64 research outputs found
Efficient Design and Inference for Multi-stage Randomized Trials of Individualized Treatment Policies
Increased clinical interest in individualized βadaptiveβ treatment policies has shifted the methodological focus for their development from the analysis of naturalistically observed strategies to experimental evaluation of a pre-selected set of strategies via multi-stage designs. Because multi-stage studies often avoid the βcurse of dimensionalityβ inherent in uncontrolled studies, and hence the need to parametrically smooth trial data, it is not surprising in this context to find direct connections among different methodological approaches. We show by asymptotic and algebraic proof that the maximum likelihood (ML) and optimal semi-parametric estimators of the mean of a treatment policy and its standard error are equal under certain experimental conditions. The two methodologies offer conceptually different formulations, which we exploit to develop a unified and efficient approach to design and inference for multi-stage trials of policies that adapt treatment according to discrete responses. We derive a sample size formula expressed in terms of a parametric (regression-based) version of the optimal semi-parametric population variance. Non-parametric (sample-based) ML estimation performed well in simulation studies, in terms of achieved power, even though sample sizes relied on parametric re-expression. For a variety of simulated scenarios, ML outperformed the semi-parametric approach, which used a priori rather than estimated randomization probabilities, because the test statistic was sensitive to even small differences arising in finite samples
Software for Distributed Computation on Medical Databases: A Demonstration Project
Bringing together the information latent in distributed medical databases
promises to personalize medical care by enabling reliable, stable modeling of
outcomes with rich feature sets (including patient characteristics and
treatments received). However, there are barriers to aggregation of medical
data, due to lack of standardization of ontologies, privacy concerns,
proprietary attitudes toward data, and a reluctance to give up control over end
use. Aggregation of data is not always necessary for model fitting. In models
based on maximizing a likelihood, the computations can be distributed, with
aggregation limited to the intermediate results of calculations on local data,
rather than raw data. Distributed fitting is also possible for singular value
decomposition. There has been work on the technical aspects of shared
computation for particular applications, but little has been published on the
software needed to support the "social networking" aspect of shared computing,
to reduce the barriers to collaboration. We describe a set of software tools
that allow the rapid assembly of a collaborative computational project, based
on the flexible and extensible R statistical software and other open source
packages, that can work across a heterogeneous collection of database
environments, with full transparency to allow local officials concerned with
privacy protections to validate the safety of the method. We describe the
principles, architecture, and successful test results for the site-stratified
Cox model and rank-k Singular Value Decomposition (SVD)
Recurrence of bipolar disorders andmajor depression: A life-longperspective
Abstract.: Objective: : It is not known whether the risk of recurrence declines with time in bipolar disorders and in major depression. This study describes the life-long recurrence risk of bipolar I, bipolar II and major depressive disorders. Method: : 160 bipolar-I, 60 bipolar-II and 186 depressive patients hospitalised between 1959 and 1963 were followed up every five years from 1965 to 1985. The course prior to the index hospitalisation was assessed in retrospect. The recurrence risk was computed by the multiplicative intensity model (Aalen et al. 1980). Results: : The cumulative intensity curves for the transition from states of remission to new episodes remained linear over 30 to 40 years after onset, indicating a constant risk of recurrence over the life-span up to the age of 70 or more. The recurrence risk of bipolar disorders (0.40 episodes per year) was about twice that of depression (0.20 episodes per year); BP-II disorders had only a slightly higher recurrence risk than BP-I disorders. There were no significant gender differences in the course of either bipolar or depressive disorders. Conclusion: : If long-term trials confirm its efficacy, these results support lifelong prophylactic treatment of severe types of mood disorder
Recurrence of bipolar disorders and major depression
Objective: : It is not known whether the risk of recurrence declines with time in bipolar disorders and in major depression. This study describes the life-long recurrence risk of bipolar I, bipolar II and major depressive disorders. Method: : 160 bipolar-I, 60 bipolar-II and 186 depressive patients hospitalised between 1959 and 1963 were followed up every five years from 1965 to 1985. The course prior to the index hospitalisation was assessed in retrospect. The recurrence risk was computed by the multiplicative intensity model (Aalen et al. 1980). Results: : The cumulative intensity curves for the transition from states of remission to new episodes remained linear over 30 to 40 years after onset, indicating a constant risk of recurrence over the life-span up to the age of 70 or more. The recurrence risk of bipolar disorders (0.40 episodes per year) was about twice that of depression (0.20 episodes per year); BP-II disorders had only a slightly higher recurrence risk than BP-I disorders. There were no significant gender differences in the course of either bipolar or depressive disorders. Conclusion: : If long-term trials confirm its efficacy, these results support lifelong prophylactic treatment of severe types of mood disorder
Effects of 12 Months of Vagus Nerve Stimulation in Treatment-Resistant Depression: A Naturalistic Study
Background: The need for effective, long-term treatment for recurrent or chronic, treatment-resistant depression is well established.
