A Modification of the Relative Weightings of Symptoms Utilizing a Logistic Function to Enhance the Linearity of the Brief Psychiatric Rating Scale: A Retrospective Analysis

Affiliation(s)

Department of Psychiatry, Tokyo Women’s Medical University, Tokyo, Japan.

Depression Prevention Medical Center, Kyoto Jujo Rehabilitation Hospital, Kyoto, Japan.

Department of Psychiatry, Tokyo Women’s Medical University, Tokyo, Japan.

Depression Prevention Medical Center, Kyoto Jujo Rehabilitation Hospital, Kyoto, Japan.

ABSTRACT

Introduction: Although the Brief Psychiatric Rating Scale (BPRS) is widely used for evaluating patients with schizophrenia, the meaning of the weights of the individual symptoms is ambiguous. The aims of the study were 1) to investigate whether the modification of relative weights of items of the BPRS is able to enhance its correlation with the Clinical Global Impression-Schizophrenia scale (CGI-SCH) and 2) to construct a potential modified BPRS. Methods: We evaluated 200 schizophrenia patients using the BPRS and the CGI-SCH and drew the scatter plot distributions of the two scales. Next, univariate regression for the CGI-SCH using individual symptoms of the BPRS was performed. Multivariate regression utilizing the ‘logistic function’ was then conducted to allocate marks to each item and Pearson’s r correlation coefficient and r-squared between the two scales were assessed. After that, we constructed an example of a potential modified BPRS. Results: With the scatter plot for the two scales, a logarithmic curve was obtained; this was described by [CGI-SCH] = 3.2248 × ln[18-item BPRS] – 7.2044 (p < 0.001). Pearson’s r for the relationship between the scales was 0.8216 and r-squared was 0.7718 (both p < 0.001). The univariate regression indicated a positive associa- tion between all symptoms of the BPRS and the CGI-SCH, although some of them were significant (p < 0.05) and others were not (p ≥ 0.05). Multivariate regression utilizing a logistic function provided the values “P_{i}” that could express the relative weights of individual symptoms. Subsequently, modification of point allocations according to “P_{i}” yielded a Pearson’s r of 0.8491 and an r-squared of 0.7718 (not changed) (both p < 0.001). An example of a potential modified BPRS was constructed. Conclusions: Within the limits of our data, the weightings of items of the BPRS improved the correlation of the BPRS with the CGI-SCH for evaluating schizophrenia.

Introduction: Although the Brief Psychiatric Rating Scale (BPRS) is widely used for evaluating patients with schizophrenia, the meaning of the weights of the individual symptoms is ambiguous. The aims of the study were 1) to investigate whether the modification of relative weights of items of the BPRS is able to enhance its correlation with the Clinical Global Impression-Schizophrenia scale (CGI-SCH) and 2) to construct a potential modified BPRS. Methods: We evaluated 200 schizophrenia patients using the BPRS and the CGI-SCH and drew the scatter plot distributions of the two scales. Next, univariate regression for the CGI-SCH using individual symptoms of the BPRS was performed. Multivariate regression utilizing the ‘logistic function’ was then conducted to allocate marks to each item and Pearson’s r correlation coefficient and r-squared between the two scales were assessed. After that, we constructed an example of a potential modified BPRS. Results: With the scatter plot for the two scales, a logarithmic curve was obtained; this was described by [CGI-SCH] = 3.2248 × ln[18-item BPRS] – 7.2044 (p < 0.001). Pearson’s r for the relationship between the scales was 0.8216 and r-squared was 0.7718 (both p < 0.001). The univariate regression indicated a positive associa- tion between all symptoms of the BPRS and the CGI-SCH, although some of them were significant (p < 0.05) and others were not (p ≥ 0.05). Multivariate regression utilizing a logistic function provided the values “P

Cite this paper

J. Sawamura, S. Morishita and J. Ishigooka, "A Modification of the Relative Weightings of Symptoms Utilizing a Logistic Function to Enhance the Linearity of the Brief Psychiatric Rating Scale: A Retrospective Analysis,"*Journal of Behavioral and Brain Science*, Vol. 2 No. 2, 2012, pp. 225-238. doi: 10.4236/jbbs.2012.22026.

