Incorporating baseline covariates to validate surrogate endpoints with a constant biomarker under control arm

Purpose The current study aimed to identify factors that distinguish between older (50+ years) hearing aid (HA) candidates who do and do not purchase HAs after having gone through an HA evaluation period (HAEP). Method Secondary data analysis of the SUpport PRogram trial was performed ( n = 267 older, 1st-time HA candidates). All SUpport PRogram participants started an HAEP shortly after study enrollment. Decision to purchase an HA by the end of the HAEP was the outcome of interest of the current study. Participants' baseline covariates (22 in total) were included as candidate predictors. Multivariable logistic regression modeling (backward selection and reclassification tables) was used. Results Of all candidate predictors, only pure-tone average (average of 1, 2, and 4 kHz) hearing loss emerged as a significant predictor (odds ratio = 1.03, 95% confidence interval [1.03, 1.17]). Model performance was weak (Nagelkerke R 2 = .04, area under the curve = 0.61). Conclusions These data suggest that, once HA candidates have decided to enter an HAEP, factors measured early in the help-seeking journey do not predict well who will and will not purchase an HA. Instead, factors that act during the HAEP may hold this predictive value. This should be examined.

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The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽

10.1027/1614-2241/a000121 ◽

2017 ◽

Vol 13 (2) ◽

pp. 41-60

Author(s):

Shahab Jolani ◽

Maryam Safarkhani

Keyword(s):

Randomized Controlled Trials ◽

Discrete Time ◽

Treatment Effect ◽

Survival Data ◽

Controlled Trials ◽

Missing Covariates ◽

Indicator Method ◽

Imputation Methods ◽

Randomized Controlled ◽

Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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Covariate-constrained Randomization Routine for Achieving Baseline Balance in Cluster-randomized Trials

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x1701700214 ◽

2017 ◽

Vol 17 (2) ◽

pp. 503-510 ◽

Cited By ~ 2

Author(s):

Eva Lorenz ◽

Sabine Gabrysch

Keyword(s):

Randomized Trials ◽

Theoretical Background ◽

Cluster Randomized Trials ◽

Cluster Randomized ◽

Baseline Covariates

In cluster-randomized trials, groups or clusters of individuals, rather than individuals themselves, are randomly allocated to intervention or control. In this article, we describe a new command, ccrand, that implements a covariate-constrained randomization procedure for cluster-randomized trials. It can ensure balance of one or more baseline covariates between trial arms by restriction to allocations that meet specified balance criteria. We provide a brief overview of the theoretical background, describe ccrand and its options, and illustrate it using an example.

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Validity of Surrogate Endpoints and Their Impact on Coverage Recommendations: A Retrospective Analysis across International Health Technology Assessment Agencies

Medical Decision Making ◽

10.1177/0272989x21994553 ◽

2021 ◽

pp. 0272989X2199455

Author(s):

Oriana Ciani ◽

Bogdan Grigore ◽

Hedwig Blommestein ◽

Saskia de Groot ◽

Meilin Möllenkamp ◽

...

Keyword(s):

Health Technology Assessment ◽

Technology Assessment ◽

International Health ◽

Surrogate Endpoint ◽

Health Technology ◽

Surrogate Endpoints ◽

Patient Centered ◽

Statistical Validation ◽

Level Of Evidence ◽

The Impact

Background Surrogate endpoints (i.e., intermediate endpoints intended to predict for patient-centered outcomes) are increasingly common. However, little is known about how surrogate evidence is handled in the context of health technology assessment (HTA). Objectives 1) To map methodologies for the validation of surrogate endpoints and 2) to determine their impact on acceptability of surrogates and coverage decisions made by HTA agencies. Methods We sought HTA reports where evaluation relied on a surrogate from 8 HTA agencies. We extracted data on the methods applied for surrogate validation. We assessed the level of agreement between agencies and fitted mixed-effects logistic regression models to test the impact of validation approaches on the agency’s acceptability of the surrogate endpoint and their coverage recommendation. Results Of the 124 included reports, 61 (49%) discussed the level of evidence to support the relationship between the surrogate and the patient-centered endpoint, 27 (22%) reported a correlation coefficient/association measure, and 40 (32%) quantified the expected effect on the patient-centered outcome. Overall, the surrogate endpoint was deemed acceptable in 49 (40%) reports ( k-coefficient 0.10, P = 0.004). Any consideration of the level of evidence was associated with accepting the surrogate endpoint as valid (odds ratio [OR], 4.60; 95% confidence interval [CI], 1.60–13.18, P = 0.005). However, we did not find strong evidence of an association between accepting the surrogate endpoint and agency coverage recommendation (OR, 0.71; 95% CI, 0.23–2.20; P = 0.55). Conclusions Handling of surrogate endpoint evidence in reports varied greatly across HTA agencies, with inconsistent consideration of the level of evidence and statistical validation. Our findings call for careful reconsideration of the issue of surrogacy and the need for harmonization of practices across international HTA agencies.

