|
|
RESEARCH METHODOLOGY |
|
Year : 2010 | Volume
: 58
| Issue : 6 | Page : 519-522 |
|
A simple nomogram for sample size for estimating sensitivity and specificity of medical tests
Rajeev Kumar Malhotra, A Indrayan
Department of Biostatistics and Medical Informatics, University College of Medical Sciences, New Delhi - 110 095, India
Date of Submission | 07-Jul-2010 |
Date of Acceptance | 24-Jul-2010 |
Date of Web Publication | 16-Oct-2010 |
Correspondence Address: Rajeev Kumar Malhotra Department of Biostatistics and Medical Informatics, University College of Medical Sciences, New Delhi - 110 095 India
Source of Support: None, Conflict of Interest: None | Check |
DOI: 10.4103/0301-4738.71699
Sensitivity and specificity measure inherent validity of a diagnostic test against a gold standard. Researchers develop new diagnostic methods to reduce the cost, risk, invasiveness, and time. Adequate sample size is a must to precisely estimate the validity of a diagnostic test. In practice, researchers generally decide about the sample size arbitrarily either at their convenience, or from the previous literature. We have devised a simple nomogram that yields statistically valid sample size for anticipated sensitivity or anticipated specificity. MS Excel version 2007 was used to derive the values required to plot the nomogram using varying absolute precision, known prevalence of disease, and 95% confidence level using the formula already available in the literature. The nomogram plot was obtained by suitably arranging the lines and distances to conform to this formula. This nomogram could be easily used to determine the sample size for estimating the sensitivity or specificity of a diagnostic test with required precision and 95% confidence level. Sample size at 90% and 99% confidence level, respectively, can also be obtained by just multiplying 0.70 and 1.75 with the number obtained for the 95% confidence level. A nomogram instantly provides the required number of subjects by just moving the ruler and can be repeatedly used without redoing the calculations. This can also be applied for reverse calculations. This nomogram is not applicable for testing of the hypothesis set-up and is applicable only when both diagnostic test and gold standard results have a dichotomous category. Keywords: Nomogram, sample size, sensitivity, specificity
How to cite this article: Malhotra RK, Indrayan A. A simple nomogram for sample size for estimating sensitivity and specificity of medical tests. Indian J Ophthalmol 2010;58:519-22 |
Sensitivity and specificity are two components that measure the inherent validity of a diagnostic test compared to the gold standard; a valid test would not only correctly detect the presence of disease but also correctly detect the absence of the disease in subjects with and without disease, respectively. Sensitivity and specificity are useful measures when the established gold standard is difficult to adopt in practice. For example, diagnosis of pancreatic carcinoma can be confirmed only by laprotomy for alive or by autopsy for dead patients. Sometimes the gold standard is expensive, less widely available, more invasive, riskier, and takes more time to produce results. Such issues compel researchers to develop new diagnostic methods as surrogate to the gold standard.
An adequate sample size is needed to ensure that the study will yield estimate of the sensitivity and specificity with acceptable precisionΎsmaller sample size produces imprecise estimate, and unduly large sample is wastage of resources especially when the new method is expensive. Furthermore, the prevalence of disease was included in the sample size formula by Buderer, because the sample size without considering the prevalence would be adequate either for sensitivity or for specificity but not for both. [1]
In practice, researchers generally decide a sample size for validating a new diagnostic test arbitrarily or at their convenience or use the previous literature. A study was conducted by Bochmann in five highest impact factor ophthalmology journals to assess the sample size calculation in diagnostic accuracy articles published in 2005 and found only 1 out of 40 studies reporting the sample size calculation before initiating the study. [2] This may be due to reluctance in using a mathematical formula or computer software. Buderer provides the sample size tables for sensitivity and specificity but they are only for the 10% precision level. Carley et al. have provided nomograms but they are separate for sensitivity and specificity. They derived them only for the 95% level of confidence; too many lines and curves make their nomogram complex to read. [3]
A nomogram is a chart consisting of three or more lines or curves so arranged that the required reading can be quickly made by just moving the ruler. They are still very popular in spite of the availability of computer. One of the main attractions is that a nomogram can be carried anywhere since it is just a piece of paper and can be repeatedly used without redoing the calculations. Various nomograms have been devised such as to calculate the sample size in diagnostic studies, to find the number of clusters required for estimating the prevalence rate in single-stage cluster-sample survey, and to find the number needed to treat in a therapeutic trial against values of absolute risk in the absence of treatment. [3],[4],[5]
We have devised a relatively very simple nomogram to read the sample size for anticipated sensitivity and specificity using the formula described by Buderer. [1] This guides the researchers about the adequate sample size to achieve specified absolute precision. The estimated prevalence of disease and confidence level 100(1 − α)% are required. The features of this nomogram are as follows: (i) a single nomogram can be used to read the sample size for both sensitivity and specificity, (ii) it is based on simple lines instead of curves, (iii) it is easy to read by just moving the ruler from one point to another, (iv) the sample size for the 95% confidence level is directly available and one can calculate the sample size for 99% and 90% levels of confidence just multiplying by 1.75 and 0.70, respectively to sample size obtained by using 95% confidence level, and (iv) the sample size can be obtained for any precision level with minor calculations.
Materials and Methods | | |
Sample size at the required absolute precision level for sensitivity and specificity can be calculated by Buderer's formula: [1]
where n = required sample size,
S N = anticipated sensitivity,
S P = anticipated specificity,
α = size of the critical region (1 − α is the confidence level),
z 1-α/2 = standard normal deviate corresponding to the specified size of the critical region (α), and
L = absolute precision desired on either side (half-width of the confidence interval) of sensitivity or specificity.
