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Diagnostic Tests - Science topic

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Hi there,
I'm currently writing my dissertation which evaluates the accuracy of 3 serological diagnostic tests (lateral flow, ELISA, and CLIA). I am collecting sensitivity and specificity values from current research on each of these tests.
Does anyone know which statistical test is best to test for significance in sensitivity and specificity values of the 3 diagnostic tests?
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As far as I know, it is by comparing AUC of the ROC curve to determine which test is the best.
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Hi,
I am working on a meta-analysis on the volume measurement of pulmonary nodules by automatic software tools. I used I^2 to calculate heterogeneity between studies but I was advised to do funnel plots. The studies are either phantom based (artificial nodules) or coffee-break in vivo studies were the actual volume of the nodule does not change. This is because there is no gold standard for the in vivo volume, since after surgery the nodule is known to shrink.
to my understanding, funnel plots apply to sensitivity / specificity studies, and I am having a hard time understanding what that means in this particular case, since all nodules, do not change (I.e., no false negatives or true positives for growth).
how else could I check for bias of publication?
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Funnel plots assess publication bias. They have nothing to do with assessing heterogeneity. Heterogeneity and publication bias are unrelated. Publication bias refers to when small studies with negative and/or nonsignificant findings tend to not be published and thus, are often omitted from systematic reviews/meta-analyses. There are several ways for assessing the presence of publication bias (e.g., Begg & Mazumdar rank correlation, Egger's regression intercept) but I consider the funnel plot to be the most useful means (at least after you've looked at a few dozen funnel plots).
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Based on the topics listed in https://www.nature.com/articles/d41586-020-03564-y the diversity of major topics categories under which research publications on COVID are grouped are:
  • Modelling epidemic, controlling spread
  • Public health
  • Diagnostics, testing
  • Mental health
  • Hospital mortality
It is obvious that Lateral Flow testing belongs to the third category on the list - diagnostics/testing. The software interface aspect of things is not addressed. One aspect that would belong to the software side is the reporting/registration of results. Please read the latest preprint of my paper on this subject at:
What are your thoughts on the impact on our preparedness for serious pandemics if software engineers develop methods to tackle online cheating at home? Please share your thoughts, let me know if I can quote you as a reviewer for this paper in a journal. Thanks in advance for your collaboration.
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One newspaper in the United Kingdom is reporting that some travelers are doctoring emails from Covid-19 testing laboratories, printing them out and simply handing them to airline staff before boarding flights.
Some Air Passengers Are Faking Negative Covid-19 Test Results, Per U.K. Reports (forbes.com)
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Greetings!
Should we establish a normal reference value of palpebral fissure length in different species?
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One of the most important reasons to measure palpebral length is that an excessive eyelid length can lead to entropion of the lateral lower eyelid in some animals. In others it leads to a macropalpebral fissure and a combination of entropion-ectropion. There are also cases of microblepharon. All of this is important clinically (i.e. to know how best to operate an animal with one or another type of entropion - different entropions require different surgical approaches) but also for breeding (i.e. to not allow affected animals to breed, or to breed them with unaffected animals to reduce the effect of the problem). There is an article on palpebral eyelid length of dogs and cats from 1992 (now considered a little old, but unique, so still relevant). You might also find interesting the guidelines of the ECVO that mention macroblepharon and microblepharon (through www.ECVO.org, under hereditary eye diseases (HED)). I have included the paper and the guidelines for your interest (I have highlighted the important part of the guidelines). Enjoy.
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When screening for COVID-19 or any other infection using existing diagnostic tests, is it possible to detect Latently infected individual? If so, how different are the chances of detection in Latently infected compared to Infectious individuals and what other factors besides viral load and test sensitivity could influence detection chances? Thanks!
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Dear Dr. Ssematimba!
I found the following resource for YOU:
1) Mellacher, P. Endogenous viral mutations, evolutionary selection, and containment policy design. J Econ Interact Coord (2022). https://doi.org/10.1007/s11403-021-00344-3, Open access: y
Yours sincerely, Bulcsu Szekely
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I have two questions.
1. When running the Trans-log SFA technique, is it necessary to carry out unit root tests?
2. What are the recommended diagnostic tests for SFA?
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Thank you Mwoya Byaro
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The metanalysis was done for the Sensitivity, Specificity, LR+, LR-, and DOR of each outcome. But the reviewer asked me to calculate and include the PPV and NVP too, however I don't think it is possible because the prevalence of the outcome (difficult laryngoscopy) is very variable (between 1,5 and 20% in the literature). What should I do?
should I calculated it with a median? or just not calculated it all.
I will put a picture of one metanalysis
Thanks
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Yes Patrick. Completely agree it would be innaproppiate to put NPV and PPV because the high variability in prevalanece of the outcome. Tough telling the reviewer its not a good thing to do. Thanks!
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Do we need to run the panel regression diagnostic tests for panel stochastic frontier analysis (xtfrontier or sfpanel)?
Could someone suggest the best read for this analysis, especially for the time-varying decay model and sfpanel with inefficiency functions explicitly mentioned?
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Regarding your problem, I can recommend the following article:
Battese, G.E., Coelli, T.J. Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India. J Prod Anal 3, 153–169 (1992). https://doi.org/10.1007/BF00158774.
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what is considered a "Good" OCT for an RFNL?
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In addition to the previous reply, here are the normal variants : The mean value for RNFL thickness in the general population is 92.9 +/- 9.4 microns. Typically, a normal, non glaucomatous eye has an RNFL thickness of 80 microns or greater. An eye with an average RNFL thickness of 70 to 79 is suspicious for glaucoma.
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What are the various diagnostic tests recommended for panel data set analysis?
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Hi Ravindra Muley thanks for your question. You can adopt a variety of tests. For instance, the Breusch and Pagan test to trace heteroskedasticity, Wooldridge test for autocorrelation, Breusch-Godfrey test for autocorrelation test. You can also use the cross-sectional dependence (CD) test
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Let us suppose we have a new cheap and simple diagnostic test we want to evaluate against the expensive and complex gold standard for a highly lethal disease.
The gold standard test is dichotomous (positive or negative), but the new test returns two continuous results: let's call them "Result A" and "Result B".
Assuming the disease can be accurately diagnosed with the gold standard test, we want to
1) estimate the posterior probability of disease given the prior and the new test results A and B, i.e. P(D+|A,B)
2) define the best threshold values for both A and B
Given the high lethality, we're more interested in avoiding false negatives.
