scholarly journals Einbezug der Väter in die stationäre Mutter-Kind-Therapie: „Mit Papa geht es besser“

2021 ◽  
Author(s):  
Jakob Johann Müller ◽  
Svenja Taubner
Keyword(s):  
Propensity Scores ◽  
Parental Bonding ◽  
Symptom Checklist ◽  
Treatment As Usual ◽  
Inverse Probability ◽  
Post Hoc ◽  
Wissenschaftliche Studien

Zusammenfassung Hintergrund Obwohl Forschungsbefunde auf einen großen väterlichen Einfluss hinweisen, gibt es bislang kaum Interventionsprogramme und wissenschaftliche Studien, die den systematischen Einbezug von Vätern in die stationäre Mutter-Kind-Behandlung zum Gegenstand haben. Ziel der Arbeit Die Studie untersucht, wie sich der Einbezug von Vätern auf das Outcome stationärer Mutter-Kind-Behandlungen bei postpartalen psychischen Störungen auswirkt. In dieser Pilotstudie wird das Programm „Mit Papa geht es besser“ vorgestellt. Methodik Fünfzehn Partner/Kindsväter von behandelten Mutter-Kind-Dyaden durchliefen ein strukturiertes Begleitprogramm parallel zur Mutter-Kind-Behandlung („Mit Papa geht es besser“). Die Mütter in Behandlung wurden in einem Prä-post-Design zu ihrer Symptomatik (Symptom-Checklist 90, SCL-90), Mutter-Kind-Bindung (Parental Bonding Questionnaire, PBQ) und Partnerschaftszufriedenheit (Kurzversion des Partnerschaftsfragebogens, PFB-K) befragt. Diese Gruppe wurden mit einer historischen Kontrollgruppe von 30 behandelten Müttern verglichen, die die Behandlung wie bisher („treatment as usual“, TAU) durchliefen. Die Gruppen wurden post hoc mithilfe einer „Inverse-probability-of-treatment-weighting“(IPTW)-Schätzung von Propensity Scores (PS) balanciert. Ergebnisse Mütter in allen Versuchsbedingungen profitierten von der stationären Behandlung. Mütter in der Interventionsgruppe wiesen im Hinblick auf die Zielvariablen ein tendenziell verbessertes Outcome auf, insbesondere für die Veränderung der Partnerschaftszufriedenheit, die Unterschiede erreichten aber keine statistische Signifikanz. Schlussfolgerung Die Befunde weisen darauf hin, dass Mütter in stationärer Mutter-Kind-Behandlung vom Einbezug der Väter profitieren könnten. Die Intervention soll nun im Rahmen eines randomisierten kontrollierten Studiendesigns an einer größeren Stichprobe auf ihre Wirksamkeit überprüft werden.

scholarly journals The Influence of Speech-Language-Hearing Therapy Duration on the Degree of Improvement in Poststroke Language Impairment

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Hitoshi Hayashi ◽  
Eisaku Okada ◽  
Yosuke Shibata ◽  
Mieko Nakamura ◽  
Toshiyuki Ojima
Keyword(s):  
Language Impairment ◽  
Propensity Scores ◽  
Rehabilitation Medicine ◽  
Functional Independence ◽  
Severe Dementia ◽  
Inverse Probability ◽  
Long Duration ◽  
Study Population ◽  
Hearing Therapy ◽  
Severity Of Dementia

Background. The relevance of speech-language-hearing therapy (ST) duration to language impairment remains unclear.Objective.To determine the effect of ST duration on improvement in language impairment as a stroke sequela and to compare the findings with those for occupational therapy (OT) and physical therapy (PT).Methods. Data regarding patients with stroke sequelae who were registered in the Japanese Association of Rehabilitation Medicine database were analyzed. Propensity scores for ST, OT, and PT duration were calculated using logistic regression, followed by inverse probability weighting in generalized estimating equations to examine the odds ratio for improvement in the Functional Independence Measures scores for comprehension, expression, and memory. Analyses stratified by age and dementia severity were also conducted.Results. Compared with short-duration ST, long-duration ST was significantly associated with improved scores for comprehension and expression in the overall study population and in some groups, with higher benefit especially for younger participants (<64 years) and those with more severe dementia. A significant but less pronounced effect was also observed for OT and PT.Conclusion. Long-duration ST is more effective than long-duration OT or PT for improving language impairment occurring as stroke sequela. However, these effects are limited by age and severity of dementia.

