BMC Medical Research Methodology - Latest Comments
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The latest comments on all articles published by BMC Medical Research Methodology2014-06-25T13:30:51ZCorrection to funding acknowledgement for this paper
http://www.biomedcentral.com/1471-2288/14/21/comments#2123698
<p>This study was funded through the East Africa International epidemiological Databases to Evaluate AIDS (IeDEA) Consortium by the US National Institutes of Health - the Eunice Kennedy Shriver National Institute Of Child Health & Human Development (NICHD) and the National Institute Of Allergy And Infectious Diseases (NIAID). Grant award 3U01AI069911-06S2.</p>
<p> </p>Annabelle Gourlay2014-06-25T13:30:51Zhttp://www.biomedcentral.com/1471-2288/14/21Gourlay et al.BMC Medical Research Methodology1421Tue Feb 11 00:00:00 GMT 2014Software
http://www.biomedcentral.com/1471-2288/13/35/comments#2030698
The functions for assessing the heterogeneity and inconsistency in network meta-analysis, for producing a net heat plot and a network graph are now implemented in the R package netmeta with version 0.4-0 and are available from the standard <a class="external-link-new-window" href="http://www.cran.r-project.org/">CRAN repository</a>.Ulrike Krahn2014-03-28T16:31:24Zhttp://www.biomedcentral.com/1471-2288/13/35Krahn et al.BMC Medical Research Methodology1335Sat Mar 09 00:00:00 GMT 2013Ridge parameter
http://www.biomedcentral.com/1471-2288/12/184/comments#1279696
<p>The paper by Hardt, Herke and Leonhart is a welcome addition to the literature. It warns against simplistic approaches that throw just anything into the imputation model. While the imputation model is generally robust against including junk variables, the paper clearly demonstrates that we should not drive this to the edge. In general building the imputation model requires appropriate care. My personal experience is that it is not beneficial to include more than -say- 25 well-chosen variables into the imputation model.
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<br/>In their simulations the authors investigate cases where the number of variables specified in the imputation model exceeds the number of cases. Many programs break down in this case, but MICE will run because it uses ridge regression instead of the usual OLS estimate. The price for this increased computational stability is -as confirmed by Hardt et all - that the parameters estimates will be biased towards zero. It is therefore likely that some of the bias observed by the authors is not intrinsic to PMM, but rather due to the setting of the ridge parameter (the default value 1E-5 may be easily changed as mice(..., ridge = 1E-6)). Would a tighter ridge setting (e.g., 1E-6 or 1E-7) appreciably reduce the bias?
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<br/>The '1 out of 3 of the complete cases' rule is interesting and easily remembered. However, a complication in practice is that there are often no complete cases in real data, especially in merged datasets. What would the authors think of the slightly more liberal rule 'n/3 variables'?
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<br/>Stef van Buuren</p>Stef van Buuren2013-02-15T14:56:37Zhttp://www.biomedcentral.com/1471-2288/12/184Hardt et al.BMC Medical Research Methodology12184Wed Dec 05 00:00:00 GMT 2012erratum
http://www.biomedcentral.com/1471-2288/7/36/comments#1042696
<p>This commentary serves to point out that in the results section (p.7) of the manuscript, we (the authors) incorrectly described the calculation of specificity of VIA for the 3-class model. The corrected text (below) describes a specificity of 0.65 versus 0.57. The calculation correction is also appended.
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<br/>"Specificity was 0.645. In that model, class 1 (p = 0.682) and class 2 (p = 0.200) combine to form non-disease. Specificity was calculated as 0.323 (probability of VIA inflammation given class 2) plus 0.117 (probability of VIA normal given class 2) multiplied by the probability of class 2 (0.200), plus the analogous values for Class 1, i.e., 0.429 (probability of VIA inflammation given class 1) plus 0.276 (probability of VIA normal given class 1) multiplied by the probability of class 1 (0.682), the entire sum being divided by the probability of non-disease (0.682 + 0.200)."
