Depressive symptoms in mothers of children with epilepsy: a comparison of growth curve and latent class modeling
Abstract Two common approaches for studying trajectories of change are standard growth curve modeling (GCM) and latent class growth modeling (LCM) (Singer and Willett, Applied longitudinal data analysis. Modeling change and event occurrence. Oxford University Press, New York, 2003; Nagin, Group-base...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ferro, Mark A. [verfasserIn] Speechley, Kathy N. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Health services and outcomes research methodology - [S.l.] : Proquest, 2000, 12(2012), 1 vom: März, Seite 44-61 |
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| Übergeordnetes Werk: |
volume:12 ; year:2012 ; number:1 ; month:03 ; pages:44-61 |
| Links: |
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| DOI / URN: |
10.1007/s10742-012-0081-2 |
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SPR012870897 |
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| 520 | |a Abstract Two common approaches for studying trajectories of change are standard growth curve modeling (GCM) and latent class growth modeling (LCM) (Singer and Willett, Applied longitudinal data analysis. Modeling change and event occurrence. Oxford University Press, New York, 2003; Nagin, Group-based modeling of development. Harvard University Press, Cambridge, 2005). The objectives were to compare the results obtained using GCM and LCM in modeling trajectories of depressive symptoms in a sample of mothers of children with epilepsy; compare the methods in predicting average trajectory and individual trajectories, and; provide general guidelines for implementing these approaches. Findings from the two modeling strategies were different: GCM suggested a quadratic change in depressive symptoms over time. Addition of the time-varying covariate, family functioning, produced a final model that explained 25, 20, 31, and 18% of the residual intra-individual, as well as inter-individual variation in the intercept and slope (linear, quadratic), respectively. Results from the LCM suggested five distinct trajectories of depressive symptoms: low stable (30%), sub-clinical (39%), moderate decreasing (15%), moderate increasing (9%), and high decreasing (7%). Adding the family functioning variable resulted in a model that replaced the sub-clinical trajectory with borderline and moderate decreasing with high increasing. Both the GCM and LCM adequately described the average trajectory of maternal depressive symptoms with signed differences of 0.61 and 0.75 and −2.39 and −2.54 for the unconditional and conditional models, respectively. There was considerable variation in capturing individual trajectories. For approximately 14 and 9% of individuals, both models under and overestimated depression scores by at least five points. Although GCM and LCM perform equally well in predicting average and individual trajectories of change, they are used most efficiently under different circumstances. Where individuals are expected to share a homogeneous trajectory, GCM should be used; however, where individuals do not follow a common trajectory, LCM is more appropriate. | ||
| 650 | 4 | |a Depressive symptoms |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Growth curve modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Latent class growth modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Longitudinal analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Modeling |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Speechley, Kathy N. |e verfasserin |4 aut | |
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10.1007/s10742-012-0081-2 doi (DE-627)SPR012870897 (SPR)s10742-012-0081-2-e DE-627 ger DE-627 rakwb eng 610 ASE 44.10 bkl Ferro, Mark A. verfasserin aut Depressive symptoms in mothers of children with epilepsy: a comparison of growth curve and latent class modeling 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Two common approaches for studying trajectories of change are standard growth curve modeling (GCM) and latent class growth modeling (LCM) (Singer and Willett, Applied longitudinal data analysis. Modeling change and event occurrence. Oxford University Press, New York, 2003; Nagin, Group-based modeling of development. Harvard University Press, Cambridge, 2005). The objectives were to compare the results obtained using GCM and LCM in modeling trajectories of depressive symptoms in a sample of mothers of children with epilepsy; compare the methods in predicting average trajectory and individual trajectories, and; provide general guidelines for implementing these approaches. Findings from the two modeling strategies were different: GCM suggested a quadratic change in depressive symptoms over time. Addition of the time-varying covariate, family functioning, produced a final model that explained 25, 20, 31, and 