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.     

The African Evaluation Guidelines: 2002. A checklist to assist in planning evaluations, negotiating clear contracts, reviewing progress and ensuring adequate completion of an evaluation

A review of the US ‘program evaluation standards’ (PES), undertaken in a series of workshops and meetings of networks of evaluators in Africa, resulted in modifications to those standards. The result was presented to a plenary session of the Inaugural Conference of the African Evaluation Association in September 1999, attended by over 300 evaluators from 35 countries. The AfrEA Conference decided that a systematic effort should be made to produce a list of African evaluation guidelines, similar to the PES, and that this checklist should be reviewed by national evaluation associations and networks in Africa and field tested in several countries. Ten national and regional networks and associations suggested modifications to the text and endorsed the final version of the guidelines.     

A modified random-effect procedure for combining risk difference in sets of 2×2 tables from clinical trials

Summary Meta-analyses of sets of clinical trials often combine risk differences from several 2×2 tables according to a random-effects model. The DerSimonian-Laird random-effects procedure, widely used for estimating the populaton mean risk difference, weights the risk difference from each primary study inversely proportional to an estimate of its variance (the sum of the between-study variance and the conditional within-study variance). Because those weights are not independent of the risk differences, however, the procedure sometimes exhibits bias and unnatural behavior. The present paper proposes a modified weighting scheme that uses the unconditional within-study variance to avoid this source of bias. The modified procedure has variance closer to that available from weighting by ideal weights when such weights are known. We studied the modified procedure in extensive simulation experiments using situations whose parameters resemble those of actual studies in medical research. For comparison we also included two unbiased procedures, the unweighted mean and a sample-size-weighted mean; their relative variability depends on the extent of heterogeneity among the primary studies. An example illustrates the application of the procedures to actual data and the differences among the results. This research was supported by Grant HS 05936 from the Agency for Health Care Policy and Research to Harvard University.     

Asymptotic bias in the linear mixed effects model under non-ignorable missing data mechanisms

Summary.  In longitudinal studies, missingness of data is often an unavoidable problem. Estimators from the linear mixed effects model assume that missing data are missing at random. However, estimators are biased when this assumption is not met. In the paper, theoretical results for the asymptotic bias are established under non-ignorable drop-out, drop-in and other missing data patterns. The asymptotic bias is large when the drop-out subjects have only one or no observation, especially for slope-related parameters of the linear mixed effects model. In the drop-in case, intercept-related parameter estimators show substantial asymptotic bias when subjects enter late in the study. Eight other missing data patterns are considered and these produce asymptotic biases of a variety of magnitudes.     

Issues in applying recent CPMP ‘Points to Consider’ and FDA guidance documents with biostatistical implications

The International Conference on Harmonisation guideline ‘Statistical Principles for Clinical Trials’ was adopted by the Committee for Proprietary Medicinal Products (CPMP) in March 1998, and consequently is operational in Europe. Since then more detailed guidance on selected topics has been issued by the CPMP in the form of ‘Points to Consider’ documents. The intent of these was to give guidance particularly to non‐statistical reviewers within regulatory authorities, although of course they also provide a good source of information for pharmaceutical industry statisticians. In addition, the Food and Drug Administration has recently issued a draft guideline on data monitoring committees. In November 2002 a one‐day discussion forum was held in London by Statisticians in the Pharmaceutical Industry (PSI). The aim of the meeting was to discuss how statisticians were responding to some of the issues covered in these new guidelines, and to document consensus views where they existed. The forum was attended by industry, academic and regulatory statisticians. This paper outlines the questions raised, resulting discussions and consensus views reached. It is clear from the guidelines and discussions at the workshop that the statistical analysis strategy must be planned during the design phase of a clinical trial and carefully documented. Once the study is complete the analysis strategy should be thoughtfully executed and the findings reported. Copyright © 2003 John Wiley & Sons, Ltd.     