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<br/>In Table 4, the specificity values for the 3-class LCA solution reference standard (#4) are likewise incorrect. These values were given as 0.568, 0.820, 0.568, 0.758, and 0.860 for, respectively, VIA Abnormal, CA; Pap LGSIL+; HPV >= 1.0 RLU; Colposcopy/Biopsy LGSIL+; and Colposcopy/Biopsy HGSIL+. The correct values are all higher and should be listed as 0.645, 0.930, 0.644, 0.860, and 0.975.
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<br/>In keeping with the above, the following text (pages 6-7) should be corrected as follows (0.645 instead of 0.568):
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<br/>"In this study, despite apparent imperfections in the reference standard, the conventionally-derived VIA results fell within the range of published data and were relatively consistent between with the 3-class LCA model (0.775 versus 0.744, respectively, for sensitivity and 0.639 and 0.645, respectively for specificity)."
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<br/>These corrections do not substantively alter the results (as noted above, the corrected LCA-derived specificity value is now almost the same as the conventionally derived value, similar to what was observed for VIA for sensitivity) and the corrections do not affect our conclusions.</p>John McGrath2012-08-02T14:47:45Zhttp://www.biomedcentral.com/1471-2288/7/36Gaffikin et al.BMC Medical Research Methodology736Tue Jul 31 01:34:30 BST 2007Correction
http://www.biomedcentral.com/1471-2288/11/96/comments#1027698
<p>Equation 12 contains errors. In the equation k_min should be replaced by k_1, k_max with k_K and k_K-j with k_K-j+1. Below equation 12, in the description of lambda_j, k_K-j should again be replaced with k_K-j+1. We would like to thank Dr. Finian Bannon at the N. Ireland Cancer Registry for pointing out these errors.</p>Therese Andersson2012-08-02T12:15:04Zhttp://www.biomedcentral.com/1471-2288/11/96Andersson et al.BMC Medical Research Methodology1196Wed Jun 22 00:00:00 BST 2011Update on methodology for NSW Ministy of Health telephone surveys
http://www.biomedcentral.com/1471-2288/11/159/comments#924696
<p>As referenced in the article landline random digit dialling (RDD) have been the method of choice for the telephone based population health survey conducted by the NSW Ministry of Health over the last decade. However because of the increase in mobile phone ownership the methology was modified to include mobile only persons using an overlapping duel-frame design in 2012. The methodology was developed in collaboration with the Centre for Statistical and Survey Methodology at the University of Wollongong. A full description of the methods and preliminary findings will be available soon.</p>Margo Barr2012-06-24T14:38:47Zhttp://www.biomedcentral.com/1471-2288/11/159Liu et al.BMC Medical Research Methodology11159Thu Nov 24 00:00:00 GMT 2011Quantum Biophysical Semeiotics plays a central role in predicting intracranial findings on CT-scans.
