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A Composite Endpoint for Treatment Benefit According to Patient Preference.

ABSTRACT: Complex disorders usually affect multiple symptom domains measured by several outcomes. The importance of these outcomes is often different among patients. Current approaches integrate multiple outcomes without considering patient preferences at the individual level. In this paper, we propose a new composite Desirability of Outcome Ranking (DOOR) that integrates individual level ranking of outcome importance and define a winning probability measuring the overall treatment effect. Stratified randomization can be performed based on the participants' baseline outcome rankings. A Wilcoxon-Mann-Whitney U-statistic is used to average the pairwise DOOR between one treated and one control patient, considering the difference in these patients' ranking of outcome importance. We use both theoretical and empirical methods to examine the statistical properties of our method and to compare with conventional approaches. We conclude that the proposed composite DOOR properly reflects patient-level preferences and can be used in pivotal trials or comparative effectiveness trials for a patient-centered evaluation of overall treatment benefits.

SUBMITTER: Lu Y

PROVIDER: S-EPMC10655937 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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A Composite Endpoint for Treatment Benefit According to Patient Preference.

Lu Ying Y Zhao Qian Q Zou Jiying J Yan Shiyan S Tamaresis John S JS Nelson Lorene L Tu Xin M XM Chen Jie J Tian Lu L

Statistics in biopharmaceutical research 20220719 4

Complex disorders usually affect multiple symptom domains measured by several outcomes. The importance of these outcomes is often different among patients. Current approaches integrate multiple outcomes without considering patient preferences at the individual level. In this paper, we propose a new composite Desirability of Outcome Ranking (DOOR) that integrates individual level ranking of outcome importance and define a winning probability measuring the overall treatment effect. Stratified rand ...[more]

PMID: 37981982

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Project description:BackgroundClinical trials commonly use multiple endpoints to measure the impact of an intervention. While this improves the comprehensiveness of outcomes, it can make trial results difficult to interpret. We examined the impact of integrating patient weights into a composite endpoint on the interpretation of Control of Hypertension in Pregnancy Study (CHIPS) Trial results.MethodsOutcome weights were extracted from a previous patient preferences study in pregnancy hypertension (N = 183 women) which identified (i) seven outcomes most important to women (taking medication, severe hypertension, pre-eclampsia, blood transfusion, Caesarean, delivery < 34 weeks, and baby born smaller-than-expected) and (ii) three preference subgroups: (1) 'equal prioritizers', 62%; (2) 'early delivery avoiders', 23%; and (3) 'medication minimizers', 14%. Outcome weights from the preference subgroups were integrated with CHIPS data for the seven outcomes identified in the preference study. A weighted composite score was derived for each participant by multiplying the preference weight for each outcome by the binary outcome if it occurred. Analyses considered equal weights and those from the preference subgroups. The mean composite scores were compared between trial arms (t-tests).ResultsComposite scores were similar between trial arms with the use of equal weights or those of subgroup (1) (95% confidence intervals [CIs]: - 0.03, 0.02; p > 0.50 for each). 'Tight' control was superior when using subgroup (2) weights (95% CIs: 0.002, 0.07; p = 0.03), and 'less-tight' control was superior when using subgroup (3) weights (95% CIs: - 0.11, - 0.04; p < 0.01).ConclusionsEvidence-based recommendations for 'tight' control are consistent with most women's preferences, but for a sixth of women, 'less-tight' control is more preference consistent. Depending on patient preferences, a single trial may support different interventions. Future trials should specify component weights to improve interpretation.Trial registrationClinicalTrials.gov NCT01192412.

Dataset Information

A Composite Endpoint for Treatment Benefit According to Patient Preference.

Publications

A Composite Endpoint for Treatment Benefit According to Patient Preference.

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