18% of the residual intra-individual, as well as inter-individual variation in the intercept and slope (linear, quadratic), respectively. Results from the LCM suggested five distinct trajectories of depressive symptoms: low stable (30%), sub-clinical (39%), moderate decreasing (15%), moderate increasing (9%), and high decreasing (7%). Adding the family functioning variable resulted in a model that replaced the sub-clinical trajectory with borderline and moderate decreasing with high increasing. Both the GCM and LCM adequately described the average trajectory of maternal depressive symptoms with signed differences of 0.61 and 0.75 and −2.39 and −2.54 for the unconditional and conditional models, respectively. There was considerable variation in capturing individual trajectories. For approximately 14 and 9% of individuals, both models under and overestimated depression scores by at least five points. Although GCM and LCM perform equally well in predicting average and individual trajectories of change, they are used most efficiently under different circumstances. Where individuals are expected to share a homogeneous trajectory, GCM should be used; however, where individuals do not follow a common trajectory, LCM is more appropriate. Depressive symptoms (dpeaa)DE-He213 Growth curve modeling (dpeaa)DE-He213 Latent class growth modeling (dpeaa)DE-He213 Longitudinal analysis (dpeaa)DE-He213 Modeling (dpeaa)DE-He213 Speechley, Kathy N. verfasserin aut Enthalten in Health services and outcomes research methodology [S.l.] : Proquest, 2000 12(2012), 1 vom: März, Seite 44-61 (DE-627)320443477 (DE-600)2005197-9 1572-9400 nnns volume:12 year:2012 number:1 month:03 pages:44-61 https://dx.doi.org/10.1007/s10742-012-0081-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 12 2012 1 03 44-61 |
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10.1007/s10742-012-0081-2 doi (DE-627)SPR012870897 (SPR)s10742-012-0081-2-e DE-627 ger DE-627 rakwb eng 610 ASE 44.10 bkl Ferro, Mark A. verfasserin aut Depressive symptoms in mothers of children with epilepsy: a comparison of growth curve and latent class modeling 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Two common approaches for studying trajectories of change are standard growth curve modeling (GCM) and latent class growth modeling (LCM) (Singer and Willett, Applied longitudinal data analysis. Modeling change and event occurrence. Oxford University Press, New York, 2003; Nagin, Group-based modeling of development. Harvard University Press, Cambridge, 2005). The objectives were to compare the results obtained using GCM and LCM in modeling trajectories of depressive symptoms in a sample of mothers of children with epilepsy; compare the methods in predicting average trajectory and individual trajectories, and; provide general guidelines for implementing these approaches. Findings from the two modeling strategies were different: GCM suggested a quadratic change in depressive symptoms over time. Addition of the time-varying covariate, family functioning, produced a final model that explained 25, 20, 31, and 18% of the residual intra-individual, as well as inter-individual variation in the intercept and slope (linear, quadratic), respectively. Results from the LCM suggested five distinct trajectories of depressive symptoms: low stable (30%), sub-clinical (39%), moderate decreasing (15%), moderate increasing (9%), and high decreasing (7%). Adding the family functioning variable resulted in a model that replaced the sub-clinical trajectory with borderline and moderate decreasing with high increasing. Both the GCM and LCM adequately described the average trajectory of maternal depressive symptoms with signed differences of 0.61 and 0.75 and −2.39 and −2.54 for the unconditional and conditional models, respectively. There was considerable variation in capturing individual trajectories. For approximately 14 and 9% of individuals, both models under and overestimated depression scores by at least five points. Although GCM and LCM perform equally well in predicting average and individual trajectories of change, they are used most efficiently under different circumstances. Where individuals are expected to share a homogeneous trajectory, GCM should be used; however, where individuals do not follow a common trajectory, LCM is more appropriate. Depressive symptoms (dpeaa)DE-He213 Growth curve modeling (dpeaa)DE-He213 Latent class growth modeling (dpeaa)DE-He213 Longitudinal analysis (dpeaa)DE-He213 Modeling (dpeaa)DE-He213 Speechley, Kathy N. verfasserin aut Enthalten in Health services and outcomes research methodology [S.l.] : Proquest, 2000 12(2012), 1 vom: März, Seite 44-61 (DE-627)320443477 (DE-600)2005197-9 1572-9400 nnns volume:12 year:2012 number:1 month:03 pages:44-61 https://dx.doi.org/10.1007/s10742-012-0081-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 12 2012 1 03 44-61 |
