Estimating the Power of Indirect Comparisons: A Simulation Study

Indirect comparisons are becoming increasingly popular for evaluating medical treatments that have not been compared head-to-head in randomized clinical trials (RCTs). While indirect methods have grown in popularity and acceptance, little is known about the fragility of confidence interval estimations and hypothesis testing relying on this method.We present the findings of a simulation study that examined the fragility of indirect confidence interval estimation and hypothesis testing relying on the adjusted indirect method.Our results suggest that, for the settings considered in this study, indirect confidence interval estimation suffers from under-coverage while indirect hypothesis testing suffers from low power in the presence of moderate to large between-study heterogeneity. In addition, the risk of overestimation is large when the indirect comparison of interest relies on just one trial for one of the two direct comparisons.Indirect comparisons typically suffer from low power. The risk of imprecision is increased when comparisons are unbalanced

Inference in partially linear models with correlated errors

We study the problem of performing statistical inference on the linear effects in partially linear models with correlated errors. To estimate these effects, we introduce usual, modified and estimated modified backfitting estimators, relying on locally linear regression. We obtain explicit expressions for the conditional asymptotic bias and variance of the usual backfitting estimators under the assumption that the model errors follow a mean zero, covariance-stationary process. We derive similar results for the modified backfitting estimators under the more restrictive assumption that the model errors follow a mean zero, stationary autoregressive process of finite order. Our results assume that the width of the smoothing window used in locally linear regression decreases at a specified rate, and the number of data points in this window increases. These results indicate that the squared bias of the considered estimators can dominate their variance in the presence of correlation between the linear and non-linear variables in the model, therefore compromising their i/n-consistency. We suggest that this problem can be remedied by selecting an appropriate rate of convergence for the smoothing parameter of the-estimators. We argue that this rate is slower than the rate that is optimal for estimating the non-linear effect, and as such it 'undersmooths' the estimated non-linear effect. For this reason, data-driven methods devised for accurate estimation of the non-linear effect may fail to yield a satisfactory choice of smoothing for estimating the linear effects. We introduce three data-driven methods for accurate estimation of the linear effects. Two of these methods are modifications of the Empirical Bias Bandwidth Selection method of Opsomer and Ruppert (1999). The third method is a non-asymptotic plug-in method. We use the data-driven choices of smoothing supplied by these methods as a basis for constructing approximate confidence intervals and tests of hypotheses for the linear effects. Our inferential procedures do not account for the uncertainty associated with the fact that the choices of smoothing are data-dependent and the error correlation structure is estimated from the data. We investigate the finite sample properties of our procedures via a simulation study. We also apply these procedures to the analysis of data collected in a time-series air pollution study.Science, Faculty ofStatistics, Department ofGraduat

A Model of Environmental Correlates of Adolescent Obesity in the United States

Purpose The purpose of this study was to test a conceptual model of proximal (home) and distal (neighborhood) environmental correlates of adolescent obesity. Methods This was a descriptive, cross-sectional study, using the 2007 National Survey of Children\u27s Health, of 39,542 children aged 11-17 years. Structural equation modeling was used to test the fit of the model, identify direct and indirect effects of the proximal and distal environmental correlates, and determine reliabilities for latent constructs (Access to Physical Activity, Neighborhood Conditions, Social Capital Home Sedentary Behavior, and Physical Activity). Results The model fitted the data well (Root Mean Square Standard Error of Approximation: .038 (90% confidence interval .038-.039), Comparative Fit Index: .950, and Tucker-Lewis Index: .934). Access to Physical Activity, Social Capital, Home Sedentary Behavior, and Physical Activity had direct effects on obesity (−.026, p = .001; .061, p \u3c .001; .110, p \u3c .001; and −.119, p \u3c .001, respectively). Neighborhood Condition had indirect effects on obesity through Access to Physical Activity, Social Capital, and Home Sedentary Behavior (−.001, p = .009; .032, p \u3c .001; and .044, p \u3c .001, respectively). Access to Physical Activity had indirect effects on obesity through Physical Activity, Social Capital, and Home Sedentary Behavior (−.013, p \u3c .001; −.005, p \u3c .001; and −.005, p = .003, respectively). Home Sedentary Behavior had indirect effect on obesity through Physical Activity (.052, p \u3c.001). Conclusions Results of this model fit to the U. S. population-based data suggest that interventions should target not only sedentary behavior and physical activity but also parent perceptions of safety, access to physical activity, and the neighborhood condition

Orientation and Transition Programme Component Predictors of New Graduate Workplace Integration

AIM: To examine the relationships between selected components of new graduate nurse transition programmes and transition experiences. BACKGROUND: Transition support for new graduates is growing increasingly multifaceted; however, an investigation of the effectiveness of the constituent components of the transition process is lacking. METHODS: An online survey was disseminated to new graduates working in acute care settings and included questions related to new graduate transition programmes. The Casey Fink Graduate Nurse Experience Survey was used to quantify the transition experience. RESULTS: New graduate nurses who participated in a formal new graduate (NG) transition programme had significantly higher total transition scores than non-programme nurses. The orientation length and the average number of hours worked in a two week period were significant predictors of transition; the percentage of preceptored shifts was statistically insignificant. CONCLUSIONS: New graduate transition is enhanced with participation in a formal transition programme. Orientation should be at least four weeks in length, and new graduates should work at least 49 hours in a two week period. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse managers are in key positions to advocate for new graduate nurse transition programmes with adequate resources to support a four week orientation phase and shift scheduling to ensure an adequate number of hours over two week periods to facilitate transition.Health and Social Development, Faculty of (Okanagan)Library, UBCNursing, School of (Okanagan)ReviewedFacult

