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An overview on the methodological and reporting quality of dose–response meta-analysis on cancer prevention

  • Chang Xu
  • Yu Liu
  • Chao Zhang
  • Joey S. W. Kwong
  • Jian-Guo Zhou
  • Long Ge
  • Jing-Yu HuangEmail author
  • Tong-Zu LiuEmail author
Original Article – Cancer Research
  • 97 Downloads

Abstract

Background

Dose–response meta-analysis (DRMA) has been widely used in exploring cancer risk factors. Understanding the quality of published DRMAs on cancer risk factors may be beneficial for informed prevention for cancer.

Methods

We searched eligible DRMAs from 1st January 2011 to 31st-July-2017. The modified AMSTAR 1.0 (15 items) and PRISMA checklist (26 items) were used to evaluate the methodological and reporting quality of included DRMAs. We compared the adherence rate of these items by journal type, publication years, region, and funding information, in prior.

Results

We included 260 DRMAs. Colorectal, breast, prostate, and lung were the four most commonly investigated cancers. For methodological quality, 6 out of 15 items were adhered by less than 30% of the DRMAs, 2 by less than 60%, only 7 of which by 80% or more. For reporting quality, 3 out of 26 items were adhered by less than 30% of the DRMAs, 1 by less than 80% (> 30%), and 20 of which by 80% or more. Those published in general journal, published more recently, and received any financial support have better methodological (Rate differences, RDs = 10–36%; P < 0.05) and reporting adherence (RDs = 12–36%; P < 0.05). DRMAs by Asian author tend to be less qualified than by European and American.

Conclusions

The methodological quality of DRMAs on cancer risk factors is worrisome that the findings of them may be deflective; more efforts are needed to improve the validity of it.

Keywords

Cancer prevention Dose–response meta-analysis Methodological quality Reporting quality 

Notes

Acknowledgements

I (XC) would like to express my deep appreciation for Prof. Suhail A.R Doi (Qatar University) for his guidance on me of synthesis methods for dose–response data.

Author contributions

LTZ, HJY, and XC conceived and designed the study; XC and ZC drafted the manuscript; XC and LY contributed to the quality assessment; LTZ, HJY, ZC, JK, LS, ZJG, and GL provided careful comments and revised the manuscript. All authors approved the final version to be published.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

432_2019_2869_MOESM1_ESM.docx (29 kb)
Supplementary material 1 (DOCX 28 KB)

