The use of frailty hazard models for unrecognized heterogeneity that interacts with treatment: Considerations of efficiency and power

Biometrics

Volumn 58, Issue 1, 2002, Pages 232-236

  Li, Y ^a   Betensky, R A ^a   Louis, D N ^b   Cairncross, J G ^c  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c UNIVERSITY OF WESTERN ONTARIO   (Canada)

Author keywords

Omitted covariate

Indexed keywords

DIAGNOSIS; EFFICIENCY;

ANALYTIC APPROXIMATION; ASYMPTOTIC RELATIVE EFFICIENCY; COVARIATES; GENETIC STUDIES; HAZARD MODELING; OMITTED COVARIATE; POWER; RANK TEST; SIMPLE++; TREATMENT EFFECTS;

TUMORS;

CANCER; MEDICAL GEOGRAPHY; STATISTICAL ANALYSIS; SURVIVAL;

ANALYTIC METHOD; CANCER SURVIVAL; COVARIANCE; GENETIC HETEROGENEITY; HUMAN; NONBIOLOGICAL MODEL; REVIEW; STATISTICAL MODEL; TREATMENT OUTCOME;

EID: 0036190971     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2002.00232.x     Document Type: Review

References (10)

1. A quantitative study of the bias in estimating the treatment effect caused by omitting a balanced covariate in survival analysis
2. Molecular subtypes of anaplastic oligodendroglioma: Implications for patient management at diagnosis
3. The role of frailty models and accelerated failure time models in describing heterogeneity due to omitted covariates
4. Properties of proportional-hazards score tests under misspecified regression models
5. Omitting covariates from the proportional hazards model
6. Frailty models and rank tests
7. Effects of covariate omission and categorization when analysing randomized trials with the Cox model
8. Sample-size formula for the proportional-hazards regression model
9. Misspecified proportional hazard models