Statistical Techniques For Comparing Measurers And Methods Of Measurement: A Critical Review
SUMMARY 1. Clinical and experimental pharmacologists and physiologists often wish to compare two methods of measurement, or two measurers. 2. Biostatisticians insist that what should be sought is not agreement between methods or measurers, but disagreement or bias. 3. If measurements have been made...
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| Veröffentlicht in: | Clinical and experimental pharmacology & physiology Jg. 29; H. 7; S. 527 - 536 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Melbourne, Australia
Blackwell Science Pty
01.07.2002
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| ISSN: | 0305-1870, 1440-1681 |
| Online-Zugang: | Volltext |
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| Abstract | SUMMARY
1. Clinical and experimental pharmacologists and physiologists often wish to compare two methods of measurement, or two measurers.
2. Biostatisticians insist that what should be sought is not agreement between methods or measurers, but disagreement or bias.
3. If measurements have been made on a continuous scale, the main choice is between the Altman–Bland method of differences and least products regression analysis. It is argued that although the former is relatively simple to execute, it does not distinguish adequately between fixed and proportional bias. Least products regression analysis, although more difficult to execute, does achieve this goal. There is almost universal agreement among biostatisticians that the Pearson product–moment correlation coefficient (r) is valueless as a test for bias.
4. If measurements have been made on a categorical scale, unordered or ordered, the most popular method of analysis is to use the kappa statistic. If the categories are unordered, the unweighted kappa statistic (K) is appropriate. If the categories are ordered, as they are in most rating scales in clinical, psychological and epidemiological research, the weighted kappa statistic (Kw) is preferable. But Kw
corresponds to the intraclass correlation coefficient, which, like r for continuous variables, is incapable of detecting bias. Simple techniques for detecting bias in the case of ordered categorical variables are described and commended to investigators. |
|---|---|
| AbstractList | 1. Clinical and experimental pharmacologists and physiologists often wish to compare two methods of measurement, or two measurers. 2. Biostatisticians insist that what should be sought is not agreement between methods or measurers, but disagreement or bias. 3. If measurements have been made on a continuous scale, the main choice is between the Altman-Bland method of differences and least products regression analysis. It is argued that although the former is relatively simple to execute, it does not distinguish adequately between fixed and proportional bias. Least products regression analysis, although more difficult to execute, does achieve this goal. There is almost universal agreement among biostatisticians that the Pearson product-moment correlation coefficient (r) is valueless as a test for bias. 4. If measurements have been made on a categorical scale, unordered or ordered, the most popular method of analysis is to use the kappa statistic. If the categories are unordered, the unweighted kappa statistic (K) is appropriate. If the categories are ordered, as they are in most rating scales in clinical, psychological and epidemiological research, the weighted kappa statistic (K(w)) is preferable. But K(w) corresponds to the intraclass correlation coefficient, which, like r for continuous variables, is incapable of detecting bias. Simple techniques for detecting bias in the case of ordered categorical variables are described and commended to investigators. 1. Clinical and experimental pharmacologists and physiologists often wish to compare two methods of measurement, or two measurers. 2. Biostatisticians insist that what should be sought is not agreement between methods or measurers, but disagreement or bias. 3. If measurements have been made on a continuous scale, the main choice is between the Altman–Bland method of differences and least products regression analysis. It is argued that although the former is relatively simple to execute, it does not distinguish adequately between fixed and proportional bias. Least products regression analysis, although more difficult to execute, does achieve this goal. There is almost universal agreement among biostatisticians that the Pearson product–moment correlation coefficient ( r ) is valueless as a test for bias. 4. If measurements have been made on a categorical scale, unordered or ordered, the most popular method of analysis is to use the kappa statistic. If the categories are unordered, the unweighted kappa statistic (K) is appropriate. If the categories are ordered, as they are in most rating scales in clinical, psychological and epidemiological research, the weighted kappa statistic (K w ) is preferable. But K w corresponds to the intraclass correlation coefficient, which, like r for continuous variables, is incapable of detecting bias. Simple techniques for detecting bias in the case of ordered categorical variables are described and commended to investigators. SUMMARY 1. Clinical and experimental pharmacologists and physiologists often wish to compare two methods of measurement, or two measurers. 