References

  1. Anderson Johnson C, Palmer PH, Chou CP et al (2006) Tobacco use among youth and adults in Mainland China: the China Seven Cities Study. Public Health 120(12):1156–1169CrossRefGoogle Scholar
  2. Bagnardi V, Zambon A, Quatto P, Corrao G (2004) Flexible meta-regression functions for modeling aggregate dose-response data, with an application to alcohol and mortality. Am J Epidemiol 159:1077–1086.  https://doi.org/10.1093/aje/kwh142 CrossRefGoogle Scholar
  3. Berlin JA, Longnecker MP, Greenland S (1993) Meta-analysis of epidemiologic dose-response data. Epidemiology (Cambridge Mass) 4:218–228CrossRefGoogle Scholar
  4. Burda BU, Holmer HK, Norris SL (2016) Limitations of a measurement tool to assess systematic reviews (AMSTAR) and suggestions for improvement. Syst Rev 5:58.  https://doi.org/10.1186/s13643-016-0237-1 CrossRefGoogle Scholar
  5. Ferlay J, Soerjomataram I, Dikshit R et al (2015) Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136:E359–E386.  https://doi.org/10.1002/ijc.29210 CrossRefGoogle Scholar
  6. Fitzmaurice C, Allen C, Barber RM et al (2017) Global, Regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: a systematic analysis for the global burden of disease study. JAMA Oncol 3:524–548.  https://doi.org/10.1001/jamaoncol.2016.5688 CrossRefGoogle Scholar
  7. GBD 2015 Risk Factors Collaborators (2016) Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388:1659–1724.  https://doi.org/10.1016/s0140-6736(16)31679-8 CrossRefGoogle Scholar
  8. Jia P, Tang L, Yu J et al (2018) Risk of bias and methodological issues in randomised controlled trials of acupuncture for knee osteoarthritis: a cross-sectional study. BMJ Open 8:e019847  https://doi.org/10.1136/bmjopen-2017-019847 CrossRefGoogle Scholar
  9. Lazaro M, Gallardo E, Doménech M et al (2016) SEOM clinical guideline for treatment of muscle-invasive and metastatic urothelial bladder cancer (2016). Clin Transl Oncol 18:1197–1205.  https://doi.org/10.1007/s12094-016-1584-z CrossRefGoogle Scholar
  10. Liu Q, Cook NR, Bergström A, Hsieh CC (2009) A two-stage hierarchical regression model for meta-analysis of epidemiologic nonlinear dose–response data. Comput Stat Data Anal 53:4157–4167CrossRefGoogle Scholar
  11. Moher D, Liberati A, Tetzlaff J, Altman DG (2010) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg 8:336–341.  https://doi.org/10.1016/j.ijsu.2010.02.007 CrossRefGoogle Scholar
  12. Mottet N, Bellmunt J, Bolla M et al (2017) EAU-ESTRO-SIOG guidelines on prostate cancer. Part 1: screening, diagnosis, and local treatment with curative intent. European urology 71:618–629  https://doi.org/10.1016/j.eururo.2016.08.003 CrossRefGoogle Scholar
  13. Orsini N, Li R, Wolk A, Khudyakov P, Spiegelman D (2012) Meta-analysis for linear and nonlinear dose-response relations: examples, an evaluation of approximations, and software. Am J Epidemiol 175:66–73.  https://doi.org/10.1093/aje/kwr265 CrossRefGoogle Scholar
  14. Page MJ, Shamseer L, Altman DG et al (2016) Epidemiology and reporting characteristics of systematic reviews of biomedical research: a cross-sectional study. PLoS Med 13:e1002028  https://doi.org/10.1371/journal.pmed.1002028 CrossRefGoogle Scholar
  15. Schulz KF, Chalmers I, Hayes RJ, Altman DG (1995) Empirical evidence of bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. JAMA 273:408–412CrossRefGoogle Scholar
  16. Shea BJ, Grimshaw JM, Wells GA et al (2007) Development of AMSTAR: a measurement tool to assess the methodological quality of systematic reviews. BMC Med Res Methodol 7:10  https://doi.org/10.1186/1471-2288-7-10 CrossRefGoogle Scholar
  17. Shea BJ, Reeves BC, Wells G et al (2017) AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ 358:j4008.  https://doi.org/10.1136/bmj.j4008 CrossRefGoogle Scholar
  18. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A (2015) Global cancer statistics, 2012. CA Cancer J Clin 65:87–108.  https://doi.org/10.3322/caac.21262 CrossRefGoogle Scholar
  19. Torre LA, Siegel RL, Ward EM, Jemal A (2016) Global cancer incidence and mortality rates and trends—an update. Cancer Epidemiol Biomark Prev 25:16–27.  https://doi.org/10.1158/1055-9965.epi-15-0578 CrossRefGoogle Scholar
  20. Vaughn K, Skinner M, Vaughn V, Wayant C, Vassar M (2018) Methodological and reporting quality of systematic reviews referenced in the clinical practice guideline for pediatric high-blood pressure. J Hypertens.  https://doi.org/10.1097/hjh.0000000000001870 Google Scholar
  21. Xu C, Doi SAR (2018) The robust error meta-regression method for dose-response meta-analysis. Int J Evid-Based Healthc 16:138–144Google Scholar
  22. Xu C, Liu TZ, Jia PL et al (2018) Improving the quality of reporting of systematic reviews of dose-response meta-analyses: a cross-sectional survey. BMC Med Res Methodol 18(1):157CrossRefGoogle Scholar
  23. Xu C, Thabane L, Liu TZ et al (2019a) Flexible piecewise linear model for investigating dose-response relationship in meta-analysis: methodology, examples, and comparison. J Evid-Based Med.  https://doi.org/10.1111/jebm.12339 Google Scholar
  24. Xu C, Liu Y, Jia PL et al (2019b) The methodological quality of dose-response meta-analyses needed substantial improvement: a cross-sectional survey and proposed recommendations. J Clin Epidemiol 107:1–11CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Chang Xu
    • 1
  • Yu Liu
    • 2
  • Chao Zhang
    • 3
  • Joey S. W. Kwong
    • 4
  • Jian-Guo Zhou
    • 5
  • Long Ge
    • 6
  • Jing-Yu Huang
    • 7
    Email author
  • Tong-Zu Liu
    • 8
    Email author
  1. 1.Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan & Chinese Evidence-based Medicine Center, West China HospitalSichuan UniversityChengduChina
  2. 2.Gansu Provincial Maternity and Child-Care HospitalGansuChina
  3. 3.Center for Evidence-Based Medicine and Clinical Research, Taihe HospitalHubei University of MedicineShiyanChina
  4. 4.JC School of Public Health and Primary Care, Faculty of MedicineThe Chinese University of Hong KongHong KongChina
  5. 5.Department of OncologyAffiliated Hospital of Zunyi Medical UniversityZunyiChina
  6. 6.Evidence-Based Medicine Center, School of Basic Medical SciencesLanzhou UniversityLanzhouChina
  7. 7.Department of Thoracic Tumor Ward, Thoracic and Cardiovascular SurgeryZhongnan Hospital of Wuhan UniversityWuhanChina
  8. 8.Department of Urology, Zhongnan Hospital of Wuhan UniversityWuhan UniversityWuhanChina

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