Let's suppose we have data like the ones in figure 1 (randomly generated data). Big red dots and small grey dots are patients whose gold standard test did result respectively positive and negative.
Which is the best model to evaluate such a test?
Logistic regression and ROC curve?
Clustering in machine learning?
Other?
Thank you.
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Hello Max Pierini. In trying to find the best balance between Se and Sp, you are using Youden's Index (or some variation on it). Let me remind you of what Jochen Wilhelm said in his reply (with emphasis added):
The performance of the new method can be evaluated by a ROC analysis. You may decide on a useful combination of sensitvity and specificity you can get (standard indices like the Youden index or similar are ignoring all practical consequences of false positives and false negatives and should not be used).
The only change I would make to what Jochen said is that Youden's index (or similar) should only be used when false positives and false negatives are deemed equally costly. But you want to limit false negatives (because of lethality), so trying to strike a balance between Se and Sp does not make sense. Rather, you need a cut-point that guarantees whatever level of Se you deem necessary (IMO). HTH.
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For example if I am comparing Rt PCR ct value with the RDT test positive or negative for Covid 19.
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Hi Pragyan
following the delta/delta Ct procedures, try this file
all the best
fred
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My research has the dependent variable that cannot have data prior to 1995 as the existence of the variable commences since that year only. So I now have 22 annual time series data. I am testing for the determinants against 6 other variables which are strongly related to the dependent variable. My test results indicate the following:
Unit root test (DF-GLS) - stationary
ARDL F-bounds test - Cointegration exists at a 1 per cent significance level
ARDL long-run cointegration - All explanatory variables are statistically significant
ECM - Cointegration across all variables shows statistical significance
Diagnostics tests - The results validate the null hypothesis assumptions of no autocorrelation (Serial Correlation LM Test - Breusch-Godfrey), the existence of homoscedasticity (Heteroscedasticity Test: Breusch-Pagan-Godfrey), normal distribution of the residuals (Normality Test: Jarque-Bera), and a correct model specification (Ramsey RESET).
Parameter stability - CUSUM and CUSUMSQ plots are within the critical lines of 5 per cent.
Can I go ahead with this econometric analysis or is there still a possibility of a spurious regression with such data? Please share your thoughts and suggestions. Are there are any studies on similar lines i.e. small sample data for ARDL estimation that I can refer to?
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For classic frequentist analysis (i.e, panel model & time series); depends on the hypothesis.
There is no rules of thumbs casted on stone, that a certain number of independent variables should be the minimum in exploring the links between dependent variables and independent variables. But having 22 observations are enough and have high degree of freedom to conduct the study. However, avoid including too many variables in the same model (i.e 7 variables) may cause multicollinearity.
You can also use Bayesian estimate, in which the small samples sizes can be simulated to larger sample by MCMC....
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My ARDL model with no trend and no intercept passed every thing like, long run co integration, sign and significant of coefficients and all diagnostic tests. What will be the interpretation of “no trend and no intercept” in ARDL model? I need comments or any paper in similar context. Thanks
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We still need more opinion on this matter
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Systematic reviews and meta-analyses of diagnostic test accuracy usually pool sensitivity and specificity estimates. However, for clinical/patient-care purposes, positive and negative predictive values (PPV and NPV) are arguably more useful.
Is including PPV and NPV as meta-analytic pooling outcomes sensible? One potential argument against this that I can think of is that these measures are influenced by disease prevalence in the studies that report them (unlike sensitivity and specificity). However, a potential counter-argument to that is some sort of stratification by pre-specified prevalence ranges can be performed.
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You are right that the prevalence of a condition in the tested population will have an important influence on the +PPV and –PPV. However, you can calculate another informative statistic – the clinical likelihood ratio (positive and negative). This is the effect of the test on the likelihood of the person having the condition. It simply compares the odds of the condition pre and post test. A positive test with a clinical likelihood ratio of 10, for example, means that the condition is ten times more likely to be present if the test is positive. And a negative likelihood ratio of 0·1 means that the test is only a tenth as likely to be present if the test is negative.
Likelihood ratios are based on odds, so they generalise across prevalences in a way that predictive values don't. This might help you deal with the prevalence effects.
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Hello Friends,
I want to prepare a Two Tier Diagnostic Test for Misconceptions of  Cell Division and Nutrition at XI grade Science students. I did the item analysis of these concept. I want information about preparation of two tier diagnostic misconception test. I would like to accept your suggestions for my further work. If any one has authentic information regarding the validation of this two Tier diagnostic test please share with me.
Thank you
Regards
Rajendra Chavan
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When preparing a diagnostic test for misconceptions among students about cell division, it must be taken into account that the test paragraph measures the definition of the concept, the distinction of the concept from related concepts, and the application of the concept in a new situation to ensure the misunderstanding of the students
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Is it by comparing the values of sensitivity, specificity, positive predictive value and negative predictive value of each test?
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It is possible. I think the attached article may be useful for you.
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I want to compare the performance of two diagnostic tests (binary yes or no) on the same population, first, I want to compare if there is a significant difference in their ability to predict my specific outcome/disease and second, I want to identify if there is a significant difference in the distribution of different characteristics between the positive diagnostic test results (both numeric and binary). Can this be done on SPSS? and if so under which test?
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For the first question, it is mainly a matter of "True and false positive rates". Have a look to the paper in the link as an example, but you can search more specific papers.
For the second question, check the previous answer.
Good luck
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I am aware of a study that has compared the use of citrated and defibrinated sheep's blood in microbiological media ( ), but can anyone tell me if sheep blood prepared in Alsever solution interferes with any microbiological diagnostic tests?
Thank you for your input.
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I recently got indications of beta-hemolysis on blood agar made with sheep's blood in Alsever's solution with bacteria that should be gamma-hemolytic. The plates were fresh, as was the blood.
Unfortunately, I won't have fresh blood again for a few months, so I may not be able to verify these results for a while.
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I am conducting Diagnostic test accuracy meta-analysis.
For publication bias, I want to conduct Deek's funnel plot test.
I am using R software with mada ant meta packages.
However, I found that there is no r package for Deek's funnel plot test.
Deek's test seems to be available only in STATA.
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library(meta)
mymeta = metabin(TP, TP+FP, FN, FN+TN, sm = “OR”)
metabias(mymeta, method.bias = “deeks”)
funnel(mymeta, y.axis = “ess”)
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Hello Colleagues
Trust you are all fine,
Please I have been struggling for days on estimating some spatial diagnostic test on my model. The stata command I am using is spatdiag command.