J. Sawamura, S. Morishita and J. Ishigooka, "A Modification of the Relative Weightings of Symptoms Utilizing a Logistic Function to Enhance the Linearity of the Brief Psychiatric Rating Scale: A Retrospective Analysis,"

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[34] J. S. Brekke, B. Kohrt and M. F. Green, “Neuropsychological Functioning as a Moderator of the Relationship between Psychosocial Functioning and the Subjective Experience of Self and Life in Schizophrenia,” Schizophrenia Bulletin, Vol. 27, No. 4, 2001, pp. 697-708. doi:10.1093/oxfordjournals.schbul.a006908

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[46] C. T. Hartrick and J. P. Kovan, “The Numeric Rating Scale for Clinical Pain Measurement: A Ratio Measure?” Pain Practice, Vol. 3, No. 4, 2003, pp. 310-316. doi:10.1111/j.1530-7085.2003.03034.x

[47] L. J. DeLoach, M. S. Higgins, A. B. Caplan and J. L. Stiff, “The Visual Analog Scale in the Immediate Postoperative Period: Intrasubject Variability and Correlation with a Numeric Scale,” Anesthesia and Analgesia, Vol. 86, No. 1, 1998, pp. 102-106. doi:10.1097/00000539-199801000-00020

[48] P. S. Myles, S. Troedel, M. Boquest and M. Reeves, “The Pain Visual Analog Scale: Is it Linear or Nonlinear?” Anesthesia and Analgesia, Vol. 89, No. 6, 1999, pp. 1517-1520.

[49] S. S. Stevens, “On the Theory of Scales of Measurement,” Science, New Series, Vol. 103, No. 2684, 1946, pp. 677-680. doi:10.1126/science.103.2684.677

[1] J. P. Lindenmayer, P. D. Harvey, A. Khan and B. Kirkpatrick, “Schizophrenia: Measurements of Psychopathology,” Psychiatric Clinics of North America, Vol. 30, No. 3, 2007, pp. 339-363. doi:10.1016/j.psc.2007.04.005

[2] J. E. Overall and D. R. Gorham, “The Brief Psychiatric Rating Scale,” Psychological Reports, Vol. 10, No. 3, 1962, pp. 799-812. doi:10.2466/PR0.10.3.799

[3] J. M. Haro, S. A. Kamath, S. Ochoa, et al., and SOHO Study Group, “The Clinical Global Impression-Schizophrenia Scale: A Simple Instrument to Measure the Diversity of Symptoms Present in Schizophrenia,” Acta Psychiatrica Scandinavica, Suppl. 416, 2003, pp. 16-23. doi:10.1034/j.1600-0447.107.s416.5.x

[4] S. R. Kay, A. Fiszbein and L. A. Opler, “The Positive and Negative Syndrome Scale (PANSS) for Schizophrenia,” Schizophrenia Bulletin, Vol. 13, No. 2, 1987, pp. 261-276. doi:10.1093/schbul/13.2.261

[5] N. C. Andreasen, “Scale for the Assessment of Positive Symptoms (SAPS),” University of Iowa, Iowa City, 1984.

[6] N. C. Andreasen, “Scale for the Assessment of Negative Symptoms (SANS),” University of Iowa, Iowa City, 1983.

[7] J. Sawamura, S. Morishita and J. Ishigooka, “Is There a Linear Relationship between the Brief Psychiatric Rating Scale and the Clinical Global Impression-Schizophrenia scale? A Retrospective Analysis,” BMC Psychiatry, Vol. 10, 2010, p. 105. doi:10.1186/1471-244X-10-105

[8] American Psychiatric Association, “Diagnostic and Statistical Manual of Mental Disorders,” 4th Edition, American Psychiatric Publishing, New York, 2000.

[9] C. R. Mehta and N. R. Patel, “Exact Logistic Regression: Theory and Examples,” Statistics in Medicine, Vol. 14, No. 19, 1995, pp. 2143-2160. doi:10.1002/sim.4780141908

[10] B. J. Friedman, T. Hastie and R. Tibshirani, “Additive Logistic Regression: A Statistical View of Boosting,” The Annals of Statistics, Vol. 28, No. 2, 2000, pp. 337-407. doi:10.1214/aos/1016218223

[11] B. Jorgensen, “Maximum Likelihood Estimation and Large-Sample Inference for Generalized Linear and Nonlinear Regression Model,” Biometrika, Vol. 70, No. 1, 1983, pp. 19-28. doi:10.1093/biomet/70.1.19

[12] F. E. Harrell, Jr., “Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis,” 2nd Edition, Springer-Verlag, New York, 2001.

[13] F. E. Harrell, Jr. and K. L. Lee, “Regression Modeling Strategies for Improved Prognostic Prediction,” Statistics in Medicine, Vol. 3, No. 2, 1984, pp. 143-152. doi:10.1002/sim.4780030207

[14] F. E. Harrell, Jr., K. L. Lee, D. B. Matchar and T. A. Richert, “Regression Models for Prognostic Prediction: Advantages, Problems, and Suggested Solutions,” Cancer Treatment Reports, Vol. 69, No. 10, 1985, pp. 1071-1077.

