Sensitivity analysis using elicited expert information for inference with coarsened data: Illustration of censored discrete event times in the AIDS Link to Intravenous Experience (ALIVE) study

Michelle Shardell, Daniel O. Scharfstein, David Vlahov, Noya Galai

Research output: Contribution to journalReview articlepeer-review

Abstract

In this paper, the authors use the rubric of "coarsened data," of which missing and censored data are special cases, to motivate the elicitation and use of expert information for performing sensitivity analyses of censored event-time data. Elicited information is important because observed data are insufficient to estimate how study participants with coarsened data compare with participants with uncoarsened data, and misspecifying this comparison may produce biased analysis results. In the presence of coarsening, performing a sensitivity analysis over a range of plausible assumptions is the best one can do. Here the authors illustrate an approach for eliciting expert information for use in sensitivity analyses to compare cumulative incidence functions of censored nonmortality outcomes. An example of such data is the AIDS Link to Intravenous Experience (ALIVE) Study, where the authors aim to estimate and compare cumulative incidence functions for human immunodeficiency virus between risk factor categories. The interval and right-censoring and censoring due to death found in the ALIVE data (1988-1998) are thought to be informative; thus, a sensitivity analysis is performed using information elicited from 2 ALIVE scientists and an expert in acquired immunodeficiency syndrome epidemiology about the relation between seroconversion and censoring.

Original languageEnglish (US)
Pages (from-to)1460-1469
Number of pages10
JournalAmerican journal of epidemiology
Volume168
Issue number12
DOIs
StatePublished - Dec 2008

Keywords

  • Bayesian analysis
  • Frequentist approach
  • HIV
  • Hypothesis test
  • Incidence
  • Interval censoring
  • Sensitivity analysis

ASJC Scopus subject areas

  • Epidemiology

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