Meta-analysis of the difference of medians.
median
meta-analysis
quantile estimation
skewed data
two-group
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
01 2020
01 2020
Historique:
received:
17
01
2019
revised:
07
06
2019
accepted:
16
06
2019
pubmed:
26
9
2019
medline:
29
12
2020
entrez:
26
9
2019
Statut:
ppublish
Résumé
We consider the problem of meta-analyzing two-group studies that report the median of the outcome. Often, these studies are excluded from meta-analysis because there are no well-established statistical methods to pool the difference of medians. To include these studies in meta-analysis, several authors have recently proposed methods to estimate the sample mean and standard deviation from the median, sample size, and several commonly reported measures of spread. Researchers frequently apply these methods to estimate the difference of means and its variance for each primary study and pool the difference of means using inverse variance weighting. In this work, we develop several methods to directly meta-analyze the difference of medians. We conduct a simulation study evaluating the performance of the proposed median-based methods and the competing transformation-based methods. The simulation results show that the median-based methods outperform the transformation-based methods when meta-analyzing studies that report the median of the outcome, especially when the outcome is skewed. Moreover, we illustrate the various methods on a real-life data set.
Identifiants
pubmed: 31553488
doi: 10.1002/bimj.201900036
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
69-98Subventions
Organisme : CIHR
Pays : Canada
Informations de copyright
© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Références
Bastian, H., Glasziou, P., & Chalmers, I. (2010). Seventy-five trials and eleven systematic reviews a day: How will we ever keep up? PLOS Medicine, 7 (9), e1000326.
Beaumont, M. A., Zhang, W., & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025-2035.
Blackwell, A. D., Trumble, B. C., Suarez, I. M., Stieglitz, J., Beheim, B., Snodgrass, J. J. … Gurven, M. (2016). Immune function in Amazonian horticulturalists. Annals of Human Biology, 43(4), 382-396.
Bland, M. (2014). Estimating mean and standard deviation from the sample size, three quartiles, minimum and maximum. International Journal of Statistics in Medical Research, 4(1), 57-64.
Boehme, C. C., Nicol, M. P., Nabeta, P., Michael, J. S., Gotuzzo, E., Tahirli, R. … Perkins, M. D. (2011). Feasibility, diagnostic accuracy and effectiveness of decentralised use of the Xpert MTB/RIF test for diagnosis of tuberculosis and multidrug resistance: a multicentre implementation study. Lancet, 377(9776), 1495-1505.
Boldin, M., Simonova, G., & Tyurin, I. (1997). Sign-based methods in linear statistical models. Translations of mathematical monographs, Providence, RI: American Mathematical Society.
Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2010). A basic introduction to fixed-effect and random-effects models for meta-analysis. Research Synthesis Methods, 1(2), 97-111.
Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2011). Introduction to meta-analysis. Chichester: John Wiley & Sons.
Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16(5), 1190-1208.
Cochran, W. G. (1954). The combination of estimates from different experiments. Biometrics, 10(1), 101-129.
Conover, W. (1980). Practical nonparametric statistics. Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics. New York: Wiley.
Cox, H. S., Mbhele, S., Mohess, N., Whitelaw, A., Muller, O., Zemanay, W. … Nicol, M. P. (2014). Impact of xpert MTB/RIF for TB diagnosis in a primary care clinic with high TB and HIV prevalence in South Africa: A pragmatic randomised trial. PLOS Medicine, 11(11), 1-12.
De Oliveira, G. S., Agarwal, D., & Benzon, H. T. (2012). Perioperative single dose ketorolac to prevent postoperative pain: A meta-analysis of randomized trials. Anesthesia & Analgesia, 114(2), 424-433.
Deeks, J. J., Altman, D. G., & Bradburn, M. J. (2008). Statistical methods for examining heterogeneity and combining results from several studies in meta-analysis. London: BMJ Publishing Group.
DerSimonian, R. & Laird, N. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials, 7(3), 177-188.
Durovni, B., Saraceni, V., van den Hof, S., Trajman, A., Cordeiro-Santos, M., Cavalcante, S. … Cobelens, F. (2014). Impact of replacing smear microscopy with xpert MTB/RIF for diagnosing tuberculosis in brazil: A stepped-wedge cluster-randomized trial. PLOS Medicine, 11(12), 1-19.
Emerson, J. D., Hoaglin, D. C., & Mosteller, F. (1993). A modified random-effect procedure for combining risk difference in sets of 2x2 tables from clinical trials. Journal of the Italian Statistical Society, 2(3), 269-290.
George, R. B., Allen, T. K., & Habib, A. S. (2013). Intermittent epidural bolus compared with continuous epidural infusions for labor analgesia: A systematic review and meta-analysis. Anesthesia & Analgesia, 116(1), 133-144.
Geraci, M. & Bottai, M. (2014). Linear quantile mixed models. Statistics and Computing, 24(3), 461-479.
