Assessing treatment benefit in the presence of placebo response using the sequential parallel comparison design.

Szczegóły
Abstrakt

Tytuł:: Assessing treatment benefit in the presence of placebo response using the sequential parallel comparison design.
Autorzy:: Liu X; Department of Biostatistics, Boston University, Boston, Massachusetts, USA.
Kim C; Department of Biostatistics, Boston University, Boston, Massachusetts, USA.; Department of Statistics, SungKyunKwan University, Seoul, Korea.
Han Z; School of Statistics, University of International Business and Economics, Beijing, China.
Lim P; Janssen Research & Development LLC, Raritan, New Jersey, USA.
Roychoudhury S; Pfizer Inc., New York, New York, USA.
Fava M; Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Doros G; Department of Biostatistics, Boston University, Boston, Massachusetts, USA.
Źródło:: Statistics in medicine [Stat Med] 2022 May 30; Vol. 41 (12), pp. 2166-2190. Date of Electronic Publication: 2022 Feb 20.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
Język:: English
Imprint Name(s):: Original Publication: Chichester ; New York : Wiley, c1982-
MeSH Terms:: Placebo Effect*
Research Design*
Bias ; Computer Simulation ; Humans
References:: Enck P, Klosterhalfen S, Weimer K, Horing B, Zipfel S. The placebo response in clinical trials: more questions than answers. Philos Trans R Soc B Biol Sci. 2011;366:1889-1895.
Khin NA, Chen YF, Yang Y, Yang P, Laughren TP. Exploratory analyses of efficacy data from major depressive disorder trials submitted to the US Food and Drug Administration in support of new drug applications. J Clin Psychiatry. 2011;72:464-472.
Walsh BT, Seidman SN, Sysko R, Gould M. Placebo response in studies of major depression: variable, substantial, and growing. JAMA. 2002;287:1840-1847.
Fava M, Evins AE, Dorer DJ, Schoenfeld DA. The problem of the placebo response in clinical trials for psychiatric disorders: culprits, possible remedies, and a novel study design approach. Psychother Psychosom. 2003;72:115-127.
Rybin D, Doros G, Pencina MJ, Fava M. Placebo non-response measure in sequential parallel comparison design studies. Stat Med. 2015;34:2281-2293.
Rybin D, Lew R, Pencina MJ, Fava M, Doros G. Placebo response as a latent characteristic: application to analysis of sequential parallel comparison design studies. J Am Stat Assoc. 2018;113:1411-1430.
Doros G, Pencina M, Rybin D, Meisner A, Fava M. A repeated measures model for analysis of continuous outcomes in sequential parallel comparison design studies. Stat Med. 2013;32:2767-2789.
Huang X, Tamura RN. Comparison of test statistics for the sequential parallel design. Stat Biopharm Res. 2010;2:42-50.
Ivanova A, Qaqish B, Schoenfeld DA. Optimality, sample size, and power calculations for the sequential parallel comparison design. Stat Med. 2011;30:2793-2803.
Tamura RN, Huang X. An examination of the efficiency of the sequential parallel design in psychiatric clinical trials. Clin Trials. 2007;4:309-317.
Chen YF, Yang Y, Hung HMJ, Wang SJ. Evaluation of performance of some enrichment designs dealing with high placebo response in psychiatric clinical trials. Contemp Clin Trials. 2011;32:592-604.
Schoenfeld DA, Doros G, Fava M. A commentary on: statistical inference problems in sequential parallel comparison designs. J Biopharm Stat. 2019;29:1130-1133.
Cui Y, Ogbagaber S, Hung HJ. Rejoinder to “Statistical inference problems in sequential parallel comparison designs”. J Biopharm Stat. 2019;29:1134-1136.
Tamura RN, Huang X, Boos DD. Estimation of treatment effect for the sequential parallel design. Stat Med. 2011;30:3496-3506.
Chi GY, Li Y, Liu Y, Lewin D, Lim P. On clinical trials with a high placebo response rate. Contemp Clin Trials Commun. 2016;2:34-53.
International Conference on Harmonisation. ICH E9 (R1) addendum on estimands and sensitivity analysis in clinical trials to the guideline on statistical principles for clinical trials; 2017.
Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics. 2002;58:21-29.
Fava M, Mischoulon D, Iosifescu D, et al. A double-blind, placebo-controlled study of aripiprazole adjunctive to antidepressant therapy among depressed outpatients with inadequate response to prior antidepressant therapy (ADAPT-A Study). Psychother Psychosom. 2012;81:87-97.
Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol. 1974;66:688-701.
Splawa-Neyman J, Dabrowska DM, Speed TP. On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Stat Sci. 1990;5:465-472.
Ivanova A, Qaqish B. Power calculations for the sequential parallel comparison design with continuous outcomes. J Biopharm Stat. 2020;30:1121-1129.
Ivanova A, Tamura RN. A two-way enriched clinical trial design: combining advantages of placebo lead-in and randomized withdrawal. Stat Methods Med Res. 2015;24:871-890.
Liu Y, Rybin D, Heeren TC, Doros G. Comparison of novel methods in two-way enriched clinical trial design. Stat Med. 2019;38:4112-4130.
Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91:444-455.
Chen YF, Zhang X, Tamura RN, Chen CM. A sequential enriched design for target patient population in psychiatric clinical trials. Stat Med. 2014;33:2953-2967.
Muthén B, Brown HC. Estimating drug effects in the presence of placebo response: causal inference using growth mixture modeling. Stat Med. 2009;28:3363-3385.
Rubin DB. Randomization analysis of experimental data: the fisher randomization test comment. J Am Stat Assoc. 1980;75:591-593.
Magnusson BP, Schmidli H, Rouyrre N, Scharfstein DO. Bayesian inference for a principal stratum estimand to assess the treatment effect in a subgroup characterized by postrandomization event occurrence. Stat Med. 2019;38:4761-4771.
Egleston BL, Scharfstein DO, Freeman EE, West SK. Causal inference for non-mortality outcomes in the presence of death. Biostatistics. 2007;8:526-545.
Gilbert PB, Bosch RJ, Hudgens MG. Sensitivity analysis for the assessment of causal vaccine effects on viral load in HIV vaccine trials. Biometrics. 2003;59:531-541.
Lou Y, Jones MP, Sun W. Estimation of causal effects in clinical endpoint bioequivalence studies in the presence of intercurrent events: noncompliance and missing data. J Biopharm Stat. 2019;29:151-173.
Lou Y, Jones MP, Sun W. Assessing the ratio of means as a causal estimand in clinical endpoint bioequivalence studies in the presence of intercurrent events. Stat Med. 2019;38:5214-5235.
U.S. Food and Drug Administration. Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Products; 2021.
Serfling RJ. Approximation Theorems of Mathematical Statistics. New York, NY: John Wiley & Sons; 1980:126-128.
Lipkovich I, Ratitch B, Mallinckrodt CH. Causal inference and estimands in clinical trials. Stat Biopharm Res. 2020;12:54-67.
Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Series B. 1977;39:1-38.
Undurraga J, Baldessarini RJ. Randomized, placebo-controlled trials of antidepressants for acute major depression: thirty-year meta-analytic review. Neuropsychopharmacology. 2012;37:851-864.
Benaglia T, Chauveau D, Hunter DR, Young DS. mixtools: an R package for analyzing mixture models. J Stat Softw. 2009;32:1-29.
VanderWeele TJ, Robins JM. Stochastic counterfactuals and stochastic sufficient causes. Stat Sin. 2012;22:379-392.
Shepherd BE, Gilbert PB, Mehrotra DV. Eliciting a counterfactual sensitivity parameter. Am Stat. 2007;61:56-63.
Evers AW, Colloca L, Blease C, et al. “Consensus on placebo and nocebo effects connects science with practice:” Reply to “Questioning the consensus on placebo and nocebo effects”. Psychother Psychosom. 2021;90:213-214.
Contributed Indexing:: Keywords: SPCD; estimand; placebo response; principal stratification; treatment effect
Entry Date(s):: Date Created: 20220220 Date Completed: 20220421 Latest Revision: 20220726
Update Code:: 20240105
DOI:: 10.1002/sim.9349
PMID:: 35184326
: Czasopismo naukowe