The procedure to construct a nomogram is described by Adam and Molnar. [6],[7] Our nomogram is depicted in [Figure 1]. This was created in MS Excel. This nomogram is for the 95% confidence level and consists of five parallel lines. The first line depicts anticipated sensitivity or specificity of the diagnostic test that can vary from 0.70 to 0.97. A test with anticipated sensitivity or specificity less than 0.70 may not be worthy of investigations. The minimum value of L on either side of anticipated sensitivity or specificity is taken as 0.03. The second line depicts the number of subjects required at 0.03 and 0.05 absolute precision and the third line depicts the number of subjects for 0.07 and 0.10 absolute precision. Fourth and fifth lines are prevalence lines and represent the expected prevalence of disease; the fourth line is to be used for L = 0.03 or 0.05 and the fifth for L = 0.07 or 0.10. | Figure 1: Nomogram for the sample size for anticipated sensitivity/specificity, and estimated prevalence
Click here to view |
Result | | |
To find the number of subjects required for estimating sensitivity, place a ruler joining the anticipated sensitivity with expected prevalence and read the number of subjects where the ruler cuts the corresponding line of the number of subjects with required absolute precision. One should choose anticipated sensitivity such that after adding the required precision it does not exceed 1. For example, when anticipated sensitivity is 0.96, a researcher cannot select required precision to be more than 0.04.
Suppose the researcher selects anticipated sensitivity S N = 0.80, precision = 0.03 with 95% confidence level (two-tailed), i.e., S N can be from 0.77 to 0.83, and expected prevalence = 0.20. Place a ruler joining the point 0.80 on the anticipated sensitivity/specificity line to point 0.20 on the estimated prevalence line of 0.03 absolute precision and read the required sample size from the number of subjects line of 0.03 absolute precision. In our example, the number of subjects required is nearly 3450 as shown in [Figure 1]. By formula, the exact value is 3415-a difference of nearly 1%. This can happen with any nomogram.
To find the required sample size for estimating specificity, first subtract the expected prevalence from 1 and place the ruler joining the anticipated specificity to (1 - prevalence) value on the prevalence line of required precision. For example, if S P = 0.80, precision = 0.05 with 95% confidence level, and prevalence is 0.20, join the point S p = 0.80 with the point (1 - 0.20) = 0.80, on the prevalence line of 0.05 absolute precision, and read the sample size from the number of subjects line for 0.05 absolute precision This is nearly 300. By calculation, the exact value is 308. Now the difference is 3%.
The final sample size depends on the interest of the researcher. If sensitivity and specificity are equally important for the study, determine the sample size for both sensitivity and specificity, separately. The final sample size of the study would be the larger of these two. But sometimes the researcher is interested more in sensitivity than specificity. In that case, the final sample size would be based on the sensitivity only. In addition, there are other considerations such as nonresponse and subgroup analysis. [8]
It is easily seen in the formula that the number of subjects is exactly four times when the length of L is halved, and one-fourth when the length of L is doubled, provided other values remain same. Following expression can be used to obtain the number of subjects needed for any precision level L1
where n0 = sample size at precision level L0 from the nomogram where L0 may be 0.03, 0.05, 0.07, and 0.10 as depicted in our nomogram and n1 = sample size at precision level L1 ; L1 may be any other acceptable precision level. Thus this nomogram can in fact be used for any precision level with minor calculation as envisaged in equation (1). Similarly the researcher can also use the nomogram for 99% and 90% confidence levels. To find the sample size for 99% and 90% confidence levels, first read the number of subjects required assuming the 95% confidence level and then multiply it with 1.75 for the 99% confidence level and 0.70 for the 90% confidence level. This is the ratio of the square of the standard normal deviate for the required confidence level 100(1 - α)% to the standard normal deviate for the 95% confidence level.
To validate the nomogram, various parameter combinations such as anticipated sensitivity/specificity, and prevalence of the disease were randomly selected. The exact sample size was calculated by the formula while at the same time independently second author determined the sample size from a nomogram for these randomly selected combinations of parameters. The percentage error was calculated ([Table 1] and [Table 2] in Appendix). The percentage error is higher when the sample size is small; for instance, the exact sample size for specificity = 0.97, prevalence = 0.60, and absolute precision = 0.10 is 28 while the nomogram shows this as 30 [Table 2]. The difference of 2 is small although percentage is 7.14%. Otherwise the sample size is within 5% of the exact value. As already stated, this kind of minor approximation is inevitable with any nomogram as it simplifies the process. | Table 1: Validation table for the sample size for estimating sensitivity with the 95% confidence level
Click here to view |
| Table 2: Validation table for the sample size for estimating specificity with the 95% confidence level
Click here to view |
Discussion | | |
A nomogram depicts the mathematical relationship among various parameters and is simple to use without redoing the calculations. Our nomogram has four parametersΎanticipated sensitivity/specificity, number of subjects, absolute precision level, and expected prevalence of disease. Researchers can also use this nomogram for reverse calculation. If any of these three parameters are known, the fourth parameter can be obtained. This nomogram does not incorporate Type II error; thus this cannot be used for testing the hypothesis on sensitivity/specificity.
