Evaluation of Cox's model and logistic regression for matched case-control data with time-dependent covariates: a simulation study

2003 ◽  
Vol 22 (24) ◽  
pp. 3781-3794 ◽  
Author(s):  
Karen Leffondré ◽  
Michal Abrahamowicz ◽  
Jack Siemiatycki
Keyword(s):  
Logistic Regression ◽  
Simulation Study ◽  
Case Control ◽  
Time Dependent ◽  
Control Data ◽  
Matched Case ◽  
Time Dependent Covariates ◽  
Cox's Model

scholarly journals Power and sample size for multivariate logistic modeling of unmatched case-control studies

2017 ◽  
Vol 28 (3) ◽  
pp. 822-834
Author(s):  
Mitchell H Gail ◽  
Sebastien Haneuse
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Case Control ◽  
Simulation Methods ◽  
Case Control Studies ◽  
Control Data ◽  
Logistic Analysis ◽  
Sample Size Calculations ◽  
Control Designs ◽  
Univariate Analyses

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.

Matched Case-Control Data Analysis with Selection Bias

Biometrics ◽  
2001 ◽  
Vol 57 (4) ◽  
pp. 1106-1112 ◽  
Author(s):  
I-Feng Lin ◽  
Myunghee Cho Paik
Keyword(s):  
Data Analysis ◽  
Selection Bias ◽  
Case Control ◽  
Control Data ◽  
Matched Case

scholarly journals Matched versus Unmatched Analysis of Matched Case-Control Studies

2021 ◽  
Author(s):  
Fei Wan ◽  
Graham A Colditz ◽  
Siobhan Sutcliffe
Keyword(s):  
Logistic Regression ◽  
Functional Form ◽  
Conditional Logistic Regression ◽  
Control Sample ◽  
Target Population ◽  
Case Control ◽  
Model Specification ◽  
Exact Matching ◽  
Case Control Studies ◽  
Matched Case

Abstract Although the need for addressing matching in the analysis of matched case-control studies is well established, debate remains as to the most appropriate analytic method when matching on at least one continuous factor. We compare the bias and efficiency of unadjusted and adjusted conditional logistic regression (CLR) and unconditional logistic regression (ULR) in the setting of both exact and non-exact matching. To demonstrate that case-control matching distorts the association between the matching variables and the outcome in the matched sample relative to the target population, we derive the logit model for the matched case-control sample under exact matching. We conduct simulations to validate our theoretical conclusions and to explore different ways of adjusting for the matching variables in CLR and ULR to reduce biases. When matching is exact, CLR is unbiased in all settings. When matching is not exact, unadjusted CLR tends to be biased and this bias increases with increasing matching caliper size. Spline smoothing of the matching variables in CLR can alleviate biases. Regardless of exact or non-exact matching, adjusted ULR is generally biased unless the functional form of the matched factors is modelled correctly. The validity of adjusted ULR is vulnerable to model specification error. CLR should remain the primary analytic approach.

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