Inference and Decision Making for 21st-Century Drug Development and Approval

ABSTRACT

The cost and time of pharmaceutical drug development continue to grow at rates that many say are unsustainable. These trends have enormous impact on what treatments get to patients, when they get them and how they are used. The statistical framework for supporting decisions in regulated clinical development of new medicines has followed a traditional path of frequentist methodology. Trials using hypothesis tests of “no treatment effect” are done routinely, and the p-value < 0.05 is often the determinant of what constitutes a “successful” trial. Many drugs fail in clinical development, adding to the cost of new medicines, and some evidence points blame at the deficiencies of the frequentist paradigm. An unknown number effective medicines may have been abandoned because trials were declared “unsuccessful” due to a p-value exceeding 0.05. Recently, the Bayesian paradigm has shown utility in the clinical drug development process for its probability-based inference. We argue for a Bayesian approach that employs data from other trials as a “prior” for Phase 3 trials so that synthesized evidence across trials can be utilized to compute probability statements that are valuable for understanding the magnitude of treatment effect. Such a Bayesian paradigm provides a promising framework for improving statistical inference and regulatory decision making.     

Assessing and communicating heterogeneity of treatment effects for patient subpopulations: The hardest problem there is

Heterogeneity is an enormously complex problem because there are so many dimensions and variables that can be considered when assessing which ones may influence an efficacy or safety outcome for an individual patient. This is difficult in randomized controlled trials and even more so in observational settings. An alternative approach is presented in which the individual patient becomes the “subgroup,” and similar patients are identified in the clinical trial database or electronic medical record that can be used to predict how that individual patient may respond to treatment.     

What matters most? Different stakeholder perspectives on estimands for an invented case study in COPD

In drug development, we ask ourselves which population, endpoint and treatment comparison should be investigated. In this context, we also debate what matters most to the different stakeholders that are involved in clinical drug development, for example, patients, physicians, regulators and payers. With the publication of draft ICH E9 addendum on estimands in 2017, we now have a common framework and language to discuss such questions in an informed and transparent way. This has led to the estimand discussion being a key element in study development, including design, analysis and interpretation of a treatment effect. At an invited session at the 2018 PSI annual conference, PSI hosted a role‐play debate where the aim of the session was to mimic a regulatory and payer scientific advice discussion for a COPD drug. Including role‐play views from an industry sponsor, a patient, a regulator and a payer. This paper presents the invented COPD case‐study design and considerations relating to appropriate estimands are discussed by each of the stakeholders from their differing viewpoints with the additional inclusion of a technical (academic) perspective. The rationale for each perspective on approaches for handling intercurrent events is presented, with a key emphasis on the application of while‐on‐treatment and treatment policy estimands in this context. It is increasingly recognised that the treatment effect estimated by the treatment policy approach may not always be of primary clinical interest and may not appropriately communicate to patients the efficacy they can expect if they take the treatment as directed.     

A continuously adaptive nonparametric two–sample test

A two–sample test statistic for detecting shifts in location is developed for a broad range of underlying distributions using adaptive techniques. The test statistic is a linear rank statistics which uses a simple modification of the Wilcoxon test; the scores are Winsorized ranks where the upper and lower Winsorinzing proportions are estimated in the first stage of the adaptive procedure using sample the first stage of the adaptive procedure using sample measures of the distribution's skewness and tailweight. An empirical relationship between the Winsorizing proportions and the sample skewness and tailweight allows for a ‘continuous’ adaptation of the test statistic to the data. The test has good asymptotic properties, and the small sample results are compared with other populatr parametric, nonparametric, and two–stage tests using Monte Carlo methods. Based on these results, this proposed test procedure is recommended for moderate and larger sample sizes.     

Assessing and communicating heterogeneity of treatment effects for patient subpopulations: Panel discussion on considerations in design and analysis

Clinical trials are primarily conducted to understand the average effects treatments have on patients. However, patients are heterogeneous in the severity of the condition and in ways that affect what treatment effect they can expect. It is therefore important to understand and characterize how treatment effects vary. The design and analysis of clinical studies play critical roles in evaluating and characterizing heterogeneous treatment effects. This panel discussed considerations in design and analysis under the recognition that there are heterogeneous treatment effects across subgroups of patients. Panel members discussed many questions including: What is a good estimate of the treatment effect in me, a 65-year-old, bald, Caucasian-American, male patient? What magnitude of heterogeneity of treatment effects (HTE) is sufficiently large to merit attention? What role can prior evidence about HTE play in confirmatory trial design and analysis? Is there anything described in the 21st Century Cures Act that would benefit from greater attention to HTE? An example of a Bayesian approach addressing multiplicity when testing for treatment effects in subgroups will be provided. We can do more or better at understanding heterogeneous treatment effects and providing the best information on heterogeneous treatment effects.     

Representing sex workers in video games: feminisms,fantasies of exceptionalism,and the value of erotic labor

This article critiques the representation of sex workers in “AAA” video games, with a focus on the devaluing of erotic labor. Existing feminist commentary has interpreted these representations as examples of the objectification of female game characters, perpetuating harmful misconceptions of sex work as fundamentally exploitative. By contrast, taking cues from feminist media studies, porn studies, and sex workers rights activism, I argue that what makes these representations of sex workers problematic is not their engagement in erotic labor but the ways that the games in which they appear devalue that labor, through both dialogue and interactive elements. Across their many appearances in AAA games, it is strikingly common for sex workers to offer their services to player-characters for free or at a discount, or for games to allow players to take their money back after erotic labor has been performed. This contributes to a gendered fantasy of exceptionalism in which a player-character’s masculinity is tied to being too attractive or too powerful to pay for sex. Critiquing these representations demonstrates how AAA video games prompt players to reenact widespread cultural biases against sex work. It also points toward the need for a diversity of feminisms within game studies.     

Efficiencies of some two–sample location tests for a broad class of distributions

Efficiencies of variety of two–sample tests are examomed for a broad class of distributions. Two new test statistics are introduced. and their potential use as part of an adaptive procedure discussed. Recommendations are made as to the utility of the various test statistics when the underlying distribution is unknown.     

Gender differences in the ultimatum game

I explore the behavior of men and women in the ultimatum game. In one treatment, players remain mutually anonymous. In the second treatment, players'gender is common knowledge. Average offers made do not differ based on the gender of player 1. Offers are affected by the gender of player 2, with men attracting higher offers, particularly from female players 1. Players 2 of both genders choose a higher minimum acceptable offer when facing a female player 1. These patterns led to substantial differences in earnings. Such striking differences in expectations and decisions could impact salary negotiations and other real-world transactions.