[15] T. R. Dawber, G. F. Meadors and F. E. Moore, Jr., “Epidemiological Approaches to Heart Disease: The Framingham Study,” American Journal of Public Health, Vol. 41, No. 3, 1951, pp. 279-286. doi:10.2105/AJPH.41.3.279

[16] T. Gordon, W. P. Castelli, M. C. Hjortland, W. B. Kannel and T. R. Dawber, “High Density Lipoprotein as a Protective Factor Against Coronary Heart Disease,” The American Journal of Medicine, Vol. 62, No. 5, 1977, pp. 707-714. doi:10.1016/0002-9343(77)90874-9

[17] S. H. Walker and D. Duncan, “Estimation of the Probability of an Event as a Function of Several Independent Variables,” Biometrika, Vol. 54, No. 1-2, 1967, pp. 167-179. doi:10.1093/biomet/54.1-2.167

[18] Stata Corporation, “Stata User’s Guide, Release 10.0,” Stata Corporation, College Station, 2007.

[19] SPSS Inc, “SPSS for Windows Version 14,” SPSS Inc, Chicago, 2007.

[20] Microsoft Corporation, “Microsoft Excel 2003,” Microsoft Corp., Redmond, Washington DC, 2003.

[21] J. L. Hedlund and B. W. Vieweg, “The Brief Psychiatric Rating Scale (BPRS): A Comprehensive Review,” Journal of Operational Psychiatry, Vol. 11, No. 1, 1980, PP. 48-65.

[22] S. Leucht and R. Engel, “The Relative Sensitivity of the Clinical Global Impressions Scale and the Brief Psychiatric Rating Scale in Antipsychotic Drug Trials,” Neuropsychopharmacology, Vol. 31, No. 2, 2006, pp. 406-412. doi:10.1038/sj.npp.1300873

[23] S. Leucht, J. M. Kane, W. Kissling, J. Hamann, E. Etschel and R. Engel, “Clinical Implications of Brief Psychiatric Rating Scale Scores,” British Journal of Psychiatry, Vol. 187, No. 4, 2005, pp. 366-371. doi:10.1192/bjp.187.4.366

[24] M. J. Müller, W. Rossbach, P. Dannigkeit, F. Müller-Siecheneder, A. Szegedi and H. Wetzel, “Evaluation of Standardized Rater Training for the Positive and Negative Syndrome Scale (PANSS),” Schizophrenia Research, Vol. 32, No. 3, 1998, pp. 151-160. doi:10.1016/S0920-9964(98)00051-6

[25] D. Naber and A. Vita, “Tools for Measuring Clinical Effectiveness,” European Neuropsychopharmacology, Vol. 14, Suppl. 4, 2004, pp. S435-S444. doi:10.1016/j.euroneuro.2004.08.002

[26] R. Tandon, R. F. Devellis, J. Han, H. Li, S. Frangou, S. Dursun, J. N. Beuzen, W. Carson, P. K. Corey-Lisle, B. Falissard, D. N. Jody, M. J. Kujawa, G. L’italien, R. N. Marcus, R. D. McQuade, S. Ray, P. Van Peborgh and IAQ Validation Study Group, “Validation of the Investigator’s Assessment Questionnaire, a New Clinical Tool for Relative Assessment of Response to Antipsychotics in Patients with Schizophrenia and Schizoaffective Disorder,” Psychiatry Research, Vol. 136, No. 2-3, 2005, pp. 211-221. doi:10.101

[27] T. Suzuki, H. Uchida, K. Nomura, H. Takeuchi, S. Nakajima, A. Tanabe, G. Yagi, K. Watanabe and H. Kashima, “Novel Rating Scales for Schizophrenia-Targeted Inventory on Problems in Schizophrenia (TIP-Sz) and Functional Assessment for Comprehensive Treatment of Schizophrenia (FACT-Sz),” Schizophrenia Research, Vol. 106, No. 2-3, 2008, pp. 328-336. doi:10.1016/j.schres.2008.08.013

[28] G. M. Burlingame, T. W. Dunn, S. Chen, A. Lehman, R. Axman, D. Earnshaw and F. M. Rees, “Selection of Outcome Assessment Instruments for Inpatients with Severe and Persistent Mental Illness,” Psychiatric Services, Vol. 56, No. 4, 2005, pp. 444-451. doi:10.1176/appi.ps.56.4.444

[29] C. M. Judd, G. H. McClelland and C. S. Ryan, “Data Analysis: A Model Comparison Approach,” 2nd Edition, Routledge/Taylor and Francis, New York, 2009.

[30] F. E. Harrell, “Regression Modeling Strategies with Applications to Linear Models, Logistic Regression, and Survival Analysis,” Springer, New York, 2001.

