A corrected profile likelihood method for survival data with covariate measurement error under the Cox model

2015 ◽  
Vol 43 (3) ◽  
pp. 454-480 ◽  
Author(s):  
Ying Yan ◽  
Grace Y. Yi
Keyword(s):  
Measurement Error ◽  
Survival Data ◽  
Profile Likelihood ◽  
Cox Model ◽  
Likelihood Method ◽  
Covariate Measurement Error

Covariate Measurement Error in the Cox Model: A Simulation Study

2004 ◽  
Vol 33 (4) ◽  
pp. 1077-1093 ◽  
Author(s):  
Kenneth Liu ◽  
Roslyn A. Stone ◽  
Sati Mazumdar ◽  
Patricia R. Houck ◽  
Charles F. Reynolds III
Keyword(s):  
Measurement Error ◽  
Simulation Study ◽  
Cox Model ◽  
Covariate Measurement Error

Accounting for covariate measurement error in a Cox model analysis of recurrence of depression

2001 ◽  
Vol 35 (3) ◽  
pp. 177-185 ◽  
Author(s):  
Kenneth Liu ◽  
Sati Mazumdar ◽  
Roslyn A. Stone ◽  
Mary Amanda Dew ◽  
Patricia R. Houck ◽  
...  
Keyword(s):  
Measurement Error ◽  
Cox Model ◽  
Model Analysis ◽  
Covariate Measurement Error

scholarly journals Sampling strategies to evaluate the prognostic value of a new biomarker on a time-to-event end-point

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Francesca Graziano ◽  
Maria Grazia Valsecchi ◽  
Paola Rebora
Keyword(s):  
Survival Data ◽  
Prognostic Value ◽  
Cox Model ◽  
Lymphoblastic Leukemia ◽  
Fixed Ratio ◽  
Case Control ◽  
Time To Event ◽  
Full Cohort ◽  
Number Of Patients ◽  
Nested Case Control

Abstract Background The availability of large epidemiological or clinical data storing biological samples allow to study the prognostic value of novel biomarkers, but efficient designs are needed to select a subsample on which to measure them, for parsimony and economical reasons. Two-phase stratified sampling is a flexible approach to perform such sub-sampling, but literature on stratification variables to be used in the sampling and power evaluation is lacking especially for survival data. Methods We compared the performance of different sampling designs to assess the prognostic value of a new biomarker on a time-to-event endpoint, applying a Cox model weighted by the inverse of the empirical inclusion probability. Results Our simulation results suggest that case-control stratified (or post stratified) by a surrogate variable of the marker can yield higher performances than simple random, probability proportional to size, and case-control sampling. In the presence of high censoring rate, results showed an advantage of nested case-control and counter-matching designs in term of design effect, although the use of a fixed ratio between cases and controls might be disadvantageous. On real data on childhood acute lymphoblastic leukemia, we found that optimal sampling using pilot data is greatly efficient. Conclusions Our study suggests that, in our sample, case-control stratified by surrogate and nested case-control yield estimates and power comparable to estimates obtained in the full cohort while strongly decreasing the number of patients required. We recommend to plan the sample size and using sampling designs for exploration of novel biomarker in clinical cohort data.

Propensity score trimming mitigates bias due to covariate measurement error in inverse probability of treatment weighted analyses: A plasmode simulation

2021 ◽  
Vol 40 (9) ◽  
pp. 2101-2112
Author(s):  
Mitchell M. Conover ◽  
Kenneth J. Rothman ◽  
Til Stürmer ◽  
Alan R. Ellis ◽  
Charles Poole ◽  
...  
Keyword(s):  
Measurement Error ◽  
Propensity Score ◽  
Inverse Probability ◽  
Covariate Measurement Error
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