I am working on a panel data with 759 observations. However, I am using a 33x33 matrix. Each time I run the spatdiag command after the regression model, I get this error "Matrix W is 33x33, regression has been carried out on 759 obs. To run -spatdiag- weights matrix dimension must equal N. of obs"
I would be glad if I can get a comment on how to go about. I look forward to hearing from you
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@Pure tone threshold audiometry
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Sensitivity, specifically, PPV and NPV are they important only for screening tests or also for diagnostic tests.
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Thank you for your valuable information.
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In order to establish new Rapid Diagnostic Test, I need the mentioned Anti-COVID-19 antibody urgently. Has anybody can assist me to prepare it?
Thanks in anticipated
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I'm following the best answer.
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Hi. I'm running a multivariate GARCH model. When I introduce un AR(1) in the mean equation, all the diagnostic tests on Standardized Residuals and squared residuals are perfect (Q-Statistics, Hosking's Multivariate Portmanteau test, Li and McLeod's Multivariate Portmanteau tests), however, the AR(1) parameter is not significant. When I remove the AR(1), Hosking's Multivariate Portmanteau Statistics are significant (autocorrelation problem is both residuals and squared residuals). It's a surprising result!!! especially that Hosking and McLeod's portmanteau tests give different results!
Do I have to keep the AR(1) even when it's not significant?
Thanks in advance for any advice.
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Thank you Leonardo Mataruna for your help. I'm not really familiar with R, I'm using Rats, STATA, EVIEWS. I'll take a look at the reference you sent me.
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What sample size formula should I use to compare the three area under the roc curve (AUC) obtained from three diagnostic methods from similar sample?
MedCalc software gives us the sample size to compare the two AUC. My question is about comparing more than 2 AUC.
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We wish to compare a new rapid test to gold standard PCR for a disease with a population prevalence of 1-. 2 %. Samples will be tested by both methods concurrently (paired analysis). We will be testing symptomatic people (not the general population) and will want a sample size which will enable detection of approx 5-15% sensitivity difference with 80% power - if you can tell me how to do this in STATA (14) I'd be grateful.
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Sensitivity and specificity are calculated on separate populations : those with and without the health state of interest. In each case, the appropriate sample size is for a single proportion. In Stata, this is power oneproportion. Your null proportion is the sensitivity/specificity of PCR, and your alternative proportion is the proportion that would represent a clinically significant difference. For example, calculation for 90% power to detect a test that's 92.5% sensitive or worse compared with PCR which is 97.5% sensitive.
. power oneproportion .975 .925, power(.9)
Performing iteration ...
Estimated sample size for a one-sample proportion test
Score z test
Ho: p = p0 versus Ha: p != p0
Study parameters:
alpha = 0.0500
power = 0.9000
delta = -0.0500
p0 = 0.9750
pa = 0.9250
Estimated sample size:
N = 166
Note that 80% power is not acceptable in real life settings. It guarantees a 20% chance of failure to detect! For real health research, 90% or 95% power is required – someone could die if you come to the wrong conclusion!
If you take a general population sample, a relatively small percentage will be true cases, so in order to get an adequate sample size for sensitivity you will have a sample that is far bigger than you need for specificity.
The solution is to sample for sensitivity separately, and to use identified cases to build up your sample quicker than you would with a general sample.
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Dear seniors and researchers,
I am currently performing ROC curve analysis in SPSS v22 to asses the diagnostic value of several laboratory tests to predict parasite density status (whether hyperparasitemia or not) of patients with malaria. I found that investigated parameters I examined showed a high AUC while the p value is not significant. Regarding the circumstance I faced, what are possible factors that might influence the significance of AUC in ROC Curve analysis? Does the number of samples contribute to this finding?
Thank you
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I would guess your standard error is very large? Perhaps because your sample is too small?
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Could you help me out how to compare the sensitivity, specificity, PPV and NPV of the same diagnostic test (vs. a gold standard) on two different samples/populations?
Presumably I should use chi square test, but I don't know how, as these are all derived parameters, not percentages. Preferably I would use SPSS.
Answers are much appreciated!
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Dear Mihály Károlyi,
Perhaps,the thread started by Katrin Skerl "How can I compare 2 AUC values of different parameters of the same sample group (ROC analysis)?" on May 11, 2015 can help you.
Best regards
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I am planning to condacting a diagnostic accuracy systematic review and meta-analysis. In order to calculate pooled sensitivity and specificity. I need to know the number of TP, TN, FP, and, FN which are rarely reported in published articles. so how can I calculate the number of TP, TN, FP, FN from just the sample size, sensitivity, and specificity reported in a study?
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Dear Binyam Girma Sisay:
That is a usual problem we usually face when we intend to perform a meta-analysis of diagnostic tests. Though STARD initiative encourages for providing 2x2 table, many papers do not contain such simple and valuable information.
First option could be asking to the authors of the different papers to provide 2x2 table. Unfortunately, my experience is that many of them do not answer.
To perform the 2x2 Table, you would need: sample size, values of sensitivity and specificity and the prevalence of the disease. Prevalence can be calculated thorough predictive values.
The reference posted down provides a simple calculator to estimate 2x2 table. You only must fill the “Diagnostic Test” table and all the valuable information is automatically provided in “Observed Contingency Table”.
It also gives you the accuracy of the estimates (essentially, when it delivers whole numbers without decimals, the accuracy will be good. The obtained accuracy is provided in “Fisher Exact Test” table.
Best regards.
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I am planning to conduct a systematic review comparing 2 diagnostic tests, being one of them the conventional reference standard to diagnose the disease. Since it has been reported that this test might have a fairly low sensitivity in some cases, it could be relevant to compare it with another test.
However, most primary studies comparing these 2 tests are not assessing the sensitivity and specificity, only the agreement between them.
Would it be possible to conduct this review with this kind of data?
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I appreciate everyone for their help! It was very enlightening and helped me reach a decision with my research group.
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I have a mix order of integration after doing the unit root tests for all my variables. That is I(0) and I(1), I can't run panel Johansen co-integration or panel least square fix/ random effect because of this. it would violate the condition or assumption underlying them. The suitable approach is panel ARDL using Eviews-11. But i can't find any diagnostic test except for Histogram normality test. I don't know how to carry out serial correlation LM test, heteroscedasticity using the panel PMG/ARDL method on Eviews-11. Can i run the diagnostic test using the ordinary regression? and still use ARDL? PLEASE HELP.
