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• W4385318805 abstract "INTRODUCTION Research aims to generate answers to relevant questions that can be applied to the population of interest to improve clinical care and outcomes. Whereas in routine practice, care is delivered within the context of several interacting, confounding factors, research designs strive to minimize these variations to discern better “signal” from “noise” and generate valid, patient-centered evidence that contributes to health policy. Evidence-based medicine (EBM) uses the best available evidence to make decisions about patient care. Traditionally, randomized controlled trials (RCTs) have been considered the gold standard in EBM due to their methodological and statistical rigor allowing for delineating causal relationships between exposure (treatment) and outcomes. However, with the generation of big data from large databases and registries, there has been a growing interest in using big data science to answer research questions or augment trial designs. Both have advantages and disadvantages, and which is better for medical research is being debated. EVIDENCE-BASED MEDICINE AND THE ROLE OF RANDOMIZED CONTROLLED TRIALS EBM involves integrating clinical expertise, patient values, and the best available evidence to make decisions about patient care.[1] However, conclusions from systematic reviews and meta-analyses often depend on the quality of included studies and, when poor, can lead to poor-quality conclusions, the “Garbage In, Garbage Out” phenomenon.[2] Further, different studies in a meta-analysis may assess different questions or combine dissimilar populations; this heterogeneity may threaten the validity of conclusions drawn. Finally, publication bias relating to smaller trials may influence meta-analysis outcomes. Due to these concerns, it has been argued that well-designed and adequately powered RCTs, when available, may be better to guide clinical decision-making.[3] WHY PERFORM RANDOMIZED CONTROLLED TRIALS? WHAT ARE THEIR LIMITATIONS? RCTs are designed to provide unbiased estimates of the effect of an intervention. The primary advantage of well-conducted RCTs is that they provide a high level of internal validity, meaning that the results are likely to be free from bias. Randomization helps ensure that the groups being compared are similar in all important aspects, reducing the risk of confounding. This is because randomization balances measured participant characteristics and inadequately measured or unseen confounding factors, when done properly. No other study design can achieve this satisfactorily. However, there are limitations to RCTs. They are often expensive, time-consuming, and may not be feasible for all research questions. For instance, we do not have a definitive answer to whether it is safe to use antidepressants during pregnancy, probably, because it will not be possible to randomize pregnant mothers for an RCT to answer this research question due to ethical reasons. RCTs are also associated with practical limitations, such as difficulty in recruiting participants, dropouts, and compliance issues. In addition, findings from RCTs, owing to their strict selection criteria, may not be generalizable to all populations or settings, limiting their external validity. Lastly, knowledge translation from RCTs to practice takes time. BIG DATA SCIENCE: ADVANTAGES AND DISADVANTAGES Big data science involves the analysis of large and complex datasets to discover patterns and relationships. It allows investigators to integrate large amounts of heterogeneous, complex data from diverse sources such as clinical data, social media information, genetic data, and electronic health records (EHR). In medical research, big data has mainly been used in large observational studies incorporating large amounts of data from one or more sources.[4] It is mostly rooted in the 5V model: volume, velocity, variety, veracity, and value.[5] Big data science can be used to identify new risk factors, predict outcomes, and personalize treatments. Big data science has several advantages over RCTs. First, it can be less expensive and faster than conducting an RCT. Second, big data can be more representative of the population and captures a wider range of outcomes than an RCT. Finally, big data can be used to generate hypotheses for further investigation. However, there are also several disadvantages to big data science. As noted in all observational studies, one of the biggest challenges is the potential for bias and confounding. Big data can be subject to selection bias, where certain groups are overrepresented in the dataset. There is also the potential for confounding, where variables not accounted for in the analysis may be responsible for the observed associations. Another limitation is that big data may not capture all relevant variables, making it difficult to control for all confounders.