Group sequential two-stage preference designs.

The two-stage preference design (TSPD) enables inference for treatment efficacy while allowing for incorporation of patient preference to treatment. It can provide unbiased estimates for selection and preference effects, where a selection effect occurs when patients who prefer one treatment respond differently than those who prefer another, and a preference effect is the difference in response caused by an interaction between the patient's preference and the actual treatment they receive. One potential barrier to adopting TSPD in practice, however, is the relatively large sample size required to estimate selection and preference effects with sufficient power. To address this concern, we propose a group sequential two-stage preference design (GS-TSPD), which combines TSPD with sequential monitoring for early stopping. In the GS-TSPD, pre-planned sequential monitoring allows investigators to conduct repeated hypothesis tests on accumulated data prior to full enrollment to assess study eligibility for early trial termination without inflating type I error rates. Thus, the procedure allows investigators to terminate the study when there is sufficient evidence of treatment, selection, or preference effects during an interim analysis, thereby reducing the design resource in expectation. To formalize such a procedure, we verify the independent increments assumption for testing the selection and preference effects and apply group sequential stopping boundaries from the approximate sequential density functions. Simulations are then conducted to investigate the operating characteristics of our proposed GS-TSPD compared to the traditional TSPD. We demonstrate the applicability of the design using a study of Hepatitis C treatment modality.

Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity.

Cluster randomized trials (CRTs) are studies where treatment is randomized at the cluster level but outcomes are typically collected at the individual level. When CRTs are employed in pragmatic settings, baseline population characteristics may moderate treatment effects, leading to what is known as heterogeneous treatment effects (HTEs). Pre-specified, hypothesis-driven HTE analyses in CRTs can enable an understanding of how interventions may impact subpopulation outcomes. While closed-form sample size formulas have recently been proposed, assuming known intracluster correlation coefficients (ICCs) for both the covariate and outcome, guidance on optimal cluster randomized designs to ensure maximum power with pre-specified HTE analyses has not yet been developed. We derive new design formulas to determine the cluster size and number of clusters to achieve the locally optimal design (LOD) that minimizes variance for estimating the HTE parameter given a budget constraint. Given the LODs are based on covariate and outcome-ICC values that are usually unknown, we further develop the maximin design for assessing HTE, identifying the combination of design resources that maximize the relative efficiency of the HTE analysis in the worst case scenario. In addition, given the analysis of the average treatment effect is often of primary interest, we also establish optimal designs to accommodate multiple objectives by combining considerations for studying both the average and heterogeneous treatment effects. We illustrate our methods using the context of the Kerala Diabetes Prevention Program CRT, and provide an R Shiny app to facilitate calculation of optimal designs under a wide range of design parameters.

Using Direct-to-Consumer Genetic Testing Results to Accelerate Alzheimer Disease Clinical Trial Recruitment.

INTRODUCTION: The apolipoprotein E (APOE) gene is the strongest known genetic risk factor for sporadic Alzheimer disease (AD). APOE can be used as an enrichment strategy or inclusion criterion for AD prevention trials. Personal genomics companies market direct-to-consumer (DTC) genetic tests, including APOE. We assessed DTC APOE testing usage among enrollees of the University of California Irvine Consent-to-Contact Registry, an online recruitment registry, and attitudes toward using this information in clinical trial recruitment. METHODS: We emailed links to an electronic survey to registry enrollees age 50 years or older. We assessed participants' use of DTC services, willingness to learn APOE status, and willingness to share genetic information. Logistic regression models assessed relationships between DTC testing usage and demographic characteristics, and with willingness to share results to assist trial recruitment. RESULTS: Among 1312 responders (57% response rate), few (7%) had used DTC testing for APOE. Non-Hispanic Asian enrollees were 93% less likely to have used DTC testing, compared with non-Hispanic Whites [95% confidence interval: (0.01, 0.67)]. Willingness to share APOE information for study recruitment was >90% for both users and nonusers. CONCLUSIONS: Matching participants to trials on the basis of DTC APOE information may be an effective way to streamline AD prevention trial recruitment.

Variance estimation for the Kappa statistic in the presence of clustered data and heterogeneous observations.

