JOURNAL ARTICLE

A causal inference approach to compare leukaemia treatment outcome in the absence of randomization and with dependent censoring

Abstract

Abstract Background One cause of poor outcomes in children of low-income countries affected by acute lymphoblastic leukaemia (ALL) is loss to follow-up due to abandonment of treatment. Assuming this type of loss to follow-up as independent censoring, as in standard Kaplan–Meier estimates, ignores the likely association of abandonment with biologic and socio-economic factors related to outcome. Moreover, when comparing treatment protocols adopted in different time periods, possible imbalances in patients’ characteristics must be considered. We aim to compare the outcome of children enrolled in two subsequent protocols for ALL treatment (2000–2007 and 2008–2015) in Honduras, taking both dependent censoring due to abandonment of treatment and imbalances between patient characteristics into account. Methods Marginal structural models based on inverse probability of treatment and censoring (IPTC) weighting allow the estimation of potential event-free survival (EFS) as if no abandonment of treatment occurred and the whole cohort was exposed, or not, to both protocols. An Aalen additive model and a logistic-regression model were used to build abandonment and treatment weights, respectively. Results The two protocols recruited 514 and 717 patients. Measured baseline covariates in both protocols were gender, age, white blood cell count, central nervous system involvement, tumour histology and socio-economic status. The potential EFS is slightly higher under the more recent protocol in the first 3 years but no difference is estimated in the long period [survival difference at 5 years (95% confidence interval) = 0.1% (−0.97%; 1.13%)]. Both protocols would allow reducing the event rate by 12–13% if there was no abandonment of treatment. Conclusions Using IPTC weighting, we found a similar potential effect of the two treatment protocols if the imbalance due to the different distribution of potential confounders and to abandonment of therapy was removed.

Keywords:
Censoring (clinical trials) Causal inference Randomization Inference Outcome (game theory) Medicine Econometrics Statistics Clinical trial Computer science Mathematics Internal medicine Artificial intelligence

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6
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1.05
FWCI (Field Weighted Citation Impact)
32
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0.77
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Citation History

Topics

Advanced Causal Inference Techniques
Physical Sciences →  Mathematics →  Statistics and Probability
Statistical Methods and Inference
Physical Sciences →  Mathematics →  Statistics and Probability
Acute Lymphoblastic Leukemia research
Health Sciences →  Medicine →  Public Health, Environmental and Occupational Health

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