How does social support shape the association between depressive symptoms and labour market participation:a four-way decomposition

Background Little is known about factors that may explain the association between depressive symptoms and poor labour market participation (LMP). The aim of this study is to examine the mediation and interaction effects of social support on the association between depressive symptoms and LMP. Methods Data were used from 985 participants (91% of the initial cohort) of the Northern Swedish Cohort, a longitudinal study of Swedish participants followed from adolescence throughout adulthood. Depressive symptoms were measured at age 16, social support at age 21 and LMP from age 30 to 43. Poor LMP was defined as being unemployed for a total of 6 months or more between the ages of 30 and 43. A four-way decomposition approach was applied to identify direct, mediation and interaction effects, together and separately. Results Both depressive symptoms during adolescence and social support at young adulthood were associated with poor LMP [odds ratio (OR) = 1.70, 95% confidence interval (CI) 1.17-2.47 and OR = 2.56, 95% CI 1.78-3.68 respectively]. The association between depressive symptoms and poor LMP was partially mediated by a lack of social support. No interaction effect of a lack of social support was found. Conclusion The results suggest that depressive symptoms influence not only later LMP but also the intermediary level of social support, and in turn influencing later LMP. Recommendations for public health are to detect and treat depressive symptoms at an early stage and to focus on the development of social skills, facilitating the increased availability of social support, thereby improving future LMP

Design of a randomized controlled trial of physical training and cancer (Phys-Can) – the impact of exercise intensity on cancer related fatigue, quality of life and disease outcome

Background: Cancer-related fatigue is a common problem in persons with cancer, influencing health-related quality of life and causing a considerable challenge to society. Current evidence supports the beneficial effects of physical exercise in reducing fatigue, but the results across studies are not consistent, especially in terms of exercise intensity. It is also unclear whether use of behaviour change techniques can further increase exercise adherence and maintain physical activity behaviour. This study will investigate whether exercise intensity affects fatigue and health related quality of life in persons undergoing adjuvant cancer treatment. In addition, to examine effects of exercise intensity on mood disturbance, adherence to oncological treatment, adverse effects from treatment, activities of daily living after treatment completion and return to work, and behaviour change techniques effect on exercise adherence. We will also investigate whether exercise intensity influences inflammatory markers and cytokines, and whether gene expressions following training serve as mediators for the effects of exercise on fatigue and health related quality of life. Methods/design: Six hundred newly diagnosed persons with breast, colorectal or prostate cancer undergoing adjuvant therapy will be randomized in a 2 × 2 factorial design to following conditions; A) individually tailored low-to-moderate intensity exercise with or without behaviour change techniques or B) individually tailored high intensity exercise with or without behaviour change techniques. The training consists of both resistance and endurance exercise sessions under the guidance of trained coaches. The primary outcomes, fatigue and health related quality of life, are measured by self-reports. Secondary outcomes include fitness, mood disturbance, adherence to the cancer treatment, adverse effects, return to activities of daily living after completed treatment, return to work as well as inflammatory markers, cytokines and gene expression. Discussion: The study will contribute to our understanding of the value of exercise and exercise intensity in reducing fatigue and improving health related quality of life and, potentially, clinical outcomes. The value of behaviour change techniques in terms of adherence to and maintenance of physical exercise behaviour in persons with cancer will be evaluated

Some Approximations of the Logistic Distribution with Application to the Covariance Matrix of Logistic Regression Title: Some Approximations of the Logistic Distribution with Application to the Covariance Matrix of Logistic Regression Some Approximations

SUMMARY We show that a two component normal mixture model provides a very close approximation to the logistic distribution. This is an improvement over using the normal distribution and is on par with using the t-distribution as approximating distributions. The result from using the mixture model is exemplified by finding an approximative analytic expression for the covariance matrix of logistic regression with normally distributed random regressors

Estimating the variance of a propensity score matching estimator for the average treatment effect

This study considers variance estimation when estimating the asymptotic variance of a propensity score matching estimator for the average treatment effect.  We investigate the role of smoothing parameters in a variance estimator based on matching.   We also study the properties of estimators using local linear estimation. Simulations demonstrate that large gains can be made in terms of mean squared error, bias and coverage rate by properly selecting smoothing parameters.  Alternatively, a residual-based local linear estimator could be used as an estimator of the asymptotic variance.   The variance estimators are implemented in analysis to evaluate the effect of right heart catheterisation

Estimating the variance of a propensity score matching estimator: A new look at right heart catheterisation data

This study considers the implementation of a variance estimator when estimating the asymptotic variance of a propensity score matching estimator for the average treatment effect. We investigate the role of smoothing parameters in the variance estimator and propose using local linear estimation. Simulations demonstrate that large gains can be made in terms of mean squared error by properly selecting smoothing parameters and that local linear estimation may lead to a more efficient estimator of the asymptotic variance. The choice of smoothing parameters in the variance estimator is shown to be crucial when evaluating the effect of right heart catheterisation, i.e. we show either a negative effect on survival or no significant effect depending on the choice of smoothing parameters

Estimating the variance of a propensity score matching estimator: A new look at right heart catheterisation data

This study considers the implementation of a variance estimator when estimating the asymptotic variance of a propensity score matching estimator for the average treatment effect. We investigate the role of smoothing parameters in the variance estimator and propose using local linear estimation. Simulations demonstrate that large gains can be made in terms of mean squared error by properly selecting smoothing parameters and that local linear estimation may lead to a more efficient estimator of the asymptotic variance. The choice of smoothing parameters in the variance estimator is shown to be crucial when evaluating the effect of right heart catheterisation, i.e. we show either a negative effect on survival or no significant effect depending on the choice of smoothing parameters

Some Aspects of Propensity Score-based Estimators for Causal Inference

This thesis consists of four papers that are related to commonly used propensity score-based estimators for average causal effects. The first paper starts with the observation that researchers often have access to data containing lots of covariates that are correlated. We therefore study the effect of correlation on the asymptotic variance of an inverse probability weighting and a matching estimator. Under the assumptions of normally distributed covariates, constant causal effect, and potential outcomes and a logit that are linear in the parameters we show that the correlation influences the asymptotic efficiency of the estimators differently, both with regard to direction and magnitude. Further, the strength of the confounding towards the outcome and the treatment plays an important role. The second paper extends the first paper in that the estimators are studied under the more realistic setting of using the estimated propensity score. We also relax several assumptions made in the first paper, and include the doubly robust estimator. Again, the results show that the correlation may increase or decrease the variances of the estimators, but we also observe that several aspects influence how correlation affects the variance of the estimators, such as the choice of estimator, the strength of the confounding towards the outcome and the treatment, and whether constant or non-constant causal effect is present. The third paper concerns estimation of the asymptotic variance of a propensity score matching estimator. Simulations show that large gains can be made for the mean squared error by properly selecting smoothing parameters of the variance estimator and that a residual-based local linear estimator may be a more efficient estimator for the asymptotic variance. The specification of the variance estimator is shown to be crucial when evaluating the effect of right heart catheterisation, i.e. we show either a negative effect on survival or no significant effect depending on the choice of smoothing parameters.   In the fourth paper, we provide an analytic expression for the covariance matrix of logistic regression with normally distributed regressors. This paper is related to the other papers in that logistic regression is commonly used to estimate the propensity score