One of the main limitations of any nomogram is reading accuracy. In place of 465, one might read 460 from the line but this minor deviation may not be important in practice. This nomogram is applicable only when both the new diagnostic test and gold standard provide result in a dichotomous category such as test+ and test-. Thus this is not applicable when the gold standard is dichotomous and the new diagnostic test is ordinal or continuous, or vice versa.
References | | |
1. | Buderer NM. Statistical methodology: I Incorporating the prevalence of disease into the sample size calculation for sensitivity and specificity. Acad Emerg Med 1996;3:895-900. [ PUBMED] [ FULLTEXT] |
2. | Bochmann F, Johnson Z, Azuara-Blanco A. Sample size in studies on diagnostic accuracy in ophthalmology: A literature survey. Br J Ophthalmol 2007;91:898-900. [ PUBMED] [ FULLTEXT] |
3. | Carley S, Dosman S, Jones SR, Harrison M. Simple nomograms to calculate simple size in diagnostic studies. Emerg Med J 2005;22:180-1. [ PUBMED] [ FULLTEXT] |
4. | Kumar R, Indrayan A. A nomogram for single-stage-cluster-sampling survey in a community for estimation of a prevalence rate. Int J Epidemiol 2002;31:463-7. [ PUBMED] [ FULLTEXT] |
5. | Chatellier G, Zapletal E, Lemaitre D, Medard J, Degoulet P. The number needed to treat: A clinically useful nomogram in its proper context. Br Med J 1996;312:426-9. |
6. | Adams DP. Nomography: Theory and Application. Hamden: Connecticut Archon Books; 1964. p. 1-17. |
7. | Molnar J. Nomographs: What they are and how to use them. Ann Arbor: Ann Arbor Sciences; 1981. |
8. | Indrayan A. Confidence intervals and principal of significance. Medical Biostatistics. 2 nd ed. Boca Raton: Chapman and Hall/ CRC Press; 2008. p. 380-2. |
[Figure 1]
[Table 1], [Table 2]
This article has been cited by | 1 |
Rethinking False Positive Exercise Electrocardiographic Stress Tests by Assessing Coronary Microvascular Function |
|
| Aish Sinha, Utkarsh Dutta, Ozan M. Demir, Kalpa De Silva, Howard Ellis, Samuel Belford, Mark Ogden, Matthew Li Kam Wa, Holly P. Morgan, Ajay M. Shah, Amedeo Chiribiri, Andrew J. Webb, Michael Marber, Haseeb Rahman, Divaka Perera | | Journal of the American College of Cardiology. 2024; 83(2): 291 | | [Pubmed] | [DOI] | | 2 |
Sensitivity and specificity of handheld one lead ECG detecting atrial fibrillation in an outpatient clinic setting |
|
| Johan Malmqvist, Johan Engdahl, Gunnar Sjölund, Piotr Doliwa | | Journal of Electrocardiology. 2024; 83: 106 | | [Pubmed] | [DOI] | | 3 |
Diagnostic validation study of rapid urinary tract infection diagnosis kit at peripheral health facilities of West Bengal, India |
|
| Debjit Chakraborty, Falguni Debnath, Agniva Majumdar, Atreyi Chakrabarti, Monica Sharma, Kamini Walia, Alok Kumar Deb, Shanta Dutta | | Scientific Reports. 2024; 14(1) | | [Pubmed] | [DOI] | | 4 |
Triage method for endometrial biopsy in postmenopausal women: a multicenter retrospective cohort study |
|
| Yufei Shen, Lucia Li, Hailong Wang, Yi Hu, Xi Deng, Xiaoling Lian, Yanlin Tan, Liling Liang, Yu Zhang, Wenqing Yang | | Menopause. 2023; | | [Pubmed] | [DOI] | | 5 |
Analytical and Clinical Evaluation of a TaqMan Real-Time PCR Assay for the Detection of Chikungunya Virus |
|
| Anna Andrew, Marimuthu Citartan, Kiing Aik Wong, Thean Hock Tang, Sum Magdline Sia Henry, Ewe Seng Ch'ng, Meghan Starolis | | Microbiology Spectrum. 2023; | | [Pubmed] | [DOI] | | 6 |
Evaluation of a diagnostic device, CL Detect rapid test for the diagnosis of new world cutaneous leishmaniasis in Peru |
|
| Max Grogl, Christie A. Joya, Maria Saenz, Ana Quispe, Luis Angel Rosales, Rocio del Pilar Santos, Maxy B. De los Santos, Ngami Donovan, Janet H. Ransom, Ana Ramos, Elmer Llanos Cuentas, Alberto Novaes Ramos | | PLOS Neglected Tropical Diseases. 2023; 17(3): e0011054 | | [Pubmed] | [DOI] | | 7 |
An Elaboration on Sample Size Planning for Performing a One-Sample Sensitivity and Specificity Analysis by Basing on Calculations on a Specified 95% Confidence Interval Width |
|
| Mohamad Adam Bujang | | Diagnostics. 2023; 13(8): 1390 | | [Pubmed] | [DOI] | | 8 |
simpleNomo: A Python Package of Making Nomograms for Visualizable Calculation of Logistic Regression Models |
|
