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Meta-Analytic Approach to Evaluation of Surrogate Endpoints
Published in Christopher H. Schmid, Theo Stijnen, Ian R. White, Handbook of Meta-Analysis, 2020
Tomasz Burzykowski, Marc Buyse, Geert Molenberghs, Ariel Alonso, Wim Van der Elst, Ziv Shkedy
Here, Var(b0) denotes the unconditional variance of the trial-specific random effect of Z on T. The smaller the conditional variance given by (20.17), the better the precision of the prediction.
Reference-Based Imputation
Published in Craig Mallinckrodt, Geert Molenberghs, Ilya Lipkovich, Bohdana Ratitch, Estimands, Estimators and Sensitivity Analysis in Clinical Trials, 2019
Craig Mallinckrodt, Geert Molenberghs, Ilya Lipkovich, Bohdana Ratitch
The conditional variance of the first term given can be derived from the variance estimates from the repeated measures model (e.g., using the covariance estimates of the LSMEAN differences from the SAS PROC MIXED analysis). The second term can be calculated using the point estimates of and , with for , for , where is the sample size for group .
Pairwise Survival Analysis of Infectious Disease Transmission Data
Published in Leonhard Held, Niel Hens, Philip O’Neill, Jacco Wallinga, Handbook of Infectious Disease Data Analysis, 2019
The variance of can be estimated using the conditional variance formula. Conditioning on V, we get Let denote the estimated hazard function corresponding to . Let and denote the estimates of and . The first term of Equation (12.65) reduces to
A dependent counting INAR model with serially dependent innovation
Published in Journal of Applied Statistics, 2021
Masoumeh Shirozhan, Mehrnaz Mohammadpour
The conditional expectation is The conditional variance is The autocovariance function of the process and innovation are
Modeling material stress using integrated Gaussian Markov random fields
Published in Journal of Applied Statistics, 2020
Peter W. Marcy, Scott A. Vander Wiel, Curtis B. Storlie, Veronica Livescu, Curt A. Bronkhorst
Properties of GMRFs (e.g. [17], Theorem 2.3) can be used to gain insight into the parameterization above: 9) is a weighted average of the neighbors, where β process is across neighbors. The conditional variance is a function of the number of neighbors, and intuitively these quantities are inversely proportional. Though the β process is not actually stationary, conditional precision: smaller
Testing for heteroskedasticity in two-way fixed effects panel data models
Published in Journal of Applied Statistics, 2020
Sanying Feng, Gaorong Li, Tiejun Tong, Shuanghua Luo
Testing for heteroskedasticity is a crucial step and an important topic in panel data analysis. In the literature, many panel data models have assumed that the disturbances have homoskedastic variances. In practice, however, this can be a rather restrictive assumption for panel data generated from the real world. For instance, cross-sectional units may vary in size so that the conditional variance may exhibit a conditional heteroskedasticity. It is also known that heteroskedasticity may lead to inefficient least squares estimates and inconsistent covariance matrix estimates, when the error terms are incorrectly specified as homoskedastic.