Methods: This naturalistic follow-up describes outpatients with nonpsychotic major depressive (n = 185) or bipolar (I or II) disorder, depressed phase (n = 20) who initially received 10 weeks of active (n = 110) or sham vagus nerve stimulation (VNS) (n = 95). The initial active group received another 9 months, while the initial sham group received 12 months of VNS. Participants received antidepressant treatments and VNS, both of which could be adjusted.
Results: The primary analysis (repeated measures linear regression) revealed a significant reduction in 24-item Hamilton Rating Scale for Depression (HRSD24) scores (average improvement, .45 points [SE = .05] per month (p \u3c .001). At exit, HRSD24 response rate was 27.2% (55/202); remission rate (HRSD24 β€ 9) was 15.8% (32/202). Montgomery Asberg Depression Rating Scale (28.2% [57/202]) and Clinical Global Impression-Improvement (34.0% [68/200]) showed similar response rates. Voice alteration, dyspnea, and neck pain were the most frequently reported adverse events.
Conclusions: These 1-year open trial data found VNS to be well tolerated, suggesting a potential long-term, growing benefit in treatment-resistant depression, albeit in the context of changes in depression treatments. Comparative long-term data are needed to determine whether these benefits can be attributed to VNS
Effects of 12 Months of Vagus Nerve Stimulation in Treatment-Resistant Depression: A Naturalistic Study
Background: The need for effective, long-term treatment for recurrent or chronic, treatment-resistant depression is well established.
Methods: This naturalistic follow-up describes outpatients with nonpsychotic major depressive (n = 185) or bipolar (I or II) disorder, depressed phase (n = 20) who initially received 10 weeks of active (n = 110) or sham vagus nerve stimulation (VNS) (n = 95). The initial active group received another 9 months, while the initial sham group received 12 months of VNS. Participants received antidepressant treatments and VNS, both of which could be adjusted.
Results: The primary analysis (repeated measures linear regression) revealed a significant reduction in 24-item Hamilton Rating Scale for Depression (HRSD24) scores (average improvement, .45 points [SE = .05] per month (p \u3c .001). At exit, HRSD24 response rate was 27.2% (55/202); remission rate (HRSD24 β€ 9) was 15.8% (32/202). Montgomery Asberg Depression Rating Scale (28.2% [57/202]) and Clinical Global Impression-Improvement (34.0% [68/200]) showed similar response rates. Voice alteration, dyspnea, and neck pain were the most frequently reported adverse events.
Conclusions: These 1-year open trial data found VNS to be well tolerated, suggesting a potential long-term, growing benefit in treatment-resistant depression, albeit in the context of changes in depression treatments. Comparative long-term data are needed to determine whether these benefits can be attributed to VNS
A point-of-care clinical trial comparing insulin administered using a sliding scale versus a weight-based regimen
Background Clinical trials are widely considered
the gold standard in comparative effectiveness research (CER) but the high cost
and complexity of traditional trials and concerns about generalizability to
broad patient populations and general clinical practice limit their appeal.