| Haoyang Hong, Shenda Hong | | Health Data Science. 2023; 3 | | [Pubmed] | [DOI] | | 9 |
Furosemide Stress Test and Renal Resistive Index for Prediction of Severity of Acute Kidney Injury in Sepsis |
|
| Pravin K Das, Sudeep K Maurya, Soumya Sankar Nath, Tushant Kumar, Namrata Rao, Neha Shrivastava | | Cureus. 2023; | | [Pubmed] | [DOI] | | 10 |
Microbial, Cytological, and Histopathological Analysis of Bronchoalveolar Lavage and Transbronchial Lung Biopsy in Diagnosis of Community-acquired Pneumonia: A Prospective Study |
|
| Nirajkumar Soni, Vrushali Khadke, Vihita Kulkarni, Arun Bahulikar, Deepak Phalgune | | The Indian Journal of Chest Diseases and Allied Sciences. 2023; 65(2): 69 | | [Pubmed] | [DOI] | | 11 |
Delirium Assessment Tools Among Hospitalized Older Adults: A Systematic Review and Metaanalysis of Diagnostic Accuracy |
|
| Chia-Jou Lin, I-Chang Su, She-Wen Huang, Pin-Yuan Chen, Victoria Traynor, Hui-Chen (Rita) Chang, I-Hsing Liu, Yun-Shuan Lai, Hsin-Chien Lee, Kaye Rolls, Hsiao-Yean Chiu | | Ageing Research Reviews. 2023; : 102025 | | [Pubmed] | [DOI] | | 12 |
Social Risk Factors Are Associated With Risk for Hospitalization in Home Health Care: A Natural Language Processing Study |
|
| Mollie Hobensack, Jiyoun Song, Sungho Oh, Lauren Evans, Anahita Davoudi, Kathryn H. Bowles, Margaret V. Mcdonald, Yolanda Barrón, Sridevi Sridharan, Andrea S. Wallace, Maxim Topaz | | Journal of the American Medical Directors Association. 2023; | | [Pubmed] | [DOI] | | 13 |
Validation of mobile health technology (mhealth tech) for cardiovascular risk detection |
|
| Vinoth Gnana Chellaiyan Devanbu, S. Vijayalakshmi, S.M. Suruliraman | | Clinical Epidemiology and Global Health. 2023; 23: 101398 | | [Pubmed] | [DOI] | | 14 |
Mixed Martial Arts: Comparing the King-Devick and Sport Concussion Assessment Tool 5 in knockouts, technical knockouts and choke holds |
|
| Eric E. Twohey, Ike B. Hasley, Patrick J. Shaeffer, George A. Ceremuga, Stephen A. Firkins, Gene C. Stringer, Mario Roberto Vaz Carneiro Filho, John H. Hollman, Rodolfo Savica, Jonathan T. Finnoff | | Archives of Rehabilitation Research and Clinical Translation. 2023; : 100301 | | [Pubmed] | [DOI] | | 15 |
Validation of the DSM-5 Level 1 Cross-Cutting Symptom Measure in a Community Sample |
|
| Robert A. Doss, Sara E. Lowmaster | | Psychiatry Research. 2022; : 114935 | | [Pubmed] | [DOI] | | 16 |
Hypermethylated CDO1 and ZNF454 in Cytological Specimens as Screening Biomarkers for Endometrial Cancer |
|
| Lei Wang, Lanlan Dong, Jun Xu, Lin Guo, Yiran Wang, Kangkang Wan, Wei Jing, Lanbo Zhao, Xue Feng, Kailu Zhang, Miao Guo, Yuliang Zou, Lianglu Zhang, Qiling Li | | Frontiers in Oncology. 2022; 12 | | [Pubmed] | [DOI] | | 17 |
Development and Implementation of a Smartphone-based Identification Instrument for Infantile Atopic Dermatitis (eCEQ): Web-based Questionnaire Study (Preprint) |
|
| Heping Fang, Lin Chen, Juan Li, Luo Ren, Yu Yin, Danleng Chen, Huaying Yin, Enmei Liu, Yan Hu, Xiaoyan Luo | | Journal of Medical Internet Research. 2022; | | [Pubmed] | [DOI] | | 18 |
Response: Commentary: Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread |
|
| Subhash Kumar Yadav, Yusuf Akhter | | Frontiers in Public Health. 2022; 9 | | [Pubmed] | [DOI] | | 19 |
Evaluation of the BioFire FilmArray Pneumonia Panel Plus to the Conventional Diagnostic Methods in Determining the Microbiological Etiology of Hospital-Acquired Pneumonia |
|
| Noha A. Kamel, Mohammad Y. Alshahrani, Khaled M. Aboshanab, Mervat I. El Borhamy | | Biology. 2022; 11(3): 377 | | [Pubmed] | [DOI] | | 20 |
A combined diagnostic approach based on serum biomarkers for sarcopenia in older patients with hip fracture |
|
| Shengwu Yu, Li Chen, Yining Zhang, Peng Wu, Congcong Wu, Junzhe Lang, Yangbo Liu, Jiandong Yuan, Keke Jin, Lei Chen | | Australasian Journal on Ageing. 2022; | | [Pubmed] | [DOI] | | 21 |
Diagnostic value of reflux episodes in gastroesophageal reflux-induced chronic cough: a novel predictive indicator |
|
| Shengyuan Wang, Siwan Wen, Xiao Bai, Mengru Zhang, Yiqing Zhu, Mingyan Wu, Lihua Lu, Cuiqin Shi, Li Yu, Xianghuai Xu | | Therapeutic Advances in Chronic Disease. 2022; 13: 2040622322 | | [Pubmed] | [DOI] | | 22 |
