• Corpus ID: 250644125

The role of the geometric mean in case-control studies

  title={The role of the geometric mean in case-control studies},
  author={Amanda Coston and Edward H. Kennedy},
Historically used in settings where the outcome is rare or data collection is expensive, outcome-dependent sampling is relevant to many modern settings where data is readily available for a biased sample of the target population, such as public administrative data. Under outcome-dependent sampling, common effect measures such as the average risk difference and the average risk ratio are not identified, but the conditional odds ratio is. Aggregation of the conditional odds ratio is challenging… 



On the need for the rare disease assumption in case-control studies.

The conditions under which matched and unmatched odds ratios are consistent estimators of the incidence-density ratio in case-control studies are examined and the odds ratio obtained under "incidence-density" sampling will in general provide a better approximation to the risk ratio.

Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions

We study causal inference under case-control and case-population sampling. For this purpose, we focus on the binary-outcome and binary-treatment case, where the parameters of interest are causal

Estimability and estimation in case-referent studies.

The concepts that case-referent studies provide for the estimation of "relative risk" only if the illness is "rare", and that the rates and risks themselves are inestimable, are overly superficial

Estimation Based on Case-Control Designs with Known Prevalence Probability

A general method of estimation relying on knowing the prevalence probability, conditional on the matching variable if matching is used is presented, which provides double robust locally efficient targeted maximum likelihood estimators of the causal relative risk and causal odds ratio for regular case Control sampling and matched case control sampling.

A weighting approach to causal effects and additive interaction in case-control studies: marginal structural linear odds models.

The authors propose an inverse probability of treatment weighting approach to causal effects and additive interaction in case-control studies under the assumption of no unmeasured confounding, which amounts to fitting a marginal structural linear odds model.

Logistic disease incidence models and case-control studies

SUMMARY The probability of disease development in a defined time period is described by a logistic regression model. A model for the regression variable, given disease status, is induced and is

Sensitivity Analysis for Selection bias and unmeasured Confounding in missing Data and Causal inference models

In both observational and randomized studies, subjects commonly drop out of the study (i.e., become censored) before end of follow-up. If, conditional on the history of the observed data up to t, the

Some surprising results about covariate adjustment in logistic regression models

Summary Results from classic linear regression regarding the effect of adjusting for covariates upon the precision of an estimator of exposure effect are often assumed to apply more generally to

The Estimation of Choice Probabilities from Choice Based Samples

Ti-H CONCERN of this paper is the estimation of the parameters of a probabilistic choice model when choices rather than decision makers are sampled. Existing estimation methods presuppose an