreatic cancer risk in a cohort of middle-aged and elderly Japanese. Nutr Cancer 2006;56:40-49.ArticlePubMed
  • 18. Larsson SC, Håkansson N, Näslund I, Bergkvist L, Wolk A. Fruit and vegetable consumption in relation to pancreatic cancer risk: a prospective study. Cancer Epidemiol Biomarkers Prev 2006;15:301-305.ArticlePubMed
  • 19. Nöthlings U, Murphy SP, Wilkens LR, Henderson BE, Kolonel LN. Dietary glycemic load, added sugars, and carbohydrates as risk factors for pancreatic cancer: the Multiethnic Cohort Study. Am J Clin Nutr 2007;86:1495-1501.ArticlePubMed
  • 20. Olsen GW, Mandel JS, Gibson RW, Wattenberg LW, Schuman LM. Nutrients and pancreatic cancer: a population-based case-control study. Cancer Causes Control 1991;2:291-297.ArticlePubMed
  • 21. Norell SE, Ahlbom A, Erwald R, Jacobson G, Lindberg-Navier I, Olin R, et al. Diet and pancreatic cancer: a case-control study. Am J Epidemiol 1986;124:894-902.ArticlePubMed
  • 22. Ji BT, Chow WH, Gridley G, Mclaughlin JK, Dai Q, Wacholder S, et al. Dietary factors and the risk of pancreatic cancer: a case-control study in Shanghai China. Cancer Epidemiol Biomarkers Prev 1995;4:885-893.PubMed
  • 23. Chan JM, Wang F, Holly EA. Vegetable and fruit intake and pancreatic cancer in a population-based case-control study in the San Francisco bay area. Cancer Epidemiol Biomarkers Prev 2005;14:2093-2097.ArticlePubMed
  • 24. Greenland S, Longnecker MP. Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. Am J Epidemiol 1992;135:1301-1309.ArticlePubMed
  • 25. Berlin JA, Longnecker MP, Greenland S. Meta-analysis of epidemiologic dose-response data. Epidemiology 1993;4:218-228.ArticlePubMed
  • 26. Aune D, Chan DS, Vieira AR, Navarro Rosenblatt DA, Vieira R, Greenwood DC, et al. Red and processed meat intake and risk of colorectal adenomas: a systematic review and meta-analysis of epidemiological studies. Cancer Causes Control 2013;24:611-627.ArticlePubMed
  • 27. Han J, Jiang Y, Liu X, Meng Q, Xi Q, Zhuang Q, et al. Dietary fat intake and risk of gastric cancer: a meta-analysis of observational studies. PLoS One 2015;10:e0138580.ArticlePubMedPMC
  • 28. Vieira AR, Abar L, Vingeliene S, Chan DS, Aune D, Navarro-Rosenblatt D, et al. Fruits, vegetables and lung cancer risk: a systematic review and meta-analysis. Ann Oncol 2016;27:81-96.ArticlePubMed
  • 29. Vieira AR, Vingeliene S, Chan DS, Aune D, Abar L, Navarro Rosenblatt D, et al. Fruits, vegetables, and bladder cancer risk: a systematic review and meta-analysis. Cancer Med 2015;4:136-146.ArticlePubMedPMC

Figure & Data



    Citations to this article as recorded by  
    • A 7-Step Guideline for Qualitative Synthesis and Meta-Analysis of Observational Studies in Health Sciences
      Marija Glisic, Peter Francis Raguindin, Armin Gemperli, Petek Eylul Taneri, Dante Jr. Salvador, Trudy Voortman, Pedro Marques Vidal, Stefania I. Papatheodorou, Setor K. Kunutsor, Arjola Bano, John P. A. Ioannidis, Taulant Muka
      Public Health Reviews.2023;[Epub]     CrossRef
    • Maternal uric acid levels and risk of gestational diabetes mellitus: A systematic review and dose–response meta‐analysis of cohort studies including 105,380 participants
      Ali Nikparast, Jamal Rahmani, Reza Bagheri, Saba Mohammadpour, Mehdi Shadnoosh, Alexei Wong, Matin Ghanavati
      Journal of Diabetes Investigation.2023; 14(8): 973.     CrossRef
    • Association between Oral Hygiene and Metabolic Syndrome: A Systematic Review and Meta-Analysis
      Cornelia Melinda Adi Santoso, Fera Ketti, Taufan Bramantoro, Judit Zsuga, Attila Nagy
      Journal of Clinical Medicine.2021; 10(13): 2873.     CrossRef
    • Hormonal Replacement Therapy and Risk of Thyroid Cancer in Women: A Meta-Epidemiological Analysis of Prospective Cohort Studies
      Jong-Myon Bae
      Journal of Menopausal Medicine.2021; 27(3): 141.     CrossRef
    • A systematic review and meta-analysis of the association between fish consumption and risk of metabolic syndrome
      Golgis Karimi, Zahra Heidari, Somayyeh Firouzi, Fahimeh Haghighatdoost
      Nutrition, Metabolism and Cardiovascular Diseases.2020; 30(5): 717.     CrossRef
    • Dietary patterns and frailty: a systematic review and meta-analysis
      Nafiseh Rashidi Pour Fard, Farzad Amirabdollahian, Fahimeh Haghighatdoost
      Nutrition Reviews.2019; 77(7): 498.     CrossRef
    • A systematic review and meta-analysis of the association between childhood infections and the risk of childhood acute lymphoblastic leukaemia
      Jeremiah Hwee, Christopher Tait, Lillian Sung, Jeffrey C Kwong, Rinku Sutradhar, Jason D Pole
      British Journal of Cancer.2018; 118(1): 127.     CrossRef

    Epidemiol Health : Epidemiology and Health