Efficient two-stage sequential arrays of proof of concept studies for pharmaceutical portfolios.

Abstract:

:Previous work has shown that individual randomized "proof-of-concept" (PoC) studies may be designed to maximize cost-effectiveness, subject to an overall PoC budget constraint. Maximizing cost-effectiveness has also been considered for arrays of simultaneously executed PoC studies. Defining Type III error as the opportunity cost of not performing a PoC study, we evaluate the common pharmaceutical practice of allocating PoC study funds in two stages. Stage 1, or the first wave of PoC studies, screens drugs to identify those to be permitted additional PoC studies in Stage 2. We investigate if this strategy significantly improves efficiency, despite slowing development. We quantify the benefit, cost, benefit-cost ratio, and Type III error given the number of Stage 1 PoC studies. Relative to a single stage PoC strategy, significant cost-effective gains are seen when at least one of the drugs has a low probability of success (10%) and especially when there are either few drugs (2) with a large number of indications allowed per drug (10) or a large portfolio of drugs (4). In these cases, the recommended number of Stage 1 PoC studies ranges from 2 to 4, tracking approximately with an inflection point in the minimization curve of Type III error.

journal_name

Stat Methods Med Res

authors

He L,Du L,Antonijevic Z,Posch M,Korostyshevskiy VR,Beckman RA

doi

10.1177/0962280220958177

subject

Has Abstract

pub_date

2020-09-21 00:00:00

pages

962280220958177

eissn

0962-2802

issn

1477-0334

pub_type

杂志文章
  • Receiver operating characteristic curve estimation for time to event with semicompeting risks and interval censoring.

    abstract::Semicompeting risks and interval censoring are frequent in medical studies, for instance when a disease may be diagnosed only at times of visit and disease onset is in competition with death. To evaluate the ability of markers to predict disease onset in this context, estimators of discrimination measures must account...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280214531691

    authors: Jacqmin-Gadda H,Blanche P,Chary E,Touraine C,Dartigues JF

    更新日期:2016-12-01 00:00:00

  • Estimation of data adaptive minimal clinically important difference with a nonconvex optimization procedure.

    abstract::Understanding the limitation of solely relying on statistical significance, researchers have proposed methods to draw biomedical conclusions based on clinical significance. The minimal clinically important significance is one of the most fundamental concepts to study clinical significance. Based on an anchor question ...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280219850191

    authors: Zhou Z,Zhao J,Bisson LJ

    更新日期:2020-03-01 00:00:00

  • Efficient Monte Carlo evaluation of resampling-based hypothesis tests with applications to genetic epidemiology.

    abstract::Monte Carlo evaluation of resampling-based tests is often conducted in statistical analysis. However, this procedure is generally computationally intensive. The pooling resampling-based method has been developed to reduce the computational burden but the validity of the method has not been studied before. In this arti...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280216661876

    authors: Fung WK,Yu K,Yang Y,Zhou JY

    更新日期:2018-05-01 00:00:00

  • Inferences about a linear combination of proportions.

    abstract::Statistical methods for carrying out asymptotic inferences (tests or confidence intervals) relative to one or two independent binomial proportions are very frequent. However, inferences about a linear combination of K independent proportions L = Σβ(i)p(i) (in which the first two are special cases) have had very little...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280209347953

    authors: Martín Andrés A,Alvarez Hernández M,Herranz Tejedor I

    更新日期:2011-08-01 00:00:00

  • Correcting for dependent censoring in routine outcome monitoring data by applying the inverse probability censoring weighted estimator.

    abstract::Censored data make survival analysis more complicated because exact event times are not observed. Statistical methodology developed to account for censored observations assumes that patients' withdrawal from a study is independent of the event of interest. However, in practice, some covariates might be associated to b...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280216628900

    authors: Willems S,Schat A,van Noorden MS,Fiocco M

    更新日期:2018-02-01 00:00:00

  • Efficient algorithms for covariate analysis with dynamic data using nonlinear mixed-effects model.

