Power Analysis for Trials With Discrete-Time Survival Endpoints

Katarzyna Jóźwiak and Mirjam Moerbeek

Journal of Educational and Behavioral Statistics, 2012, vol. 37, issue 5, 630-654

Abstract: Studies on event occurrence aim to investigate if and when subjects experience a particular event. The timing of events may be measured continuously using thin precise units or discretely using time periods. The latter metric of time is often used in social science research and the generalized linear model (GLM) is an appropriate model for data analysis. While the design of trials with continuous-time survival endpoints has been extensively studied, hardly any guidelines are available for trials with discrete-time survival endpoints. This article studies the relationship between sample size and power to detect a treatment effect in a trial with two treatment conditions. The authors use the exponential and the Weibull survival functions to represent constant and varying hazard rates. Furthermore, logit and complementary log–log link functions are used. For constant hazard rates, the power depends on the event proportions at the end of the trial in both treatment arms and on the number of time periods. For varying hazard rates, the power also depends on the shape of the survival functions and different power levels are observed in each time period in case the logit link function is used. For any survival function, power decreases if attrition is present. The authors provide R code to perform the power calculations.

Keywords: power; sample size; discrete-time longitudinal data; survival analysis; generalized linear model (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.3102/1076998611424876 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:37:y:2012:i:5:p:630-654

DOI: 10.3102/1076998611424876

Access Statistics for this article

More articles in Journal of Educational and Behavioral Statistics
Bibliographic data for series maintained by SAGE Publications ().