- Email: [email protected]
Spine Stats
The Spine Journal 1 (2001) 75
Spine Stats The Mantel-Haenszel Chi-Square test For evaluation of a possible linear trend across groups or a time period, when the dependent measure evaluated is continuous (e.g., number of days of disability, pain ratings on an analog scale, etc.), a parametric statistical analysis can be routinely applied, such as an analysis of variance, which can also test for linear trends. Such parametric procedures are well known and widely applied in the scientific literature. However, a less well-known method is available when the dependent measure is categorical and, thus, requires a nonparametric test. One such nonparametric test for evaluating a linear trend across groups or time periods is the Mantel-Haenszel Chi-Square test, which uses an asymptotic chi-square design and measures linear association between the row and column variables in a contingency table. As an example of the use of this test, a recent study by Mayer et al. [1] evaluated whether a patient’s age had an effect on socioeconomic outcomes following a functional restoration program. In this study, patients were placed into five groups based on age. One-year socioeconomic variables were subsequently collected after completion of the functional restoration program. Many of these outcome variables were categorical in nature (e.g., return to work, work retention, etc.). A Mantel-Haenszel Chi-Square test for trend across the five age groups was therefore applied to these data. The results of these tests revealed that the percentage of patients who
returned to work declined progressively from 100% in the youngest group to 69% in the oldest group (P.001). The same trend was also revealed for the percentage of patients retaining work at the end of 1 year (98% to 62%, P.001). This nonparametric test allows investigators to evaluate trends across time or groups when the outcome measure is categorical in nature. Because the test is based on asymptotic theory, it is most appropriate for large samples. This form of the test is not well known and is not routinely described in textbooks on biomedical statistics. However, there are statistical packages that include the Mantel-Haenszel Chi-Square test (SAS) or the equivalent linear-by-linear association (SPSS) as part of their inventory of tests. A more detailed discussion of this test can be found in texts by Altman [2] and Rosner [3]. References [1] Mayer TG, Gatchel RJ, Evans T. Effect of age on outcomes of tertiary rehabilitation for chronic disabling spinal disorders. Spine 2000 (in press). [2] Altman DG. Practical statistics for medical research. London: Chapman and Hall, 1991. [3] Rosner BA. Fundamentals of biostatistics. 2nd ed. Boston: Duxbury Press, 1986.
1529-9430/01/$ – see front matter © 2001 Elsevier Science Inc. All rights reserved. PII: S1529-9430(01)00 0 3 4 - 1
Robert J. Gatchel, Ph.D. Dallas, TX
Spine Stats The Mantel-Haenszel Chi-Square test For evaluation of a possible linear trend across groups or a time period, when the dependent measure evaluated is continuous (e.g., number of days of disability, pain ratings on an analog scale, etc.), a parametric statistical analysis can be routinely applied, such as an analysis of variance, which can also test for linear trends. Such parametric procedures are well known and widely applied in the scientific literature. However, a less well-known method is available when the dependent measure is categorical and, thus, requires a nonparametric test. One such nonparametric test for evaluating a linear trend across groups or time periods is the Mantel-Haenszel Chi-Square test, which uses an asymptotic chi-square design and measures linear association between the row and column variables in a contingency table. As an example of the use of this test, a recent study by Mayer et al. [1] evaluated whether a patient’s age had an effect on socioeconomic outcomes following a functional restoration program. In this study, patients were placed into five groups based on age. One-year socioeconomic variables were subsequently collected after completion of the functional restoration program. Many of these outcome variables were categorical in nature (e.g., return to work, work retention, etc.). A Mantel-Haenszel Chi-Square test for trend across the five age groups was therefore applied to these data. The results of these tests revealed that the percentage of patients who
returned to work declined progressively from 100% in the youngest group to 69% in the oldest group (P.001). The same trend was also revealed for the percentage of patients retaining work at the end of 1 year (98% to 62%, P.001). This nonparametric test allows investigators to evaluate trends across time or groups when the outcome measure is categorical in nature. Because the test is based on asymptotic theory, it is most appropriate for large samples. This form of the test is not well known and is not routinely described in textbooks on biomedical statistics. However, there are statistical packages that include the Mantel-Haenszel Chi-Square test (SAS) or the equivalent linear-by-linear association (SPSS) as part of their inventory of tests. A more detailed discussion of this test can be found in texts by Altman [2] and Rosner [3]. References [1] Mayer TG, Gatchel RJ, Evans T. Effect of age on outcomes of tertiary rehabilitation for chronic disabling spinal disorders. Spine 2000 (in press). [2] Altman DG. Practical statistics for medical research. London: Chapman and Hall, 1991. [3] Rosner BA. Fundamentals of biostatistics. 2nd ed. Boston: Duxbury Press, 1986.
1529-9430/01/$ – see front matter © 2001 Elsevier Science Inc. All rights reserved. PII: S1529-9430(01)00 0 3 4 - 1
Robert J. Gatchel, Ph.D. Dallas, TX