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10.1007/s10742-012-0081-2 doi (DE-627)SPR012870897 (SPR)s10742-012-0081-2-e DE-627 ger DE-627 rakwb eng 610 ASE 44.10 bkl Ferro, Mark A. verfasserin aut Depressive symptoms in mothers of children with epilepsy: a comparison of growth curve and latent class modeling 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Two common approaches for studying trajectories of change are standard growth curve modeling (GCM) and latent class growth modeling (LCM) (Singer and Willett, Applied longitudinal data analysis. Modeling change and event occurrence. Oxford University Press, New York, 2003; Nagin, Group-based modeling of development. Harvard University Press, Cambridge, 2005). The objectives were to compare the results obtained using GCM and LCM in modeling trajectories of depressive symptoms in a sample of mothers of children with epilepsy; compare the methods in predicting average trajectory and individual trajectories, and; provide general guidelines for implementing these approaches. Findings from the two modeling strategies were different: GCM suggested a quadratic change in depressive symptoms over time. Addition of the time-varying covariate, family functioning, produced a final model that explained 25, 20, 31, and 18% of the residual intra-individual, as well as inter-individual variation in the intercept and slope (linear, quadratic), respectively. Results from the LCM suggested five distinct trajectories of depressive symptoms: low stable (30%), sub-clinical (39%), moderate decreasing (15%), moderate increasing (9%), and high decreasing (7%). Adding the family functioning variable resulted in a model that replaced the sub-clinical trajectory with borderline and moderate decreasing with high increasing. Both the GCM and LCM adequately described the average trajectory of maternal depressive symptoms with signed differences of 0.61 and 0.75 and −2.39 and −2.54 for the unconditional and conditional models, respectively. There was considerable variation in capturing individual trajectories. For approximately 14 and 9% of individuals, both models under and overestimated depression scores by at least five points. Although GCM and LCM perform equally well in predicting average and individual trajectories of change, they are used most efficiently under different circumstances. Where individuals are expected to share a homogeneous trajectory, GCM should be used; however, where individuals do not follow a common trajectory, LCM is more appropriate. Depressive symptoms (dpeaa)DE-He213 Growth curve modeling (dpeaa)DE-He213 Latent class growth modeling (dpeaa)DE-He213 Longitudinal analysis (dpeaa)DE-He213 Modeling (dpeaa)DE-He213 Speechley, Kathy N. verfasserin aut Enthalten in Health services and outcomes research methodology [S.l.] : Proquest, 2000 12(2012), 1 vom: März, Seite 44-61 (DE-627)320443477 (DE-600)2005197-9 1572-9400 nnns volume:12 year:2012 number:1 month:03 pages:44-61 https://dx.doi.org/10.1007/s10742-012-0081-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 12 2012 1 03 44-61 |
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10.1007/s10742-012-0081-2 doi (DE-627)SPR012870897 (SPR)s10742-012-0081-2-e DE-627 ger DE-627 rakwb eng 610 ASE 44.10 bkl Ferro, Mark A. verfasserin aut Depressive symptoms in mothers of children with epilepsy: a comparison of growth curve and latent class modeling 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Two common approaches for studying trajectories of change are standard growth curve modeling (GCM) and latent class growth modeling (LCM) (Singer and Willett, Applied longitudinal data analysis. Modeling change and event occurrence. Oxford University Press, New York, 2003; Nagin, Group-based modeling of development. Harvard University Press, Cambridge, 2005). The objectives were to compare the results obtained using GCM and LCM in modeling trajectories of depressive symptoms in a sample of mothers of children with epilepsy; compare the methods in predicting average trajectory and individual trajectories, and; provide general guidelines for implementing these approaches. Findings from the two modeling strategies were different: GCM suggested a quadratic change in depressive symptoms over time. Addition of the time-varying covariate, family functioning, produced a final model that explained 25, 20, 31, and 18% of the residual intra-individual, as well as inter-individual variation in the intercept and slope (linear, quadratic), respectively. Results from the LCM suggested five distinct trajectories of depressive symptoms: low stable (30%), sub-clinical (39%), moderate decreasing (15%), moderate increasing (9%), and high decreasing (7%). Adding the family functioning variable resulted in a model that replaced the sub-clinical trajectory with borderline and moderate decreasing with high increasing. Both the GCM and LCM adequately described the average trajectory of maternal depressive