Ҏ߱ Ҏલɿ – ਓͷ͏ͭපෆ҆ʹର͢Δ$#5ͷ3$5݅ • ରর܈5"6 8- QJMMQMBDFCPʹݶఆ – ཚੜ ׂΓ͚ͷӅณԽ ධՁऀͷݕԽ ΞτΧϜଌ ఆͷશੑ *55Λ࣮ࢪ͍ͯ͠Δ͔ʣΛͭΛ९क͍ͯ͠Ε IJHIRVBMJUZͱஅ Cuijpers, P., Cristea, I. A., Karyotaki, E., Reijnders, M., & Huibers, M. J. (2016). How effecMve are cogniMve behavior therapies for major depression and anxiety disorders? A meta-analyMc update of the evidence. World Psychiatry, 15(3), 245-258.
C. (2017). Is cogniMve behavioral therapy the gold standard for psychotherapy?: the need for plurality in treatment and research. Jama, 318(14), 1323-1324. David, D., Cristea, I., & Hofmann, S. G. (2018). Why cogniMve behavioral therapy is the current gold standard of psychotherapy. FronMers in psychiatry, 9, 4.
( Leichsenring, F., & Steinert, C. (2017). Is cogniMve behavioral therapy the gold standard for psychotherapy?: the need for plurality in treatment and research. Jama, 318(14), 1323-1324.
- - TAU TAU David, D., Cristea, I., & Hofmann, S. G. (2018). Why cogniMve behavioral therapy is the current gold standard of psychotherapy. FronMers in psychiatry, 9, 4.
de Oliveira, G. S. D. A., Tacon-Ramos, T., Manfro, G. G., & Salum, G. A. (2019). Anxiety and stress-related disorders and mindfulness-based intervenMons: a systemaMc review and mulMlevel meta-analysis and meta-regression of mulMple outcomes. Mindfulness, 10(6), 996-1005.
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Friede, T., Hegerl, U., Mohr, D. C., & Oce, C. (2017). Control condiMons for randomised trials of behavioural intervenMons in psychiatry: a decision framework. The Lancet Psychiatry, 4(9), 725-732.
Hegerl, U., Mohr, D. C., & Oce, C. (2017). Control condiMons for randomised trials of behavioural intervenMons in psychiatry: a decision framework. The Lancet Psychiatry, 4(9), 725-732. ױऀͷϦεΫ ࣬ױͷಛੑʣ அͷҧ͍ ྟচతಛͷҧ͍ ౷੍܈ͷ༧ظظ ϊηϘͷੜ͢͡͞ ෳ༗ޮͳ࣏ྍ͕͋Δ߹ʹ ະ࣏ྍ8-Λબ͢Δ߹ͷྙཧత
Hegerl, U., Mohr, D. C., & Oce, C. (2017). Control condiMons for randomised trials of behavioural intervenMons in psychiatry: a decision framework. The Lancet Psychiatry, 4(9), 725-732. ྟচݚڀͷϑΣʔζ 1IBTF୳ࡧతͳ༰ྔ୳ࡧͳͲGFBTJCJMJUZ 1IBTF༧උతͳޮՌݚڀ લޙൺֱ খن3$5 1IBTFݕূతͳޮՌݚڀ େن3$5 1IBTF༗ޮੑݚڀ ࣮ࢼݧʣ
Hegerl, U., Mohr, D. C., & Oce, C. (2017). Control condiMons for randomised trials of behavioural intervenMons in psychiatry: a decision framework. The Lancet Psychiatry, 4(9), 725-732. ݚڀࢿݯ ΞΫςΟϒͳ౷੍܈ίετ͕͔͔Δ հೖऀͷޏ༻ͳͲ খ͞ͳޮՌΛݕग़͢ΔͨΊʹɺେ͖ͳαϯ ϓϧαΠζ͕ඞཁ ˠαϯϓϧαΠζ࠶ਪఆదԠܕσβΠϯ༗ޮ
Hegerl, U., Mohr, D. C., & Oce, C. (2017). Control condiMons for randomised trials of behavioural intervenMons in psychiatry: a decision framework. The Lancet Psychiatry, 4(9), 725-732. 3$5ͷ࣮ࢪऀɺࢼݧͷϓϩτίϧจʹ ࣄલʹ౷੍܈બͷਖ਼ੑʹ͍ͭͯৄࡉʹه ࡌ͖͢ ౷੍܈ΛબͿͱ͖ɺױऀूஂͱؔ࿈ϦεΫɺ ݚڀͷϑΣʔζΛߟྀ ϝλੳ࣏ྍΨΠυϥΠϯͰ౷੍܈ͷใ Λ໌͖ࣔ͢ ࣏ྍΨΠυϥΠϯʹɺػϦετ܈Ͱͷ3$5 Ͱ͔͠༗ޮੑ͕ࣔ͞Ε͍ͯͳ͍ͷܝࡌ͠ ͳ͍
Hegerl, U., Mohr, D. C., & Oce, C. (2017). Control condiMons for randomised trials of behavioural intervenMons in psychiatry: a decision framework. The Lancet Psychiatry, 4(9), 725-732. 1IBTF୳ࡧతͳ༰ྔ୳ࡧͳͲGFBTJCJMJUZ 1IBTF༧උతͳޮՌݚڀ લޙൺֱ খن3$5 4$&% 1IBTFݕূతͳޮՌݚڀ ( RCT) 1IBTF༗ޮੑݚڀ ࣮ࢼݧ 4$&% ʣ
T. R., Hitchcock, J., Horner, R. H., Levin, J. R., Odom, S. L., Rindskopf, D. M & Shadish, W. R. (2010). Single-case designs technical documentaMon. Retrieved from What Works Clearinghouse website: hcp://ies.ed.gov/ncee/wwc/pdf/wwc_scd.pdf. ϕʔεϥΠϯ ظ A հೖظ
• ྫूੵͷݱ࣮తͳࠔ • ౷੍܈ͷׂΓͯͷྙཧత • RCTࣗମͷݶքͷࢦఠ RCTͱ࣮ྟচͷױऀͱͷဃݸਓࠩ Barlow, D. H., Nock, M., & Hersen, M. (2009). Single case experimental designs: Strategies for studying behavior for change (No. Sirsi) i9780205474554).