[6] Additional challenges with big data science involve the need for a specialized workforce to handle big data and legitimate concerns over data privacy, quality, and security. There is a need to evolve software solutions and good governance practices that guarantee big data privacy and security. WHICH IS BETTER? Whether RCTs or big data science is better depends on the research question being asked. RCTs continue to be the gold-standard study design for ascribing causal relationships owing to the power of random assignment. RCTs are best suited for evaluating the efficacy of interventions, particularly for treatments where the mechanism of action is well understood. RCTs are also appropriate for evaluating the adverse effects of interventions. Readers will immediately understand that both these situations involve drawing causal inferences about the effect (efficacy) and tolerability (side effects) of interventions, and therefore, RCTs may be preferred when feasible. In contrast, big data offers a relatively less expensive way of collecting large amounts of information that is both adequately narrow (specific to individuals) and adequately broad (representative of the population). The diversity of data generated by integrating several sources facilitates real-time tailored decision-making in patient care, removing hurdles in knowledge translation. In the example of the safety of antidepressants during pregnancy, where an RCT may not be possible for ethical reasons, big data approaches are an alternative; since no individuals are randomized in observational studies, ethical issues are less. Big data is better suited for generating hypotheses and exploring associations between variables. Approaches like machine learning can also be used to identify subgroups of patients who may benefit from a particular intervention. HOW TO SUCCESSFULLY COMBINE BIG DATA WITH RANDOMIZED TRIALS? Big data can augment clinical trial design, conduct, and knowledge translation in several ways. First, investigators can identify potential participants for clinical trials by pinpointing individuals matching a priori selection criteria with the help of EHR data. This can help reduce recruitment time and costs. Second, big data can be used to address the problem of generalizability of RCT results. By nesting RCTs within the EHR, the enrolment of patients can be broadened to make the sample more representative of the phenotypic heterogeneity of a disease or disorder. This will not only generate treatment effects that are more generalizable but also help in understanding the heterogeneity of treatment effects.[7] Another way big data can augment clinical trial designs is by providing information on the likelihood of benefit of treatment A versus treatment B, for a given eligible subject, based on continuous analysis of the odds of treatment success seen in similar, already recruited patients. Over time, a trial can use this information in a response-adaptive randomization technique to modify the random allocation ratios from 50:50 to, say, 70:30. Such an approach, when truly actualized, would combine the best of both worlds: it would tailor treatment decisions for a trial recruitee based on big data science but do so in the context of a randomized trial. This would allow for a trial where one can draw stronger causal inferences and one which is ethically more permissible: patients, on average, will get randomized to the more effective treatment for them.[8] Taking it one step further, a complete integration of big data and RCT would lead to a new trial design called a randomized, embedded, multifactorial, adaptive platform (REMAP) trial. An example is the REMAP-Community-Acquired Pneumonia study, wherein a point-of-care RCT is combined with an adaptive platform trial to result in a study design where recruited patients would be preferentially randomized to the better-performing intervention over time.[9] Thus, there is an element of self-learning in the trial and immediate translation of the learning into practice by modifying the random allocation ratios over time. The advantage is that this learning is still based on robust causal inferences using the RCT design, thus bridging the knowledge translation gap that is a problem with conventional RCTs. CONCLUSION RCTs and big data science have advantages and disadvantages in medical research. RCTs are the gold standard for evaluating the efficacy and safety of interventions; however, they can be costly, time-consuming, and may not be feasible for all research questions. On the other hand, big data science can provide a wealth of information and generate hypotheses for further investigation; however, it can be subject to bias and confounding. The most effective approach to medical research will depend on the specific research question. In many cases, combining RCTs and big data science may be the most effective approach. Researchers can use big data science to generate hypotheses, identify subgroups of patients who may benefit from a particular intervention, and then, use RCTs to evaluate the efficacy and safety of the intervention. Overall, the key is to use the best available evidence to make decisions about patient care. By incorporating clinical expertise, patient values, and the best available evidence, clinicians can provide patients with the highest quality of care." @default.
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• W4385318805 title "Balancing evidence-based medicine: Weighing the pros and cons of randomized controlled trials and big data science" @default.
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