Coronary Wave Intensity Analysis as an Invasive and Vessel-Specific Index of Myocardial Viability |
|
| Matthew Ryan, Kalpa De Silva, Holly Morgan, Kevin O’Gallagher, Ozan M. Demir, Haseeb Rahman, Howard Ellis, Luke Dancy, Daniel Sado, Julian Strange, Narbeh Melikian, Michael Marber, Ajay M. Shah, Amedeo Chiribiri, Divaka Perera | | Circulation: Cardiovascular Interventions. 2022; 15(12) | | [Pubmed] | [DOI] | | 23 |
A novel blood based triage test for colorectal cancer in primary care: a pilot study |
|
| Cerys Jenkins, Freya Woods, Susan Chandler, Kym Carter, Rhys Jenkins, Andrew Cunningham, Kayleigh Nelson, Rachel Still, Jenna A Walters, Non Gwynn, Wilson Chea, Rachel Harford, Claire O'Neill, Julie Hepburn, Ian Hill, Heather Wilkes, Greg Fegan, Peter Dunstan, Dean A Harris | | BJGP Open. 2022; : BJGPO.2022 | | [Pubmed] | [DOI] | | 24 |
Heat Acclimation Improves Heat Tolerance Test Specificity in a Criteria-dependent Manner |
|
| KATHERINE M. MITCHELL, ROY M. SALGADO, KARLEIGH E. BRADBURY, NISHA CHARKOUDIAN, ROBERT W. KENEFICK, SAMUEL N. CHEUVRONT | | Medicine & Science in Sports & Exercise. 2021; 53(5): 1050 | | [Pubmed] | [DOI] | | 25 |
Performance evaluation of Baermann techniques: The quest for developing a microscopy reference standard for the diagnosis of Strongyloides stercoralis |
|
| Woyneshet Gelaye, Nana Aba Williams, Stella Kepha, Augusto Messa Junior, Pedro Emanuel Fleitas, Helena Marti-Soler, Destaw Damtie, Sissay Menkir, Alejandro J. Krolewiecki, Lisette van Lieshout, Wendemagegn Enbiale, Peter Steinmann | | PLOS Neglected Tropical Diseases. 2021; 15(2): e0009076 | | [Pubmed] | [DOI] | | 26 |
Diagnostic Accuracy of Detecting Diabetic Retinopathy by Using Digital Fundus Photographs in the Peripheral Health Facilities of Bangladesh: Validation Study |
|
| Tahmina Begum, Aminur Rahman, Dilruba Nomani, Abdullah Mamun, Alayne Adams, Shafiqul Islam, Zara Khair, Zareen Khair, Iqbal Anwar | | JMIR Public Health and Surveillance. 2021; 7(3): e23538 | | [Pubmed] | [DOI] | | 27 |
Dengue pre-vaccination screening test evaluation for the use of dengue vaccine in an endemic area |
|
| Umaporn Limothai, Sasipha Tachaboon, Janejira Dinhuzen, Taweewun Hunsawong, Prapapun Ong-ajchaowlerd, Butsaya Thaisomboonsuk, Stefan Fernandez, Supachoke Trongkamolchai, Mananya Wanpaisitkul, Chatchai Chulapornsiri, Anongrat Tiawilai, Thawat Tiawilai, Terapong Tantawichien, Usa Thisyakorn, Nattachai Srisawat, Ray Borrow | | PLOS ONE. 2021; 16(9): e0257182 | | [Pubmed] | [DOI] | | 28 |
A Step-by-Step Process on Sample Size Determination for Medical Research |
|
| Mohamad Adam Bujang | | Malaysian Journal of Medical Sciences. 2021; 28(2): 15 | | [Pubmed] | [DOI] | | 29 |
Identifying and Avoiding Risk of Bias in Caries Diagnostic Studies |
|
| Jan Kühnisch, Mila Janjic Rankovic, Svetlana Kapor, Ina Schüler, Felix Krause, Stavroula Michou, Kim Ekstrand, Florin Eggmann, Klaus W. Neuhaus, Adrian Lussi, Marie-Charlotte Huysmans | | Journal of Clinical Medicine. 2021; 10(15): 3223 | | [Pubmed] | [DOI] | | 30 |
Scope and limitations of a multiplex conventional PCR for the diagnosis of S. stercoralis and hookworms |
|
| Pedro E. Fleitas, Paola A. Vargas, Nicolás Caro, M. Cristina Almazan, Adriana Echazú, Marisa Juárez, Pamela Cajal, Alejandro J. Krolewiecki, Julio R. Nasser, Rubén O. Cimino | | The Brazilian Journal of Infectious Diseases. 2021; 25(6): 101649 | | [Pubmed] | [DOI] | | 31 |
Development and validation of a Screening Tool to Evaluate and Warrant Anticoagulation Treatment prior to Discharge in inpatients with Atrial Fibrillation (STEWARxD-AF) |
|
| Charlotte Quintens, Lorenz Van der Linden, Kaat Meeusen, Egon Nijns, Rik Willems, Isabel Spriet | | International Journal of Medical Informatics. 2021; 154: 104555 | | [Pubmed] | [DOI] | | 32 |
Validity of near-infrared light transillumination for the assessment of proximal caries in permanent teeth |
|
| F Wang, C Su, C Yang, JW Hoff, Z Bian, L Meng | | Australian Dental Journal. 2021; | | [Pubmed] | [DOI] | | 33 |
Smartphone-based Ophthalmic Imaging Compared With Spectral-domain Optical Coherence Tomography Assessment of Vertical Cup-to-disc Ratio Among Adults in Southwestern Uganda |
|