    abstract::Nonlinear mixed-effects modeling is one of the most popular tools for analyzing repeated measurement data, particularly for applications in the biomedical fields. Multiple integration and nonlinear optimization are the two major challenges for likelihood-based methods in nonlinear mixed-effects modeling. To solve thes...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280220949898

    authors: Yuan M,Zhu Z,Yang Y,Zhao M,Sasser K,Hamadeh H,Pinheiro J,Xu XS

    更新日期:2020-08-24 00:00:00

  • Projections of cancer mortality risks using spatio-temporal P-spline models.

    abstract::Cancer mortality risk estimates are essential for planning resource allocation and designing and evaluating cancer prevention and management strategies. However, mortality figures generally become available after a few years, making necessary to develop reliable procedures to provide current and near future mortality ...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280212446366

    authors: Ugarte MD,Goicoa T,Etxeberria J,Militino AF

    更新日期:2012-10-01 00:00:00

  • Bayesian variable selection in the accelerated failure time model with an application to the surveillance, epidemiology, and end results breast cancer data.

    abstract::Accelerated failure time model is a popular model to analyze censored time-to-event data. Analysis of this model without assuming any parametric distribution for the model error is challenging, and the model complexity is enhanced in the presence of large number of covariates. We developed a nonparametric Bayesian met...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280215626947

    authors: Zhang Z,Sinha S,Maiti T,Shipp E

    更新日期:2018-04-01 00:00:00

  • Reference-based pattern-mixture models for analysis of longitudinal binary data.

    abstract::Pattern-mixture model (PMM)-based controlled imputations have become a popular tool to assess the sensitivity of primary analysis inference to different post-dropout assumptions or to estimate treatment effectiveness. The methodology is well established for continuous responses but less well established for binary res...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280220941880

    authors: Lu K

    更新日期:2020-12-01 00:00:00

  • Pattern discovery of health curves using an ordered probit model with Bayesian smoothing and functional principal component analysis.

    abstract::This article is motivated by the need for discovering patterns of patients' health based on their daily settings of care to aid the health policy-makers to improve the effectiveness of distributing funding for health services. The hidden process of one's health status is assumed to be a continuous smooth function, cal...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280220951834

    authors: Wang S,Nie Y,Sutherland JM,Wang L

    更新日期:2020-09-25 00:00:00

  • A robust imputation method for missing responses and covariates in sample selection models.

    abstract::Sample selection arises when the outcome of interest is partially observed in a study. Although sophisticated statistical methods in the parametric and non-parametric framework have been proposed to solve this problem, it is yet unclear how to deal with selectively missing covariate data using simple multiple imputati...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280217715663

    authors: Ogundimu EO,Collins GS

    更新日期:2019-01-01 00:00:00

  • A Bayesian semiparametric approach with change points for spatial ordinal data.

    abstract::The change-point model has drawn much attention over the past few decades. It can accommodate the jump process, which allows for changes of the effects before and after the change point. Intellectual disability is a long-term disability that impacts performance in cognitive aspects of life and usually has its onset pr...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280212463415

    authors: Cai B,Lawson AB,McDermott S,Aelion CM

    更新日期:2016-04-01 00:00:00

  • Estimating the effect of treatment on binary outcomes using full matching on the propensity score.

    abstract::Many non-experimental studies use propensity-score methods to estimate causal effects by balancing treatment and control groups on a set of observed baseline covariates. Full matching on the propensity score has emerged as a particularly effective and flexible method for utilizing all available data, and creating well...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280215601134

    authors: Austin PC,Stuart EA

    更新日期:2017-12-01 00:00:00

  • A mixed-effects, spatially varying coefficients model with application to multi-resolution functional magnetic resonance imaging data.