Clinical Writing of a Therapy in Progress: Ethical Questions and Therapeutic Challenges

Clinical implications and ethical dilemmas of the use of confidential case material in clinical writing are examined, including a review of the discourse among professionals who publish clinical work. This literature is applied to a clinical illustration of psychotherapy with a client who gave consent for publication and read the clinical write-up of her case material. It is suggested that clinical writing may increase client “self-reflection” if there is a “secure base” of attachment between therapist/author and the client. The impact on the client’s treatment process is examined, in addition to a discussion of ethical questions and professional recommendations. ²Dr. Susanne Bennett is an Assistant Professor, National School of Social Service of the Catholic University of America. Dr. Bennett is also a psychotherapist in private practice in Falls Church, VA.     

Predictive Hierarchic Modeling of Operational Characteristics in Clinical Trials

An analytic methodology for patient enrollment modeling using a Poisson-gamma model is developed by Anisimov & Fedorov (2005–2007). For modeling hierarchic processes associated with enrollment, a new methodology using evolving stochastic processes is proposed. This provides rather general and unified framework to describe various operational processes associated with enrollment. The technique for calculating predictive distributions, mean, and credibility bounds for evolving processes is developed. Some applications to modeling operational characteristics in clinical trials are considered with focus to modeling events associated with incoming and follow-up patients in different settings. For these models, predictive characteristics are derived in a closed form.     

Shared learning on an international clinical placement: Promoting symbiotic midwifery practice knowledge

AimThe aim of this study was to explore the experiences of shared learning between Australian and Balinese midwifery students during a two-week clinical placement in Bali Indonesia.BackgroundCultural safety in midwifery is a key concept that is underpinned by the provision of holistic quality midwifery care to all women. Therefore, culturally safe midwifery care identifies, protects and promotes women’s individual cultures and is a key concept that is fostered in midwifery education. To educate culturally safe midwives, international placements to resource limited countries have become more common within midwifery education programs.MethodsThis study used a qualitative research design with a convenience sampling design. The participants were enrolled in midwifery courses in a University in the Northern Territory of Australia (n = 9), a Balinese private midwifery school (n = 4) and a Balinese public midwifery school (n = 4). Thematic analysis was used to analyse the data.FindingsThe findings were categorised into major themes under the headings of “Learning together despite differences”; “Cultural differences”, “Communication, Resources”, and “Recommendations for future placements”.ConclusionThis study provides a valuable insight into how shared learning increases students’ midwifery knowledge and is fundamental in understanding cultural differences that could be applied to students’ clinical midwifery practice.     

Implementing a college mental health program – an overview of the first twelve months

Objective: To evaluate the demographics and clinical utilization patterns among college students during the initial 12 months of a novel, multi-disciplinary, collaborative, college mental health program (CMHP). Participants: Undergraduate and graduate students receiving treatment at the CMHP from Jan-Dec 2015. Methods: De-identified data was obtained via electronic health records for all students receiving care through the CMHP. Results: 1.2 FTE clinical providers treated 278 undergraduate and graduate students during the year (65.1% < age 26, 53.6% female, 49.6% caucasian). There were 1822 CMHP outpatient visits, 318 other medical visits and 103 total emergency room (ER)/inpatient visits. Ten students were identified as high utilizers of ER/inpatient services, while charges to the CMHP totaled $470,157 and total charges to the Health System were $2,378,315. Conclusions: Students with complex psychiatric/medical co-morbidities received cost effective, convenient and integrative treatment. Over time, we hope to intervene earlier and decrease ER/inpatient visits.     

What influences the size of child maintenance payments? A register‐based study of maintenance payments in Finland

The aim of this study was to explore how particular economic and demographic factors contribute to the level of the child maintenance payment (CMP) paid by the non‐resident parent. For this study, we used 5‐year longitudinal panel data from years 2009 to 2013 consisting of over 80,000 non‐resident parents from the Finnish Tax Administration and The Finnish Population Register Centre. Results from regression models indicate that the single biggest factor affecting the size of CMPs is the number of dependent children. We found that the non‐resident parent's higher income is associated with higher CMPs and that non‐resident fathers pay on average larger CMPs than non‐resident mothers, even after accounting for differences in income. Unexpectedly, we found that the age of the dependent child did not predict changes in CMPs. This suggests that once formal CMP‐contracts are determined between the parents, they are seldom changed. Our results suggest that some degree of mandatory periodic review for maintenance contracts is worth considering.