Table 1 Features of Rare Diseases, Interventions, or Outcomes Measures that Could Impact Study Design Decisions [8,9,10]

Disease Characteristics

Diseases that are life threatening

In placebo controlled RCTs, time on placebo should be minimized

Diseases in which individuals are often diagnosed when they first have the condition

Prospective inception cohort designs may be useful in establishing temporality among study variables

Diseases that have an unpredictable disease course

Several experimental designs cannot be used including crossover designs, latin square designs, n-of-1 trials, and randomized withdrawal designs

Intervention Characteristics

Whether the anticipated response to the intervention is non-reversible

Several experimental designs cannot be used including crossover designs, latin square designs, n-of-1 trials, randomized withdrawal designs, early escape, and delayed start designs

Whether the anticipated response to the intervention is delayed rather than immediate

Several experimental designs cannot be used including crossover designs, latin square designs, n-of-1 trials, early escape designs, and designs that involve adaptive randomization

Whether the effects on the outcomes are influenced by the order of interventions received*

Several experimental designs cannot be used including crossover designs, latin square designs, and n-of-1 trials

Outcome and prognostic tool characteristics

Whether meaningful surrogate outcomes or composite measures are available or whether statistical techniques for analyzing repeated outcome measures are applicable

In these situations, it may be possible to reduce the sample size needed to answer the study question

Whether tools are available that can be used to accurately predict prognosis

In these situations, risk-based allocation designs are feasible and it may be possible to reduce the sample size needed if the study focuses on recruiting only patients who are at high risk of progressing. However, enrolling only high risk patients will also reduce the pool of eligible individuals.

Whether existing research infrastructure exists for the condition of interest, such as a patient registry

In situations where there is existing infrastructure, that infrastructure may be leveraged to recruit eligible participants more rapidly and to implement a study more efficiently

Acceptable levels of uncertainty

Whether decision-makers expected to use the study data are willing accept results from a trial with an alpha >0.05

In these situations, it may be possible to reduce the sample size needed to address the study question are the study would not need to be powered at an alpha ≤0.05