Getnet, F., Demissie, M., Assefa, N., Mengistie, B., & Worku, A. (2017). Delay in diagnosis of pulmonary tuberculosis in low-and middle-income settings: Systematic review and meta-analysis. BMC Pulmonary Medicine, 17(1), 202.
Grocott, M. P. W., Dushianthan, A., Hamilton, M. A., Mythen, M. G., Harrison, D., Rowan, K., & Optimisation Systematic Review Steering Group. (2013). Perioperative increase in global blood flow to explicit defined goals and outcomes after surgery: A Cochrane Systematic Review? British Journal of Anaesthesia, 111(4), 535-548.
Hagiwara, M. A., Bremer, A., Claesson, A., Axelsson, C., Norberg, G., & Herlitz, J. (2014). The impact of direct admission to a catheterisation lab/CCU in patients with ST-elevation myocardial infarction on the delay to reperfusion and early risk of death: Results of a systematic review including meta-analysis. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 22(1), 67.
Hedges, L. V. & Vevea, J. L. (1998). Fixed-and random-effects models in meta-analysis. Psychological Methods, 3(4), 486.
Higgins, J. & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21(11), 1539-1558.
Higgins, J. P. & Green, S. (2011). Cochrane handbook for systematic reviews of interventions 5.1.0 (pp. 33-49). Cochrane Collaboration. Retrieved from http://handbook.cochrane.org
Hozo, S. P., Djulbegovic, B., & Hozo, I. (2005). Estimating the mean and variance from the median, range, and the size of a sample. BMC Medical Research Methodology, 5(1), 13.
Kenney, J. F. (1962). Mathematics of statistics, part 1 (3rd ed.). Princeton, NJ: Van Nostrand.
Khadjesari, Z., Murray, E., Hewitt, C., Hartley, S., & Godfrey, C. (2010). Can stand-alone computer-based interventions reduce alcohol consumption? A systematic review. Addiction, 106(2), 267-282.
de Kieviet, J. F., Piek, J. P., Aarnoudse-Moens, C. S., & Oosterlaan, J. (2009). Motor development in very preterm and very low-birth-weight children from birth to adolescence: A meta-analysis. JAMA, 302(20), 2235-2242.
Kwon, D. & Reis, I. M. (2015). Simulation-based estimation of mean and standard deviation for meta-analysis via approximate Bayesian computation (ABC). BMC Medical Research Methodology, 15(1), 61.
Kwon, D. & Reis, I. M. (2016). Approximate Bayesian computation (ABC) coupled with Bayesian model averaging method for estimating mean and standard deviation. Preprint, arXiv:1607.03080v1.
Laird, N., Fitzmaurice, G., & Ding, X. (2010). Comments on, “Empirical vs natural weighting in random effects meta-analysis,”. Statistics in Medicine, 29(12), 1266-1267.
Lee, J., Hahn, S., Kim, D.-W., Kim, J., Kang, S. N. … Park, B.-J. (2013). Evaluation of survival benefits by platinums and taxanes for an unfavourable subset of carcinoma of unknown primary: A systematic review and meta-analysis. British Journal of Cancer, 108(1), 39-48.
Luo, D., Wan, X., Liu, J., & Tong, T. (2016). Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Statistical Methods In Medical Research, 27, 1785-1805.
Martin, C., Jacob, M., Vicaut, E., Guidet, B., Van Aken, H., & Kurz, A. (2013). Effect of waxy maize-derived hydroxyethyl starch 130/0.4 on renal function in surgical patients. Anesthesiology, 118(2), 387-394.
McGrath, S., Zhao, X., Qin, Z. Z., Steele, R., & Benedetti, A. (2018a). One-sample aggregate data meta-analysis of medians. Statistics in Medicine, 38(6), 969-984.
McGrath, S., Zhao, X., Steele, R., & Benedetti, A. (2018b). metamedian: Meta-Analysis of Medians. R package version 0.1.1. Retrieved from https://CRAN.R-project.org/package=metamedian
Mitchell, E., Macdonald, S., Campbell, N. C., Weller, D., & Macleod, U. (2008). Influences on pre-hospital delay in the diagnosis of colorectal cancer: A systematic review. British Journal of Cancer, 98(1), 60-70.
Nnoaham, K. E. & Clarke, A. (2008). Low serum vitamin D levels and tuberculosis: A systematic review and meta-analysis. International Journal of Epidemiology, 37(1), 113-119.
Omrani, A. S., Al-Otaibi, M. F., Al-Ateah, S. M., Al-Onazi, F. M., Baig, K., El-Khizzi, N. A., & Albarrak, A. M. (2014). GeneXpert MTB/RIF testing in the management of patients with active tuberculosis: A real life experience from saudi arabia. Infection & Chemotherapy, 46(1), 30-34.
Peng, L., Xu, L., & Ouyang, W. (2013). Role of peripheral inflammatory markers in postoperative cognitive dysfunction (POCD). A meta-analysis. PLOS ONE, 8(11), e79624.
Qin, Z. Z. (2016). Delays in diagnosis and treatment of pulmonary tuberculosis, and patient care-seeking pathways in China: A systematic review and meta-analysis (Ph.D. thesis). McGill University, Montreal.
