Biostatistics: Bayesian and Frequentist Approaches Frequency of Use

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Table of Contents

Overview of Bayesian and frequentist approaches
Aspects to consider
Areas of Application
Frequency of Use of Different Methods
Reference List

Overview of Bayesian and frequentist approaches

The research methods may be chosen by the researchers to influence the results of the investigation which can be later compared for different types of research methods. In this respect, the effectiveness of the method selected for the trial depends on the appropriateness of this method for processing specific data and on the experience of a researcher in selecting the research method. The Bayesian and frequentist approaches are both related to statistics and probability whereas some experts prefer one method to another.

Aspects to consider

As reported by Winkler (1), the Bayesian approach is rarely used because researchers do not realize its advantages for the experiments in health care while the frequentist methods often appear to be inappropriate and lading to false data selection. Some researchers vigorously support the Bayesian method (4) while others prefer the frequentist one or stay neutral in this dispute. The preference of one of the statistical methods is mostly explained through the applicability of the method to the research or through the comparison of both methods when they are either both applied to the same case or are compared in terms of effectiveness, advantages, the number of cases when false findings were received with the help of theoretical data excluding practical investigations.

Areas of Application

Cost-efficiency. The cost-effectiveness in health care institutions is one of the most important components for effective management and operation of the entire health care unit because the effective distribution of resources may save costs and enable the medical staff to provide health care services to a greater number of patients to desperately need it. The use of frequentist and Bayesian methods for the analysis of cost-efficiency is also discussed in different sources while some researchers prefer one approach to another. For instance, In contrast to frequentist methods, Bayesian methods answer the right questions and agree with natural common sense. (1,p58) Blood pressure control is brought into discussion in terms of the cost-effectiveness of the procedure in the study by Briggs (3,11). The cost-effectiveness of some trials and health care institutions as well as the resource management in these institutions can be questioned and assessed with the help of statistical methods applied to various institutions. The cost-effectiveness of health care interventions should be compared to the available budget the health care institution can afford; this is one of the reasons why studies evaluating cost-effectiveness (9) are so important. The analysis of cost-data and trials aimed at assessing the cost-effectiveness of different health care interventions is one of the most important approaches used in health care to improve the operation and management of health care institutions. Randomized trials can be found in different sources (21) that explain the significance of assessment.
Medical trials. Some researchers use medical trials to find other ways to cure diseases while others investigate the effectiveness of hospitals in terms of health care services provided to patients. In other words, the assessment of hospitals can also be a subject of the research with the help of a frequentist or a Bayesian approach. As such, Austin et al. (2) study the cases of myocardial infarction patients in different hospitals and mortality rates related to these cases to evaluate the work of hospitals and the quality of acute health care services they are able to provide to their patients. The clinical evidence is claimed to be better measured with the help of the Bayesian method (5). The profiling of the medical staff is another area for research using statistical data and comparing findings in different health care institutions. The decision-making is evaluated in terms of the health care providers in case of Alzheimers disease (8).

Comparison of statistical methods used in health care investigations is very important for the understanding of the approaches the researchers use while selecting this or that method and preferring one of them and criticizing the other (10). Another type of study that can be found on the Bayesian/frequentist methods is the one by Berry (12) that outlines the main parts of the research procedures. At the same time, Goodman (13) discusses the Bayesian method and its application in research methodology. The Bayesian method is used for data analysis (14) as a method alternative to the observation and evaluation. The contrast of the frequentist and Bayesian methods is available in the study by Coory et al. (15) where the one-off cancer cluster is regarded. At the same time, the study by Lee (16) contains criticism of the frequentist method and suggests Bayesian as an alternative for the modern health care system. The Bayesian method is frequently used as the only method of the research though in different medical cases to compare and contrast the findings received using this approach. In cases where there is a parametric alternative hypothesis, likelihood or Bayesian methods might be preferable (18, p233).

The application of the simplest statistical approaches can be found in bootstrapping which is the computational application of statistics which makes the procedures of comparison easier. The analysis of this method can be seen in different sources (19) that explain the applicability of this statistical method. This method can also be found in Johnson (20) where the author explains the method and its advantages with regard to the disadvantages including the assessment of errors that may occur in the process of evaluation adding details that may be useful for inexperienced researchers who want to study this method and apply it in practice.