http://www.biomedcentral.com/1471-2288/11/143/comments#628695
<p>Editors, <br/>in order to recognize, among individuals involved by suspected traumatic brain damage, those who really is suffering from such a disorder, physicians nowadays can utilise Quantum Biophysical Semeiotics (1-4). <br/>For instance, in health, light digital pressure, applied upon the closed eye, brings about gastric aspecific reflex (= both stomach fundus and body dilates) after a latency time of 8 sec. <br/>On the contrary, in presence of a pathological condition latency time results smaller, in relation to the seriousness of underlying disorder. <br/>An awful number of other signs allow doctor to make a correct differential diagnosis. <br/> <br/> <br/>References <br/> <br/> <br/>1) Stagnaro S., Percussione Ascoltata degli Attacchi Ischemici Transitori. Ruolo dei Potenziali Cerebrali Evocati. Min. Med. 76, 1211 [Pub-Med indicizzato per Medline] <br/>2) Stagnaro Sergio, Stagnaro-Neri Marina. Introduzione alla Semeiotica Biofisica. Il Terreno oncologico”. Travel Factory SRL., Roma, 2004. http://www.travelfactory.it/semeiotica_biofisica.htm <br/>3) Stagnaro S., Stagnaro-Neri M., Single Patient Based Medicine.La Medicina Basata sul Singolo Paziente: Nuove Indicazioni della Melatonina. Travel Factory SRL., Roma, 2005. http://www.travelfactory.it/semeiotica_biofisica.htm <br/>4) Stagnaro Sergio. Inherited Real Risk of Brain Disorders. www.plos.org, 24 July 2009. http://www.plosone.org/article/comments/info%3Adoi%2F10.1371%2Fjournal.pone.0006354;jsessionid=9AC82C42FA9F57C913844806BF96DDC1 <br/></p>Sergio Stagnaro2011-12-02T17:25:34Zhttp://www.biomedcentral.com/1471-2288/11/143van der Ploeg et al.BMC Medical Research Methodology11143Tue Oct 25 00:00:00 BST 2011Further early references to sample sizes with fixed number of clusters
http://www.biomedcentral.com/1471-2288/11/102/comments#570694
<p>Hemming et al (2011) give a useful review of sample size calculations with a fixed number of clusters. As they acknowledge their equations (17) to (19) are derived from Donner and Klar (200). I would like to point out the issue of fixed number of clusters was also discussed by Campbell (2000) that the authors’ equation (13) was first given as equation (2) in that paper and as equation (9.7) in Machin and Campbell (2005). <br/> <br/>Mike Campbell <br/> <br/>References <br/>Campbell MJ Cluster randomized trials in general (family) practice research. Statistical Methods in Medical Research 2000, 9; 81-94 <br/> <br/>Donner A and Klar N. Design and analysis of cluster randomized trials. London, Arnold 2000 <br/> <br/>Machin D and Campbell MJ Design of Studies for Medical Research. Chichester, John Wiley and Sons Ltd, 2005 <br/></p>Michael Campbell2011-09-12T16:34:29Zhttp://www.biomedcentral.com/1471-2288/11/102Hemming et al.BMC Medical Research Methodology11102Thu Jun 30 00:00:00 BST 2011Two errors in this article?
http://www.biomedcentral.com/1471-2288/11/23/comments#535685
<p>I can't find a definition for FORM in the article. it appears for the first time in the Method section on page 3 but does not have a definition. Is there one? <br/> <br/>Also, there appears to be an error in Table 1, in the cell combining "Consistency" and "D Poor". It says 'Evidence is consistent' however it should read 'Evidence is inconsistent'. </p>Emma Friesen2011-07-04T10:54:28Zhttp://www.biomedcentral.com/1471-2288/11/23Hillier et al.BMC Medical Research Methodology1123Mon Feb 28 09:14:39 GMT 2011Standard measures of differences between outcome rates are problematic for identifying subgroup effects
http://www.biomedcentral.com/1471-2288/5/15/comments#515697
<p>White and Elbourne[1] address the way that interaction tests are affected by whether one compares relative changes in risk of an outcome, relative changes in risk of the opposite outcome, absolute changes in outcome rates, or odds ratios. They recommend a conservative approach to identifying interaction that involves examining the measure that is least likely to show a statistically significant subgroup effect. <br/> <br/>The fact that, for example, when an intervention reduces one adverse outcome rate from 12.7% to 5.0% and another from 21.7% to 10.0%, whether an interaction test finds a statistically significant difference between the two changes may depend on what measure of change is employed suggests that something may be amiss with interaction tests generally. <br/> <br/>More important, the authors overlook the way