2. Biostatisticians insist that what should be sought is not agreement between methods or measurers, but disagreement or bias. 3. If measurements have been made on a continuous scale, the main choice is between the Altman–Bland method of differences and least products regression analysis. It is argued that although the former is relatively simple to execute, it does not distinguish adequately between fixed and proportional bias. Least products regression analysis, although more difficult to execute, does achieve this goal. There is almost universal agreement among biostatisticians that the Pearson product–moment correlation coefficient (r) is valueless as a test for bias. 4. If measurements have been made on a categorical scale, unordered or ordered, the most popular method of analysis is to use the kappa statistic. If the categories are unordered, the unweighted kappa statistic (K) is appropriate. If the categories are ordered, as they are in most rating scales in clinical, psychological and epidemiological research, the weighted kappa statistic (Kw) is preferable. But Kw corresponds to the intraclass correlation coefficient, which, like r for continuous variables, is incapable of detecting bias. Simple techniques for detecting bias in the case of ordered categorical variables are described and commended to investigators. 1. Clinical and experimental pharmacologists and physiologists often wish to compare two methods of measurement, or two measurers. 2. Biostatisticians insist that what should be sought is not agreement between methods or measurers, but disagreement or bias. 3. If measurements have been made on a continuous scale, the main choice is between the Altman-Bland method of differences and least products regression analysis. It is argued that although the former is relatively simple to execute, it does not distinguish adequately between fixed and proportional bias. Least products regression analysis, although more difficult to execute, does achieve this goal. There is almost universal agreement among biostatisticians that the Pearson product-moment correlation coefficient (r) is valueless as a test for bias. 4. If measurements have been made on a categorical scale, unordered or ordered, the most popular method of analysis is to use the kappa statistic. If the categories are unordered, the unweighted kappa statistic (K) is appropriate. If the categories are ordered, as they are in most rating scales in clinical, psychological and epidemiological research, the weighted kappa statistic (K(w)) is preferable. But K(w) corresponds to the intraclass correlation coefficient, which, like r for continuous variables, is incapable of detecting bias. Simple techniques for detecting bias in the case of ordered categorical variables are described and commended to investigators.1. Clinical and experimental pharmacologists and physiologists often wish to compare two methods of measurement, or two measurers. 2. Biostatisticians insist that what should be sought is not agreement between methods or measurers, but disagreement or bias. 3. If measurements have been made on a continuous scale, the main choice is between the Altman-Bland method of differences and least products regression analysis. It is argued that although the former is relatively simple to execute, it does not distinguish adequately between fixed and proportional bias. Least products regression analysis, although more difficult to execute, does achieve this goal. There is almost universal agreement among biostatisticians that the Pearson product-moment correlation coefficient (r) is valueless as a test for bias. 4. If measurements have been made on a categorical scale, unordered or ordered, the most popular method of analysis is to use the kappa statistic. If the categories are unordered, the unweighted kappa statistic (K) is appropriate. If the categories are ordered, as they are in most rating scales in clinical, psychological and epidemiological research, the weighted kappa statistic (K(w)) is preferable. But K(w) corresponds to the intraclass correlation coefficient, which, like r for continuous variables, is incapable of detecting bias. Simple techniques for detecting bias in the case of ordered categorical variables are described and commended to investigators. |
| Author | Ludbrook, John |
| Author_xml | – sequence: 1 givenname: John surname: Ludbrook fullname: Ludbrook, John organization: The University of Melbourne, Parkville, Victoria, Australia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/12060093$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1177/001316446002000104 10.1054/jocn.2001.0960 10.2307/2529310 10.1136/bmj.292.6522.746 10.1016/0895-4356(93)90018-V 10.1016/0895-4356(94)90137-6 10.1037/h0026256 10.1016/0895-4356(93)90173-X 10.1161/01.CIR.64.6.1227 10.1111/j.1440-1681.1997.tb01807.x 10.1098/rsta.1896.0007 10.1038/clpt.1981.18 10.1161/01.STR.20.7.864 10.1037/h0028106 10.1016/0010-4825(90)90013-F 10.2307/2987937 10.1177/096228029900800204 10.1177/001316447303300309 |
