How does the physician make a medical decision? A spectrum of deductive and inductive techniques are used in this process. They contain economic, social, scientific, traditional, among other, influences. I want to examine this process,
The published medical literature that constitutes the effable basis for medical decisions comes in two forms: models (basic science) andclinical trial data. The two work off of one another. Both are intrinsically flawed by the same problem: heterogeneity. The purist doctor ( devoid of extraneous influences like money and trying to look good) has the problem of balancing the inputs and resolving the internal inconsistencies.
Models, often expresses as molecular mechanisms, describe how things work, They are useful, simplified, ways of thinking about how things happen. There is no claim that these models are complete. They are a framework that can be tested, to an extent, by laboratory research and clinical trials
The clinical trial is very different. It tests an intervention. It looks at the percentage of people who benefited or were harmed ( or both) by the intervention This is strong, hard to obtain, human experimentation evidence.
How these two interact constitutes the conceptual basis of medical practice. The correlation between clinical trail data and the model is often not very good. What does that mean for the model? What does that mean for the clinical study?
Models
Models depend upon the interpretation of laboratory experiments that are subject to limitations of currently available technique. They are often appealing. At the time of presentation, they seem to account for much of the available data.
Models are very fragile, and they should be. Contradictory laboratory or clinical evidence should promptly collapse the model and clear the space for the generation of a new model. The checking of results is limited because of the expense and the esoteric nature of the work. For practitioners who are not directly involved in the research endeavor, the model is an act of faith, a belief based upon its beauty and trust in the investigators
Models are supported by a scaffold of economic and social interactions. Over time, careers and corporations are built on these models. The models generate communities, that came to their visions based upon underlying common beliefs, Some of these believes are stated, others are long-standing assumptions that are hidden from view, There is a “common knowledge,” a set of propositions that evolve into postulates. Based upon what they “know” the community generates a structure that protects the model.
Clincal Trials
The outcomes of clinical trials are usually expressed in statistical terms.The trial asks if there is a difference between two ( or more) courses of action. But differences occur by chance. A difference that would be predicted by a Gaussian distribution to occur by chance less than 5% of the time ( P<.05) is considered significant: That is the arbitrary Holy Grail.
The tested population in a clinical trial is always heterogeneous. That is a recognized issue. That is the reason that larger trials are “more powerful”. Large numbers are believed to compensate for heterogeneity, “random” variations are canceled,
This approach puts the clinical trial in the role of testing an idea, not necessarily looking for truth. The heterogeneity in clinical trials is an asset to the trial, not something to be canceled, it is a truth to be discovered. It is a way to identify responders, victims of toxicity, and new diseases. But the heterogeneity stands in the way of the ( P<0.05) declaration of significance.
The result of the clinical trial is correct. If the trial is well designed, it predicts what would happen in a similar population treated in an identical manner. This information is useful for insurance companies, drug manufacturers, epidemiologists, health care planners. etc. It is an accurate portrayal of the selected outcomes in a heterogeneous population.
For the treating physician theses results should be far less useful. It is rare ( perhaps, impossible) for the patient that needs treatment to match the “average” patient in the trial. Worse, the information about the treatment and outcomes of those patients , included in the trial, that most closely match the physician’s actual patient, are obscured in the statistical smoke. The data on those patients has been sacrificed to statistical “significance,”
The trialists look for sources of heterogeneity, but the raw data is generally not accessible to the practitioner, the consumer of the trial data. S/he only has a statistical summary of the outcomes and cannot search the trial data for the closest match to the patient at hand.
Making the Decision
How should the physician behave? The current dogma is that practice should remain the same until an adequately powered clinical trial has demonstrated the need for change. This is the practice of “evidence based medicine”. Discoveries in basic science and changes in models, are not recognized. Among the consequence of this approach: insurance companies are allowed to save money by avoiding payment for expensive new therapies; and there is stability of the market for drug manufacturers.
We would hope that a clinical decision weighs all the factors. Clinical trials apply to populations. An individual is never a perfect representative of a population. Hence, the application of a clinical trial is always flawed.The models are always tenuous, but they are the best we have at the moment. As models account for increasing amounts of data, they become more credible, they become a way to summarize and check the applicability of the clinical trial data. Which patients in the clinical trial fit the model?
Clinical trials should turn to identifying heterogeneity instead of burying it. Populations that vary in outcome should be investigated for corresponding variations on biology The raw data should be available for analysis, so that the practitioner can examine the outcomes of the best matches to the patient needing treatment.
Models should both predict, and be tested by, looking at more completely defined populations.
It is time to start using the technology we already have.
