In-Brief:
- Healthcare data analytics solutions provide a powerful approach to understand the spectrum of disease with applications in observational and analytic epidemiology, screening and diagnosis randomized clinical trials, and prognosis in the clinical research organization.
- Causes of measurement errors of biomarkers differ with the individual to the laboratory. Discussing problems that affect the biomarker analysis along with recommendations on how to concern with bias and confounding
- This Pepgra blog review describes the concerns before using biomarkers in clinical investigation.
Introducing Biomarkers
Biological markers are “cellular, biochemical alterations that are accountable in biological media such as human tissues, or fluids.” the definition broadens as an indicator of normal biological processes, or pharmacological responses, pathogenic processes to a therapeutic intervention. Biomarkers include tools that can aid in understanding the prediction, diagnosis, progression, cause, regression, or outcome of therapeutics of disease. In the nervous system, there is a wide range of techniques that gain information about the brain in a healthy and diseased state. Healthcare Analytics Services may involve measurements directly on biological media or sizes such as brain imaging which do not include a direct sampling of physical media using Clinical trial Monitoring Services.
Generations of physicians, epidemiologists, have used biomarkers, and scientists to study and treat human disease. The benefits of biomarkers in the diagnosis and management of cardiovascular disease, immunological and genetic disorders, infections, and cancer are well known. The neuroscientists have relied on biomarkers to assist in the diagnosis and therapeutics of nervous system disorders and to investigate their cause. The rapid growth of cellular biology and laboratory has expanded using the application of technically advance biomarkers. Molecular biomarkers will be in clinical investigators, provide a dynamic and powerful approach to understanding the spectrum of neurological disease with obvious applications in analytic epidemiology, diagnosis, and disease management, clinical trials and disease prevention.
Practical considerations before using biomarkers:
Improper measurement of the biomarker would naturally lead to low validity of the relation to the disease. There are many types of errors other than those errors that occur in the cellular laboratory. Problems for transporting the specimens to the laboratory can alter the measurement of the biomarker. Storing samples improperly or changes in storage surroundings can also disturb the size of biomarkers. Technicians handle most of the specimens, and so appropriate training of personnel is there. Finally, receipt and control errors such as in the transcription of identification numbers if done by hand can always be a source of error. A well-organized procedures manual outlining the details for documentation, storage, monitoring of specimens and maintaining records, can alleviate many of these issues. The laboratories in an institute, the quality assurance and quality-control program conduction to reduce measurement errors with Clinical Biostatistics services.
Healthcare data analytics services evade the bias that occurs in any study, with the help of biomarkers. When biases arise without regard to the outcome, so-called nondifferential bias, the effects on the survey are less severe but favour the null hypothesis of no association. Problems occur when the availability of the biomarker is differentially related to either the disease or exposure of the specimen acquisition, storage, measurement, or specific procedures differ with the disease compared to those without the disease. Differential biases tend to favour an association in either direction; the real relationship has a high response rate from all cases and controls have maintenance. The investigators should have an objective review board review and monitor the conduct of the study, observing possible biases in subject participation or specimen ascertainment to reduce such biases for pharmaceutical regulatory consulting services.
Astounding is the failure to identify factors that may change the measurement of the biomarker. It can be internal, the weight of the subject, or external, a batch of laboratory kits used. Individual properties of biomarkers should influence the choice and interpretation for its inclusion in any investigation. The effects of confounders like age, gender, diet, and other metabolic factors must have examinations before initiating the study. Biologic stability is critical mainly if the biomarker storage for any length of time. Banked serum or plasma is of great value in any review unless it affects the pharmacologic properties of the biomarker in the healthcare data analytics services.
For example, some nutrients such as vitamins do not store well because they are light-sensitive. Storage of all tissues, including lymphocytes and extracted DNA, can be expensive, and the evaluating stability of the biomarker if storage needed for extended intervals. These are often overlooked in the analyses and can seriously affect the outcome. One should use data on potential confounders when designing the study and collect relevant internal and external information that might affect the measurement of clinical study design, including the analysis of the relation between the biomarker and the outcome of interest.
The choice of the biomarker for research should be guided by the scientific question and by the financial resources. Cost is always a concern. In a small clinical trial, this may be important; if an epidemiologic study includes thousands of subjects, the price can be quite high unless the laboratory procedure is automated and relatively simple. In fact, for some investigations, larger sample sizes can bring down the cost per subject. It generally implies that the biomarker is readily available, and its inclusion in the study is feasible. For example, automated procedures have made the inclusion of lipid profiles in clinical studies of stroke quite possible. Methods have improved to the point that a “finger-stick” can provide the necessary amount of blood. Healthcare data analytics works depends on the type of investigation, researchers should have an idea of the false-positive or false-negative profile of the biomarker.
As might be expected “false positives” create extra work regardless of whether it is a biomarker of exposure, susceptibility, or disease. “False negatives” increase the overall cost of the study. Tolerance for this problem depends on the funding available even in therapeutics of clinical research.
Healthcare data analysis service provides data from biomarkers of human tissues or body fluids, the choice of biomarkers are not trivial. Biomarkers possess some degree of risk. In Healthcare Analytics Solutions the clinical trials, that is less a concern because the patient will possibly benefit from the “new treatment.” In quasi-experimental studies, the source of the biomarker may be critical. Body fluids, such as blood and urine, are usually well tolerated.
However, biopsy and collection of cerebrospinal fluid is more complicated and associated with slight risks. Risk-benefit will be an issue for the investigator to resolve. Pilot studies are always beneficial for convincing institutional review boards that your research is safe and that the risk-benefit ratio favours a benefit for biomedical research.
Conclusion:
Many studies using biomarkers never achieve their full potential because of the failure to adhere to the same rules that would apply for the use of variables that are not biological. The development of any biomarker should precede or go in parallel with the standard design of any epidemiological project or healthcare analytics protocol and the formal investigations listed in this Pepgra blog.
References:
- Dobbin, K. K., Cesano, A., Alvarez, J., Hawtin, R., Janetzki, S., Kirsch, I., … & Zhang, J. (2016). Validation of biomarkers to predict response to immunotherapy in cancer: Volume II—clinical validation and regulatory considerations. Journal for immunotherapy of cancer, 4(1), 77.
- Gazerani, P. (2019). Current evidence on potential uses of MicroRNA biomarkers for migraine: from diagnosis to treatment. Molecular Diagnosis & Therapy, 23(6), 681-694.
- Mayeux, R. (2004). Biomarkers: potential uses and limitations. NeuroRx, 1(2), 182-188.
