Diagnostic tests for bias of estimating equations in weighted regression with missing covariates

2001 ◽  
Vol 29 (2) ◽  
pp. 239-250 ◽  
Author(s):  
S.-Y. Claire Lei ◽  
Suojin Wang
Keyword(s):  
Diagnostic Tests ◽  
Estimating Equations ◽  
Weighted Regression ◽  
Missing Covariates

scholarly journals Weighted estimating equations for additive hazards models with missing covariates

2018 ◽  
Vol 71 (2) ◽  
pp. 365-387
Author(s):  
Lihong Qi ◽  
Xu Zhang ◽  
Yanqing Sun ◽  
Lu Wang ◽  
Yichuan Zhao
Keyword(s):  
Estimating Equations ◽  
Missing Covariates ◽  
Additive Hazards

Images of Aging in the Psychotherapeutic Context

GeroPsych ◽  
2015 ◽  
Vol 28 (2) ◽  
pp. 47-55 ◽  
Author(s):  
Eva-Marie Kessler ◽  
Catherine E. Bowen
Keyword(s):  
Old Age ◽  
Diagnostic Tests ◽  
Aging Process ◽  
Developmental Psychology ◽  
Mental Representations ◽  
Later Life ◽  
Future Research ◽  
Psychotherapeutic Process ◽  
Images Of Aging ◽  
Client Communication

Both psychotherapists and their clients have mental representations of old age and the aging process. In this conceptual review, we draw on available research from gerontology, social and developmental psychology, and communication science to consider how these “images of aging” may affect the psychotherapeutic process with older clients. On the basis of selected empirical findings we hypothesize that such images may affect the pathways to psychotherapy in later life, therapist-client communication, client performance on diagnostic tests as well as how therapists select and apply a therapeutic method. We posit that interventions to help both older clients and therapists to reflect on their own images of aging may increase the likelihood of successful treatment. We conclude by making suggestions for future research.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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