Literature DB >> 7722564

Planning for precision in survival studies.

M Borenstein1.   

Abstract

If the purpose of a clinical study is not only to test the null hypothesis but also to estimate the magnitude of the treatment effect, the study design should ensure not only that the study will have adequate power but also that it will enable the researcher to report the relevant parameters with an appropriate level of precision. This paper discusses the factors that control precision in survival studies and shows how a computer program may be used to address these issues. The program allows the user to systematically modify assumptions about the population (e.g. the magnitude of the hazard ratio or the attrition rate) and elements of the study design (e.g. sample size and trial duration), quickly identify the impact of these factors on the study's precision, and modify the study design accordingly. The program may also be used to compute power for a planned study, and confidence intervals for a completed study.

Mesh:

Year:  1994        PMID: 7722564     DOI: 10.1016/0895-4356(94)90133-3

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  4 in total

Review 1.  Estimating sample sizes for binary, ordered categorical, and continuous outcomes in two group comparisons.

Authors:  M J Campbell; S A Julious; D G Altman
Journal:  BMJ       Date:  1995-10-28

2.  ATF5 polymorphisms influence ATF function and response to treatment in children with childhood acute lymphoblastic leukemia.

Authors:  Julie Rousseau; Vincent Gagné; Malgorzata Labuda; Cyrielle Beaubois; Daniel Sinnett; Caroline Laverdière; Albert Moghrabi; Stephen E Sallan; Lewis B Silverman; Donna Neuberg; Jeffery L Kutok; Maja Krajinovic
Journal:  Blood       Date:  2011-10-04       Impact factor: 22.113

3.  Study protocol: the DOse REsponse Multicentre International collaborative initiative (DO-RE-MI).

Authors:  Detlef Kindgen-Milles; Didier Journois; Roberto Fumagalli; Sergio Vesconi; Javier Maynar; Anibal Marinho; Irene Bolgan; Alessandra Brendolan; Marco Formica; Sergio Livigni; Mariella Maio; Mariano Marchesi; Filippo Mariano; Gianpaola Monti; Elena Moretti; Daniela Silengo; Claudio Ronco
Journal:  Crit Care       Date:  2005-06-14       Impact factor: 9.097

4.  Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes.

Authors:  Richard D Riley; Kym Ie Snell; Joie Ensor; Danielle L Burke; Frank E Harrell; Karel Gm Moons; Gary S Collins
Journal:  Stat Med       Date:  2018-10-24       Impact factor: 2.373

  4 in total

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