Participation of Older People in Preauthorization Trials of Recently Approved Medicines

Beers, Erna; Moerkerken, D.C.; Leufkens, Hubert G. M.; Egberts, Toine C. G.; Jansen, Paul A. F.

doi:10.1111/jgs.13067

Cited by 38 publications

(31 citation statements)

References 33 publications

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“…Relaxing the age limit in the eligibility criteria alone may not improve the representativeness of elderly due to other exclusion criteria [39]. Beers et al [40] reported that older patients were underrepresented in Phase II and III trials of medical conditions characteristically associated with aging, partly because of age-sensitive exclusion criteria that are often not well justified.…”

Section: Discussionmentioning

confidence: 99%

Multivariate analysis of the population representativeness of related clinical studies

Ryan

Hoxha

et al. 2016

Journal of Biomedical Informatics

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Objective To develop a multivariate method for quantifying the population representativeness across related clinical studies and a computational method for identifying and characterizing underrepresented subgroups in clinical studies. Methods We extended a published metric named Generalizability Index for Study Traits (GIST) to include multiple study traits for quantifying the population representativeness of a set of related studies by assuming the independence and equal importance among all study traits. On this basis, we compared the effectiveness of GIST and multivariate GIST (mGIST) qualitatively. We further developed an algorithm called “Multivariate Underrepresented Subgroup Identification” (MAGIC) for constructing optimal combinations of distinct value intervals of multiple traits to define underrepresented subgroups in a set of related studies. Using Type 2 diabetes mellitus (T2DM) as an example, we identified and extracted frequently used quantitative eligibility criteria variables in a set of clinical studies. We profiled the T2DM target population using the National Health and Nutrition Examination Survey (NHANES) data. Results According to the mGIST scores for four example variables, i.e., age, HbA1c, BMI, and gender, the included observational T2DM studies had superior population representativeness than the interventional T2DM studies. For the interventional T2DM studies, Phase I trials had better population representativeness than Phase III trials. People at least 65 years old with HbA1c value between 5.7% and 7.2% were particularly underrepresented in the included T2DM trials. These results confirmed well-known knowledge and demonstrated the effectiveness of our methods in population representativeness assessment. Conclusions mGIST is effective at quantifying population representativeness of related clinical studies using multiple numeric study traits. MAGIC identifies underrepresented subgroups in clinical studies. Both data-driven methods can be used to improve the transparency of design bias in participation selection at the research community level.

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Section: Discussionmentioning

confidence: 99%

Multivariate analysis of the population representativeness of related clinical studies

Ryan

Hoxha

et al. 2016

Journal of Biomedical Informatics

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“…Compared with healthy individuals of the same age, patients with Alzheimer’s disease have a six- to tenfold increased risk of developing clinical seizures during the course of their illness 9. The paucity of comparative-effectiveness research involving patients with chronic neurological conditions, such as epilepsy and Alzheimer’s disease, as well as their lowered medication tolerance, limits the application of results from randomized clinical trials in this population 9,38,39…”

Section: Clinical Trialsmentioning

confidence: 99%

“…The International Conference on Harmonisation (ICH) guidelines on geriatrics (E7) were adopted in 1994 to improve elderly representation in clinical trials; however, low enrollment numbers persist 38. In clinical drug trials involving medications frequently prescribed to the elderly, less than 10% of participants are above age 65 years, and only 1% are above age 75 years.…”

Section: Clinical Trialsmentioning

confidence: 99%

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Causal inference as an emerging statistical approach in neurology: an example for epilepsy in the elderly

Moura¹,

Westover²,

Kwasnik³

et al. 2016

CLEP

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The elderly population faces an increasing number of cases of chronic neurological conditions, such as epilepsy and Alzheimer’s disease. Because the elderly with epilepsy are commonly excluded from randomized controlled clinical trials, there are few rigorous studies to guide clinical practice. When the elderly are eligible for trials, they either rarely participate or frequently have poor adherence to therapy, thus limiting both generalizability and validity. In contrast, large observational data sets are increasingly available, but are susceptible to bias when using common analytic approaches. Recent developments in causal inference-analytic approaches also introduce the possibility of emulating randomized controlled trials to yield valid estimates. We provide a practical example of the application of the principles of causal inference to a large observational data set of patients with epilepsy. This review also provides a framework for comparative-effectiveness research in chronic neurological conditions.

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“…However, older adults are systematically underrepresented in clinical studies across major types of cancer [3] as well as other chronic conditions such as dementia [4], and diabetes [5]. This practice may significantly hamper the generalizability of the efficacy and safety findings of clinical studies to broader patient populations [6]. As a consequence, some drugs were later withdrawn from the market after serious adverse drug reactions (e.g., death, organ damage, and toxicity) were observed when they were administered to a broad patient population [7].…”

Section: Introductionmentioning

confidence: 99%

Analysis of temporal constraints in qualitative eligibility criteria of cancer clinical studies

Chen

Bian

2016

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Clinical studies, especially randomized controlled trials, generate gold-standard medical evidence. However, the lack of population representativeness of clinical studies has hampered their generalizability to the real-world population. Overly restrictive qualitative criteria are often applied to exclude patients. In this work, we develop a lexical-pattern-based tool to structure qualitative eligibility criteria with temporal constraints, with which we analyzed over 10,800 cancer clinical studies. Our results showed that restrictive temporal constraints are often applied on qualitative criteria in cancer studies, limiting the generalizability of their results.

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Participation of Older People in Preauthorization Trials of Recently Approved Medicines

Cited by 38 publications

References 33 publications

Multivariate analysis of the population representativeness of related clinical studies

Multivariate analysis of the population representativeness of related clinical studies

Causal inference as an emerging statistical approach in neurology: an example for epilepsy in the elderly

Analysis of temporal constraints in qualitative eligibility criteria of cancer clinical studies

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