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Power and sample size determinations in dynamic risk prediction

Sun, Zhaowen (2017) Power and sample size determinations in dynamic risk prediction. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Dynamic risk prediction is a powerful tool to estimate the future risk of study subjects with data involves time-dependent information, including repeatedly measured covariates, intermediate events, and time-varying covariate effects. The quantity of interest for dynamic risk prediction is the probability of failure at the prediction horizon time conditional on the status at the prediction baseline (aka landmark time}. For a clinical study, a series of horizon and landmark time points are usually planned in the design stage. This conditional probability can be estimated from a standard Cox proportional hazards model (for data without competing risks) or a Fine and Gray subdistributional hazards model (for data with competing risks) by appropriately setting up a landmark dataset. In this dissertation, I propose test statistics for testing the equal conditional probability between two patient groups according to their response to treatment at the prediction baseline under the scenarios of data with and without competing risks, respectively. The dissertation provides three different methods for estimating the variance of risk difference. In designing a randomized clinical trial for comparing risk difference between the two study arms, I derived formulas for power, the number of events, and the total sample size required with respect to the aforementioned hypothesis tests. Simulations were conducted to evaluate the impact of each design parameter on the power and sample size calculations.
Public health significance: This study aims to introduce new risk prediction methods that can incorporate time-dependent information and update risk estimation during the course of study follow-up, also provide researchers with references on the power and sample size requirements at the planning phase of studies involving dynamic risk prediction.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Sun, Zhaowenzhs17@pitt.eduzhs17
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChang, Chung-Chou H.changj@pitt.eduCHANGJ
Committee MemberAnderson, Stewart J.sja@pitt.eduSJA
Committee MemberKang, Chaeryoncrkang@pitt.eduCRKANG
Committee MemberSnitz, Beth E.snitbe@upmc.eduSNITBE
Date: 25 September 2017
Date Type: Publication
Defense Date: 8 August 2017
Approval Date: 25 September 2017
Submission Date: 25 July 2017
Access Restriction: 3 year -- Restrict access to University of Pittsburgh for a period of 3 years.
Number of Pages: 59
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Dynamic risk prediction; power; sample size
Date Deposited: 25 Sep 2017 14:29
Last Modified: 01 Sep 2020 05:15


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