222 research outputs found
Treatment compliance and effectiveness of a cognitive behavioural intervention for low back pain : a complier average causal effect approach to the BeST data set
Background:
Group cognitive behavioural intervention (CBI) is effective in reducing low-back pain and disability in comparison to advice in primary care. The aim of this analysis was to investigate the impact of compliance on estimates of treatment effect and to identify factors associated with compliance.
Methods:
In this multicentre trial, 701 adults with troublesome sub-acute or chronic low-back pain were recruited from 56 general practices. Participants were randomised to advice (control n = 233) or advice plus CBI (n = 468). Compliance was specified a priori as attending a minimum of three group sessions and the individual assessment. We estimated the complier average causal effect (CACE) of treatment.
Results:
Comparison of the CACE estimate of the mean treatment difference to the intention-to-treat (ITT) estimate at 12 months showed a greater benefit of CBI amongst participants compliant with treatment on the Roland Morris Questionnaire (CACE: 1.6 points, 95% CI 0.51 to 2.74; ITT: 1.3 points, 95% CI 0.55 to 2.07), the Modified Von Korff disability score (CACE: 12.1 points, 95% CI 6.07 to 18.17; ITT: 8.6 points, 95% CI 4.58 to 12.64) and the Modified von Korff pain score (CACE: 10.4 points, 95% CI 4.64 to 16.10; ITT: 7.0 points, 95% CI 3.26 to 10.74). People who were non-compliant were younger and had higher pain scores at randomisation.
Conclusions:
Treatment compliance is important in the effectiveness of group CBI. Younger people and those with more pain are at greater risk of non-compliance
Presynaptic Cholinergic Modulators as Potent Cognition Enhancers and Analgesic Drugs. 2. 2-Phenoxy-, 2-(phenylthio)- and 2-(phenylamino)alkanoic Acid Esters
Applying Heterogeneous Transition Models in Labour Economics: the role of youth training in labour market transitions
Measuring the Vulnerability of Children in Developing Countries: An Application to Guatemala
Anti-poverty policy in developing countries has focused mainly on the measurement and location of poverty and the targeting of policy towards those who are currently poor. Recently, the research effort has been extended to cover those judged to be not poor at present but vulnerable to poverty in the future. We concentrate on two aspects: inadequate education and child labor, which are closely associated with chronic poverty. We develop and apply new methods for the measurement and empirical analysis of vulnerability to future premature school leaving and/or onset of child labor. Guatemalan survey data are used for the illustrative application.
Measuring the Vulnerability of children in developing countries: an application to Guatemala
Anti-poverty policy in developing countries has focused mainly on the measurement and location of poverty and the targeting of policy towards those who are currently poor. Recently, the research effort has been extended to cover those judged to be not poor at present but vulnerable to poverty in the future. We concentrate on two aspects: inadequate education and child labor, which are closely associated with chronic poverty. We develop and apply new methods for the measurement and empirical analysis of vulnerability to future premature school leaving and/or onset of child labor. Guatemalan survey data are used for the illustrative application.
Analyzing a Randomized Trial on Breast Self Examination with Noncompliance and Missing Outcomes
Recently, instrumental variables methods have been used to address non-compliance in randomized experiments. Complicating such analyses is often the presence of missing data. The standard model for missing data, missing at random (MAR), has some unattractive features in this context. In this paper
we compare MAR-based estimates of the complier average causal effect (CACE) with an estimator based on an alternative, nonignorable model for the missing data process, developed by Frangakis and
Rubin (1999). We also introduce a new missing data model that, like the Frangakis\u2013Rubin model, is specially suited for models with instrumental variables, but makes different substantive assumptions. We analyze these issues in the context of a randomized trial of breast self-examination (BSE). In the
study two methods of teaching BSE, consisting of either mailed information about BSE (the standard treatment) or the attendance of a course involving theoretical and practical sessions (the new treatment), were compared with the aim of assessing whether teaching programs could increase BSE practice and improve examination skills. The study was affected by the two sources of bias mentioned above: only 55% of women assigned to receive the new treatment complied with their assignment and 35% of the women did not respond to the post-test questionnaire. Comparing the causal estimand of the new treatment using the MAR, Frangakis\u2013Rubin, and our new approach, the results suggest that for these data the MAR assumption appears least plausible, and that the new model appears most plausible among the three choices
Identification of Principal Causal Effects Using Additional Outcomes in Concentration Graphs
d-Agostic Interactions and C-H Bond Activation Reactions in Late Transition Metal Complexes
Occupational pensions and job mobility in Britain Estimation of a random effects competing risks model
SIGLEGBUnited Kingdo
- …