Selecting the primary endpoint in a randomized clinical trial: the ARE method

The decision on the primary endpoint in a randomized clinical trial is of paramount importance and the combination of several endpoints might be a reasonable choice. Gómez and Lagakos (2013) have developed a method that quantifies how much more efficient it could be to use a composite instead of an individual relevant endpoint. From the information provided by the frequencies of observing the component endpoints in the control group and by the relative treatment effects on each individual endpoint, the asymptotic relative efficiency (ARE) can be computed. This article presents the applicability of the ARE method as a practical and objective tool to evaluate which components, among the plausible ones, are more efficient in the construction of the primary endpoint. The method is illustrated with two real cardiovascular clinical trials and is extended to allow for different dependence structures between the times to the individual endpoints. The influence of this choice on the recommendation on whether or not to use the composite endpoint as the primary endpoint for the investigation is studied. We conclude that the recommendation between using the composite or the relevant endpoint only depends on the frequencies of the endpoints and the relative effects of the treatment.Peer ReviewedPostprint (author's final draft

Extension of the asymptotic relative efficiency method to select the primary endpoint in a randomized clinical trial

We extend the ARE method proposed in Gómez and Lagakos (2013) devised to decide which primary endpoint to choose when comparing two treatments in a randomized clinical trial. The ARE method is based on the Asymptotic Relative Efficiency (ARE) between two logrank tests to compare two treatments: one is based on a relevant endpoint E1 while the other is based on a composite endpoint E* = E1 ¿ E2, where E2 is an additional endpoint. The ARE depends, besides some intuitive parameters, on the joint law of the times T1 and T2 from randomization to E1 and E2, respectively. Gómez and Lagakos (2013) characterize this joint law by means of Frank’s copula. In our work, several families of copulas can be chosen for the bivariate survival function of (T1, T2) so that different dependence struc- tures between T1 and T2 are feasible. We motivate the problem and show how to apply the method through a real cardiovascular clinical trial. We explore the influence of the copula chosen into the ARE value by means of a simulation study. We conclude that the recommendation on whether or not to use the composite endpoint as the primary endpoint for the investigation is, almost always, independent of the copula chosen.Preprin

Clinical trial designs using CompARE. An on-line exploratory tool for investigators

Report de Recerca aprovat per la Comissió de doctorat i de recerca del Departament d'EIOConclusions from randomized clinical trials (RCT) rely primarily on the primary endpoint (PE) chosen at the design stage of the study. There should generally be only one PE which should be able to provide the most clinically relevant and scientific evidence regarding the potential eficacy of the new treatment. Therefore, it is of utmost importance to select it appropriately. Composite endpoints, consisting of the union of several endpoints, are often used as PE in RCT. Gomez and Lagakos (2013) develop a statistical methodology to evaluate the convenience of using a CE as opposed to one of its components. Their strategy is based on the asymptotic relative eficiency (ARE), relating the efi is based on the asymptotic relative eficiency (ARE), relating the eciency of using the logrank test based on the CE versus the eficiency based on one of its components. This paper introduces the freeware online platform CompARE that facilitates the study of the performance of different candidate endpoints which could be used as PE at the design stage of a trial. CompARE, through an intuitive interface, implements the novel ARE method.Preprin

Seosrehuruokintakartoitus Suomessa : Lypsylehmien seosrehuruokinnassa käytettävät rehukomponentit