| Baimba R. Idriss, Tu M. Tran, Daniel Atwine, Robert T. Chang, David Myung, John Onyango | | Journal of Glaucoma. 2021; 30(3): e90 | | [Pubmed] | [DOI] | | 34 |
Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies |
|
| Ceyhan Ceran Serdar, Murat Cihan, Dogan Yücel, Muhittin A Serdar | | Biochemia medica. 2021; 31(1): 27 | | [Pubmed] | [DOI] | | 35 |
Performance of a tool to identify different types of self-reported sexual risk among women attending a contraception and sexual health clinic: results of a cross-sectional survey |
|
| Natalie Edelman, Jennifer Whetham, Jackie Cassell, Richard de Visser, Catherine Mercer, Christopher Jones, Abbey Gersten, Stephen Bremner | | BMJ Sexual & Reproductive Health. 2021; 47(2): 117 | | [Pubmed] | [DOI] | | 36 |
Diagnostic accuracy of point-of-care ultrasound (PoCUS) for shoulder dislocations and reductions in the emergency department: a diagnostic randomised control trial (RCT) |
|
| Mark Anthony Attard Biancardi, Robert David Jarman, Tania Cardona | | Emergency Medicine Journal. 2021; : emermed-20 | | [Pubmed] | [DOI] | | 37 |
Diagnostic Accuracy of HemotypeSC as a Point-of-Care Testing Device for Sickle Cell Disease: Findings from a Southwestern State in Nigeria and Implications for Patient Care in Resource-Poor Settings of sub-Saharan Africa |
|
| Oladele S. Olatunya, Dulcinea M. Albuquerque, Adeniyi F. Fagbamigbe, Opeyemi A. Faboya, Ayotunde E. Ajibola, Oluwatoyin A. Babalola, Adewale O. Adebisi, Adeyinka G. Falusi, Adekunle Adekile, Fernando F. Costa | | Global Pediatric Health. 2021; 8: 2333794X21 | | [Pubmed] | [DOI] | | 38 |
Multicentre prospective observational study exploring the predictive value of functional echocardiographic indices for early identification of preterm neonates at risk of developing chronic pulmonary hypertension secondary to chronic neonatal lung disease |
|
| Laura Thomas, Michelle Baczynski, Poorva Deshpande, Ashraf Kharrat, Sébastien Joye, Faith Zhu, Daniel Ibarra-Rios, Prakesh S Shah, Luc Mertens, Robert P Jankov, Xiang Y Ye, Elaine Neary, Joseph Ting, Michael Castaldo, Philip Levy, Aisling Smith, Afif F El-Khuffash, Regan E Giesinger, Patrick J McNamara, Dany E Weisz, Amish Jain | | BMJ Open. 2021; 11(3): e044924 | | [Pubmed] | [DOI] | | 39 |
Serum CYR61 as a potential biomarker for the diagnosis of esophagogastric junction tumor |
|
| Ling-Yu Chu, Jian-Yuan Zhou, Yi-Xuan Zhao, Yan-Ting Ou, Tian Yang, Yu-Hui Peng, Wang-Kai Fang, Yi-Wei Xu, Jian-Jun Xie | | Bioscience Reports. 2021; 41(6) | | [Pubmed] | [DOI] | | 40 |
Using Patient Completed Screening Tools to Predict Risk of Malnutrition in Patients With Inflammatory Bowel Disease |
|
| Lorian M Taylor, Tannaz Eslamparast, Kamal Farhat, Karen Kroeker, Brendan Halloran, Nusrat Shommu, Ankush Kumar, Quinn Fitzgerald, Leah Gramlich, Juan G Abraldes, Puneeta Tandon, Maitreyi Raman | | Crohn's & Colitis 360. 2021; 3(3) | | [Pubmed] | [DOI] | | 41 |
Diagnostic value of copeptin combined with hypersensitive cardiac troponin T detection in early acute myocardial infarction |
|
| Yan Yang, Songtao Gao, Qiuju Fang, Jing Yang | | Medicine. 2021; 100(1): e23949 | | [Pubmed] | [DOI] | | 42 |
Diagnostic performance of a SARS-CoV-2 rapid antigen test in a large, Norwegian cohort |
|
| Elisabeth Toverud Landaas, Margrethe Larsdatter Storm, Mette Christophersen Tollånes, Regine Barlinn, Anne-Marte Bakken Kran, Karoline Bragstad, Andreas Christensen, Trude Andreassen | | Journal of Clinical Virology. 2021; 137: 104789 | | [Pubmed] | [DOI] | | 43 |
Algorithms Effectiveness comparison in solving Nonogram boards |
|
| Jakub Wieckowski, Andrii Shekhovtsov | | Procedia Computer Science. 2021; 192: 1885 | | [Pubmed] | [DOI] | | 44 |
Rendimiento diagnóstico de la pregunta concerniente a la actividad física del cuestionario GINA para la detección de asma y broncoconstricción inducidas por el ejercicio |
|
| Daniele Schiwe, João Paulo Heinzmann-Filho, Cláudia Silva Schindel, Mailise Fátima Gheller, Natália Evangelista Campos, Giovana Santos, Márcio Vinícius Fagundes Donadio, Paulo Márcio Pitrez | | Anales de Pediatría. 2021; 95(1): 40 | | [Pubmed] | [DOI] | | 45 |
Validation of Sinhala version of Psoriasis Epidemiology Screening Tool |
|