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Quantitative
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I have a short panel of data in my analysis.(286 cross sections, over 3 years) I'm wondering what tests should i conduct to determine the validity of my model??? Are there any specific assumptions that i should check? I also tried running the unit root test however, e views wont let me run the test due to the short time period.
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How would you perform heterogeneity assessment (chi-square) for diagnostic tests accuracy.
if my question about the performance of a computed test and the outcomes are in percentage.
i would want to assess the heterogeneity of different studies in the type of computed test and it's accuracy.
should i use chi-square? if yes, how to perform it while the outcomes are in percentage or proportions
thank you
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Related to your query, I suggest you follow https://online.stat.psu.edu/stat509/node/152/
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I want to get the total Covid-19 gene number from the 50ml patient sample. I used the Sansure kit for RT-PCR analysis. In RT-PCR analysis, we only get the Ct value, not the gene number, or copy number of the virus. How can I get a copy number of the virus from the sample?
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You need to construct a standard curve of at least five 10-fold serial dilutions of a known standard with predetermined copies/ml or IU/ml. After that it is a simple math to determine the concentration of your Ct from the curve. However, as per my knowledge the RT-qPCR COVID-19 test is qualitative in nature not quantitative. Otherwise, the kit manufacturers would have provided known standards for construction of a standard curve.
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I looked for the daily number of samples being tested in "Our World in Data" "R" or "WHO"'s publicly available databases. I got a maximum of 80 countries data from "Our World in Data". However, WHO reported COVID-19 from more than 200 countries or territories and I need all the country's COVID-19 tests data. Are there any publicly available sources? Suggestions for the sources of the individual countries are very welcome.
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Following...
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I and my team, have planned to conduct a systematic review and meta-analysis that aimed to study the performance of a diagnostic test. We planned to use the QUADAS-2 tool to assess eligible studies for our systematic review. We need your help on how to utilize this tool.
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@Mohamed Abdel-Mabboud, thank you for the advice.
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I am new in ARDL estimation model.
its been required from me to do some diagnostic tests for the ARDL selected model.
one of these diagnostic tests is the adjusted R2. i am using Eviews 11 for my analysis. but i dont know what is the R2 means ? and how to find it, and what is the critical value for it? what is good R2 , and what is the bad R2 of the models,
i appropriate your answers and advice
thank you.
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Hi Mohammed,
I hope you are doing fine.
I agree with Dr Frain, considering the fact that you are new to the estimation technique, you need to consult someone close to you to take you through the entire process including the condition under which you can even use the ardl else the whole exercise you are undertaking will be very hectic for you.
Besides, The coefficient of determination (R squared) comes with the estimation of the ardl model (conditional ols) before the short and long run results.
I will also suggest you get good econometric text books and read for more insight. Good luck
Thank you
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Dear Reader,
Before I ask my question I would like to mention that I am NOT so good in econometrics and would request to please consider this, before answering, I will be very much Obliged.
Context:
I have used a panel Data for 6 years with 3 Dependent Variables and 3 Independent Variables. Hence, I have developed 3 Models as follows:
Dep. Var 1 = Alpha + B1 Ind.Var1 + B2Ind Var 2 + B3InvVar3 + error term
Dep. Var 2 = Alpha + B1 Ind.Var1 + B2Ind Var 2 + B3InvVar3 + error term
Dep. Var 3 = Alpha + B1 Ind.Var1 + B2Ind Var 2 + B3InvVar3 + error term
I have 6 years of Panel Data and applied GMM also included results of Fix. Eff and Rand. Eff Models for comparison.
Issue:
My research has been challenged that I have used "Insufficient Tests" (It is not mentioned whether they are referring to Data Diagnostic/Normality- If these terms are different, please help me understand) so I have assumed they are referring to Normality Testing. As far as I know, Data normality is not an issue for Panel Data. However, Just to be sure, I have applied regression and VIF Tests to check for multicollinearity all values are well within an acceptable range (All Correlation Coefficients are below 0.05 and Mean VIF value is1.26).
Question:
Since multicollinearity is not an issue, What other tests are required for panel data? I believe there shouldn't be any since I am using a panel data set. If so How can I justify my assumption?
P.S I would sincerely appreciate it if you could please explain what the hell is the difference with a Diagnostic test and a Normal Distribution test? Please help me justify my data set.
I will be very much grateful for the assistance and hope to return the favor someday.
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Dear Umair,
"The Oxford Handbook of Panel Data," edited by Badi Baltagi, Oxford University Press, 2015 shows a number of tests you can do. Some of them listed in the index include constant-correlated effects, cross-section dependence, incidental parameters, moment conditions, noncointegration, overidentifing restrictions, residual based, sphericity, state dependence, unit roots etc.
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Hello everyone,
what are some ideas for developing cheaper test kits (it can be PCR-based or others) for COVID-19 diagnostics?
For those experienced in such test kits development, what would be the major challenges in terms of cost and how to overcome it?
Any tips for cutting cost would be much appreciated. Thanks!
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The cheaper kit can be made on the basis antigen-antibody reactions. This technique requires a number of samples available to the developers.
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Hello everyone,
I am running a logistic regression analysis where the outcome is a status for disease (positive, negative). The diagnostic test for the disease has a specificity of 100% and a sensitivity of 80%.
Can anyone tell me how to account for these spec. and sens. figures in my regression?
Cheers
Dusan
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The specificity and sensitivity do not seem to affect the regression analysis. Once the regression is done it cannot account for the specificity and sensitivity of the dependent variable.
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Hello, in order to do my end of study work in physiotherapy, I was wondering if it was possible to use SEBT as a diagnostic. And what would be the most relevant articles on this subject
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Please find attached an interesting paper:
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Usually, RT-PCR or qPCR based test assay is performed for the diagnostic tests, which seems a little bit time-consuming. On the other hand, some rapid tests based on IgG-IgM interaction have been mentioned, which probably is not a specific test for coronavirus COVID-19. That is why, finding specific biomarkers (like antibody, protein or others) could be a very useful means to produce effective testing kits especially microdevices like lateral flow immunoassay strip or electrochemical sensor (lab on a chip). Can anybody, please, give me specific information or reference regarding this issue?