Rice, K., Higgins, J. P. T., & Lumley, T. (2018). A re-evaluation of fixed effect(s) meta-analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society), 181(1), 205-227.
van Rijssen, L. B., Narwade, P., van Huijgevoort, N. C. M., Tseng, D. S. J., van Santvoort, H. C., Molenaar, I. Q. & Besselink, M. G. (2016). Prognostic value of lymph node metastases detected during surgical exploration for pancreatic or periampullary cancer: A systematic review and meta-analysis. HPB: The Official Journal of the International Hepato Pancreato Biliary Association, 18(7), 559-566.
Rücker, G., Schwarzer, G., Carpenter, J., & Schumacher, M. (2010). Comments on, “Empirical vs natural weighting in random effects meta-analysis,”. Statistics in Medicine, 29(28), 2963-2965.
Rukhin, A. L. (2013). Estimating heterogeneity variance in meta-analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75(3), 451-469.
Samal, J. (2016). Health seeking behaviour among tuberculosis patients in India: A systematic review. Journal of Clinical and Diagnostic Research: JCDR, 10(10), LE01-LE06.
Shapiro, S. S. & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591-611.
Shuster, J. J. (2010). Empirical vs natural weighting in random effects meta-analysis. Statistics in Medicine, 29(12), 1259-1265.
Siemieniuk, R. A., Meade, M. O., Alonso-Coello, P., Briel, M., Evaniew, N., Prasad, M. … Guyatt, G. H. (2015). Corticosteroid therapy for patients hospitalized with community-acquired pneumonia: A systematic review and meta-analysis. Annals of Internal Medicine, 163(7), 519-528.
Siempos, I. I., Ntaidou, T. K., & Falagas, M. E. (2010). Impact of the administration of probiotics on the incidence of ventilator-associated pneumonia: A meta-analysis of randomized controlled trials. Critical Care Medicine, 38(3), 954-962.
Sohn, H. (2016). Improving tuberculosis diagnosis in vulnerable populations: impact and cost-effectiveness of novel, rapid molecular assays. Ph.D. thesis, McGill University, Montreal.
Sreeramareddy, C. T., Panduru, K. V., Menten, J., & Van den Ende, J. (2009). Time delays in diagnosis of pulmonary tuberculosis: A systematic review of literature. BMC Infectious Diseases, 9, 91-91.
Sreeramareddy, C. T., Qin, Z. Z., Satyanarayana, S., Subbaraman, R., & Pai, M. (2014). Delays in diagnosis and treatment of pulmonary tuberculosis in India: A systematic review. The International Journal of Tuberculosis and Lung Disease: The Official Journal of the International Union Against Tuberculosis and Lung Disease, 18(3), 255-266.
Sutton, A. J. & Higgins, J. P. T. (2008). Recent developments in meta-analysis. Statistics in Medicine, 27(5), 625-650.
Theron, G., Zijenah, L., Chanda, D., Clowes, P., Rachow, A., Lesosky, M. & TB-NEAT Team (2014). Feasibility, accuracy, and clinical effect of point-of-care Xpert MTB/RIF testing for tuberculosis in primary-care settings in Africa: A multicentre, randomised, controlled trial. Lancet, 383(9915), 424-435.
Thompson, S. G. & Higgins, J. P. (2010). Comments on, “Empirical vs natural weighting in random effects meta-analysis,”. Statistics in Medicine, 29(12), 1270-1271.
Timbrook, T. T., Morton, J. B., McConeghy, K. W., Caffrey, A. R., Mylonakis, E., & LaPlante, K. L. (2017). The effect of molecular rapid diagnostic testing on clinical outcomes in bloodstream infections: A systematic review and meta-analysis. Clinical Infectious Diseases, 64(1), 15-23.
Veroniki, A. A., Jackson, D., Viechtbauer, W., Bender, R., Bowden, J., Knapp, G. … Salanti, G. (2016). Methods to estimate the between-study variance and its uncertainty in meta-analysis. Research Synthesis Methods, 7(1), 55-79.
Viechtbauer, W. (2005). Bias and efficiency of meta-analytic variance estimators in the random-effects model. Journal of Educational and Behavioral Statistics, 30(3), 261-293.
Waksman, J. A. (2010). Comments on, “Empirical vs natural weighting in random effects meta-analysis,”. Statistics in Medicine, 29(12), 1268-1269.
Wan, X., Wang, W., Liu, J., & Tong, T. (2014). Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Medical Research Methodology, 14(1), 135.
Wao, H., Mhaskar, R., Kumar, A., Miladinovic, B., & Djulbegovic, B. (2013). Survival of patients with non-small cell lung cancer without treatment: A systematic review and meta-analysis. Systematic Reviews, 2, 10-10.
White, M. T., Conteh, L., Cibulskis, R., & Ghani, A. C. (2011). Costs and cost-effectiveness of malaria control interventions-A systematic review. Malaria Journal, 10(1), 337.