    abstract::Spatial resolution plays an important role in functional magnetic resonance imaging studies as the signal-to-noise ratio increases linearly with voxel volume. In scientific studies, where functional magnetic resonance imaging is widely used, the standard spatial resolution typically used is relatively low which ensure...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280217752378

    authors: Liu Z,Bartsch AJ,Berrocal VJ,Johnson TD

    更新日期:2019-04-01 00:00:00

  • A corrected formulation for marginal inference derived from two-part mixed models for longitudinal semi-continuous data.

    abstract::For semi-continuous data which are a mixture of true zeros and continuously distributed positive values, the use of two-part mixed models provides a convenient modelling framework. However, deriving population-averaged (marginal) effects from such models is not always straightforward. Su et al. presented a model that ...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280213509798

    authors: Tom BD,Su L,Farewell VT

    更新日期:2016-10-01 00:00:00

  • The application of multidimensional scaling methods to epidemiological data.

    abstract::This paper illustrates the use of multidimensional scaling methods (MDS) to examine space-time patterns in epidemic data. The paper begins by outlining the principles of MDS. The model is then formally specified and illustrated by application to two data sets. The first is partly a tutorial example. It uses monthly re...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/096228029500400202

    authors: Cliff AD,Haggett P,Smallman-Raynor MR,Stroup DF,Williamson GD

    更新日期:1995-06-01 00:00:00

  • Latent mixture models for multivariate and longitudinal outcomes.

    abstract::Repeated measures and multivariate outcomes are an increasingly common feature of trials. Their joint analysis by means of random effects and latent variable models is appealing but patterns of heterogeneity in outcome profile may not conform to standard multivariate normal assumptions. In addition, there is much inte...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章,评审

    doi:10.1177/0962280209105016

    authors: Pickles A,Croudace T

    更新日期:2010-06-01 00:00:00

  • Parametric models for incomplete continuous and categorical longitudinal data.

    abstract::This paper reviews models for incomplete continuous and categorical longitudinal data. In terms of Rubin's classification of missing value processes we are specifically concerned with the problem of nonrandom missingness. A distinction is drawn between the classes of selection and pattern-mixture models and, using sev...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章,评审

    doi:10.1177/096228029900800105

    authors: Kenward MG,Molenberghs G

    更新日期:1999-03-01 00:00:00

  • Multilevel growth curve models that incorporate a random coefficient model for the level 1 variance function.

    abstract::Aim To present a flexible model for repeated measures longitudinal growth data within individuals that allows trends over time to incorporate individual-specific random effects. These may reflect the timing of growth events and characterise within-individual variability which can be modelled as a function of age. Subj...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280217706728

    authors: Goldstein H,Leckie G,Charlton C,Tilling K,Browne WJ

    更新日期:2018-11-01 00:00:00

  • Random-effects models for multivariate repeated measures.

    abstract::Mixed models are widely used for the analysis of one repeatedly measured outcome. If more than one outcome is present, a mixed model can be used for each one. These separate models can be tied together into a multivariate mixed model by specifying a joint distribution for their random effects. This strategy has been u...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280206075305

    authors: Fieuws S,Verbeke G,Molenberghs G

    更新日期:2007-10-01 00:00:00

  • The cross-validated AUC for MCP-logistic regression with high-dimensional data.

    abstract::We propose a cross-validated area under the receiving operator characteristic (ROC) curve (CV-AUC) criterion for tuning parameter selection for penalized methods in sparse, high-dimensional logistic regression models. We use this criterion in combination with the minimax concave penalty (MCP) method for variable selec...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280211428385

    authors: Jiang D,Huang J,Zhang Y

    更新日期:2013-10-01 00:00:00

  • Modelling of zero-inflation improves inference of metagenomic gene count data.

