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Methods and management: NIH administrators, federal oversight, and the Framingham Heart Study.

ABSTRACT: This article explores the 1965 controversy over the Framingham Heart Study in the midst of growing oversight into the management of science at the National Institutes of Health (NIH). It describes how, beginning in the early 1960s, federal overseers demanded that NIH administrators adopt particular management styles in administering programs and how these growing pressures led administrators to favor investigative pursuits that allowed for easy prospective accounting of program payoffs, especially those based on experimental methods designed to examine discrete interventions or outcomes of interest. In light of this changing managerial culture within the NIH, the Framingham study and other population laboratories-with their bases in observation and in open-ended study designs-became harder for NIH administrators to justify and defend.

SUBMITTER: Patel SS

PROVIDER: S-EPMC3368344 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Methods and management: NIH administrators, federal oversight, and the Framingham Heart Study.

Patel Sejal S SS

Bulletin of the history of medicine 20120101 1

This article explores the 1965 controversy over the Framingham Heart Study in the midst of growing oversight into the management of science at the National Institutes of Health (NIH). It describes how, beginning in the early 1960s, federal overseers demanded that NIH administrators adopt particular management styles in administering programs and how these growing pressures led administrators to favor investigative pursuits that allowed for easy prospective accounting of program payoffs, especial ...[more]

PMID: 22643985

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Project description:BackgroundStatistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome.MethodsIn this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times.ResultsSimulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years.ConclusionsTraditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

Dataset Information

Methods and management: NIH administrators, federal oversight, and the Framingham Heart Study.

Publications

Methods and management: NIH administrators, federal oversight, and the Framingham Heart Study.

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