that, due to factors inherent in the shape of normal distributions of risks of experiencing an outcome, standard measures of changes in outcome rates tend to be affected by the overall prevalence of an outcome. As a result of such factors, there is reason to expect that an intervention that similarly affects groups with different base rates of experiencing an outcome will commonly cause a larger proportionate change in the outcome for the group with the lower base rate while causing a larger proportionate change in the opposite outcome for the other group. Thus, statistical significance issues aside, a determination of which group benefited more in relative terms will often turn on whether one examines changes in the adverse outcome or the favorable outcome. Changes in absolute differences and odds ratios tend also to be systematically affected by the overall prevalence of an outcome. While typical patterns of relative differences and odds ratios are more difficult to describe, the two measures commonly yield contrasting interpretations as to which group benefited more from an intervention.[2-6] <br/> <br/>The hip trial data examined by White and Elbourne may have provided an unfortunate example. Among the strong suspicion group, ultrasound appeared to reduce the risk of surgery and the risk of any hip treatment. But among the moderate suspicion group, those treated with ultrasound had a slightly higher surgery rate and only a negligibly lower rate of any hip treatment than those not treated. So the above-described patterns as to the comparative size of relative changes in one outcome and relative changes in the opposite outcome are not present. But, given that the shapes of normal distributions provide a statistical basis for expecting such patterns absent countervailing forces, the patterns must be taken into account. <br/> <br/>In order to identify a meaningful subgroup effect one must employ a measure that is unaffected by the overall prevalence of an outcome. The only apparent such measure is an estimate, derived from the outcome rates of subjects receiving an intervention and not receiving it, of the difference between the means of the hypothesized underlying risk distributions.[4,6] In the case of the figures cited above, such measures would show the two changes to be exactly the same. Each reflects a situation where the intervention shifted the means of the underlying distributions by half a standard deviation. <br/> <br/>These considerations must be borne in mind both in appraising subgroup effects and in interpreting meta-analyses when the base rates differ from study to study or within studies.[7] The considerations must also be borne in mind in the crucial estimating of the absolute risk reduction for a particular subgroup in the absence of reliable information for that subgroup. <br/> <br/>References: <br/> <br/>1. White IA, Elbourne D. Assessing subgroup effects with binary data: can the use of different effects measures lead to different conclusions? BMC Medical Research Methodology 2005, 5;15: http://www.biomedcentral.com/1471-2288/5/15 (Accessed June 7, 2010). <br/> <br/>2. Scanlan JP. Race and mortality. Society 2000;37(2):19-35: http://www.jpscanlan.com/images/Race_and_Mortality.pdf (Accessed June 7, 2011.) <br/> <br/>3. Scanlan JP. Divining difference. Chance 1994;7(4):38-9,48: http://jpscanlan.com/images/Divining_Difference.pdf (Accessed June 7, 2011.) <br/> <br/>4. Scanlan JP. Interpreting Differential Effects in Light of Fundamental Statistical Tendencies, presented at 2009 Joint Statistical Meetings of the American Statistical Association, International Biometric Society, Institute for Mathematical Statistics, and Canadian Statistical Society, Washington, DC, Aug. 1-6, 2009: http://www.jpscanlan.com/images/JSM_2009_ORAL.pdf; http://www.jpscanlan.com/images/Scanlan_JSM_2009.ppt (Accessed June 7, 2011.) <br/> <br/>5. Scanlan’s Rule page of jpscanlan.com: http://jpscanlan.com/scanlansrule.html (Accessed June 7, 2011) <br/> <br/>6. Subgroup Effects sub-page of Scanlan’s Rule page of jpscanlan.com: http://www.jpscanlan.com/scanlansrule/subgroupeffects.html (Accessed June 7, 2011.) <br/> <br/>7. Meta-analysis sub-page of Scanlan’s Rule page of jpscanlan.com: http://jpscanlan.com/scanlansrule/metaanalysis.html (Accessed June 7, 2011.) <br/></p>James Scanlan2011-06-08T16:50:49Zhttp://www.biomedcentral.com/1471-2288/5/15White et al.BMC Medical Research Methodology515Fri Apr 29 00:00:00 BST 2005