Show simple item record

dc.contributor.advisorCai, Tianxi
dc.contributor.authorParast, Layla
dc.date.accessioned2013-02-12T15:54:17Z
dc.date.issued2013-02-12
dc.date.submitted2012
dc.identifier.citationParast, Layla. 2012. Landmark Prediction of Survival. Doctoral dissertation, Harvard University.en_US
dc.identifier.otherhttp://dissertations.umi.com/gsas.harvard:10085en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:10288618
dc.description.abstractThe importance of developing personalized risk prediction estimates has become increasingly evident in recent years. In general, patient populations may be heterogenous and represent a mixture of different unknown subtypes of disease. When the source of this heterogeneity and resulting subtypes of disease are unknown, accurate prediction of survival may be difficult. However, in certain disease settings the onset time of an observable intermediate event may be highly associated with these unknown subtypes of disease and thus may be useful in predicting long term survival. Throughout this dissertation, we examine an approach to incorporate intermediate event information for the prediction of long term survival: the landmark model. In Chapter 1, we use the landmark modeling framework to develop procedures to assess how a patient’s long term survival trajectory may change over time given good intermediate outcome indications along with prognosis based on baseline markers. We propose time-varying accuracy measures to quantify the predictive performance of landmark prediction rules for residual life and provide resampling-based procedures to make inference about such accuracy measures. We illustrate our proposed procedures using a breast cancer dataset. In Chapter 2, we aim to incorporate intermediate event time information for the prediction of survival. We propose a fully non-parametric procedure to incorporate intermediate event information when only a single baseline discrete covariate is available for prediction. When a continuous covariate or multiple covariates are available, we propose to incorporate intermediate event time information using a flexible varying coefficient model. To evaluate the performance of the resulting landmark prediction rule and quantify the information gained by using the intermediate event, we use robust non-parametric procedures. We illustrate these procedures using a dataset of post-dialysis patients with end-stage renal disease. In Chapter 3, we consider improving efficiency by incorporating intermediate event information in a randomized clinical trial setting. We propose a semi-nonparametric two-stage procedure to estimate survival by incorporating intermediate event information observed before the landmark time. In addition, we present a testing procedure using these resulting estimates to test for a difference in survival between two treatment groups. We illustrate these proposed procedures using an AIDS dataset.en_US
dc.language.isoen_USen_US
dash.licenseMETA_ONLY
dc.subjectlandmark modelen_US
dc.subjectresamplingen_US
dc.subjectrisk predictionen_US
dc.subjectsurvival analysisen_US
dc.subjectbiostatisticsen_US
dc.subjectepidemiologyen_US
dc.titleLandmark Prediction of Survivalen_US
dc.typeThesis or Dissertationen_US
dash.embargo.until10000-01-01
thesis.degree.date2012en_US
thesis.degree.disciplineBiostatisticsen_US
thesis.degree.grantorHarvard Universityen_US
thesis.degree.leveldoctoralen_US
thesis.degree.namePh.D.en_US
dc.contributor.committeeMemberWei, Lee-Jenen_US
dc.contributor.committeeMemberHarrington, Daviden_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record