The Helpfulness and Timing of Transition Program Education

The purpose of this study was to examine relationships between transition program education and new graduate nurse transition. Although new graduates preferred hands-on learning, the helpfulness of workshops was associated with better transition. New graduates, many of whom were from the Millennial Generation, liked a variety of educational modalities. Access to support was better for nurse graduates who received education delivered throughout the first year of transition.Health and Social Development, Faculty of (Okanagan)Nursing, School of (Okanagan)Library, UBCReviewedFacult

A Structural Equation Model of Environmental Correlates of Adolescent Obesity for Age and Gender Groups

Background The relationships between environmental correlates of adolescent obesity are complex and not yet well defined by current research, especially when considering age and gender. Objective The purpose of this study was to test a model of proximal (home) and distal (neighbourhood) environmental correlates of obesity for adolescent age and gender groups. Methods This was a descriptive, cross-sectional study, using the 2007 National Survey of Children\u27s Health of 39 542 children ages 11-17 years. Results The model fit the data well for early adolescents (ages 11-14 years) (root mean square standard error of approximation [ RMSEA] 0.040, 90% confidence interval [ CI]: 0.039-0.041; comparative fit index [ CFI] 0.947; Tucker- Lewis index [ TLI] 0.929) and middle adolescents (ages 15-17 years) ( RMSEA 0.037, 90% CI: 0.036-0.038; CFI 0.052; TLI 0.937). The model also fit the data well for boy adolescents ( RMSEA 0.038, 90% CI: 0.037-0.039; CFI 0.951; TLI 0.935) and girl adolescents ( RMSEA 0.038, 90% CI: 0.037-0.040; CFI 0.949; TLI 0.932). Conclusions All models provide loadings of the environmental correlates of adolescent obesity for specific age and gender groups that can be used for early identification of risks and targeted interventions. [ABSTRACT FROM AUTHOR

A Model of Environmental Correlates of Adolescent Obesity in the United States

Purpose The purpose of this study was to test a conceptual model of proximal (home) and distal (neighborhood) environmental correlates of adolescent obesity. Methods This was a descriptive, cross-sectional study, using the 2007 National Survey of Children\u27s Health, of 39,542 children aged 11-17 years. Structural equation modeling was used to test the fit of the model, identify direct and indirect effects of the proximal and distal environmental correlates, and determine reliabilities for latent constructs (Access to Physical Activity, Neighborhood Conditions, Social Capital Home Sedentary Behavior, and Physical Activity). Results The model fitted the data well (Root Mean Square Standard Error of Approximation: .038 (90% confidence interval .038-.039), Comparative Fit Index: .950, and Tucker-Lewis Index: .934). Access to Physical Activity, Social Capital, Home Sedentary Behavior, and Physical Activity had direct effects on obesity (−.026, p = .001; .061, p \u3c .001; .110, p \u3c .001; and −.119, p \u3c .001, respectively). Neighborhood Condition had indirect effects on obesity through Access to Physical Activity, Social Capital, and Home Sedentary Behavior (−.001, p = .009; .032, p \u3c .001; and .044, p \u3c .001, respectively). Access to Physical Activity had indirect effects on obesity through Physical Activity, Social Capital, and Home Sedentary Behavior (−.013, p \u3c .001; −.005, p \u3c .001; and −.005, p = .003, respectively). Home Sedentary Behavior had indirect effect on obesity through Physical Activity (.052, p \u3c.001). Conclusions Results of this model fit to the U. S. population-based data suggest that interventions should target not only sedentary behavior and physical activity but also parent perceptions of safety, access to physical activity, and the neighborhood condition

A Structural Equation Model of Environmental Correlates of Adolescent Obesity for Age and Gender Groups

Background The relationships between environmental correlates of adolescent obesity are complex and not yet well defined by current research, especially when considering age and gender. Objective The purpose of this study was to test a model of proximal (home) and distal (neighbourhood) environmental correlates of obesity for adolescent age and gender groups. Methods This was a descriptive, cross-sectional study, using the 2007 National Survey of Children\u27s Health of 39 542 children ages 11-17 years. Results The model fit the data well for early adolescents (ages 11-14 years) (root mean square standard error of approximation [ RMSEA] 0.040, 90% confidence interval [ CI]: 0.039-0.041; comparative fit index [ CFI] 0.947; Tucker- Lewis index [ TLI] 0.929) and middle adolescents (ages 15-17 years) ( RMSEA 0.037, 90% CI: 0.036-0.038; CFI 0.052; TLI 0.937). The model also fit the data well for boy adolescents ( RMSEA 0.038, 90% CI: 0.037-0.039; CFI 0.951; TLI 0.935) and girl adolescents ( RMSEA 0.038, 90% CI: 0.037-0.040; CFI 0.949; TLI 0.932). Conclusions All models provide loadings of the environmental correlates of adolescent obesity for specific age and gender groups that can be used for early identification of risks and targeted interventions. [ABSTRACT FROM AUTHOR