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| References | Altman DG, Bland JM. Measurement in medicine: The analysis of method comparison studies. Statistician 1983; 32: 307-17. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat. Meth. Med. Res. 1999; 8: 135-60. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977; 33: 159-74. Fleiss JL, Cohen J, Everitt BS. Large sample standard errors of kappa and weighted kappa. Psychol. Bull. 1969; 72: 323-7. Cohen J. Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychol. Bull. 1968; 70: 213-20. Bland JM, Altman DG. A note on the use of the intraclass correlation coefficient in the evaluation of agreement between two methods of measurement. Comput. Biol. Med. 1990; 20: 337-40. Goldman L, Hashimoto B, Cook EF, Loscalzo A. Comparative reproducibility and validity of systems for assessing cardiovascular functional class: Advantages of a new specific activity scale. Circulation 1981; 64: 1227-34. Mahoney F, Barthel D. Functional evaluation: The Barthel Index. Md State Med. J. 1965; 14: 61-5. Byrt T, Bishop J, Carlin JB. Bias, prevalence and kappa. J. Clin. Epidemiol. 1993; 46: 423-9. Gardner MJ, Altman DG. Confidence intervals rather than P values: Estimation rather than hypothesis testing. BMJ 1986; 292: 746-50. Pearson K. Mathematical contributions to the theory of evolution. III. Regression, heredity and panmixia. Phil. Trans. R. Soc. Lond. A 1896; 187: 253-318. Fleiss JL, Cohen J. The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educ. Psychol. Meas. 1973; 33: 613-19. Kramer MS, Feinstein AR. Clinical biostatistics. LIV. The biostatistics of concordance. Clin. Pharmacol. Ther. 1981; 29: 111-23. Graham P, Jackson R. The analysis of ordinal agreement data: Beyond weighted kappa. J. Clin. Epidemiol. 1993; 46: 1055-62. Teasdale G, Murray G, Parker L, Jennett B. Adding up the Glasgow Coma Score. Acta Neurochir. Suppl. 1979; 28: 13-16. Brott T, Adams HP, Olinger CP et al. Measurements of acute cerebral infarction: A clinical examination scale. Stroke 1989; 20: 864-70. Ludbrook J. Comparing methods of measurement. Clin. Exp. Pharmacol. Physiol. 1997; 24: 193-203. Cohen J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 1960; 20: 37-46. May SM. Modelling observer agreement: An alternative to kappa. J. Clin. Epidemiol. 1994; 47: 1315-24. 1979; 28 1990; 20 1993; 46 1989; 20 1965; 14 1960; 20 1973; 33 1969; 72 1986; 292 1997; 24 1983; 32 1994; 47 1981; 29 1977; 33 1896; 187 1999; 8 1968; 70 1981; 64 e_1_2_5_14_2 e_1_2_5_13_2 e_1_2_5_9_2 e_1_2_5_16_2 e_1_2_5_24_2 e_1_2_5_8_2 e_1_2_5_15_2 e_1_2_5_25_2 e_1_2_5_7_2 e_1_2_5_22_2 e_1_2_5_6_2 e_1_2_5_23_2 e_1_2_5_5_2 e_1_2_5_20_2 e_1_2_5_4_2 e_1_2_5_11_2 e_1_2_5_21_2 e_1_2_5_3_2 e_1_2_5_2_2 Mahoney F (e_1_2_5_10_2) 1965; 14 e_1_2_5_18_2 e_1_2_5_17_2 e_1_2_5_19_2 Teasdale G (e_1_2_5_12_2) 1979; 28 |
| References_xml | – reference: Ludbrook J. Comparing methods of measurement. Clin. Exp. Pharmacol. Physiol. 1997; 24: 193-203. – reference: Byrt T, Bishop J, Carlin JB. Bias, prevalence and kappa. J. Clin. Epidemiol. 1993; 46: 423-9. – reference: Fleiss JL, Cohen J. The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educ. Psychol. Meas. 1973; 33: 613-19. – reference: Goldman L, Hashimoto B, Cook EF, Loscalzo A. Comparative reproducibility and validity of systems for assessing cardiovascular functional class: Advantages of a new specific activity scale. Circulation 1981; 64: 1227-34. – reference: Altman DG, Bland JM. Measurement in medicine: The analysis of method comparison studies. Statistician 1983; 32: 307-17. – reference: Brott T, Adams HP, Olinger CP et al. Measurements of acute cerebral infarction: A clinical examination scale. Stroke 1989; 20: 864-70. – reference: Kramer MS, Feinstein AR. Clinical biostatistics. LIV. The biostatistics of concordance. Clin. Pharmacol. Ther. 1981; 29: 111-23. – reference: Mahoney F, Barthel D. Functional evaluation: The Barthel Index. Md State Med. J. 1965; 14: 61-5. – reference: Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977; 33: 159-74. – reference: May SM. Modelling observer agreement: An alternative to kappa. J. Clin. Epidemiol. 1994; 47: 1315-24. – reference: Cohen J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 1960; 20: 37-46. – reference: Gardner MJ, Altman DG. Confidence intervals rather than P values: Estimation rather than hypothesis testing. BMJ 1986; 292: 746-50. – reference: Cohen J. Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychol. Bull. 1968; 70: 213-20. – reference: Fleiss JL, Cohen J, Everitt BS. Large sample standard errors of kappa and weighted kappa. Psychol. Bull. 1969; 72: 323-7. – reference: Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat. Meth. Med. Res. 1999; 8: 135-60. – reference: Teasdale G, Murray G, Parker L, Jennett B. Adding up the Glasgow Coma Score. Acta Neurochir. Suppl. 