Bootstrap confidence intervals and bias correction in the estimation of HIV incidence from surveillance data with testing for recent infection

Nicole Bohme Carnegie

doi:10.1002/sim.4134

Bootstrap confidence intervals and bias correction in the estimation of HIV incidence from surveillance data with testing for recent infection

Statistics in Medicine ◽

10.1002/sim.4134 ◽

2010 ◽

Vol 30 (8) ◽

pp. 854-865 ◽

Cited By ~ 3

Author(s):

Nicole Bohme Carnegie

Keyword(s):

Confidence Intervals ◽

Bias Correction ◽

Surveillance Data ◽

Hiv Incidence ◽

Recent Infection ◽

Bootstrap Confidence Intervals

Bootstrap confidence intervals in nonparametric regression with built-in bias correction

Statistics & Probability Letters ◽

10.1016/j.spl.2008.02.032 ◽

2008 ◽

Vol 78 (15) ◽

pp. 2463-2469 ◽

Cited By ~ 21

Author(s):

Timothy L. McMurry ◽

Dimitris N. Politis

Keyword(s):

Confidence Intervals ◽

Nonparametric Regression ◽

Bias Correction ◽

Bootstrap Confidence Intervals

Two-Condition Within-Participant Statistical Mediation Analysis: A Path-Analytic Framework

10.31234/osf.io/4su2j ◽

2019 ◽

Author(s):

Amanda Kay Montoya ◽

Andrew F. Hayes

Keyword(s):

Confidence Intervals ◽

Indirect Effect ◽

Mediation Analysis ◽

Analytic Approach ◽

Analytic Framework ◽

Hypothesis Tests ◽

Bootstrap Confidence Intervals ◽

Complex Models ◽

Path Analytic ◽

Statistical Mediation Analysis

Researchers interested in testing mediation often use designs where participants are measured on a dependent variable Y and a mediator M in both of two different circumstances. The dominant approach to assessing mediation in such a design, proposed by Judd, Kenny, and McClelland (2001), relies on a series of hypothesis tests about components of the mediation model and is not based on an estimate of or formal inference about the indirect effect. In this paper we recast Judd et al.’s approach in the path-analytic framework that is now commonly used in between-participant mediation analysis. By so doing, it is apparent how to estimate the indirect effect of a within-participant manipulation on some outcome through a mediator as the product of paths of influence. This path analytic approach eliminates the need for discrete hypothesis tests about components of the model to support a claim of mediation, as Judd et al’s method requires, because it relies only on an inference about the product of paths— the indirect effect. We generalize methods of inference for the indirect effect widely used in between-participant designs to this within-participant version of mediation analysis, including bootstrap confidence intervals and Monte Carlo confidence intervals. Using this path analytic approach, we extend the method to models with multiple mediators operating in parallel and serially and discuss the comparison of indirect effects in these more complex models. We offer macros and code for SPSS, SAS, and Mplus that conduct these analyses.