Frequency of Use of Different Methods

The current report is aimed at investigating the frequency of using a frequentist approach and a Bayesian approach in health care. As such, we can see that there are many researches that include either both methods or compare and contrast the results received using these methods. For instance, some studies (2,7) include both methods or question the usability of one of the methods (1) while others use one method because they consider it more appropriate than the other (3,5,8,9,11,17). At the same time, some studies (7,16) are aimed at investigating the false approaches used in case of positive findings while the actual state of things is absolutely different; another category of studies contains a comparison of both methods (10) or description of one of them (13). Though there are studies that question the applicability of the frequentist or Bayesian methods, every researcher may select one of the approaches for the assessment, clinical trial, or data analysis in case the method is appropriate for the investigation and would benefit the findings of it.

Reference List

Winkler RL. Why Bayesian analysis hasnt caught on in healthcare decision-making. Int J Tech Assess Health Care [Internet]. 2001.

Austin PC, Naylor CD, Tu JV. A comparison of a Bayesian vs. a frequentist method for profiling hospital performance. J of Evaluation in Clinical Prac [Internet]. 2001.

Briggs AH. A Bayesian approach to stochastic cost-effectiveness analysis: an illustration and application to blood pressure control in type 2 diabetes. Int J Tech Assess Health Care [Internet]. 2001. Web.

Spiegelhalter DJ, Myles JP, Jones DR, Abrams KR. Methods in health service research: an introduction to Bayesian methods in health technology assessment. BMJ [Internet]. 1999.

Freedman L and affiliations. Bayesian statistical methods: a natural way to assess clinical evidence. BMJ [Internet]. 1996.

Wacholder S, Chanock S, Garcia-Closas M, Ghormli LE, Rothman N. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst [Internet]. 2004.

Christiansen CL, Morris CN. Improving the statistical approach to health care provider profiling. Ann Intern Med [Internet]. 1997.

Claxton K, Neumann PJ, Araki S, Weinstein MC. Bayesian value-of-information analysis an application to a policy model of Alzheimers disease. Int J Tech Assess Health Care [Internet]. 2001.

OBrien BJ, Briggs AH. Analysis of uncertainty in health care cost-effectiveness studies: an introduction to statistical issues and methods. Stat Meth Med Research [Internet]. 2002.

Bland JM, Altman DG. Bayesians and frequentists: statistics notes. BMJ [Internet]. 1998.

Briggs AH. Briggs AH. A Bayesian approach to stochastic cost-effectiveness analysis. Health Econ [Internet]. 1999.

Berry SM. Bayesian clinical trials. [Internet]. 2009. Web.

Goodman SN. Toward evidence-based medical statistics. 2: the Bayes factor. Ann Intern Med [Internet]. 1999.

Gurrin LC, Kurinczuk JJ, Burton PR. Bayesian statistics in medical research: an intuitive alternative to conventional data analysis. J Eval Clinical Prac [Internet]. 1999. Web.

Coory MD, Wills RA, Barnett AG. Bayesian versus frequentist statistical inference for investigating a one-off cancer cluster reported to a health department. BMC Medical Research Methodology [Internet]. 2009.

Lee JJ. Demystify statistical significancetime to move on from the p value to Bayesian analysis. JNCI [Internet]. 2011;103(1):2-3. Web.

Stephens M, Donnelly P. A comparison of Bayesian methods for haplotype reconstruction from population genotype data. Am J Hum Genet [Internet]. 2003;73:1162-1169.

Efron B, Tibshirani RJ. An introduction to the bootstrap. London: Chapman & Hall/CRC; 1993.

Davison AC, Kuonen D. An introduction to the bootstrap with applications in R. Statistical Computing and Statistical Graphics Newsletter [Internet]. 2000;13(1):6-11.

Johnson RW. An introduction to the bootstrap. Teaching Statistics [Internet]. 2001; 23(2):49-54. Web.

Barber JA, Thompson SG. Analysis of cost data in randomized trials: an application of the non-parametric bootstrap. Statist Med. 2000; 19:3219-3236.

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Biostatistics: Bayesian and Frequentist Approaches Frequency of Use

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