Impact of Low-Dose Hydrocortisone on the Incidence of Atrial Fibrillation in Patients With Septic Shock: A Propensity Score-Inverse Probability of Treatment Weighting Cohort Study

2017 ◽  
Vol 34 (3) ◽  
pp. 238-244 ◽  
Author(s):  
Yoann Launey ◽  
Sigismond Lasocki ◽  
Karim Asehnoune ◽  
Baptiste Gaudriot ◽  
Claire Chassier ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Intensive Care Unit ◽  
Septic Shock ◽  
Lower Risk ◽  
Low Dose ◽  
Propensity Scores ◽  
Weighting Method ◽  
Inverse Probability ◽  
History Of ◽  
Attending Physician

Purpose: Atrial fibrillation (AF) is common in the intensive care unit (ICU), notably in patients with septic shock for whom inflammation is an already identified risk factor. The aim of this study was to evaluate the effect of low-dose hydrocortisone on AF occurrence in patients with septic shock. Methods: We performed a prospective nonrandomized observational study in 5 academic ICUs in France. From November 2012 to June 2014, all patients ≥16 years having septic shock were included, except those who had a history of AF, had a pacemaker, and/or experienced AF during hospitalization before the onset of shock or in whom the onset of shock occurred prior to admission to the ICU. Hydrocortisone was administered at the discretion of the attending physician. The incidence of AF was compared among patients who received hydrocortisone, and the effect of low-dose hydrocortisone on AF was estimated using the inverse probability treatment weighting method based on propensity scores. Results: A total of 261 patients were included (no-hydrocortisone group, n = 138; hydrocortisone group, n = 123). Atrial fibrillation occurred in 57 (22%) patients. Atrial fibrillation rates were 33 (24%) and 24 (19%) in no-hydrocortisone patients and hydrocortisone patients, respectively. In the weighted sample, the proportion of patients who developed AF was 28.8% in the no-hydrocortisone group and 16.8% in the hydrocortisone group (difference: −11.9%; 95% confidence interval: −23.4% to −0.5%; P = .040). Conclusion: In patients with septic shock, low-dose hydrocortisone was associated with a lower risk of developing AF during the acute phase.

scholarly journals A Case Study of the Impact of Data-Adaptive Versus Model-Based Estimation of the Propensity Scores on Causal Inferences from Three Inverse Probability Weighting Estimators

2016 ◽  
Vol 12 (1) ◽  
pp. 131-155 ◽  
Author(s):  
Romain Neugebauer ◽  
Julie A. Schmittdiel ◽  
Mark J. van der Laan
Keyword(s):  
Propensity Scores ◽  
Adaptive Estimation ◽  
Inverse Probability Weighting ◽  
Model Specification ◽  
Probability Weighting ◽  
Consistent Estimation ◽  
Causal Inferences ◽  
Inverse Probability ◽  
Estimation Strategy ◽  
Data Adaptive

Abstract:Objective: Consistent estimation of causal effects with inverse probability weighting estimators is known to rely on consistent estimation of propensity scores. To alleviate the bias expected from incorrect model specification for these nuisance parameters in observational studies, data-adaptive estimation and in particular an ensemble learning approach known as Super Learning has been proposed as an alternative to the common practice of estimation based on arbitrary model specification. While the theoretical arguments against the use of the latter haphazard estimation strategy are evident, the extent to which data-adaptive estimation can improve inferences in practice is not. Some practitioners may view bias concerns over arbitrary parametric assumptions as academic considerations that are inconsequential in practice. They may also be wary of data-adaptive estimation of the propensity scores for fear of greatly increasing estimation variability due to extreme weight values. With this report, we aim to contribute to the understanding of the potential practical consequences of the choice of estimation strategy for the propensity scores in real-world comparative effectiveness research.Method: We implement secondary analyses of Electronic Health Record data from a large cohort of type 2 diabetes patients to evaluate the effects of four adaptive treatment intensification strategies for glucose control (dynamic treatment regimens) on subsequent development or progression of urinary albumin excretion. Three Inverse Probability Weighting estimators are implemented using both model-based and data-adaptive estimation strategies for the propensity scores. Their practical performances for proper confounding and selection bias adjustment are compared and evaluated against results from previous randomized experiments.Conclusion: Results suggest both potential reduction in bias and increase in efficiency at the cost of an increase in computing time when using Super Learning to implement Inverse Probability Weighting estimators to draw causal inferences.