Unsuccessful implementation of CER results limits the value of even the highest
quality trials. Planning for a trial comparing two standard strategies of
insulin administration for hospitalized patients led us to develop a new method
for a clinical trial designed to be embedded directly into the clinical care
setting thereby lowering the cost, increasing the pragmatic nature of the
overall trial, strengthening implementation, and creating an integrated
environment of research-based care
ΠΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΠΉ ΠΌΡΠ·ΡΠΊΠ°Π»ΡΠ½ΡΠΉ ΠΊΠΎΠ½ΠΊΡΡΡ Π² ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ (Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ 2017)
ΠΠ½Π½ΠΎΡΠ°ΡΠΈΡ Π²ΡΠΏΡΡΠΊΠ½ΠΎΠΉ ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ ΠΡΠ»ΠΎΠ² ΠΠΈΠΊΠΈΡΠ° Π‘Π΅ΡΠ³Π΅Π΅Π²ΠΈΡ Β«ΠΠΠΠΠ£ΠΠΠ ΠΠΠΠ«Π ΠΠ£ΠΠ«ΠΠΠΠ¬ΠΠ«Π ΠΠΠΠΠ£Π Π‘ Π Π€ΠΠ ΠΠΠ ΠΠΠΠΠΠ ΠΠΠΠΠΠ Π‘Π’Π ΠΠΠ« ΠΠ ΠΠΠΠΠΠΠΠ― (ΠΠ ΠΠ ΠΠΠΠ Π ΠΠΠ ΠΠΠΠΠΠΠΠ―-2017)Β» Π. ΡΡΠΊ. - ΠΡΠΊΠΎΠ²Π° ΠΠ»Π΅Π½Π° ΠΠ»Π°Π΄ΠΈΠΌΠΈΡΠΎΠ²Π½Π°, Π΄ΠΎΠΊΡΠΎΡ ΡΠΈΠ»ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
Π½Π°ΡΠΊ, Π΄ΠΎΡΠ΅Π½Ρ ΠΠ°ΡΠ΅Π΄ΡΠ° ΡΠ²ΡΠ·Π΅ΠΉ Ρ ΠΎΠ±ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΡΡΡΡ ΠΡΠ½Π°Ρ ΡΠΎΡΠΌΠ° ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ: ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΠΉ ΠΌΡΠ·ΡΠΊΠ°Π»ΡΠ½ΡΠΉ ΠΊΠΎΠ½ΠΊΡΡΡ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΠ΅ ΠΊΠ°ΠΊ ΡΠ°ΠΌΠΎΠ΅ ΠΌΠ°ΡΡΡΠ°Π±Π½ΠΎΠ΅ ΡΠ΅Π³ΡΠ»ΡΡΠ½ΠΎΠ΅ Π²ΡΡΠΎΠΊΠΎΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ½ΠΎΠ΅ ΡΠ΅Π»Π΅Π²ΠΈΠ·ΠΈΠΎΠ½Π½ΠΎΠ΅ ΠΈ ΠΌΠ΅Π΄ΠΈΠ°-ΡΠΎΠ±ΡΡΠΈΠ΅, ΠΊΠΎΡΠΎΡΠΎΠ΅ . Π Π΅ΠΆΠ΅Π³ΠΎΠ΄Π½ΠΎ Π°ΠΊΡΠ΅Π½ΡΠΈΡΡΠ΅Ρ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π°ΡΠ΄ΠΈΡΠΎΡΠΈΠΈ Π½Π° Π½Π°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎ-ΠΊΡΠ»ΡΡΡΡΠ½ΡΡ
ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡΡ
ΡΡΡΠ°Π½Ρ-ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΎΡΠ° ΠΊΠΎΠ½ΠΊΡΡΡΠ°, ΡΠΎΡΠΌΠΈΡΡΠ΅Ρ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΡΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠΎΡΠΎΠΊΠΈ ΠΈ ΡΠ΅ΠΌ ΡΠ°ΠΌΡΠΌ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΈ. ΠΠΎΠ»Π΅Π΅ ΡΠΎΠ³ΠΎ, ΠΏΠΎΠ±Π΅Π΄Π° ΡΡΡΠ°Π½Ρ-ΡΡΠ°ΡΡΠ½ΠΈΡΡ ΠΊΠΎΠ½ΠΊΡΡΡΠ° ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ Π·Π°ΡΠ°ΡΡΡΡ ΠΎΡΡΠ°ΠΆΠ°Π΅Ρ ΠΈΠ΄Π΅ΠΎΠ»ΠΎΠ³ΠΎ-ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π²Π΅ΠΊΡΠΎΡ ΠΠ²ΡΠΎΠΏΡ ΠΈ ΠΏΠΎ ΡΡΡΠΈ Π΄Π΅Π»Π° Π²ΡΠΏΠΎΠ»Π½ΡΠ΅Ρ ΡΡΠ½ΠΊΡΠΈΡ ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ PR ΡΡΡΠ°Π½Ρ-ΠΏΠΎΠ±Π΅Π΄ΠΈΡΠ΅Π»Ρ ΠΈ ΡΡΡΠ°Π½Ρ-Ρ
ΠΎΠ·ΡΠΉΠΊΠΈ ΠΌΠ΅ΡΠΎΠΏΡΠΈΡΡΠΈΡ. Π‘Π»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎ Π°Π½Π°Π»ΠΈΠ· ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΡ
Π½Π° ΠΌΠ΅ΡΠΎΠΏΡΠΈΡΡΠΈΠΈ ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠ²Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΡΠ²Π»ΡΠ΅ΡΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΠΌ ΠΈ Π²ΠΎΡΡΡΠ΅Π±ΠΎΠ²Π°Π½Π½ΡΠΌ Π΄Π»Ρ ΡΠΎΠ±ΡΡΠΈΠΉΠ½ΠΎΠ³ΠΎ ΠΈ ΡΡΡΡΠΎΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ PR ΠΠ±ΡΠ΅ΠΊΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΌΡΠ·ΡΠΊΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΠΊΡΡΡΠ° (Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ Π² ΠΠΈΠ΅Π²Π΅ Π² 2017 Π³.). ΠΡΠ΅Π΄ΠΌΠ΅Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΡΡΠ½ΠΊΡΠΈΡ ΡΡΠ°ΡΡΡΠ½ΠΎΠ³ΠΎ PR-ΠΌΠ΅ΡΠΎΠΏΡΠΈΡΡΠΈΡ Π² ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ. Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: Π΄ΠΎΠΊΠ°Π·Π°ΡΡ, ΡΡΠΎ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΠΉ ΠΌΡΠ·ΡΠΊΠ°Π»ΡΠ½ΡΠΉ ΠΊΠΎΠ½ΠΊΡΡΡ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΠ΅ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ. ΠΠ°Π΄Π°ΡΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°ΡΡ ΡΠ΅ΡΠΌΠΈΠ½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°ΠΏΠΏΠ°ΡΠ°Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π½Π°ΡΡΠ½ΠΎΠΉ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ ΠΏΠΎ ΠΈΠΌΠΈΠ΄ΠΆΠΌΠ΅ΠΉΠΊΠΈΠ½Π³Ρ, Π±ΡΠ΅Π½Π΄ΠΈΠ½Π³Ρ ΠΈ ΠΈΠ²Π΅Π½Ρ-ΠΌΠ΅Π½Π΅Π΄ΠΆΠΌΠ΅Π½ΡΡ; ΠΎΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΠ΅ ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ, ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΡΠ΅ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΡ
ΡΠΎΠ±ΡΡΠΈΠΉ Π΄Π»Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ; ΠΎΠΏΠΈΡΠ°ΡΡ ΡΠΎΠ»Ρ ΠΠ²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΎΠ³ΠΎ ΠΠ΅ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ Π‘ΠΎΡΠ·Π° ΠΊΠ°ΠΊ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΎΡΠ° ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ Π² ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΊΠΎΠ½ΠΊΡΡΡΠ°; ΠΎΡΠ΅Π½ΠΈΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΡΠ΅Π°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π½ΡΡ
ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠ²Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ; Π΄Π°ΡΡ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΈ ΠΏΠΎ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ. Π’Π΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π±Π°Π·Π°: Π½Π°ΡΡΠ½ΡΠ΅ ΡΡΡΠ΄Ρ Π. ΠΡΠΊΠΎΠ²ΠΎΠΉ, Π. ΠΠ°Π²ΡΡ, Π. ΠΠ°Π½ΠΊΡΡΡ
ΠΈΠ½Π°, Π. ΠΠΆΠ΅Π½Π΅ΡΠ°, Π. ΠΠ°Π²Π΅ΡΠΈΠ½ΠΎΠΉ, Π£. Π₯Π°Π»ΡΡΠ±Π°ΡΡΠ°, ΠΠΆ. ΠΠΎΠ»Π΄Π±Π»Π°ΡΡΠ° Π° ΡΠ°ΠΊΠΆΠ΅ ΡΡΡΠ΄Ρ Π. ΠΠ°ΡΡΠΌΠ°Π½Π° ΠΎ ΠΌΡΠ·ΡΠΊΠ°Π»ΡΠ½ΠΎΠΌ Π±ΠΈΠ·Π½Π΅ΡΠ΅, Π. ΠΠΆΠΎΡΠ΄Π°Π½Π° ΠΎ ΠΏΡΠΎΠ΄Π²ΠΈΠΆΠ΅Π½ΠΈΠΈ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ ΠΈ Π΄Ρ. ΠΠΌΠΏΠΈΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π±Π°Π·Π°: PR-Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΡ, ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½Π½ΡΠ΅ Π½Π° ΡΠ°ΠΉΡΠ΅ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ ΠΈ ΠΠ²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΎΠ³ΠΎ ΠΠ΅ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ Π‘ΠΎΡΠ·Π°; Π±ΠΎΠ»Π΅Π΅ ΠΏΠΎΠ»ΡΡΠΎΡΠ° ΠΌΠΈΠ»Π»ΠΈΠΎΠ½Π° ΡΡΠ°ΡΠ΅ΠΉ ΠΎΠ± Π£ΠΊΡΠ°ΠΈΠ½Π΅ Π² Π΅Π²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΈΡ
Π‘ΠΠ, ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½Π½ΡΠ΅ Π² Π±Π°Π·Π΅ ΠΏΡΠΎΠ΅ΠΊΡΠ° ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΈΠΌΠΈΠ΄ΠΆΠ° Π£ΠΊΡΠ°ΠΈΠ½Ρ Β«OkoΒ»; Π΄Π°Π½Π½ΡΠ΅ Π±Π°Π·Ρ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠ² Π‘ΠΠ ΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
ΠΌΠ΅Π΄ΠΈΠ° Factiva; Π΄Π°Π½Π½ΡΠ΅ Google.Analytics. ΠΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ: ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π΄ΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΡΡΠΎ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΠΉ ΠΌΡΠ·ΡΠΊΠ°Π»ΡΠ½ΡΠΉ ΠΊΠΎΠ½ΠΊΡΡΡ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΠ΅ ΡΠΎΡΠΌΠΈΡΡΠ΅Ρ ΠΈΠΌΠΈΠ΄ΠΆ ΡΡΡΠ°Π½Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π½Π΅Π·Π°Π²ΠΈΡΠΈΠΌΠΎ ΠΎΡ ΡΡΠΏΠ΅ΡΠ½ΠΎΡΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ. Π’Π΅Π·ΠΈΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ»ΠΈ Π°ΠΏΡΠΎΠ±ΠΈΡΠΎΠ²Π°Π½Ρ Π½Π° ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠΌ Π½Π°ΡΡΠ½ΠΎΠΌ ΡΠΎΡΡΠΌΠ΅ Β«ΠΠ΅Π΄ΠΈΠ° Π² ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΌ ΠΌΠΈΡΠ΅. 57-Π΅ ΠΠ΅ΡΠ΅ΡΠ±ΡΡΠ³ΡΠΊΠΈΠ΅ ΡΡΠ΅Π½ΠΈΡΒ», ΠΎΠΏΡΠ±Π»ΠΈΠΊΠΎΠ²Π°Π½Ρ Π² ΡΠ±ΠΎΡΠ½ΠΈΠΊΠ΅ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠ² ΡΡΠ°ΡΠ΅ΠΉ ΡΠΎΡΡΠΌΠ° ΠΈ ΠΈΠΌΠ΅ΡΡ ΡΡΠ°ΡΡΡ Π½Π°ΡΡΠ½ΠΎΠΉ ΡΡΠ°ΡΡΠΈ, ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½Π½ΠΎΠΉ Π² Π±Π°Π·Π΅ Π ΠΠΠ¦. Π‘ΡΡΡΠΊΡΡΡΠ° ΡΠ°Π±ΠΎΡΡ: Π Π°Π±ΠΎΡΠ° ΡΠΎΡΡΠΎΠΈΡ ΠΈΠ· Π²Π²Π΅Π΄Π΅Π½ΠΈΡ, 3 Π³Π»Π°Π²: Β«ΡΡΠ½ΠΊΡΠΈΡ ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΎΠ±ΡΡΠΈΡ Π² ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½ΡΒ», Β«ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΠ΅ ΠΊΠ°ΠΊ ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠ΅ ΡΠΎΠ±ΡΡΠΈΠ΅ ΠΠ²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΎΠ³ΠΎ ΠΠ΅ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ Π‘ΠΎΡΠ·Π°Β» ΠΈ Β«ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠΉ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π» ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ ΠΊΠ°ΠΊ ΠΏΠ»ΠΎΡΠ°Π΄ΠΊΠΈ Π΄Π»Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½ΡΒ», Π·Π°ΠΊΠ»ΡΡΠ΅Π½ΠΈΡ, ΡΠΏΠΈΡΠΊΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π½ΠΎΠΉ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ ΠΈΠ· 67 ΠΏΠΎΠ·ΠΈΡΠΈΠΉ ΠΈ 12 ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ. ΠΠ±ΡΠΈΠΉ ΠΎΠ±ΡΠ΅ΠΌ 76 ΡΡΡΠ°Π½ΠΈΡ.Abstract of graduating qualification thesis Mikita Arlou INTERNATIONAL MUSIC CONTEST IN HOST COUNTRY IMAGE FORMATION (ON THE EXAMPLE OF EUROVISION 2017) Supervisor associate professor Elena Bykova, doctor of philology Department of PR in business