Biomaterial Database

Blinded sample size recalculation in clinical trials incorporating historical data. 2017

Katharina Hees, and Meinhard Kieser

Institute for Medical Biometry and Informatics, University of Heidelberg, Germany. Electronic address: Hees@imbi.uni-heidelberg.de.

Recruiting sufficient patients within an acceptable time horizon is an issue for most clinical trials and is especially challenging in the field of rare diseases. It is therefore an attractive option to include historical data from previous (pilot) trials in the current study thus reducing the recruitment burden. In clinical trials with binary endpoint, the required sample size does not only depend on the type I error rate, the power, and the treatment group difference but additionally on the overall event rate. However, there is usually some uncertainty in the planning phase about the value of this nuisance parameter. We present methods for blinded sample size recalculation in the setting of two-arm superiority trials with historical control data where the overall rate is estimated mid-course and the sample size is recalculated accordingly. The operating characteristics of the method are investigated in terms of actual type I error rate, power, and expected sample size. Application is illustrated with a clinical trial example in patients with systemic sclerosis, a rare connective tissue disorder.

UI	MeSH Term	Description	Entries
D010865	Pilot Projects	Small-scale tests of methods and procedures to be used on a larger scale if the pilot study demonstrates that these methods and procedures can work.	Pilot Studies,Pilot Study,Pilot Project,Project, Pilot,Projects, Pilot,Studies, Pilot,Study, Pilot
D012107	Research Design	A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly.	Experimental Design,Data Adjustment,Data Reporting,Design, Experimental,Designs, Experimental,Error Sources,Experimental Designs,Matched Groups,Methodology, Research,Problem Formulation,Research Methodology,Research Proposal,Research Strategy,Research Technics,Research Techniques,Scoring Methods,Adjustment, Data,Adjustments, Data,Data Adjustments,Design, Research,Designs, Research,Error Source,Formulation, Problem,Formulations, Problem,Group, Matched,Groups, Matched,Matched Group,Method, Scoring,Methods, Scoring,Problem Formulations,Proposal, Research,Proposals, Research,Reporting, Data,Research Designs,Research Proposals,Research Strategies,Research Technic,Research Technique,Scoring Method,Source, Error,Sources, Error,Strategies, Research,Strategy, Research,Technic, Research,Technics, Research,Technique, Research,Techniques, Research
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000074099	Equivalence Trials as Topic	Works about trials that aim to show a new treatment is no better and no worse than the standard treatment.	Non-Inferiority Trials as Topic,NonInferiority Trials as Topic,Superiority Trial as Topic,Non Inferiority Trials as Topic
D012595	Scleroderma, Systemic	A chronic multi-system disorder of CONNECTIVE TISSUE. It is characterized by SCLEROSIS in the SKIN, the LUNGS, the HEART, the GASTROINTESTINAL TRACT, the KIDNEYS, and the MUSCULOSKELETAL SYSTEM. Other important features include diseased small BLOOD VESSELS and AUTOANTIBODIES. The disorder is named for its most prominent feature (hard skin), and classified into subsets by the extent of skin thickening: LIMITED SCLERODERMA and DIFFUSE SCLERODERMA.	Sclerosis, Systemic,Systemic Scleroderma,Systemic Sclerosis
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D016032	Randomized Controlled Trials as Topic	Works about clinical trials that involve at least one test treatment and one control treatment, concurrent enrollment and follow-up of the test- and control-treated groups, and in which the treatments to be administered are selected by a random process, such as the use of a random-numbers table.	Clinical Trials, Randomized,Controlled Clinical Trials, Randomized,Trials, Randomized Clinical
D053698	Interleukin-6 Receptor alpha Subunit	A low affinity interleukin-6 receptor subunit that combines with the CYTOKINE RECEPTOR GP130 to form a high affinity receptor for INTERLEUKIN-6.	Antigens, CD126,CD126 Antigens,CD126 Receptor,Interleukin 6 Receptor alpha Subunit,Receptor, CD126
D018401	Sample Size	The number of units (persons, animals, patients, specified circumstances, etc.) in a population to be studied. The sample size should be big enough to have a high likelihood of detecting a true difference between two groups. (From Wassertheil-Smoller, Biostatistics and Epidemiology, 1990, p95)	Sample Sizes,Size, Sample,Sizes, Sample
D023381	Endpoint Determination	Establishment of the level of a quantifiable effect indicative of a biologic process. The evaluation is frequently to detect the degree of toxic or therapeutic effect.	Endpoint Assay,End Point Assay,End Point Determination,Assay, End Point,Assay, Endpoint,Assays, End Point,Assays, Endpoint,Determination, Endpoint,Determinations, End Point,Determinations, Endpoint,End Point Assays,End Point Determinations,Endpoint Assays,Endpoint Determinations,Point Assay, End,Point Assays, End,Point Determinations, End