Logistic Regression: A Self-learning Text, Third Edition (Statistics in the Health Sciences)

Introduction We begin this chapter by giving the rationale for having a strategy to determine a “best” model. Focus is on a logi ...

Objectives Upon completion of this chapter, the learner should be able to: State and recognize the three stages of the recommen ...

Presentation I. Overview FOCUS Guidelines for “best” models Three-stage strategy Valid estimate of E–D relationship (con- foundi ...

Guidelines applicable to: Logistic regression Multiple linear regression Cox PH regression Two modeling goals: (1) To obtain a v ...

Interaction prior to confounding:  If strong interaction, then confounding irrelevant Assess interaction before confounding Int ...

If interaction present:  Do not assess confounding for effect modifiers  Assessing confounding for other variables difficult a ...

Confounding:no statistical testing # Validity------systematic error (Statistical testing — random error) Confounding in logistic ...

Influential observations:  Individual data may influence regression coefficients, e.g., outlier  Coefficients may change if ou ...

üSimplest choice forVs: TheCs themselves (or a subset ofCs) SpecifyWs: (in model asEW): RestrictWstobeVs themselves or products ...

Variable Specification Summary Flow Diagram Choose D, E, C 1 ,... , Cp Choose Vs from Cs Choose Ws from Cs as Vi or ViVj, i.e., ...

Lung cancer Above causal diagram Bias if we condition on X-ray status Smoking Abnormal chest X-ray ⇐ Explanation: Among smokers ...

Causal Diagram for Confounding Cis a common cause ofEandD C E D NoncausalE–Dassociation C confoundstheE–Drelationship The pathE– ...

F is a common effect ofEandD: E D F Conditioning on F creates a spur- ious association betweenEandD E D F E D F Backdoor pathE–F ...

U 1 and U 2 are unmeasured Should we control for C? E U 1 C U 2 D E U 1 U 2 C D By controlling for C we create an unblocked path ...

VI. Other Considerations for Variable Specification Data quality: Measurement error, misclassifica- tion? Correct or remove miss ...

Get to know your data! Perform thorough descriptive ana- lyses before modeling.  Useful for finding data errors  Gain insight ...

In contrast, the model given by logit P(X) equalsaplusbEplusg 1 V 1 plusg 2 V 2 plus the product termsd 1 EV 1 plusd 2 EV 2 is h ...

To illustrate this point, we return to the first example considered above, where the model is given by logit P(X) equalsaplusbEp ...

For example, even though, in the HWF model being considered here, a test for EV 1 V 2 is not dependent on the coding, a test for ...

As described in later sections, theEViVjand EVi product terms can be eliminated using appropriate statistical testing methods. H ...