symptoms with signed differences of 0.61 and 0.75 and −2.39 and −2.54 for the unconditional and conditional models, respectively. There was considerable variation in capturing individual trajectories. For approximately 14 and 9% of individuals, both models under and overestimated depression scores by at least five points. Although GCM and LCM perform equally well in predicting average and individual trajectories of change, they are used most efficiently under different circumstances. Where individuals are expected to share a homogeneous trajectory, GCM should be used; however, where individuals do not follow a common trajectory, LCM is more appropriate. Depressive symptoms (dpeaa)DE-He213 Growth curve modeling (dpeaa)DE-He213 Latent class growth modeling (dpeaa)DE-He213 Longitudinal analysis (dpeaa)DE-He213 Modeling (dpeaa)DE-He213 Speechley, Kathy N. verfasserin aut Enthalten in Health services and outcomes research methodology [S.l.] : Proquest, 2000 12(2012), 1 vom: März, Seite 44-61 (DE-627)320443477 (DE-600)2005197-9 1572-9400 nnns volume:12 year:2012 number:1 month:03 pages:44-61 https://dx.doi.org/10.1007/s10742-012-0081-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 12 2012 1 03 44-61 |
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10.1007/s10742-012-0081-2 doi (DE-627)SPR012870897 (SPR)s10742-012-0081-2-e DE-627 ger DE-627 rakwb eng 610 ASE 44.10 bkl Ferro, Mark A. verfasserin aut Depressive symptoms in mothers of children with epilepsy: a comparison of growth curve and latent class modeling 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Two common approaches for studying trajectories of change are standard growth curve modeling (GCM) and latent class growth modeling (LCM) (Singer and Willett, Applied longitudinal data analysis. Modeling change and event occurrence. Oxford University Press, New York, 2003; Nagin, Group-based modeling of development. Harvard University Press, Cambridge, 2005). The objectives were to compare the results obtained using GCM and LCM in modeling trajectories of depressive symptoms in a sample of mothers of children with epilepsy; compare the methods in predicting average trajectory and individual trajectories, and; provide general guidelines for implementing these approaches. Findings from the two modeling strategies were different: GCM suggested a quadratic change in depressive symptoms over time. Addition of the time-varying covariate, family functioning, produced a final model that explained 25, 20, 31, and 18% of the residual intra-individual, as well as inter-individual variation in the intercept and slope (linear, quadratic), respectively. Results from the LCM suggested five distinct trajectories of depressive symptoms: low stable (30%), sub-clinical (39%), moderate decreasing (15%), moderate increasing (9%), and high decreasing (7%). Adding the family functioning variable resulted in a model that replaced the sub-clinical trajectory with borderline and moderate decreasing with high increasing. Both the GCM and LCM adequately described the average trajectory of maternal depressive symptoms with signed differences of 0.61 and 0.75 and −2.39 and −2.54 for the unconditional and conditional models, respectively. There was considerable variation in capturing individual trajectories. For approximately 14 and 9% of individuals, both models under and overestimated depression scores by at least five points. Although GCM and LCM perform equally well in predicting average and individual trajectories of change, they are used most efficiently under different circumstances. Where individuals are expected to share a homogeneous trajectory, GCM should be used; however, where individuals do not follow a common trajectory, LCM is more appropriate. Depressive symptoms (dpeaa)DE-He213 Growth curve modeling (dpeaa)DE-He213 Latent class growth modeling (dpeaa)DE-He213 Longitudinal analysis (dpeaa)DE-He213 Modeling (dpeaa)DE-He213 Speechley, Kathy N. verfasserin aut Enthalten in Health services and outcomes research methodology [S.l.] : Proquest, 2000 12(2012), 1 vom: März, Seite 44-61 (DE-627)320443477 (DE-600)2005197-9 1572-9400 nnns volume:12 year:2012 number:1 month:03 pages:44-61 https://dx.doi.org/10.1007/s10742-012-0081-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 12 2012 1 03 44-61 |
