title: Longitudinal clinical trials with adaptive choice of follow-up time.
abstract: In longitudinal studies comparing two treatments with a maximum follow-up time there may be interest in examining treatment effects for intermediate follow-up times. One motivation may be to identify the time period with greatest treatment difference when there is a non-monotone treatment effect over time; another motivation may be to make the trial more efficient in terms of time to reach a decision on whether a new treatment is efficacious or not. Here, we test the composite null hypothesis of no difference at any follow-up time versus the alternative that there is a difference at at least one follow-up time. The methods are applicable when a few measurements are taken over time, such as in early longitudinal trials or in ancillary studies. Suppose the test statistic Z(t(k)) will be used to test the hypothesis of no treatment effect at a fixed follow-up time t(k). In this context a common approach is to perform a pilot study on N1 subjects, and evaluate the treatment effect at the fixed time points t1,…,t(K) and choose t* as the value of t(k) for which Z(t(k)) is maximized. Having chosen t* a second trial can be designed. In a setting with group sequential testing we consider several adaptive alternatives to this approach that treat the pilot and second trial as a seamless, combined entity and evaluate Type I error and power characteristics. The adaptive designs we consider typically have improved power over the common, separate trial approach.
authors.affiliation: Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland 20892, U.S.A.
authors.forename: Neal
authors.initials: N
authors.lastname: Jeffries
authors.affiliation: Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland 20892, U.S.A.
authors.forename: Nancy L
authors.initials: NL
authors.lastname: Geller
mesh_terms: D001699:Biometry; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D005500:Follow-Up Studies; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D010865:Pilot Projects; D011336:Probability; D011788:Quality of Life; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D013997:Time Factors; D018487:Ventricular Dysfunction, Left / Q000188:drug therapy / Q000503:physiopathology
keywords: Adaptive design; Adaptive follow-up time; Adaptive longitudinal trial; Longitudinal study; Model-free longitudinal analysis
references.citation: Biometrics. 1999 Dec;55(4):1188-92
references.pmid: 11315066
references.citation: Stat Med. 2003 Mar 15;22(5):689-703
references.pmid: 12587100
references.citation: Biometrics. 2004 Mar;60(1):134-45
references.pmid: 15032783
references.citation: Stat Med. 2004 Aug 30;23(16):2497-508
references.pmid: 15287080
references.citation: Biometrics. 1979 Sep;35(3):549-56
references.pmid: 497341
references.citation: Biometrics. 1985 Jun;41(2):353-9
references.pmid: 4027318
references.citation: Stat Med. 2010 Jan 30;29(2):219-28
references.pmid: 19827011
references.citation: Biometrics. 1992 Sep;48(3):765-79
references.pmid: 1420840
references.citation: J Am Coll Cardiol. 1994 Feb;23(2):393-400
references.pmid: 8294693
references.citation: Biometrics. 1994 Dec;50(4):1029-41
references.pmid: 7786985
references.citation: Biometrics. 1995 Dec;51(4):1315-24
references.pmid: 8589224
references.citation: J Biopharm Stat. 2005;15(4):641-58
references.pmid: 16022169
references.citation: Stat Med. 2005 Aug 30;24(16):2457-75
references.pmid: 15977295
references.citation: N Engl J Med. 1991 Aug 1;325(5):293-302
references.pmid: 2057034