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To conduct a study with people with severe/profound intellectual (and multiple) disabilities, it is important to ensure that the participating persons actually meet these sample criteria. -> From an international perspective, what are the most common ways/assessments/approaches to achieve this? Based on a little literature review, there are three possibilities:
  1. Questioning the direct support persons about their estimation within an open interview.
  2. Questioning the direct support person with a (semi-)structured interview/questionnaire (e.g. Vineland Adaptive Behavior Scales -> Arthur-Kelly et al. 2017; Mechling & Bishop 2011; Lancioni et al. 2015).
  3. Direct testing (e.g. Bayley Scales or Kent Infant Development -> Nijs et al. 2016).
(In terms of an assessment/questionnaire, it would be great if there were an English and a German version.) I am really interested in your feedback and your experiences.
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I'm working on rehabilitative programs to promote adaptive behaviors in individuals with severe to profound developmental disabilities and multiple disabilities. I usually use the VABS to assess the severity of the disability and found it valid and reliable. Accordingly, I feel that the VABS constitutes a solid way to evaluate multiple disabilities. I wonder whether a german version is actually available in the literature.
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I've recently completed a receiver operator curve analysis, where the goal is to identify the optimal cutpoint on a diagnostic test. We have two versions of the diagnostic test. The original test, and one scale where one of the six items was changed with alternate wording.
For the original scale, the AUC was .88, and the cutoff of two looks best (.82 sensitivity, .84 specificity, .66 youdens)
For the alternate scale, the AUC was .90, and the optimal cutoff looks like it was 1 (.93 sensitivity, .76 specificity, .68 youdens)
So, we are trying to decide if it is ultimately worth changing the wording of one of six items (i.e., selecting alternative scale), which looks like it may change the cutpoint of the overall test if adopted.
So, some questions:
1. Is a 2 percent increase in AUC important? Anyone have any guidelines for what constitutes an important change in AUC?
2. Would you take an 8% drop in specificity (.84 to.76), if you have an 11 percent increase in Sensitivity (.93 vs. .83)?
I look forward to your replies!
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Choice of optimal cut off depends on the objective of your study. Do you require more sensitive or specific test? If both sensitivity and specificity are equally important, you should use cut off with 0.66 youdens with good sensitivity as well as specificity. Anyway, in both the scenarios, AUC is good because it is >0.7. there are many other aspects are also involved while choosing cut off such as cost. Following article may help you.
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i am working on panel data. it is perfectly balanced panel, t>N , and all variables are I(1)
moreover, they are cointegrated. now my question is , which diagnostic testing i can use in this situation ?
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1- If you run Traditional Panel, then the following diagnostic tests have to run:
Hausman Test, Normality Test, Multicollinearity Test, Hitroskedasticity Test, Serial correlation. Linearity Test.
2- If you run panel ARDL, then in addition to the above tests, you have to run Unit root test.
All the best.
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Dear All.
I trying to build a model and conduct cost effectiveness analysis of diagnostic test. I have sensitivity data of different diagnostic test like ultrasonography, MRI, Tomosynthesis. What is the most appropriate way to calculate probabilities from sensitivity data.
Thanks
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How would you like to define 'most cost-effective' for this? Do you have a willingness to pay in mind?
I would like to warn that if you are working with a group of women with a positive screening mammogram, then incidence and prevalence numbers for the general population are not relevant.
Are you data on sensitivity and specificity based on a population of women with positive screening mammogram? Might the different tests even be performed at the same time on the same women?
At this stage, it may be best to read up on cost-effectiveness in general.
There are books on the subject, e.g.:
Methods for the Economic Evaluation of Health Care Programmes (Oxford Medical Publications)
by Michael F. Drummond , Mark J. Sculpher , et al.
Cost-Effectiveness in Health and Medicine 2nd Edition
by Peter J. Neumann, Gillian D. Sanders, et el.
Decision Modelling for Health Economic Evaluation (Handbooks in Health Economic Evaluation) 1st Edition
by Andrew Briggs, Karl Claxton, Mark Sculpher
Decision Making in Health and Medicine: Integrating Evidence and Values
by M. G. Myriam Hunink , Milton C. Weinstein, et al.
ISPOR focuses on drugs, which works a bit differently than evaluation of tests, but they do make some good background readily available:
While old, I think that I should still recommend chapter 10 from my dissertation concerning specific aspects of breast cancer screening evaluation:
I also uploaded it to the ResearchGate web site:
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The signs and symptoms of malaria are quite nonspecific - one of the few real distinguishing features is its periodic febrile paroxysms. Hence, what should be the basis for administration of pre-referral artesunate? Is just a history of periodic fever enough? Should you just use a Rapid Diagnostic Test (RDT) to confirm the diagnosis?
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Please, always try to confirm the diagnosis by microscopy, RDT or PCR. Never give Artesunate alone, always in a combination therapy, due to the emergence of possible resistance to artemisinins.
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I am currently designing a trial to evaluate the performance of a diagnostic test by comparing it against an established standard. The outcomes are ordinal in nature, integer values ranging from 0 to 4. I understand that for binary outcomes, estimating sample size is very straightforward, however i have not found simple explanations on how to calculate the required sample size for ordinal multi class comparisons.
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As the goal is to establish inter-rater agreement between two instruments, I suggest the use of Weighted Kappa statistic. For detailed justification see:
For sample size calculations and pre-calculated sample size tables, refer to
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I have very little experience in ststistics so my question is very basic I am testing a new assay for detecting B12 deficiency, active b12 compared to the current test in use total B12. I Am analyzing the two tests on the same patients and have 357 patients in total. I am using spss and excel so what would the most appropriate test to use.
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Dear Aileen,
You may also want to check the NICE guidance for the Diagnostic Assessment Program.
Please check the Methods section of the programme manual (link to pdf at the bottom of the page).
Practical applications are published in HTA journal:
Good luck!
Isaac
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What I heard from my neurology colleague, that migraine is usually a diagnosis by exclusion. That means if your physician suspects you have migraine, other investigations will be done to exclude that you don't have another diagnosis.
Why is it so until this time of high tech and advanced medical diagnostic tests?