| Achala Liyanage, S. Verni, G. Liyanage, V. De Silva, J. Akarawita, C. Gunasekera, J. Rubasinghe, S. Imafuku, S. Lekamwasam | | Clinical Rheumatology. 2021; 40(8): 3127 | | [Pubmed] | [DOI] | | 46 |
Diagnostic performance of the physical activity-related question of the GINA questionnaire to detect exercise-induced bronchoconstriction in asthma |
|
| Daniele Schiwe, João Paulo Heinzmann-Filho, Cláudia Silva Schindel, Mailise Fátima Gheller, Natália Evangelista Campos, Giovana Santos, Márcio Vinícius Fagundes Donadio, Paulo Márcio Pitrez | | Anales de Pediatría (English Edition). 2021; 95(1): 40 | | [Pubmed] | [DOI] | | 47 |
Diagnostic accuracy of the Tilburg Frailty Indicator (TFI) for early frailty detection in elderly people in Iran |
|
| Faezeh Mazoochi, Robbert J.J. Gobbens, Mohammad-sajjad Lotfi, Reza Fadayevatan | | Archives of Gerontology and Geriatrics. 2020; 91: 104187 | | [Pubmed] | [DOI] | | 48 |
Development of a Fluorescence-Based Caries Scoring System for an Intraoral Scanner: An in vitro Study |
|
| Stavroula Michou, Ana Raquel Benetti, Christoph Vannahme, Pétur Gordon Hermannsson, Azam Bakhshandeh, Kim Rud Ekstrand | | Caries Research. 2020; 54(4): 324 | | [Pubmed] | [DOI] | | 49 |
Validation of an Overnight Wireless High-Resolution Oximeter plus Cloud-Based Algorithm for the Diagnosis of Obstructive Sleep Apnea |
|
| George do Lago Pinheiro, Andrea Fonseca Cruz, Diego Munduruca Domingues, Pedro Rodrigues Genta, Luciano F. Drager, Patrick J. Strollo, Geraldo Lorenzi-Filho | | Clinics. 2020; 75 | | [Pubmed] | [DOI] | | 50 |
Utility of fractional flow reserve in moderate in-stent re-stenosis and jailed side branches and comparison of fractional flow reserve with single-photon emission computed tomography-myocardial perfusion imaging in native coronary artery stenosis |
|
| Ajitkumar Jadhav, DeepakSadashiv Phalgune, Suhas Hardas | | Heart India. 2020; 8(1): 21 | | [Pubmed] | [DOI] | | 51 |
Comparison of loop-mediated isothermal amplification (LAMP) and PCR for the diagnosis of infection with Trypanosoma brucei ssp. in equids in The Gambia |
|
| Lauren Gummery, Saloum Jallow, Alexandra G. Raftery, Euan Bennet, Jean Rodgers, David G. M. Sutton, Adriana Calderaro | | PLOS ONE. 2020; 15(8): e0237187 | | [Pubmed] | [DOI] | | 52 |
Exploring the barriers to SMEs’ open innovation adoption in Ghana |
|
| Stephen Oduro | | International Journal of Innovation Science. 2020; 12(1): 21 | | [Pubmed] | [DOI] | | 53 |
Diagnostic meta-analysis of the Pediatric Sleep Questionnaire, OSA-18, and pulse oximetry in detecting pediatric obstructive sleep apnea syndrome |
|
| Chia-Rung Wu, Yu-Kang Tu, Li-Pang Chuang, Christopher Gordon, Ning-Hung Chen, Pin-Yuan Chen, Faizul Hasan, Maria D. Kurniasari, Sri Susanty, Hsiao-Yean Chiu | | Sleep Medicine Reviews. 2020; 54: 101355 | | [Pubmed] | [DOI] | | 54 |
Screening instrument to identify skin prick test-negative patients with asthma and/or rhinitis |
|
| Ebru Celebioglu, Ahmet Ugur Demir, Gul Karakaya, Ali Fuat Kalyoncu | | The Clinical Respiratory Journal. 2019; 13(5): 314 | | [Pubmed] | [DOI] | | 55 |
How to publish diagnostic imaging studies: Common mistakes and recommendations |
|
| Anthony Pease, Celia M. Marr | | Equine Veterinary Journal. 2019; 51(1): 7 | | [Pubmed] | [DOI] | | 56 |
Diagnostic Accuracy of Point-of-Care Gastric Ultrasound |
|
| Richelle Kruisselbrink, Angineh Gharapetian, Luis E. Chaparro, Noam Ami, Dustin Richler, Vincent W. S. Chan, Anahi Perlas | | Anesthesia & Analgesia. 2019; 128(1): 89 | | [Pubmed] | [DOI] | | 57 |
Squamous cell carcinoma antigen concentration in fine needle aspiration samples: A new method to detect cervical lymph node metastases of head and neck squamous cell carcinoma |
|
| Jeroen E. van Schaik, Anna C. Muller Kobold, Bernard F. A. M. van der Laan, Bert van der Vegt, Bettien M. van Hemel, Boudewijn E. C. Plaat | | Head & Neck. 2019; 41(8): 2561 | | [Pubmed] | [DOI] | | 58 |
Label-Free Biomolecule Detection in Physiological Solutions With Enhanced Sensitivity Using Graphene Nanogrids FET Biosensor |
|
| R. Ray, J. Basu, W. A. Gazi, N. Samanta, K. Bhattacharyya, C. RoyChaudhuri | | IEEE Transactions on NanoBioscience. 2018; 17(4): 433 | | [Pubmed] | [DOI] | | 59 |
Comparison of Natural Language Processing and Manual Coding for the Identification of Cross-Sectional Imaging Reports Suspicious for Lung Cancer |