Full Text Finder

In clinical trials, placebo response is considered a beneficial effect arising from multiple factors, including the patient's expectations for the treatment. Its presence makes the classical parallel study design suboptimal and can bias the inference. The sequential parallel comparison design (SPCD), a two-stage design where the first stage is a classical parallel study design, followed by another parallel design among placebo subjects from the first stage, was proposed to address the shortcomings of the classical design. In SPCD, in lieu of treatment effect, a weighted average of the mean treatment difference in Stage I among all randomized subjects and the mean treatment difference in Stage II among placebo non-responders was proposed as the efficacy measure. However, by linking two possibly different populations, this weighted average lacks interpretability, and the choice of weight remains controversial. In this work, under the principal stratification framework, we propose a causal estimand for the treatment effect under each of three clinically important principal strata: Always Responders, Never Responders, and Drug-only Responders. To make the stratum treatment effect identifiable, we introduce a set of assumptions and two sensitivity parameters. By further considering the strata as latent characteristics, the sensitivity parameters can be estimated. An extensive simulation study is conducted to evaluate the operating characteristics of the proposed method. Finally, we apply our method on the ADAPT-A study data to assess the benefit of low-dose aripiprazole adjunctive to antidepressant therapy treatment.
(© 2022 John Wiley & Sons Ltd.)

Informacja