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Modeling change and event occurrence. Oxford University Press, New York, 2003; Nagin, Group-based modeling of development. Harvard University Press, Cambridge, 2005). The objectives were to compare the results obtained using GCM and LCM in modeling trajectories of depressive symptoms in a sample of mothers of children with epilepsy; compare the methods in predicting average trajectory and individual trajectories, and; provide general guidelines for implementing these approaches. Findings from the two modeling strategies were different: GCM suggested a quadratic change in depressive symptoms over time. Addition of the time-varying covariate, family functioning, produced a final model that explained 25, 20, 31, and 18% of the residual intra-individual, as well as inter-individual variation in the intercept and slope (linear, quadratic), respectively. Results from the LCM suggested five distinct trajectories of depressive symptoms: low stable (30%), sub-clinical (39%), moderate decreasing (15%), moderate increasing (9%), and high decreasing (7%). Adding the family functioning variable resulted in a model that replaced the sub-clinical trajectory with borderline and moderate decreasing with high increasing. Both the GCM and LCM adequately described the average trajectory of maternal depressive symptoms with signed differences of 0.61 and 0.75 and −2.39 and −2.54 for the unconditional and conditional models, respectively. There was considerable variation in capturing individual trajectories. For approximately 14 and 9% of individuals, both models under and overestimated depression scores by at least five points. Although GCM and LCM perform equally well in predicting average and individual trajectories of change, they are used most efficiently under different circumstances. 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Ferro, Mark A. |
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Ferro, Mark A. ddc 610 bkl 44.10 misc Depressive symptoms misc Growth curve modeling misc Latent class growth modeling misc Longitudinal analysis misc Modeling Depressive symptoms in mothers of children with epilepsy: a comparison of growth curve and latent class modeling |
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610 ASE 44.10 bkl Depressive symptoms in mothers of children with epilepsy: a comparison of growth curve and latent class modeling Depressive symptoms (dpeaa)DE-He213 Growth curve modeling (dpeaa)DE-He213 Latent class growth modeling (dpeaa)DE-He213 Longitudinal analysis (dpeaa)DE-He213 Modeling (dpeaa)DE-He213 |
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ddc 610 bkl 44.10 misc Depressive symptoms misc Growth curve modeling misc Latent class growth modeling misc Longitudinal analysis misc Modeling |
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Depressive symptoms in mothers of children with epilepsy: a comparison of growth curve and latent class modeling |
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depressive symptoms in mothers of children with epilepsy: a comparison of growth curve and latent class modeling |
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Depressive symptoms in mothers of children with epilepsy: a comparison of growth curve and latent class modeling |
| abstract |
Abstract Two common approaches for studying trajectories of change are standard growth curve modeling (GCM) and latent class growth modeling (LCM) (Singer and Willett, Applied longitudinal data analysis. Modeling change and event occurrence. Oxford University Press, New York, 2003; Nagin, Group-based modeling of development. Harvard University Press, Cambridge, 2005). The objectives were to compare the results obtained using GCM and LCM in modeling trajectories of depressive symptoms in a sample of mothers of children with epilepsy; compare the methods in predicting average trajectory and individual trajectories, and; provide general guidelines for implementing these approaches. Findings from the two modeling strategies were different: GCM suggested a quadratic change in depressive symptoms over time. Addition of the time-varying covariate, family functioning, produced a final model that explained 25, 20, 31, and 18% of the residual intra-individual, as well as inter-individual variation in the intercept and slope (linear, quadratic), respectively. Results from the LCM suggested five distinct trajectories of depressive symptoms: low stable (30%), sub-clinical (39%), moderate decreasing (15%), moderate increasing (9%), and high decreasing (7%). Adding the family functioning variable resulted in a model that replaced the sub-clinical trajectory with borderline and moderate decreasing with high increasing. Both the GCM and LCM adequately described the average trajectory of maternal depressive symptoms with signed differences of 0.61 and 0.75 and −2.39 and −2.54 for the unconditional and conditional models, respectively. There was considerable variation in capturing individual trajectories. For approximately 14 and 9% of individuals, both models under and overestimated depression scores by at least five points. Although GCM and LCM perform equally well in predicting average and individual trajectories of change, they are used most efficiently under different circumstances. Where individuals are expected to share a homogeneous trajectory, GCM should be used; however, where individuals do not follow a common trajectory, LCM is more appropriate. |
| abstractGer |