|
| Roxanne Wadia, Kathleen Akgun, Cynthia Brandt, Brenda T. Fenton, Woody Levin, Andrew H. Marple, Vijay Garla, Michal G. Rose, Tamar Taddei, Caroline Taylor | | JCO Clinical Cancer Informatics. 2018; (2): 1 | | [Pubmed] | [DOI] | | 60 |
Prospective, single UK centre, comparative study of the predictive values of contrast-enhanced ultrasound compared to time-resolved CT angiography in the detection and characterisation of endoleaks in high-risk patients undergoing endovascular aneurysm re |
|
| Iain Nicholas Roy, Tze Yuan Chan, Gabriela Czanner, Steve Wallace, Srinivasa Rao Vallabhaneni | | BMJ Open. 2018; 8(4): e020835 | | [Pubmed] | [DOI] | | 61 |
Diagnostic accuracy of flexion-extension radiography for the detection of ligamentous cervical spine injury following a normal cervical spine computed tomography |
|
| Jason Jaeseong Oh, Stephen Edward Asha, Kate Curtis | | Emergency Medicine Australasia. 2016; 28(4): 450 | | [Pubmed] | [DOI] | | 62 |
Diagnostic Accuracy of Urinary Cytokeratin 19 Fragment for Endometriosis |
|
| B. A. Lessey, R. F. Savaris, S. Ali, S. Brophy, S. Tomazic-Allen, K. Chwalisz | | Reproductive Sciences. 2015; 22(5): 551 | | [Pubmed] | [DOI] | | 63 |
Comparison of rapid diagnostic test Plasmotec Malaria-3, microscopy, and quantitative real-time PCR for diagnoses of Plasmodium falciparum and Plasmodium vivax infections in Mimika Regency, Papua, Indonesia |
|
| Liony Fransisca,Josef Hari Kusnanto,Tri Baskoro T Satoto,Boni Sebayang,? Supriyanto,Eko Andriyan,Michael J Bangs | | Malaria Journal. 2015; 14(1) | | [Pubmed] | [DOI] | | 64 |
Diagnostic Accuracy of the Cognitive State Test in the Detection of Dementia Among Iranian Older Adults |
|
| Mohammad-Sajjad Lotfi, Zahra Tagharrobi, Khadijeh Sharifi, Javad Abolhasani | | Research in Gerontological Nursing. 2015; 8(6): 293 | | [Pubmed] | [DOI] | | 65 |
Comparing the DN4 tool with the IASP grading system for chronic neuropathic pain screening after breast tumor resection with and without paravertebral blocks |
|
| Faraj W. Abdallah,Pamela J. Morgan,Tulin Cil,Jaime M. Escallon,John L. Semple,Vincent W. Chan | | PAIN. 2015; 156(4): 740 | | [Pubmed] | [DOI] | | 66 |
Sample size estimation in diagnostic test studies of biomedical informatics |
|
| Karimollah Hajian-Tilaki | | Journal of Biomedical Informatics. 2014; | | [Pubmed] | [DOI] | | 67 |
Statistical methods for bioimpedance analysis |
|
| Christian Tronstad, Are H. Pripp | | Journal of Electrical Bioimpedance. 2014; 5(1): 14 | | [Pubmed] | [DOI] | | 68 |
Metabolic system alterations in pancreatic cancer patient serum: potential for early detection |
|
| Shawn A Ritchie,Hirofumi Akita,Ichiro Takemasa,Hidetoshi Eguchi,Elodie Pastural,Hiroaki Nagano,Morito Monden,Yuichiro Doki,Masaki Mori,Wei Jin,Tolulope T Sajobi,Dushmanthi Jayasinghe,Bassirou Chitou,Yasuyo Yamazaki,Thayer White,Dayan B Goodenowe | | BMC Cancer. 2013; 13(1): 416 | | [Pubmed] | [DOI] | | 69 |
A feasibility study on bedside upper airway ultrasonography compared to waveform capnography for verifying endotracheal tube location after intubation |
|
| Osman Adi,Tan Chuan,Manikam Rishya | | Critical Ultrasound Journal. 2013; 5(1): 7 | | [Pubmed] | [DOI] | | 70 |
Receiver operating characteristic (ROC) curve for medical researchers |
|
| Rajeev Kumar,Abhaya Indrayan | | Indian Pediatrics. 2011; 48(4): 277 | | [Pubmed] | [DOI] | | 71 |
Receiver operating characteristic (ROC) curve for medical researchers |
|
| Kumar, R., Indrayan, A. | | Indian Pediatrics. 2011; 48(4): 277-287 | | [Pubmed] | | 72 |
Cut-off scores for the Minimal Eating Observation and Nutrition Form - Version II (MEONF-II) among hospital inpatients |
|
| Westergren, A., Norberg, E., Vallén, C., Hagell, P. | | Food and Nutrition Research. 2011; 55(1) | | [Pubmed] | | 73 |
Cut-off scores for the Minimal Eating Observation and Nutrition Form–Version II (MEONF-II) among hospital inpatients |
|
| Albert Westergren,Erika Norberg,Christina Vallén,Peter Hagell | | Food & Nutrition Research. 2011; 55(00) | | [Pubmed] | [DOI] | | 74 |
Understanding the relevance of sample size calculation |
|
| Nayak, B.K. | | Indian Journal of Ophthalmology. 2010; 58(6): 469-470 | | [Pubmed] | |
|
|
|
|