Tämän opinnäytetyön tavoitteena oli kartoittaa lypsylehmien seosrehuruokinnassa käytettävät rehukomponentit Suomessa. Tarkoitus oli saada aikaan kattava läpileikkaus suomalaisista seosrehulla ruokkivista lypsykarjatiloista sekä niiden käyttämistä rehukomponenteista. Erityisesti elintarviketeollisuuden sivutuotteiden käyttöä ruokinnassa haluttiin selvittää. Myös käytettyjen rehukomponenttien käyttäjämääriä selvitettiin. Pyrin selvittämään myös seosrehulla ruokkivien tilojen tyytyväisyyttä ruokinnan suhteen. Selvitys toteutettiin puhelinhaastatteluina syksyn 2009 aikana. Kysely laadittiin Webropol-sovellukseen, jonne myös vastaukset kirjattiin. Kyselyyn vastasi yhteensä 113 tilaa. Kaikki vastanneet olivat seosrehulla ruokkivia lypsykarjatiloja. Kyselyyn vastanneista tiloista 67 tilaa (59,3 %) ilmoitti ruokkivansa karjansa täydennetyllä seosrehuruokinnalla (PMR). PMR-ruokinnassa käytettiin useammin teollisia rehuseoksia kuin TMR-ruokinnassa. Vastaavasti TMR-ruokinnassa elintarviketeollisuuden sivutuotteita käytettiin suhteessa enemmän, näistä tiloista 60,5 % käytti ruokinnassa muitakin elintarviketeollisuuden sivutuotteita kuin rypsiä. PMR-ruokinnassa vastaava luku oli 52,2 %. Käytettävät rehut ja ruokintatavat ovat aina tilakohtaisia ratkaisuja. Myös seosrehulla ruokkivia tiloja löytyy monenlaisia, eikä yhtä oikeaa toimintatapaa ole olemassa.The aim of this study was to look into the dairy cattle mixed feed and the feed components commonly used in Finland. The purpose was to provide a comprehensive cross-section of Finnish mixed feed on dairy farms. Particular interest was paid to the use of different food industry by-products and their user volumes. User satisfaction was also looked into. The study was conducted by telephone interviews to 113 dairy farmers who use mixed feed, during the autumn 2009. The questionnaire was drawn up in the Webropol application, into which the answers from were registered. Of the respondents 67 farms (59.3%) reported using Partial Mixed Ration (PMR). Industrial by-product components were more commonly used in PMR than in Total Mixed Ration (TMR) feeding. On the other hand, in TMR feeding the food industry by-products were used proportionally more, these farms fed 60.5% of the feed food industry by-products, e.g. from vegetable oil production. In PMR feeding the corresponding figure was 52.2%. Feeds and feeding practices are always individual solutions. Every farm is different and there is no single path to correct feeding

The asymptotic relative efficiency and the ratio of sample sizes when testing two different null hypotheses

Composite endpoints, consisting of the union of two or more outcomes, are often used as the primary endpoint in time-to-event randomized clinical trials. Previously, Gómez and Lagakos provided a method to guide the decision between using a composite endpoint instead of one of its components when testing the effect of a treatment in a randomized clinical trial. Consider the problem of testing the null hypotheses of no treatment effect by means of either the single component or the composite endpoint. In this paper we prove that the usual interpretation of the asymptotic relative efficiency as the reciprocal ratio of the sample sizes required for two test procedures, for the same null and alternative hypothesis, and attaining the same power at the same significance level, can be extended to the test procedures considered here for two different null and alternative hypotheses. A simulation to study the relationship between asymptotic relative efficiency and finite sample sizes is carried out.Peer ReviewedPostprint (published version

Nonparametric bivariate estimation for successive survival times

Several aspects of the analysis of two successive survival times are considered. All the analyses take into account the dependent censoring on the second time induced by the first. Three nonparametric methods are described, implemented and applied to the data coming from a multicentre clinical trial for HIV-infected patients. Visser's and Wang and Wells methods propose an estimator for the bivariate survival function while G'omez and Serrat's method presents a conditional approach for the second time given the first. The three approaches are compared and discussed at the end of the paper

Recommendations to choose the primary endpoint in cardiovascular clinical trials

Background – A composite endpoint is often used as the primary endpoint to assess the efficacy of a new treatment in randomized clinical trials (RCT). In cardiovascular trials, the often rare event of the relevant primary endpoint (individual or composite), such as cardiovascular death (CV death), Myocardial Infarction (MI), or both, is combined with a more common secondary endpoint, such as target lesion revascularization, with the aim to increase the statistical power of the study. Methods – Gómez and Lagakos developed statistical methodology to be used at the design stage of a RCT for deciding whether to expand a study relevant primary endpoint e1 to e*, the composite of e1 and a secondary endpoint e2. The method uses the asymptotic relative efficiency of the logrank test for comparing treatment groups based on e1 versus the logrank test based on e*. The method is used to assess, in the cardiovascular research area, the characteristics of the candidate individual endpoints that should govern the choice of using a composite endpoint as the primary endpoint in a clinical trial. Results and conclusions – A set of recommendations is provided based on the reported values of the frequencies of observing each candidate endpoint as well as on the magnitude of the effect of treatment as expressed by the hazard ratio, supported by cardiovascular RCTs published in 2008.Preprin