Abstract Two common approaches for studying trajectories of change are standard growth curve modeling (GCM) and latent class growth modeling (LCM) (Singer and Willett, Applied longitudinal data analysis. Modeling change and event occurrence. Oxford University Press, New York, 2003; Nagin, Group-based modeling of development. Harvard University Press, Cambridge, 2005). The objectives were to compare the results obtained using GCM and LCM in modeling trajectories of depressive symptoms in a sample of mothers of children with epilepsy; compare the methods in predicting average trajectory and individual trajectories, and; provide general guidelines for implementing these approaches. Findings from the two modeling strategies were different: GCM suggested a quadratic change in depressive symptoms over time. Addition of the time-varying covariate, family functioning, produced a final model that explained 25, 20, 31, and 18% of the residual intra-individual, as well as inter-individual variation in the intercept and slope (linear, quadratic), respectively. Results from the LCM suggested five distinct trajectories of depressive symptoms: low stable (30%), sub-clinical (39%), moderate decreasing (15%), moderate increasing (9%), and high decreasing (7%). Adding the family functioning variable resulted in a model that replaced the sub-clinical trajectory with borderline and moderate decreasing with high increasing. Both the GCM and LCM adequately described the average trajectory of maternal depressive symptoms with signed differences of 0.61 and 0.75 and −2.39 and −2.54 for the unconditional and conditional models, respectively. There was considerable variation in capturing individual trajectories. For approximately 14 and 9% of individuals, both models under and overestimated depression scores by at least five points. Although GCM and LCM perform equally well in predicting average and individual trajectories of change, they are used most efficiently under different circumstances. Where individuals are expected to share a homogeneous trajectory, GCM should be used; however, where individuals do not follow a common trajectory, LCM is more appropriate. |
| abstract_unstemmed |
Abstract Two common approaches for studying trajectories of change are standard growth curve modeling (GCM) and latent class growth modeling (LCM) (Singer and Willett, Applied longitudinal data analysis. Modeling change and event occurrence. Oxford University Press, New York, 2003; Nagin, Group-based modeling of development. Harvard University Press, Cambridge, 2005). The objectives were to compare the results obtained using GCM and LCM in modeling trajectories of depressive symptoms in a sample of mothers of children with epilepsy; compare the methods in predicting average trajectory and individual trajectories, and; provide general guidelines for implementing these approaches. Findings from the two modeling strategies were different: GCM suggested a quadratic change in depressive symptoms over time. Addition of the time-varying covariate, family functioning, produced a final model that explained 25, 20, 31, and 18% of the residual intra-individual, as well as inter-individual variation in the intercept and slope (linear, quadratic), respectively. Results from the LCM suggested five distinct trajectories of depressive symptoms: low stable (30%), sub-clinical (39%), moderate decreasing (15%), moderate increasing (9%), and high decreasing (7%). Adding the family functioning variable resulted in a model that replaced the sub-clinical trajectory with borderline and moderate decreasing with high increasing. Both the GCM and LCM adequately described the average trajectory of maternal depressive symptoms with signed differences of 0.61 and 0.75 and −2.39 and −2.54 for the unconditional and conditional models, respectively. There was considerable variation in capturing individual trajectories. For approximately 14 and 9% of individuals, both models under and overestimated depression scores by at least five points. Although GCM and LCM perform equally well in predicting average and individual trajectories of change, they are used most efficiently under different circumstances. Where individuals are expected to share a homogeneous trajectory, GCM should be used; however, where individuals do not follow a common trajectory, LCM is more appropriate. |
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| container_issue |
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| title_short |
Depressive symptoms in mothers of children with epilepsy: a comparison of growth curve and latent class modeling |
| url |
https://dx.doi.org/10.1007/s10742-012-0081-2 |
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| author2 |
Speechley, Kathy N. |
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| doi_str |
10.1007/s10742-012-0081-2 |
| up_date |
2024-07-03T15:53:24.747Z |
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| score |
7.398529 |