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Improving interim decisions in randomized trials by exploiting information on short‐term endpoints and prognostic baseline covariates

Pharmaceutical Statistics ◽

10.1002/pst.2014 ◽

2020 ◽

Vol 19 (5) ◽

pp. 583-601 ◽

Cited By ~ 1

Author(s):

Kelly Van Lancker ◽

An Vandebosch ◽

Stijn Vansteelandt

Keyword(s):

Randomized Trials ◽

Short Term ◽

Baseline Covariates

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Impact of Baseline Covariates and Prior Therapy on the Efficacy of Second-Line Panitumumab (Pmab) + Folfiri Vs Folfiri Treatment

Annals of Oncology ◽

10.1093/annonc/mdu333.50 ◽

2014 ◽

Vol 25 ◽

pp. iv187 ◽

Cited By ~ 1

Author(s):

M. Peeters ◽

T. Price ◽

A. Cervantes ◽

A. Sobrero ◽

M.P. Ducreux ◽

...

Keyword(s):

Second Line ◽

Prior Therapy ◽

Baseline Covariates

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VALIDATION OF SURROGATE ENDPOINTS IN ADVANCED SOLID TUMORS: SYSTEMATIC REVIEW OF STATISTICAL METHODS, RESULTS, AND IMPLICATIONS FOR POLICY MAKERS

International Journal of Technology Assessment in Health Care ◽

10.1017/s0266462314000300 ◽

2014 ◽

Vol 30 (3) ◽

pp. 312-324 ◽

Cited By ~ 36

Author(s):

Oriana Ciani ◽

Sarah Davis ◽

Paul Tappenden ◽

Ruth Garside ◽

Ken Stein ◽

...

Keyword(s):

Solid Tumors ◽

Statistical Methods ◽

Surrogate Endpoint ◽

Cytotoxic Agents ◽

Surrogate Endpoints ◽

Treatment Level ◽

Cancer Type ◽

Policy Makers ◽

New Therapies ◽

Advanced Tumor

Objectives: Licensing of, and coverage decisions on, new therapies should rely on evidence from patient-relevant endpoints such as overall survival (OS). Nevertheless, evidence from surrogate endpoints may also be useful, as it may not only expedite the regulatory approval of new therapies but also inform coverage decisions. It is, therefore, essential that candidate surrogate endpoints be properly validated. However, there is no consensus on statistical methods for such validation and on how the evidence thus derived should be applied by policy makers.Methods: We review current statistical approaches to surrogate-endpoint validation based on meta-analysis in various advanced-tumor settings. We assessed the suitability of two surrogates (progression-free survival [PFS] and time-to-progression [TTP]) using three current validation frameworks: Elston and Taylor's framework, the German Institute of Quality and Efficiency in Health Care's (IQWiG) framework and the Biomarker-Surrogacy Evaluation Schema (BSES3).Results: A wide variety of statistical methods have been used to assess surrogacy. The strength of the association between the two surrogates and OS was generally low. The level of evidence (observation-level versus treatment-level) available varied considerably by cancer type, by evaluation tools and was not always consistent even within one specific cancer type.Conclusions: Not in all solid tumors the treatment-level association between PFS or TTP and OS has been investigated. According to IQWiG's framework, only PFS achieved acceptable evidence of surrogacy in metastatic colorectal and ovarian cancer treated with cytotoxic agents. Our study emphasizes the challenges of surrogate-endpoint validation and the importance of building consensus on the development of evaluation frameworks.

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