A class of two-sample nonparametric statistics for binary and time-to-event outcomes

© The Author(s) 2021We propose a class of two-sample statistics for testing the equality of proportions and the equality of survival functions. We build our proposal on a weighted combination of a score test for the difference in proportions and a Weighted Kaplan-Meier statistic-based test for the difference of survival functions. The proposed statistics are fully non-parametric and do not rely on the proportional hazards assumption for the survival outcome. We present the asymptotic distribution of these statistics, propose a variance estimator and show their asymptotic properties under fixed and local alternatives. We discuss different choices of weights including those that control the relative relevance of each outcome and emphasize the type of difference to be detected in the survival outcome. We evaluate the performance of these statistics with a simulation study, and illustrate their use with a randomized phase III cancer vaccine trial. We have implemented the proposed statistics in the R package SurvBin, available on GitHub (this https URL).This work was supported by the Ministerio de Ciencia e Innovación (Spain) under Grants PID2019-104830RB-I00; the Departament d’Empresa i Coneixement de la Generalitat de Catalunya (Spain) under Grant 2017 SGR 622 (GRBIO); and the Ministerio de Economía y Competitividad (Spain), through the María de Maeztu Programme for Units of Excellence in R&D under Grant MDM-2014-0445 to M. Bofill Roig.Peer ReviewedPostprint (published version

Multiple imputation approach for interval-censored time to HIV RNA viral rebound within a mixed effects Cox model

This is the peer reviewed version of the following article: “Alarcón-Soto, Y, Langohr K., Fehér, C., García, F., and Gómez, G. (2018) Multiple imputation approach for interval-censored time to HIV RNA viral rebound within a mixed effects Cox Model.Biometrical journal, December 13th ”which has been published in final form at [doi: 10.1002/bimj.201700291]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.We present a method to fit a mixed effects Cox model with interval-censored data. Our proposal is based on a multiple imputation approach that uses the truncated Weibull distribution to replace the interval-censored data by imputed survival times and then uses established mixed effects Cox methods for right-censored data. Interval-censored data were encountered in a database corresponding to a recompilation of retrospective data from eight analytical treatment interruption (ATI) studies in 158 human immunodeficiency virus (HIV) positive combination antiretroviral treatment (cART) suppressed individuals. The main variable of interest is the time to viral rebound, which is defined as the increase of serum viral load (VL) to detectable levels in a patient with previously undetectable VL, as a consequence of the interruption of cART. Another aspect of interest of the analysis is to consider the fact that the data come from different studies based on different grounds and that we have several assessments on the same patient. In order to handle this extra variability, we frame the problem into a mixed effects Cox model that considers a random intercept per subject as well as correlated random intercept and slope for pre-cART VL per study. Our procedure has been implemented in R using two packages: truncdist and coxme, and can be applied to any data set that presents both interval-censored survival times and a grouped data structure that could be treated as a random effect in a regression model. The properties of the parameter estimators obtained with our proposed method are addressed through a simulation study.Peer ReviewedPostprint (author's final draft

Design of phase III trials with long-term survival outcomes based on short-term binary results

Pathologic complete response (pCR) is a common primary endpoint for a phase II trial or even accelerated approval of neoadjuvant cancer therapy. If granted, a two-arm confirmatory trial is often required to demonstrate the efficacy with a time-to-event outcome such as overall survival. However, the design of a subsequent phase III trial based on prior information on the pCR effect is not straightforward. Aiming at designing such phase III trials with overall survival as primary endpoint using pCR information from previous trials, we consider a mixture model that incorporates both the survival and the binary endpoints. We propose to base the comparison between arms on the difference of the restricted mean survival times, and show how the effect size and sample size for overall survival rely on the probability of the binary response and the survival distribution by response status, both for each treatment arm. Moreover, we provide the sample size calculation under different scenarios and accompany them with the R package survmixer where all the computations have been implemented. We evaluate our proposal with a simulation study, and illustrate its application through a neoadjuvant breast cancer trial.Generalitat de Catalunya, 2017 SGR 622; Ministerio de Ciencia e Innovación, MTM2015-64465-C2-1-R; PID2019-104830RB-I00; Ministerio de Economía y Competitividad, MDM-2014-0445; National Cancer Institute, National Institutes of Health, CA016672Peer ReviewedPostprint (author's final draft