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Dear Mohamad-Hani, maybe the following papers will help you on the subject:
Kunkel RS. Migraine aura without headache: benign, but a diagnosis of exclusion. Cleve Clin J Med 2005;72(6):529-34. https://mdedge-files-live.s3.us-east-2.amazonaws.com/files/s3fs-public/issues/articles/content_72_529.pdf
Tinsley A, Rothrock JF. What Are We Missing in the Diagnostic Criteria for Migraine? Curr Pain Headache Rep 2018;22(12):84. https://link.springer.com/article/10.1007%2Fs11916-018-0733-1
Yang H, Zhang J, Liu Q, Wang Y. Multimodal MRI-based classification of migraine: using deep learning convolutional neural network. Biomed Eng Online 2018;17(1):138. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6186044/pdf/12938_2018_Article_587.pdf
van der Meer HA, Visscher CM, Vredeveld T, Nijhuis van der Sanden MW, Hh Engelbert R, Speksnijder CM. The diagnostic accuracy of headache measurement instruments: A systematic review and meta-analysis focusing on headaches associated with musculoskeletal symptoms. Cephalalgia. 2019 Apr 18:333102419840777. doi: 10.1177/0333102419840777. [Epub ahead of print]. https://journals.sagepub.com/doi/pdf/10.1177/0333102419840777
Šukalo A, Merdžanović E, Alic A, Vrabac-Mujčinagić M, Alibašić E, Janković SM. Screening general practice patients for migraine without aura: construction and validation of the Balkan Migraine Screening Questionnaire (BMSQ). Med Glas (Zenica). 2019 Aug 1;16(2). doi: 10.17392/1021-19. [Epub ahead of print]. https://www.ncbi.nlm.nih.gov/pubmed/31127712
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Is a skin biopsy, stored in saline, refrigerated one month at +4 °C, still expected to be suitable for a bacterial PCR-DNA analysis?
How fast does the DNA break down, and does it depend on which organism the DNA is from?
The purpose is diagnostic testing, so what is needed is a "yes/no" finding of the pathological bacteria borrelia burgdorferi spp. (causing Lyme disease).
It doesn't matter whether additional bacteria will grow (due to contamination or originating from the microflora of the skin).
Since (late) cutaneous Lyme disease, causing acrodermatitis chronica atrophicans, is a slow-growing infection, and borrelia burgdorferi spp. are very slow-growing bacteria, only a small amount of bacteria can be expected to exist in the sample, if present.
The question is whether the possible borrelia burgdorferi DNA is still intact enough, or is expected to be too broken down for analysis.
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I think it depends on the reason for doing the analysis. Some organisms will continue to grow even at low temperatures so the relative abundance of all organisms will change with storage. If it is just to show that an organism is present then pcr only needs short fragments of dna so degradation will not do any harm ( small fragment dna may even amplify better than large dna as they melt easier in the early cycles). The main issue is whether large amounts of other organisms grew when they may produce enzymes that digest the dna of the organism that you are looking for since the skin sample will not have been sterile
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I'm aware that a few millennia ago, humans have observed that ants were attracted to urine which led to the practice of tasting urine for diagnosis. Would we see ants getting a comeback and work with physicians as an effective and cheap alternative for chemical tests?
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Joshua;
When using sugar bait traps for general ant collecting I use 30-40 g/L solution. The threshold for diagnosing diabetes is about 0.126 g/L. It would be a simple lab experiment to determine the detection threshold of some of the nectar-gathering species of ants. In the southwestern US where I live there are an abundance of species that do that. Members of the genera Prenolepis, Myrmecocystus, Formica and Lasius are common and are readily maintained in the lab...and are commonly found in sugar bait traps.
See if you can find some data on that question. It's only a matter of personal curiosity because Nemanja is correct in his assessment.
Best regards, Jim Des Lauriers
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Dear research community,
After running several tests (including F-Test, Breusch and Pagan’s (1980) Lagrange Multiplier (LM) Test and Hausman (1978) Test) I came to the conclusion that a fixed effects model is the most appropriate one for my data.
To ensure that the estimates are efficient I run a couple of diagnostic tests.
Following a modified Wald statistic the idiosyncratic errors seem to be heteroskedastic. However there is no evidence of serial correlation following the test proposed by Wooldridge (2002).--> here the sub-question if it is correct to run the command "xtserial" after:
egen countrynum = group(Country)
xtset countrynum
xtset countrynum Year, yearly
xtreg DV IVs, fe
The main questions is whether I can make use of robust (sandwich) estimators to correct for heteroskedasticity even though there seems to be no autocorrelation problems?
Thanks for your help in advance.
Best
Frederic
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Thank you for your reply Dilesha,
actually I came to the conclusion that the non-clustered robust estimator is known to be invalid for panel data. I have used the clustered standard error as it is, itself, also robust to heteroskedasticity.
Apparently, if you, incorrectly, give Stata the command -xtreg DV Ivs, fe vce(robust)-, Stata will actually disobey this command and substitute vce(cluster country_num) without even adverting to it. As such, results are identical for both commands.
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Hello all
We have conducted a meta-analysis on diagnostic test accuracy studies that their method was similar to comparison method studies (We had data on index test, Reference test and the outcome was limits of agreement between the two methods to calculate precision). Simultaneous assessment of the two methods and setting were the two items important for us. The analysis was done with STATA 12.
For assessing the quality of studies, we have modified some of QUADAS-2 checklist questions but reviewers said that we must use GRADE approach too.
We have studies the GRADE guidelines. Unfortunately, we don’t understand how to do it and how to make specific questions in GRADE?
Any help would be greatly appreciated
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Dear Sara
I hereby send you 3 articles dealing with the how to use the GRADE approach in diagnostic studies - and I agree with Gopalakrishna et al that it is a challange bu doable
Gopalakrishna G, Mustafa RA, Davenport C, Scholten RJ, Hyde C, Brozek J, Schünemann HJ, Bossuyt PM, Leeflang MM, Langendam MW. Applying Grading of Recommendations Assessment, Development and Evaluation (GRADE) to diagnostic tests was challenging but doable. J Clin Epidemiol. 2014 Jul;67(7):760-8.
Singh S, Chang SM, Matchar DB, Bass EB. Chapter 7: grading a body of evidence on diagnostic tests. J Gen Intern Med. 2012;27 Suppl 1(Suppl 1):S47-55.
Schünemann HJ, Oxman AD, Brozek J, et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies. BMJ. 2008;336(7653):1106-10.
Best Carsten
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doctor called it as an allergy & i got anti allergic injections but of no use .. i am much worried .. kindly suggest some diagnostic tests etc ?
TIA
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The Clinical picture seems to be urticaria. May be contact urticaria ( if localized only on the distal arms) or other causes if generalized. In any case firstly treat it by anti-histamine drugs and after almost two weecks of suspension consult an allergist and make the appropriate investigation on ethiology of hives.
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(assume the tests are applied to the same population - if that matters)
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Dear Adrienne,
that could actually happen. This can occur due to a choice of cut-offs for the two diagnostic tests.