reading panel) • ࠃཱڭҭධՁɾҬࢧԉηϯλʔͷWhat Works ClearinghouseʹΑΔਪ Kratochwill, T. R., & Levin, J. R. (2014). Single-case intervention research: Methodological and statistical advances. American Psychological Association.
(2000)͕JAMAץߦจͰ ΤϏσϯε֊ͷτοϓ • ΞϝϦΧҩֶձ(2000)ͰΤϏσϯε֊ ͷτοϓ • Oxford center for Evidence based medicineͷΤϏσϯεϨϕϧ(2011) Ͱ”ڧݻͳΤϏσϯε”ʹ֘ Mirza, R. D., Punja, S., Vohra, S., & Guyac, G. (2017). The history and development of N-of-1 trials. Journal of the Royal Society of Medicine, 110(8), 330-340. Guyac, G. H., Haynes, R. B., Jaeschke, R. Z., Cook, D. J., Green, L., Naylor, C. D., ... & Evidence- Based Medicine Working Group. (2000). Users' guides to the medical literature: XXV. Evidence- based medicine: principles for applying the users' guides to paMent care. Jama, 284(10), 1290-1296.
Λ෮ 4. ֤ϑΣʔζͰͷϨϕϧɺੑɺมಈੑͷ໌ࣔ 5. ޮՌͷ໌ࣔ ޮՌͷimmediacy / ॏෳ / ϑΣʔζؒͰͷσʔλͷҰ؏ੑ ؍σʔλͱ༧ଌσʔλͷύλʔϯΛൺֱ 6. ֎తཁҼͱಛҟੑͷݕ౼ Institute of Education Sciences. (2014). What Works Clearinghouse procedures and standards handbook.
Rosenkoecer, U., Wakim, D., Godbee, K., Togher, L., & McDonald, S. (2013). Revision of a method quality raMng scale for single-case experimental designs and n-of-1 trials: The 15-item Risk of Bias in N-of-1 Trials (RoBiNT) Scale. Neuropsychological rehabilitaMon, 23(5), 619-638. Tate, R. L., Perdices, M., Rosenkoecer, U., McDonald, S., Togher, L., Shadish, W., ... & Sampson, M. (2016). The Single-Case ReporMng Guideline In BEhavioural IntervenMons (SCRIBE) 2016: ExplanaMon and elaboraMon. Archives of ScienMfic Psychology, 4(1), 10.
• ฏۉࠩ1)4ͷඪ४ԽޮՌྔ ෳ໊ͷ࣌ܥྻσʔλΛ౷߹͠ޮՌྔΛࢉग़ ࣗݾ૬ؔ τϨϯυΛߟྀ ύϥϝτϦοΫͳࢦඪ ਖ਼نҎ֎ͷʹ֦ுՄೳ Parker, R. I., Vannest, K. J., Davis, J. L., & Sauber, S. B. (2011). Combining nonoverlap and trend for single-case research: Tau- U. Behavior Therapy, 42(2), 284-299. Pustejovsky, J. E., Hedges, L. V., & Shadish, W. R. (2014). Design-comparable effect sizes in mulMple baseline designs: A general modeling framework. Journal of EducaMonal and Behavioral StaMsMcs, 39(5), 368-393.
ແ࡞ҝԽൺֱࢼݧͷΑ͏ͳ܈ؒൺֱࢼݧͷ݁ Ռͱ୯Ұࣄྫ࣮ݧͷ݁ՌΛൺֱͰ͖Δɻ ୯Ұࣄྫ࣮ݧͷ݁Ռͱଞͷ܈ؒൺֱσβΠϯ ͷݟΛ౷߹Ͱ͖Δɻ Shadish, W. R., Hedges, L. V., Horner, R. H., & Odom, S. L. (2015). The Role of Between-Case Effect Size in ConducMng, InterpreMng, and Summarizing Single-Case Research. NCER 2015-002. NaMonal Center for EducaMon Research.