scholarly journals Cognitive–behavioural therapy for severe and recurrent bipolar disorders

2006 ◽  
Vol 188 (4) ◽  
pp. 313-320 ◽  
Author(s):  
Jan Scott ◽  
Eugene Paykel ◽  
Richard Morriss ◽  
Richard Bentall ◽  
Peter Kinderman ◽  
...  
Keyword(s):  
Cognitive Behavioural Therapy ◽  
Mental Healthcare ◽  
Bipolar Disorders ◽  
Behavioural Therapy ◽  
Recurrence Rates ◽  
Treatment As Usual ◽  
Psychological Therapies ◽  
Cognitive Behavioural ◽  
Randomised Controlled ◽  
Post Hoc

BackgroundEfficacy trials suggest that structured psychological therapies may significantly reduce recurrence rates of major mood episodes in individuals with bipolar disorders.AimsTo compare the effectiveness of treatment as usual with an additional 22 sessions of cognitive–behavioural therapy (CBT).MethodWe undertook a multicentre, pragmatic, randomised controlled treatment trial (n=253). Patients were assessed every 8 weeks for 18 months.ResultsMore than half of the patients had a recurrence by 18 months, with no significant differences between groups (hazard ratio=1.05; 95% CI 0.74–1.50). Post hoc analysis demonstrated a significant interaction (P=0.04) such that adjunctive CBT was significantly more effective than treatment as usual in those with fewer than 12 previous episodes, but less effective in those with more episodes.ConclusionsPeople with bipolar disorder and comparatively fewer previous mood episodes may benefit from CBT. However, such cases form the minority of those receiving mental healthcare.

scholarly journals Long-term survival in laparoscopic vs open resection for colorectal liver metastases: inverse probability of treatment weighting using propensity scores

HPB ◽  
2016 ◽  
Vol 18 (2) ◽  
pp. 183-191 ◽  
Author(s):  
Joel W. Lewin ◽  
Nicholas A. O'Rourke ◽  
Adrian K.H. Chiow ◽  
Richard Bryant ◽  
Ian Martin ◽  
...  
Keyword(s):  
Liver Metastases ◽  
Colorectal Liver Metastases ◽  
Propensity Scores ◽  
Open Resection ◽  
Term Survival ◽  
Long Term Survival ◽  
Inverse Probability

Propensity score analysis methods with balancing constraints: A Monte Carlo study

2021 ◽  
pp. 096228022098351
Author(s):  
Yan Li ◽  
Liang Li
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Propensity Scores ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Propensity Score Model ◽  
Propensity Score Weighting ◽  
Estimation Performance ◽  
Score Model ◽  
Weighting Methods

The inverse probability weighting is an important propensity score weighting method to estimate the average treatment effect. Recent literature shows that it can be easily combined with covariate balancing constraints to reduce the detrimental effects of excessively large weights and improve balance. Other methods are available to derive weights that balance covariate distributions between the treatment groups without the involvement of propensity scores. We conducted comprehensive Monte Carlo experiments to study whether the use of covariate balancing constraints circumvent the need for correct propensity score model specification, and whether the use of a propensity score model further improves the estimation performance among methods that use similar covariate balancing constraints. We compared simple inverse probability weighting, two propensity score weighting methods with balancing constraints (covariate balancing propensity score, covariate balancing scoring rule), and two weighting methods with balancing constraints but without using the propensity scores (entropy balancing and kernel balancing). We observed that correct specification of the propensity score model remains important even when the constraints effectively balance the covariates. We also observed evidence suggesting that, with similar covariate balance constraints, the use of a propensity score model improves the estimation performance when the dimension of covariates is large. These findings suggest that it is important to develop flexible data-driven propensity score models that satisfy covariate balancing conditions.

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