Treatment efficacy and effectiveness studies nearly always use randomized controlled trials, which require large samples and funding pools and are therefore not feasible for numerous rare diseases. This R21 proposal for a NCATS Exploratory CTSA Collaborative Innovation Award is to create tools to assist researchers of rare diseases in conducting idiographic clinical trials (ICTs), which use subject-as-own-control experimental designs, time series data, and hierarchical modeling that is tailored for small samples including N=1. This approach to conducting clinical trials requires far fewer resources than randomized controlled trials; enables rigorous small sample clinical trials; and allows efficacy testing for rare diseases, hard-to- reach locales, and underrepresented peoples. ICTs logic and results are easily understood by lay persons, can be overlaid onto normal clinical services, and can generate detailed information regarding heterogeneity- of-outcomes. In ICTs, every participant receives the novel treatment being tested and treatment efficacy can be estimated for each participant (termed ?impact?), both of which are strong incentives for study participation and completion. However, additional adaptations to the analytic techniques and statistical software are needed because the small populations of many rare diseases are incongruent with statistical assumptions that are based on sampling from large populations. Three Monte Carlo simulation studies are proposed to determine the conditions and adaptations that are needed for analysis of ICTs with small samples. To lessen risk for biased estimates, the simulation studies will use three sizes of finite populations that reflect prevalence of many rare diseases to delineate the roles of (1) study design features such as sample sizes, number of observations per participant, effect sizes, and between- and within-individual variability; (2) effects of skewness and kurtosis on model estimates; and (3) identification of when individuals' error covariance structure(s) need to be modeled in place of the traditional single sample-level error covariance structure. This project will prepare the PAtient-centered Clinical Trial (PACT) software for ICTs with rare diseases to (1) streamline and simplify the analytic process, (2) use as defaults the known statistical adjustments that are needed for ICTs with small samples, and (3) provide automated adjustments to ICT analysis and output that are indicated by results from the proposed simulation studies. Two rounds of usability testing will be conducted with statistician end-users to first identify elements of PACT, its user's manual, and practice exercises that are unclear, difficult to navigate, require additions, or serve as barriers to proper and efficient use of PACT. Round 2 usability testing will ensure that revisions sufficiently addressed the limitations that are identified during Round 1 (or guide additional refinements as needed). Upon project completion, PACT will be available for power analysis for a priori planning of rare-disease ICTs and careful analysis of ICT data to support rigorous clinical trials of treatments for rare diseases.
Traditional randomized control trial methods for testing new treatments are unsuitable for numerous rare diseases due to the relatively few affected individuals and limited funding. Recent innovations in rigorous techniques require further adaptations for rare diseases and software implementation. This proposal would make these methods more readily available to clinical researchers of rare diseases, demonstrate them in simulation studies, and broaden the methods available for medical and public health clinical trials.