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Cited by 63Advanced Causal Inference TechniquesGenetic Associations and EpidemiologyStatistical Methods in Clinical Trials

Capitalizing on Mendelian randomization to assess the effects of treatments

Open access
Smith, George Davey·2007·Journal of the Royal Society of Medicine
DOI 10.1258/jrsm.100.9.432PMC PMC1963388
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Abstract

The stage of disease and general fitness of patients usually influences the treatments they receive. Because of this, tests of treatments and treatment comparisons need to ensure that treatment comparison groups are made up of patients who are as similar as possible. Without this assurance, any differences in the progress of patients receiving different treatments cannot confidently be ascribed to differential effects of treatments: they may simply reflect differences in the characteristics of patients receiving the different treatments. This is the rationale underlying random allocation (randomization) to treatment groups in randomized trials—to ensure that whether treatment is given or withheld is unrelated to factors influencing the prognoses of patients. Sometimes, for one or more of a variety of reasons, the organization of randomized controlled trials (RCTs) presents challenges that are judged likely to be very difficult to overcome. One such example relates to the need to assess the effects of bone marrow transplantation in the treatment of acute myeloid leukaemia in children. Randomized comparisons of treatment with and without bone marrow transplantation in this form of leukaemia have been judged unlikely to be achieved successfully, yet it is clearly important to obtain reliable, unbiased estimates of the effects of transplantation—wanted and unwanted—because the disadvantages may outweigh any advantages of this invasive treatment. In 1991, Richard Gray and Keith Wheatley reported an ingenious method for obtaining unbiased estimates of the effects of bone marrow transplantation without conducting a traditional randomized trial.1 They pointed out that unbiased comparisons could be made between child patients who had a genetically compatible sibling, and so in principle could receive a matched sibling bone marrow transplant, with other child patients who had no genetically compatible sibling, and so were incapable of receiving a matched sibling bone marrow transplant. Because having or not having a genetically compatible sibling is a matter of chance—it is determined by random assortment of genes at the time of gamete formation and conception—this situation produces what is effectively a randomized comparison. Whether a child with leukaemia belongs to the group with genetically compatible siblings or the group without such siblings will not be related to potential confounding factors such as disease stage and general fitness at the time of diagnosis. In a form of ‘intention-to-treat analysis’, Gray and Wheatley noted that an unbiased comparison meant comparing all the patients with a genetically compatible sibling with all the patients without such a sibling, regardless of whether or not every patient with a potential donor actually received a transplant (Figure 1).1 Figure 1 Intention to treat analysis of children with leukaemia Gray and Wheatley appear to have been the first authors to refer to this particular way of capitalizing on chance events in nature to create unbiased comparison groups to assess the effects of a treatment. They called it ‘Mendelian randomization’. Several further studies have now been carried out using their design,2-4 including studies to assess the effects of treatment for acute lymphoblastic leukaemia.5-7 Some of these studies have confirmed that like is being compared with like in comparison groups defined in this way.3-6 They have also shown that there are differences in prognostic factors between groups defined by the treatment they received; differences that would confound a conventional observational analysis comparing different treatments.4-6 The basic design outlined in Figure 1 might be improved by taking into account the number of siblings each patient has.8 Patients with more siblings have a greater chance of having a genetically compatible donor, and therefore groups defined by having a compatible donor will differ according to average number of siblings and thus will differ by factors that may be related to prognosis. Indeed, a later study found that the number of siblings could itself be related to survival.9 The study by Gray and Wheatley (Gray, personal communication) and another more recent study4 applied an analysis restricted to patients with at least one sibling, but exact stratification or matching on number of siblings might be a more robust approach.

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Authors
Smith, G. D.
Affiliations
University of Bristol (GB)
Corresponding author
Smith, George Davey
ORCID
Smith, George Davey
Venue
Journal of the Royal Society of Medicine
Publisher
SAGE Publications
Published
2007-09
Volume
100
Issue
9
Pages
432-435
ISSN
  • 0141-0768 (ISSN-L)
  • 1758-1095
DOI
10.1258/jrsm.100.9.432
PMC
PMC1963388
OpenAlex
W2141275050
Cited by
60
References
43
Citations by year
2026: 5 · 2025: 7 · 2024: 9 · 2023: 1 · 2022: 2 · 2021: 5 · 2020: 1 · 2019: 3 · 2018: 1 · 2017: 3 · 2016: 2 · 2015: 4 · 2013: 3 · 2012: 2
FWCI
0.61
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Journal of the Royal Society of Medicine (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords
Mendelian randomization, treatment effect estimation, intention-to-treat analysis, genetic compatibility, confounding, randomized controlled trials, causal inference, natural experiment
MeSH terms
Genetics, Random Allocation, Bias, Treatment Outcome

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