1979; 28: 13-16. – reference: Graham P, Jackson R. The analysis of ordinal agreement data: Beyond weighted kappa. J. Clin. Epidemiol. 1993; 46: 1055-62. – reference: Pearson K. Mathematical contributions to the theory of evolution. III. Regression, heredity and panmixia. Phil. Trans. R. Soc. Lond. A 1896; 187: 253-318. – reference: Bland JM, Altman DG. A note on the use of the intraclass correlation coefficient in the evaluation of agreement between two methods of measurement. Comput. Biol. Med. 1990; 20: 337-40. – volume: 46 start-page: 1055 year: 1993 end-page: 62 article-title: The analysis of ordinal agreement data: Beyond weighted kappa. publication-title: J. Clin. Epidemiol. – volume: 28 start-page: 13 year: 1979 end-page: 16 article-title: Adding up the Glasgow Coma Score. publication-title: Acta Neurochir. Suppl. – volume: 70 start-page: 213 year: 1968 end-page: 20 article-title: Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. publication-title: Psychol. Bull. – volume: 33 start-page: 613 year: 1973 end-page: 19 article-title: The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. publication-title: Educ. Psychol. Meas. – volume: 20 start-page: 337 year: 1990 end-page: 40 article-title: A note on the use of the intraclass correlation coefficient in the evaluation of agreement between two methods of measurement. publication-title: Comput. Biol. Med. – volume: 33 start-page: 159 year: 1977 end-page: 74 article-title: The measurement of observer agreement for categorical data. publication-title: Biometrics – volume: 29 start-page: 111 year: 1981 end-page: 23 article-title: Clinical biostatistics. LIV. The biostatistics of concordance. publication-title: Clin. Pharmacol. Ther. – volume: 46 start-page: 423 year: 1993 end-page: 9 article-title: Bias, prevalence and kappa. publication-title: J. Clin. Epidemiol. – volume: 47 start-page: 1315 year: 1994 end-page: 24 article-title: Modelling observer agreement: An alternative to kappa. publication-title: J. Clin. 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J. – volume: 20 start-page: 37 year: 1960 end-page: 46 article-title: A coefficient of agreement for nominal scales. publication-title: Educ. Psychol. Meas. – volume: 292 start-page: 746 year: 1986 end-page: 50 article-title: Confidence intervals rather than values: Estimation rather than hypothesis testing. publication-title: BMJ – volume: 64 start-page: 1227 year: 1981 end-page: 34 article-title: Comparative reproducibility and validity of systems for assessing cardiovascular functional class: Advantages of a new specific activity scale. publication-title: Circulation – volume: 24 start-page: 193 year: 1997 end-page: 203 article-title: Comparing methods of measurement. publication-title: Clin. Exp. Pharmacol. Physiol. – volume: 8 start-page: 135 year: 1999 end-page: 60 article-title: Measuring agreement in method comparison studies. publication-title: Stat. Meth. Med. Res. – ident: e_1_2_5_13_2 doi: 10.1177/001316446002000104 – volume: 14 start-page: 61 year: 1965 ident: e_1_2_5_10_2 article-title: Functional evaluation: The Barthel Index. publication-title: Md State Med. J. – ident: e_1_2_5_16_2 – ident: e_1_2_5_19_2 doi: 10.1054/jocn.2001.0960 – ident: e_1_2_5_22_2 doi: 10.2307/2529310 – ident: e_1_2_5_5_2 doi: 10.1136/bmj.292.6522.746 – ident: e_1_2_5_15_2 – ident: e_1_2_5_23_2 doi: 10.1016/0895-4356(93)90018-V – ident: e_1_2_5_25_2 doi: 10.1016/0895-4356(94)90137-6 – ident: e_1_2_5_14_2 doi: 10.1037/h0026256 – ident: e_1_2_5_17_2 – ident: e_1_2_5_24_2 doi: 10.1016/0895-4356(93)90173-X – ident: e_1_2_5_6_2 – ident: e_1_2_5_9_2 doi: 10.1161/01.CIR.64.6.1227 – ident: e_1_2_5_2_2 doi: 10.1111/j.1440-1681.1997.tb01807.x – ident: e_1_2_5_7_2 doi: 10.1098/rsta.1896.0007 – ident: e_1_2_5_18_2 doi: 10.1038/clpt.1981.18 – volume: 28 start-page: 13 year: 1979 ident: e_1_2_5_12_2 article-title: Adding up the Glasgow Coma Score. publication-title: Acta Neurochir. 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1. Clinical and experimental pharmacologists and physiologists often wish to compare two methods of measurement, or two measurers.
2. Biostatisticians... 1. Clinical and experimental pharmacologists and physiologists often wish to compare two methods of measurement, or two measurers. 2. Biostatisticians insist... 1. Clinical and experimental pharmacologists and physiologists often wish to compare two methods of measurement, or two measurers. 2. Biostatisticians insist... |
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| SubjectTerms | Bias categorical variables continuous variables correlation fixed bias kappa statistic least products regression analysis limits of agreement log-linear modelling McNemar test method of differences proportional bias Reproducibility of Results Research Design - statistics & numerical data Statistics as Topic - methods |
| Title | Statistical Techniques For Comparing Measurers And Methods Of Measurement: A Critical Review |
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