Challenges for PAIC in Rare Diseases: Results of a Systematic Literature Review
Author(s)
Mikolaj Parkitny1, Samuel Aballea, MSc, PhD2, Piotr Wojciechowski, Msc3, Mondher Toumi, Sr., MSc, PhD, MD4.
1Student, Aix-Marseille University, Kraków, Poland, 2Inovintell, Paris, France, 3Clever-Access, Kraków, Poland, 4Aix-Marseille University, Aix-Marseille, France.
1Student, Aix-Marseille University, Kraków, Poland, 2Inovintell, Paris, France, 3Clever-Access, Kraków, Poland, 4Aix-Marseille University, Aix-Marseille, France.
OBJECTIVES: Population-adjusted indirect comparisons (PAICs) are methods used to compare therapies particularly in the absence of head-to-head clinical trials. These methods often face challenges when applied to orphan drugs, where evidence typically comes from small, single-arm studies. This systematic literature review (SLR) aimed to identify and characterize the difficulties associated with applying PAIC in rare diseases.
METHODS: The scope of the SLR was publications focusing on the methodological aspects of PAICs. The search was conducted in Embase and PubMed using a strategy built around keywords related to the methodology or simulation studies of PAICs. Studies using PAIC to compare specific treatments were excluded. The extracted data included study design, detailed descriptions of PAIC method applications, and their strengths and limitations with a particular emphasis on challenges arising from small sample sizes and unanchored comparisons typical of PAICs in rare diseases.
RESULTS: A total of 74 unique records were identified from the databases and screened. Of these, 25 were retained for full-text review and data extraction. Among them, 23 included a matching-adjusted indirect comparison (MAIC), 10 a simulated treatment comparison (STC), and 4 other related methods. 15 studies focused on methodological aspects, while 13 presented results from simulation studies. Challenges typical of small sample studies included the inability to account for all prognostic factors and effect modifiers, and poor overlap between trial populations. These issues result in an increased risk of bias, underestimated standard errors, unstable treatment effect estimates, and a lack of feasible solutions. Methods such as weight truncation, effective sample size optimization, and STC with G-Computation have been proposed to address the encountered challenges.
CONCLUSIONS: The constraints inherent to trials in rare diseases pose significant challenges to the feasibility and reliability of PAIC. To enhance credibility, additional sensitivity analyses and methods to quantify the risk of bias should be employed.
METHODS: The scope of the SLR was publications focusing on the methodological aspects of PAICs. The search was conducted in Embase and PubMed using a strategy built around keywords related to the methodology or simulation studies of PAICs. Studies using PAIC to compare specific treatments were excluded. The extracted data included study design, detailed descriptions of PAIC method applications, and their strengths and limitations with a particular emphasis on challenges arising from small sample sizes and unanchored comparisons typical of PAICs in rare diseases.
RESULTS: A total of 74 unique records were identified from the databases and screened. Of these, 25 were retained for full-text review and data extraction. Among them, 23 included a matching-adjusted indirect comparison (MAIC), 10 a simulated treatment comparison (STC), and 4 other related methods. 15 studies focused on methodological aspects, while 13 presented results from simulation studies. Challenges typical of small sample studies included the inability to account for all prognostic factors and effect modifiers, and poor overlap between trial populations. These issues result in an increased risk of bias, underestimated standard errors, unstable treatment effect estimates, and a lack of feasible solutions. Methods such as weight truncation, effective sample size optimization, and STC with G-Computation have been proposed to address the encountered challenges.
CONCLUSIONS: The constraints inherent to trials in rare diseases pose significant challenges to the feasibility and reliability of PAIC. To enhance credibility, additional sensitivity analyses and methods to quantify the risk of bias should be employed.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
PT17
Topic
Methodological & Statistical Research
Disease
SDC: Rare & Orphan Diseases