Sensitivity and specificity depend upon a cut-off set by the diagnostician. The ROC (and thus the AUC) does not depend on your choice of a cut-off, because it is a depiction of the tests accuracy independent of a cut-off.
I find the following website very instructive in order to illustrate what I just wrote. It becomes clear, if one plays a little with the settings there.
Best,
Daniel
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Hi,
I am looking to find a method to compare the likelihood ratio for two non-binary diagnostic tests, performed on the one group of patients.
Specifically, I have the results of two different antibody staining results that have 4 possible scores (e.g. 0, 1+, 2+, 3+). I then compared the results to the gold standard, which is a binary result (e.g. FISH +ve, or FISH -ve). Using this information, I was able to generate a likelihood ratio and 95% CI for each score.
I found a paper on how to calculate interactions between two estimates (e.g. risk ratios or LRs) by analyzing the values on a log scale to generate a z-score [Altman and Bland (2003). Interaction revisited: the difference between two estimates]. Would this be valid on my sample? I think there was a mention that the test only works for independent measurements, and should not be used on two estimates from the same patients. The two antibodies are independent tests, but does it make the test invalid if I compared their results on the same set of patients?
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As the biology of a patient has some influence on each of your results, those estimates are in a way dependent on each other. Let's say a subject has low health - this will cause his antibody results be lower in both results compared to someone who is healthy. I assume that the test you used depends on random samples (at least) because of that matter.
My suggestion is to split your subjects sample randomly and use one part for diagnostic test 1, the second part of the sample for test 2. Then compare those results as you did. (be sure to have a large enough sample and to split the sample randomly or else the effect will be biased)
I hope this helps you a little.
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For disease X there exists no real, modern gold standard. The disease can be diagnosed with 4 diagnostic tests (A,B,C,D), all leading to a yes or no answer (binary).
I have a data set with results of those 4 tests of 100 persons. Not every test has been run in every person. Now I want to calculate the sensitivity of test A.
In order to receive a relative sensitivity of test A (relative to B,C,D), could I
- summ up the number of all the positive test results of B,C and D of those cases (I believe all positive, "BP"), in which test A has been run
- and divide this number by all positive results of test A?
Is that legit?
Furthermore I want to to calculate the sensitivity of test B (summ up the number of all the positive test results of A,C and D of those cases, in which test B has been run and divide this number by all positive results of test B), C (same procedure) and D (same procedure).
Is that a legit way to compare relative sensitivities in my data set? Is there any literature confirming / strengthening this procedure?
Looking forward to your answers!
Thank you very much!
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Why not use culture or PCR as a gold standard for calculations?
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I wish to calculate pooled sensitivity and pooled specificity for a diagnostic test as part of a systematic review. Is there a journal article, book or crystal ball that provides STEP-BY-STEP instructions on doing this? One that is easy enough for a statistical moron like myself to understand.
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Sensitivity and specificity, positive and negative predictive values, and positive and negative likelihood ratios are common indicators of diagnostic test accuracy. However, as illustrated by Glas et al., these paired indicators are not valid for comparing multiple diagnostic tests especially if one test was not superior to the other in both indicators. In addition, these parameters cannot be analysed in the traditional framework of the network meta-analysis or indirect comparison models. Alternatively, you can use the diagnostic odds ratio (DOR) as the effect estimate for your Meta-analysis. DOR is the single global indicator for diagnostic test performance and is defined as the ratio of the odds of positive test results in subjects with the disease compared to the odds in those without the disease. This effect estimate can be calculated according to the following equations.
To calculate the DOR
📷
To calculate the SE of Log DOR
📷
To calculate the 95% CI of the Log DOR
CI=InDOR ± 1.96 SE (InDOR)
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Hello everyone! Is there a formal way (such as Brooks-Gelman-Rubin (BGR) statistics or or Geweke diagnostic statistic) to determine convergence of Markov chain Monte Carlo (MCMC) if one estimates an econometric model using the latest bayes command in Stata 15? Stata 15 seems to only rely on graphical methods to determine convergence of the MCMCs but I am also interested in formal tests. However, I have not found anything yet on the same in Stata 15. Any help will be appreciated. Thank You!!
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Hi Niladri, thanks for your suggestion. I think I felt lazy to do the coding... Based on your suggestion, I will try to do it. In case I do not succeed in Stata, I will just look to R or WinBUGS! Thank you.
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Good Day All,
I have new molecular-based diagnostic test which focuses on detecting SNPs in a 10 different genes. The DNA diagnostic test is actually probe based so it is looking at relatively small sequences (e.g. 16-24 base pairs long).
I would like to assess the test's overall accuracy as well as do an in-depth analysis on a set of well characterized clinical samples (perhaps a n~100-200) and am curious about thoughts on how the appropriate statistical test. The test is not a true sequencing 'platform' but it is rather similar.
In my opinion, there are several layers which I can assessed accuracy
  1. At a macro level, if any nucleotide is wrong the test is wrong. Run 100 tests and calculate test sensitivity/specificity and use McNemar's Test (crudest).
  2. At a micro level, look at each nucleotide (e.g. truth vs test) for the small sequences (therefore at a minimum 10*16=160 pairs per 'run') and then run the McNemar's on each run (so 160 pairs and 100 runs)
I am sure there are many other permutations so I am curious as to the appropriate statistical techniques to evaluate the accuracy, sensitivity and specificity of this test?
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Hi Ajit, yes that is the traditional method for determining a test's sensitivity and/or specificity (also known as true positive rate/recall and true negative rate, depending on the discipline).
I do plan on calculating that and I was looking for a more rigorous statistical test to assess the new test's performance.
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If I have an option to choose between two diagnostic tests with the following qualities:
A: conf.level of 95% and error of +/- 3.5%
B: conf.level of 99% and error of +/-5%
Both have more/less the same sensitivity and specificity.
Which of the upper two should I use?
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confidence interval of 99% represents close to 3 standard deviations, while 95% is 2. An 99% CI with 5% error is about 3.3% at 95% CI, which is within rounding error (all figures provided are severely rounded) of the first test.
I am assuming that the figures quoted are for the test as used (including the fixed number of sub-samples or replicates used per sample).
If CIs are based on different numbers of replicates then it may be useful to work backwards to find the precision for comparing systems - one system may have less precise measurements but compensate by taking more measurements to achieve the same level of confidence in the result.