[31] J. Lee, “Covariance Adjustment of Rates Based on the Multiple Logistic Regression Model,” Journal of Chronic Diseases, Vol. 34, No. 8, 1981, pp. 415-426. doi:10.1016/0021-9681(81)90040-0

[32] K.-Y. Liang and S. L. Zeger, “Longitudinal Data Analysis Using Generalized Linear Models,” Biometrika, Vol. 73, No. 1, 1986, pp. 13-22. doi:10.1093/biomet/73.1.13

[33] D. Mullins, B. Prefferbaum, H. Schultz and J. E. Overall, “Brief Psychiatric Rating Scale for Children: Quantitative Scoring of Medical Records,” Psychiatric Research, Vol. 19, No. 1, 1986, pp. 43-49. doi:10.1016/0165-1781(86)90091-0

[34] J. S. Brekke, B. Kohrt and M. F. Green, “Neuropsychological Functioning as a Moderator of the Relationship between Psychosocial Functioning and the Subjective Experience of Self and Life in Schizophrenia,” Schizophrenia Bulletin, Vol. 27, No. 4, 2001, pp. 697-708. doi:10.1093/oxfordjournals.schbul.a006908

[35] M. H. S. Hayes and D. G. Patterson, “Experimental Development of the Graphic Rating Method,” Psychological Bulletin, Vol. 18, 1921, pp. 98-99.

[36] M. Freyd, “The Graphic Rating Scale,” Journal of Educational Psychology, Vol. 14, No. 2, 1923, pp. 83-102. doi:10.1037/h0074329

[37] N. Crichton, “Information Point: Visual Analogue Scale (VAS),” Journal of Clinical Nursing, Vol. 10, No. 5, 2001, pp. 697-706.

[38] G. B. Langley and H. Sheppeard, “The Visual Analogue Scale: Its Use in Pain Measurement,” Rheumatology International, Vol. 5, No. 4, 1985, pp. 145-148. doi:10.1007/BF00541514

[39] V. A. L. Williams, R. Morlock and D. Feltner, “Psychometric Evaluation of Visual Analog Scale for the Assessment of Anxiety,” Health and Quality of Life Outcomes, Vol. 8, 2010, p. 57. doi:10.1186/1477-7525-8-57

[40] D. C. Turk, T. E. Rudy and B. A. Sorkin, “Neglected Topics in Chronic Pain Treatment Outcome Studies: Determination of Success,” Pain, Vol. 53, No. 1, 1993, pp. 3-16. doi:10.1016/0304-3959(93)90049-U

[41] J. T. Farrar, J. P. Young, Jr., L. LaMoreaux, J. L. Werth and R. M. Poole, “Clinical Importance of Changes in Chronic Pain Intensity Measured on an 11-Point Numerical Pain Rating Scale,” Pain, Vol. 94, No. 2, 2001, pp. 149-158. doi:10.1016/S0304-3959(01)00349-9

[42] D. D. Price, P. A. McGrath, A. Rafii and B. Buckingham, “The Validation of Visual Analogue Scales as Ratio Scale Measures for Chronic and Experimental Pain,” Pain, Vol. 17, No. 1, 1983, pp. 45-56. doi:10.1016/0304-3959(83)90126-4

[43] J. S. Dixon and H. A. Bird, “Reproducibility along a 10 cm vertical Visual Analogue Scale,” Annals of the Rheumatic Diseases, Vol. 40, No. 1, 1981, pp. 87-89. doi:10.1136/ard.40.1.87

[44] C. Maxwell, “Sensitivity and Accuracy of the Visual Analogue Scale: A Psycho-Physical Classroom Experiment,” British Journal of Clinical Pharmacology, Vol. 6, No. 1, 1978, pp. 15-24.

[45] S. M. Grunberg, S. Groshen, S. Steingrass, S. Zaretsky and B. Meyerowitz, “Comparison of Conditional Quality of Life Terminology and Visual Analogue Scale Measurements,” Quality of Life Research, Vol. 5, No. 1, 1996, pp. 65-72. doi:10.1007/BF00435970

[46] C. T. Hartrick and J. P. Kovan, “The Numeric Rating Scale for Clinical Pain Measurement: A Ratio Measure?” Pain Practice, Vol. 3, No. 4, 2003, pp. 310-316. doi:10.1111/j.1530-7085.2003.03034.x

[47] L. J. DeLoach, M. S. Higgins, A. B. Caplan and J. L. Stiff, “The Visual Analog Scale in the Immediate Postoperative Period: Intrasubject Variability and Correlation with a Numeric Scale,” Anesthesia and Analgesia, Vol. 86, No. 1, 1998, pp. 102-106. doi:10.1097/00000539-199801000-00020

[48] P. S. Myles, S. Troedel, M. Boquest and M. Reeves, “The Pain Visual Analog Scale: Is it Linear or Nonlinear?” Anesthesia and Analgesia, Vol. 89, No. 6, 1999, pp. 1517-1520.

[49] S. S. Stevens, “On the Theory of Scales of Measurement,” Science, New Series, Vol. 103, No. 2684, 1946, pp. 677-680. doi:10.1126/science.103.2684.677