De‐Mystifying the Clone‐Censor‐Weight Method for Causal Research Using Observational Data: A Primer for Cancer Researchers
Charles GaberArmen A. GhazarianPaula D. StrassleTatiane Bomfim RibeiroMaribel SalasCamille MaringeXabier García‐AlbénizRichard WyssWei DuJennifer L. Lund
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ABSTRACT Background Regulators and oncology healthcare providers are increasingly interested in using observational studies of real‐world data (RWD) to complement clinical evidence from randomized controlled trials for informed decision‐making. To generate valid evidence, RWD studies must be carefully designed to avoid systematic biases. The clone‐censor‐weight (CCW) method has been proposed to address immortal time and other time‐related biases. Methods The objective of this manuscript is to de‐mystify the CCW method for cancer researchers by describing and presenting its core components in an accessible and digestible format, using visualizations and examples from cancer‐relevant studies. The CCW method has been applied in diverse settings, including investigations of the effects of surgery within a certain time after cancer diagnosis, the continuation of annual screening mammography, and chemotherapy duration on survival. Results The method handles complex data wherein the treatment group to which an individual belongs is unknown at the start of follow‐up. The three steps of the CCW method involve cloning or duplicating the patient population and assigning one clone to each treatment strategy, artificially censoring the clones when their observed data are inconsistent with the assigned strategy and weighting the cloned and censored population to address selection bias created by the artificial censoring. Conclusions The CCW method is a powerful tool for designing RWD studies in cancer that are free from time‐related biases and successfully, to the extent possible, emulate features of a randomized clinical trial.Keywords:
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For many years it has been claimed that observational studies find stronger treatment effects than randomized, controlled trials. We compared the results of observational studies with those of randomized, controlled trials.
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In Brief Objectives: To compare the results of randomized controlled trials versus observational studies in meta-analyses of digestive surgical topics. Summary Background Data: While randomized controlled trials have been recognized as providing the highest standard of evidence, claims have been made that observational studies may overestimate treatment benefits. This debate has recently been renewed, particularly with regard to pharmacotherapies. Methods: The PubMed (1966 to April 2004), EMBASE (1986 to April 2004) and Cochrane databases (Issue 2, 2004) were searched to identify meta-analyses of randomized controlled trials in digestive surgery. Fifty-two outcomes of 18 topics were identified from 276 original articles (96 randomized trials, 180 observational studies) and included in meta-analyses. All available binary data and study characteristics were extracted and combined separately for randomized and observational studies. In each selected digestive surgical topic, summary odds ratios or relative risks from randomized controlled trials were compared with observational studies using an equivalent calculation method. Results: Significant between-study heterogeneity was seen more often among observational studies (5 of 12 topics) than among randomized trials (1 of 9 topics). In 4 of the 16 primary outcomes compared (10 of 52 total outcomes), summary estimates of treatment effects showed significant discrepancies between the two designs. Conclusions: One fourth of observational studies gave different results than randomized trials, and between-study heterogeneity was more common in observational studies in the field of digestive surgery. Comparison of the results of randomized controlled trials versus observational studies in meta-analyses of digestive surgical topics was performed. One fourth of observational studies gave different results than randomized trials, and between-study heterogeneity was more common in observational studies in the field of digestive surgery.
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When using observational data, quantifying the effect of treatment duration on survival outcomes is not straightforward because only people who live for a long time can receive treatment for a long time. This problem doesn’t apply to randomised trials because people are classified based on the treatment duration they are assigned, rather than the treatment duration that they achieve. This approach accepts that dead people do not deviate from their assigned treatment strategy. By transferring this insight to the analysis of observational data, we can follow three steps to estimate the effect of treatment duration from observational data without the bias of naive comparisons between long term and short term users. The first step is cloning people to assign them to multiple treatment strategies. The second step is censoring clones when they deviate from their assigned treatment strategy. The third step is performing inverse probability weighting to adjust for the potential selection bias introduced by censoring. The procedure can be used to compare any treatment strategies that are sustained over time. Cloning, censoring, and weighting eliminates immortal time bias in the estimates of absolute and relative risk, which helps researchers focus their attention on other biases that may be present in observational analyses and are not so easily eliminated.
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The role of observational studies in the evaluation of treatments is a long-standing and contentious topic.1 In this issue of the Journal, Concato et al.2 and Benson and Hartz3 report that observational studies give results similar to those of randomized, controlled trials. If these claims lead to more observational studies of therapeutic interventions and fewer randomized, controlled trials, we see considerable dangers to clinical research and even to the well-being of patients.Any systematic review of evidence on a therapeutic topic needs to take into account the quality of the evidence. Any study, whether randomized or observational, may have flaws . . .
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Background: In randomized controlled trials, due to the randomization process the covariates are equally distributed between the groups. For these reason at the end of the study the researcher can assume that the outcomes happens only because of interventions. This strict control of bias leads the randomized controlled trials to become the gold standard in clinical research. However in some cases, like in surgical studies, it is very difficult to run a randomized controlled trial. Observational studies have some strengths and can play an important role in studies of surgical specialties. Discussion: Observational studies are very commonly used nowadays, once that they are easy to conduct, needs less time and budget than randomized controlled trials. It is also a great design to test new hypothesis that could be developed in future studies. Observational design could be an excellent option to surgical research when randomized controlled trials have an ethical or feasibility issue. Besides the ethical and feasibility issues the research could have a rare outcome that is difficult to assess and measure. To all this issues observational study offers a solution.
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In regulatory evaluations, high-quality randomized controlled trials (RCTs) are considered the gold standard for assessing the efficacy of medical interventions. However, during the COVID-19 pandemic, the urgent need for treatment options led to regulatory approvals being made based on evidence from non-randomized, observational studies. In this study we contrast results from observational studies and RCTs of six drugs to treat COVID-19 infection. Across a range of studies evaluating hydroxychloroquine, remdesivir, ivermectin, aspirin, molnupiravir and tenofovir for COVID-19, there was statistically significant evidence of benefit from non-randomized observational studies, which was then not seen in RCTs. We propose that all observational studies need to be labelled as 'non-randomized' in the title. This should indicate that they are not as reliable for evaluating the efficacy of a drug and should not be used independently for regulatory approval decisions.
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