) = Shadish, W. R., Hedges, L. V., Horner, R. H., & Odom, S. L. (2015). The Role of Between-Case Effect Size in ConducMng, InterpreMng, and Summarizing Single-Case Research. NCER 2015-002. NaMonal Center for EducaMon Research.
M. W., Petrocchi, N., Barlow, D. H., & Litz, B. T. (2017). Compassion-based therapy for trauma-related shame and poscraumaMc stress: IniMal evaluaMon using a mulMple baseline design. Behavior therapy, 48(2), 207-221. Ruiz, F. J., Flórez, C. L., García-Martín, M. B., Monroy-Cifuentes, A., Barreto-Montero, K., García-Beltrán, D. M., ... & Gil-Luciano, B. (2018). A multiple-baseline evaluation of a brief acceptance and commitment therapy protocol focused on repetitive negative thinking for moderate emotional disorders. Journal of Contextual Behavioral Science.
N., Barlow, D. H., & Litz, B. T. (2017). Compassion-based therapy for trauma- related shame and poscraumaMc stress: IniMal evaluaMon using a mulMple baseline design. Behavior therapy, 48(2), 207-221. : An overall, standardized mean difference effect size for each outcome variable was also calculated, using a d-sta:s:c specifically developed for singlecase designs (Shadish, Hedges, & Pustejovsky, 2014). The resulMng d- staMsMc takes into account autocorrelaMon, between- and within-case variance, and corrects for small sample bias using Hedges’ g. It is in the same metric as the d-staMsMc used for between-subjects designs and can be pooled with those staMsMcs in meta-analyses.
N., Barlow, D. H., & Litz, B. T. (2017). Compassion-based therapy for trauma- related shame and poscraumaMc stress: IniMal evaluaMon using a mulMple baseline design. Behavior therapy, 48(2), 207-221. ACT : To obtain an overall esMmate of the effect size of the intervenMon, the design-comparable effect size for mulMple-baseline designs developed by Pustejovsky, Hedges, and Shadish (2014) was computed. This standardized mean difference effect size for SCED shares the same metric as the Cohen's d typically used in group designs, which facilitates the direct comparison and integraMon through meta-analysis of the results obtained in both types of designs. This d-staMsMc has a formal mathemaMcal development, requires at least three cases for computaMon, and corrects for small sample bias using Hedges’ g. It is an extension of the standardized mean difference advocated by Hedges, Pustejovsky, and Shadish (2012, 2013) that uses restricted maximum likelihood esMmaMon and offers the possibility of obtaining the d-staMsMc by controlling for baseline trend and taking into account change in slope. The R package scdhlm was used to compute this d-staMsMc (Pustejovsky, 2016) following the guidelines provided by ValenMne, Tanner- Smith, and Pustejovsky (2016). According to the global visual inspecMon of the dataset, we modelled baselines without trends including both fixed and random effects for level. The treatment phase was modelled with linear trends with both fixed and random effects at level and slope. We computed the d-staMsMc to esMmate the overall effect sizes at the 4-week follow-up (i.e., just before implemenMng the second session of the protocol) and at the 12-week follow-up.
#SBUUMBOEFUBM • ϝϯλϧΫϦχοΫͷױऀ໊Λରʹ3$5 • 30.࣮ࢪ܈ͱ௨ৗ࣏ྍ܈ʹແ࡞ҝׂΓͯ • ͍ͣΕͷ܈ͷױऀਫ਼ਆྍ๏Λఏڙ͞Εͨɻ • 30.࣮ࢪ܈௨ৗ࣏ྍ܈ͱൺͯɺഒΞτΧϜ͕վળ Brattland, H., Koksvik, J. M., Burkeland, O., Gråwe, R. W., Klöckner, C., Linaker, O. M., ... & Iversen, V. C. (2018). The effects of routine outcome monitoring (ROM) on therapy outcomes in the course of an implementation process: A randomized clinical trial. Journal of counseling psychology, 65(5), 641. Shimokawa, K., Lambert, M. J., & Smart, D. W. (2010). Enhancing treatment outcome of patients at risk of treatment failure: meta-analytic and mega-analytic review of a psychotherapy quality assurance system. Journal of consulting and clinical psychology, 78(3), 298.
Wee, N. J., Van Veen, T., & Zitman, F. G. (2012). Efficacy versus effecMveness: a direct comparison of the outcome of treatment for mild to moderate depression in randomized controlled trials and daily pracMce. Psychotherapy and psychosomaMcs, 81(4), 226-234. 30.σʔλΛͬͯɺ͏ͭප࣏ྍͷQIBTFݚڀ F⒎FDUJWFOFTTTUVEZ 1IBTFͷྟচࢼݧσʔλͷϝλੳσʔλͱɺ30.Ͱଌఆ͠ ͨݱͰͷΞτΧϜͷ܈ޮՌྔ ղ Λൺֱ Clinical pracMce < RCT (32 vs.40–74%). Clinical pracMve < RCT (21 vs. 34–47%) : Clinical pracice < RCT (27 vs. 34–58% : Clinical pracMce < RCT (21 vs. 45–63%)
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