full-time study Relevance: the international music contest Eurovision as the most wide scale regular high tech TV and Media event which annually emphasizes audience attention on national cultural features of the host country, forms tourist flows which have huge influence on territorial image formation. Besides the win of a participating in the Eurovision country often shows the ideological and political European vector and in fact serves as political PR of the winning or host country. Consequently the analysis of applied communication technologies is relevant and in-demand for event PR. Research object: communication activities of international music contest (on the example of Eurovision in Kyiv in 2017). Research subject: function of status PR event in country image formation. The aim of research: to prove that international music contest Eurovision contributes host country image formation. The tasks of research: to develop research terminology based on scientific literature on image making, branding and event management; to define actual communication technologies applied in special PR events on country image formation; to describe European Broadcasting Union role in host country image formation; to appreciate effectiveness of applied communication technologies on host country image formation in Eurovision; to give recommendations for host country image formation with the help of Eurovision. Theoretical base: scientific works written by E. Bykova, D. Gavra, A. Pankrukhin, B. Jenes, E. Kaverina, U. Halcbaur, J. Goldblatt and D. PassmanΒ΄s works on music business and P. Jordan on county image building with the help of Eurovision, etc. The empirical base: PR documents from official Eurovision and European Broadcasting Union websites; more than 1.5 million articles on Ukraine in European media stored in the base of international Ukrainian image monitoring project Oko; content of the mass media and social media base Factiva; Google.Analytics data. Practical significance: the research proves that international music contest Eurovision is relevant for the host country image formation independently of the success level of applied country image formation communication technologies. Approbation: General positions of current thesis were aprobated on international scientific forum Media in modern world and were published at the collection of articles of the forum and have the status of a scientific article posted in the RINC database. Thesis structure: Research consists of introduction, 3 chapters: Special event function in country image formation, Eurovision as EBU special event and communication potential of Eurovision as a platform for image formation; conclusion, literature list from 67 positions and 12 attachments. The total volume is 76 pages
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