If as well as confidence intervals the sensitivity and specificity are similar then the choice is likely to come down to practical considerations and require a benefit/risk analysis. Is there a different in capital cost, running cost, throughput, scalability etc.
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Cancer drug hobbled by diagnostic-test confusion
A landmark cancer drug was approved last year to target tumours with specific mutations, no matter where in the body the cancer first took root. Yet physicians are struggling to identify which patients are likely to respond to the treatment, because of limitations in diagnostic tests to pick out molecular markers that indicate susceptible tumours.
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Decision making regarding therpeutic strategies in cancer patients is complicated. How you can identify the mutations, how much you rely on the results, heterogeneity of tumoral cells, cost benefit of the treatment, insurance coverage, patient cooperation and many other factors.
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I want to perform a meta-analysis on diagnostic tests. So, I will be pooling sensitivity and specificity. Some studies do not report the table that has TP, TN, FP, FN. I am wondering if there is any method (equation) that I can use to get these values or if there is any website/program that can do that rapidly.
It is worth mentioning that in these studies, the sample size, sensitivity, specificity and sometime CI are reported.
I would really appreciate any help from you
Mahmoud
Thanks
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Following
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Your suggestions would be highly appreciated.
Best wishes,
Socheat
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Thank you VERY MUCH Zoltan.
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Hi all, I have few questions here:
1. If using one year time frame for households data, is it not considered as cross-section data? Because someone told me that household data is a micro data, if I use it to analyse on aggregate result, it is not cross-section.
2.I read from many articles that used households data especially on household expenditure survey data, but they did not mention about diagnostic tests. What kind of common or MUST diagnostic tests should be performed? Especially for OLS.
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Following
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hi, ive tried with sci-hub and ask at the author, unsuccesfully.
This is the article:
Thank you in advance if u could help me.
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molecular diagnosis
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NGS sequencing of a clinical exome gene panel (TS1) and analysis limited to 8 genes associated with PKD (BICC1, LRP5, NOTCH2, PKD1, PKD2, PKHD1, PRKCSH, SEC63). If the potential candidate variant is detected in PKD1 gene (it has 6 highly homologous pseudogenes) then it is confirmed by long-range PCR of selected region, followed by exon-specific Sanger sequencing. If the variant is detected in any other gene it is confirmed by classic PCR followed by Sanger seq.
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There are different types of diagnostic tests for Alzheimer's disease. as far as I know, one of them is positron emission tomography (PET) scanning. what exactly does cause the sign of the disease on a PET image? what percentage of Alzheimer's disease can be diagnosed by this procedure? do prescription drugs affect these signs after we take the test again?
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Hi,
Currently, International Working Group (IWG) and National Institute on Aging Alzheimer's Association (NIA-AA) has proposed several biomarkers as diagnostic criteria which includes cerebro spinal fluid (CSF) amyloid beta (Aβ) and tau, atrophy on MRI, glucose metabolism on [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) and fibrillar Aβ burden on amyloid PET.
There are few regions, which acts as definite Hallmark regions for AD. You could refer to below attached papers.
(11)C-PIB-PET for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI)
Structural MRI and Amyloid PET Imaging for Prediction of Conversion to Alzheimer's Disease in Patients with Mild Cognitive Impairment: A Meta-Analysis
18F-FDG PET for the early diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI)
FDG-PET for Prediction of AD Dementia in Mild Cognitive Impairment. A Review of the State of the Art with Particular Emphasis on the Comparison with Other Neuroimaging Modalities (MRI and Perfusion SPECT)
Biomarker-based prediction of progression in MCI: Comparison of AD signature and hippocampal volume with spinal fluid amyloid-β and tau
Biomarker-based prediction of progression in MCI: Comparison of AD signature and hippocampal volume with spinal fluid amyloid-β and tau
I hope these article will help you.
Further, regarding the medication you could look into these articles
Drugs for Alzheimer’s Disease: Are They Effective?
Pharmacogenomics and therapeutic prospects in Alzheimer's disease
Cholinesterase inhibitors for Alzheimer's disease
Gud Luck !!!!
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30 year old patient diagnosed with primary hyperaldosteronism after presenting with severe hypertension and hypokalemia. On the next clinic visit to discuss further diagnostic tests and therapy, patient was found to be pregnant. How can this case be approached?
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Since spironolactone is contraindicated during pregnancy, a case report on primary hyperaldosteronism during pregnancy reported a favourable outcome for both mother and baby after switching to amiloride.
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We are testing a new diagnostic tool and comparing it to the actual gold standard for this diagnosis.
Briefly, we examined 25 patients with the new diagnostic tool (test A) and the gold standard diagnostic tool (test B). Test A gives a positive result or a negative result (no variability or range in numbers, just "positive" or "negative" as outcome). We then performed test B which also gives a "positive" or "negative" results and which is considered the true result since this is the gold standard diagnostic tool.
All patients having a positive result on test A (n=18), had a positive result on test B (n=18).
Of all patients having a negative result on test A (n=7), 5 were negative on test B but 2 were positive on test B.
Overall, 23 patients had the same outcome on test A and test B, 2 were different, which means that our new diagnostic test has a sensitivity of 92% (if we consider test B to have 100% sensitivity).
Can you recommend me any more statistics on this data, to draw conclusions? Any idea to look at this data from another perspective? Any help or insight is appreciated.
Thank you
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good question I follow
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Hello all, I'm working on Varian 4000 GC-MS. Since last week I've got the communicate "the trap filament is not being properly controlled". It seems strange because all diagnostic tests were ok, and the filament is new - it was changed in last week (the last one was broken). All other tests (air, water level, auto tune) passed without any errors, but when it comes to acquisition, the communicate appears once again. If anyone can help it would be great.
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In general siloxanes deriving from the column might cause electrical leaks across insulators. Therefore the conditioning of the column (which should not be connected to the MS !) is a crucial point.
Troubleshooting the internal ion source is more difficult. I have no further advices for that. The remark of Christopher Lee regarding a kind of jet separator is worth thinking about.
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Large CT-registry based studies have shown that unrevascularized CTO has more underpriveledged prognosis, than conservatively treated one.
On the other hand, due to pretty evident lack of coronary flow in the CTO, stress tests, particularly those combined with imaging typically show hypoperfusion (actually you do not need additional tests beyond coronarography for that since CTO represents lack of flow).
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Coronary angiography is the gold standard test.
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Want to know The diagnostic tests and screening tests for bird flu H5N1in India.
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Antigen detection ELISA is rapid. we are using