How does the physician make a medical decision? A spectrum of deductive and inductive techniques are used in this process. They contain economic, social, scientific, traditional, among other, influences. I want to examine this process,
The published medical literature that constitutes the effable basis for medical decisions comes in two forms: models (basic science) andclinical trial data. The two work off of one another. Both are intrinsically flawed by the same problem: heterogeneity. The purist doctor ( devoid of extraneous influences like money and trying to look good) has the problem of balancing the inputs and resolving the internal inconsistencies.
Models, often expresses as molecular mechanisms, describe how things work, They are useful, simplified, ways of thinking about how things happen. There is no claim that these models are complete. They are a framework that can be tested, to an extent, by laboratory research and clinical trials
The clinical trial is very different. It tests an intervention. It looks at the percentage of people who benefited or were harmed ( or both) by the intervention This is strong, hard to obtain, human experimentation evidence.
How these two interact constitutes the conceptual basis of medical practice. The correlation between clinical trail data and the model is often not very good. What does that mean for the model? What does that mean for the clinical study?
Models
Models depend upon the interpretation of laboratory experiments that are subject to limitations of currently available technique. They are often appealing. At the time of presentation, they seem to account for much of the available data.
Models are very fragile, and they should be. Contradictory laboratory or clinical evidence should promptly collapse the model and clear the space for the generation of a new model. The checking of results is limited because of the expense and the esoteric nature of the work. For practitioners who are not directly involved in the research endeavor, the model is an act of faith, a belief based upon its beauty and trust in the investigators
Models are supported by a scaffold of economic and social interactions. Over time, careers and corporations are built on these models. The models generate communities, that came to their visions based upon underlying common beliefs, Some of these believes are stated, others are long-standing assumptions that are hidden from view, There is a “common knowledge,” a set of propositions that evolve into postulates. Based upon what they “know” the community generates a structure that protects the model.
The outcomes of clinical trials are usually expressed in statistical terms.The trial asks if there is a difference between two ( or more) courses of action. But differences occur by chance. A difference that would be predicted by a Gaussian distribution to occur by chance less than 5% of the time ( P<.05) is considered significant: That is the arbitrary Holy Grail.
The tested population in a clinical trial is always heterogeneous. That is a recognized issue. That is the reason that larger trials are “more powerful”. Large numbers are believed to compensate for heterogeneity, “random” variations are canceled,
This approach puts the clinical trial in the role of testing an idea, not necessarily looking for truth. The heterogeneity in clinical trials is an asset to the trial, not something to be canceled, it is a truth to be discovered. It is a way to identify responders, victims of toxicity, and new diseases. But the heterogeneity stands in the way of the ( P<0.05) declaration of significance.
The result of the clinical trial is correct. If the trial is well designed, it predicts what would happen in a similar population treated in an identical manner. This information is useful for insurance companies, drug manufacturers, epidemiologists, health care planners. etc. It is an accurate portrayal of the selected outcomes in a heterogeneous population.
For the treating physician theses results should be far less useful. It is rare ( perhaps, impossible) for the patient that needs treatment to match the “average” patient in the trial. Worse, the information about the treatment and outcomes of those patients , included in the trial, that most closely match the physician’s actual patient, are obscured in the statistical smoke. The data on those patients has been sacrificed to statistical “significance,”
The trialists look for sources of heterogeneity, but the raw data is generally not accessible to the practitioner, the consumer of the trial data. S/he only has a statistical summary of the outcomes and cannot search the trial data for the closest match to the patient at hand.
Making the Decision
How should the physician behave? The current dogma is that practice should remain the same until an adequately powered clinical trial has demonstrated the need for change. This is the practice of “evidence based medicine”. Discoveries in basic science and changes in models, are not recognized. Among the consequence of this approach: insurance companies are allowed to save money by avoiding payment for expensive new therapies; and there is stability of the market for drug manufacturers.
We would hope that a clinical decision weighs all the factors. Clinical trials apply to populations. An individual is never a perfect representative of a population. Hence, the application of a clinical trial is always flawed.The models are always tenuous, but they are the best we have at the moment. As models account for increasing amounts of data, they become more credible, they become a way to summarize and check the applicability of the clinical trial data. Which patients in the clinical trial fit the model?
Clinical trials should turn to identifying heterogeneity instead of burying it. Populations that vary in outcome should be investigated for corresponding variations on biology The raw data should be available for analysis, so that the practitioner can examine the outcomes of the best matches to the patient needing treatment.
Models should both predict, and be tested by, looking at more completely defined populations.
It is time to start using the technology we already have.
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