We present a methodology motivated by a controlled trial designed to validate SPOT GRADE, a novel surgical bleeding severity scale. Briefly, the study was designed to quantify inter- and intra-surgeon agreement for characterizing the severity of surgical bleeds via a Kappa statistic. Multiple surgeons were presented with a randomized sequence of controlled bleeding videos and asked to apply the rating system to characterize each wound. Each video was shown multiple times to quantify intra-surgeon reliability, creating clustered data. In addition, videos within the same category may have had different classification probabilities due to changes in blood flow rates and wound sizes. In this work, we propose a new variance estimator for the Kappa statistic, for use in clustered data as well as heterogeneity among items within the same classification category. We then apply this methodology to data from the SPOT GRADE trial.

Reasons for undergoing amyloid imaging among cognitively unimpaired older adults.

OBJECTIVES: Preclinical Alzheimer's disease (AD) clinical trials screen cognitively unimpaired older adults for biomarker criteria and disclose their results. We examined whether participants in the Anti-Amyloid Treatment in Asymptomatic Alzheimer's disease Study with "elevated" and "not elevated" amyloid differed in scores on the "Views and Perceptions of Amyloid Imaging" questionnaire. We hypothesized that, prior to disclosure, those with elevated amyloid would score higher than those with not elevated amyloid. We also quantified how responses changed after result disclosure. METHODS: We assessed data from 4327 individuals who completed the questionnaire at screening visit 1 and after amyloid disclosure. We used linear regression models to assess the relationship between questionnaire category scores and amyloid status. We also quantified the relationship between category score changes and amyloid status. RESULTS: Overall, participants scored altruism and contribution to research as the strongest motivations for undergoing amyloid imaging. Those with elevated amyloid scored 0.23 points higher in the Perceived Risk category, on average, than those who had not elevated amyloid prior to disclosure; this effect attenuated towards zero after adjusting for Cognitive Function Instrument score. After disclosure, participants with elevated amyloid demonstrated less within-subject change in Perceived Risk, on average, compared to those with similar pre-disclosure scores who had not elevated amyloid, while demonstrating greater changes in the altruism and planning categories. INTERPRETATION: Altruism and learning disease risk motivated enrollment in this preclinical AD trial. Participants with elevated amyloid differed from their not elevated counterparts in their perceptions of amyloid imaging, even before undergoing the procedure.

Participant and study partner prediction and identification of cognitive impairment in preclinical Alzheimer's disease: study partner vs. participant accuracy.

BACKGROUND: Preclinical Alzheimer's disease (AD) clinical trials require participants to enroll with a study partner, a person who can attend visits and report changes in the participant's cognitive ability. Whether study partners, compared to participants themselves, provide added information about participant cognition in preclinical AD trials is an open question. We tested the hypothesis that study partners provide meaningful information related to participant cognition cross-sectionally and longitudinally, and assessed whether amyloid status modified observed effects. METHODS: We assessed participant and study partner Everyday Cognition (ECog) scores and participant Alzheimer's Disease Assessment Scale 13-item cognitive subscale (ADAS13) data from 335 cognitively normal participant-partner dyads in the AD Neuroimaging Initiative. We used random forest and linear mixed effects (LME) models to predict ADAS13 scores as a function of participant and/or study partner ECog scores over time. LME models were adjusted for potential confounding factors, including APOE4 status, amyloid status, baseline age, years of education, and sex. Random forest models were split into the above factors, as well as race/ethnicity and other available neuropsychological battery test scores. RESULTS: In random forest models predicting ADAS13 12 months from baseline, we observed no difference in the estimated mean variable importance (eMVI) associated with baseline study partner ECog compared to the baseline participant ECog (eMVI = 0.15, 95%CB 0.13, 0.16 for partner; eMVI = 0.15, 95%CB 0.14, 0.16 for participant). In models predicting ADAS13 48 months after baseline, the eMVI associated with baseline study partner ECog was slightly lower than that associated with baseline participant ECog (eMVI = 0.21, 95%CB 0.20, 0.22 for partner; eMVI = 0.24, 95%CB 0.22, 0.25 for participant). In cross-sectional models, study partner eMVI was twice as large as participant eMVI at 12 months (eMVI = 0.20, 95%CB 0.19, 0.21 for partner; eMVI = 0.09, 95%CB 0.09, 0.10 for participant) and three times as large at 48 months (eMVI = 0.38, 95%CB 0.36, 0.39 for partner; eMVI = 0.13, 95%CB 0.12, 0.14 for participant). We did not observe qualitative differences by amyloid status. CONCLUSIONS: While baseline participant reports reasonably predict subsequent cognitive change, informants perform better at cross-sectionally recognizing cognitive status as observation time grows. The study partner requirement may be essential to ensure trial data integrity, especially in longer trials.