    abstract::Metagenomics enables the study of gene abundances in complex mixtures of microorganisms and has become a standard methodology for the analysis of the human microbiome. However, gene abundance data is inherently noisy and contains high levels of biological and technical variability as well as an excess of zeros due to ...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280218811354

    authors: Jonsson V,Österlund T,Nerman O,Kristiansson E

    更新日期:2019-12-01 00:00:00

  • Small sample sizes: A big data problem in high-dimensional data analysis.

    abstract::In many experiments and especially in translational and preclinical research, sample sizes are (very) small. In addition, data designs are often high dimensional, i.e. more dependent than independent replications of the trial are observed. The present paper discusses the applicability of max t-test-type statistics (mu...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280220970228

    authors: Konietschke F,Schwab K,Pauly M

    更新日期:2020-11-24 00:00:00

  • Designs in partially controlled studies: messages from a review.

    abstract::The ability to evaluate effects of factors on outcomes is increasingly important for studies that control some but not all of the factors. Although important advances have been made in methods of analysis for such partially controlled studies, work on designs has been limited. To help understand why, we review the mai...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1191/0962280205sm405oa

    authors: Li F,Frangakis CE

    更新日期:2005-08-01 00:00:00

  • Estimation of half-life periods in nonlinear data with fractional polynomials.

    abstract::Regression models are frequently used to model the functional relationship between an interesting outcome parameter and one or more potentially relevant explanatory variables. Objectives can be to set up as a prognostic model, for example, or an estimation model for a certain parameter of interest. Determining half-li...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280213502403

    authors: Mayer B,Keller F,Syrovets T,Wittau M

    更新日期:2016-10-01 00:00:00

  • A curvilinear bivariate random changepoint model to assess temporal order of markers.

    abstract::In biomedical research, various longitudinal markers measuring different quantities are often collected over time. For example, repeated measures of psychometric scores are very informative about the degradation process toward dementia. These trajectories are generally nonlinear with an acceleration of the decline a f...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280219898719

    authors: Segalas C,Helmer C,Jacqmin-Gadda H

    更新日期:2020-09-01 00:00:00

  • Letter to the editor: Fitting truncated normal distributions.

    abstract::I comment here on a recent paper in this journal, on the fitting of truncated normal distributions by the EM algorithm. I show that the fitting of such distributions by direct numerical maximization of likelihood (rather than EM) is straightforward, contrary to an assertion made by the authors of that paper. ...

    journal_title:Statistical methods in medical research

    pub_type: 评论,信件

    doi:10.1177/0962280217712089

    authors: MacDonald IL

    更新日期:2018-12-01 00:00:00

  • An intuitive Bayesian spatial model for disease mapping that accounts for scaling.

    abstract::In recent years, disease mapping studies have become a routine application within geographical epidemiology and are typically analysed within a Bayesian hierarchical model formulation. A variety of model formulations for the latent level have been proposed but all come with inherent issues. In the classical BYM (Besag...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280216660421

    authors: Riebler A,Sørbye SH,Simpson D,Rue H

    更新日期:2016-08-01 00:00:00

  • Nonparametric estimation of risk tracking indices for longitudinal studies.

    abstract::Tracking a subject's risk factors or health status over time is an important objective in long-term epidemiological studies with repeated measurements. An important issue of time-trend tracking is to define appropriate statistical indices to quantitatively measure the tracking abilities of the targeted risk factors or...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280219839427

    authors: Wu CO,Tian X,Tian L,Reis JP,Zhao L,Allen NB,Bae S,Liu K

    更新日期:2020-02-01 00:00:00

  • A frequentist approach to estimating the force of infection for a respiratory disease using repeated measurement data from a birth cohort.

    abstract::This article aims to develop a probability-based model involving the use of direct likelihood formulation and generalised linear modelling (GLM) approaches useful in estimating important disease parameters from longitudinal or repeated measurement data. The current application is based on infection with respiratory sy...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280210385749

    authors: Mwambi H,Ramroop S,White Lj,Okiro E,Nokes Dj,Shkedy Z,Molenberghs G

    更新日期:2011-10-01 00:00:00