I need the way biostatistics is applied in various fields?

the way biostatistics is applied in :


Public health, including epidemiology, health services research, nutrition, and environmental health


Design and analysis of clinical trials in medicine


Genomics, population genetics, and statistical genetics in populations in order to link variation in genotype with a variation in phenotype. genetics


Ecology


Biological sequence analysis

I need the way biostatistics is applied in various fields?
Biostatistics (biological statistics) or biometry is the application of statistics to a wide range of topics in biology. It has particular applications to medicine and to agriculture.


Biostatistical reasoning and modeling were of critical importance to the foundation theories of modern biology. In the early 1900s, after the rediscovery of Mendel's work, the conceptual gaps in understanding between genetics and evolutionary Darwinism led to vigorous debate between biometricians such as Walter Weldon and Karl Pearson and Mendelians such as Charles Davenport, William Bateson and Wilhelm Johannsen. By the 1930s statisticians and models built on statistical reasoning had helped to resolve these differences and to produce the neo-Darwinian modern evolutionary synthesis.





The leading figures in the establishment of this synthesis all relied on statistics and developed its use in biology.





Sir Ronald A. Fisher developed several basic statistical methods in support of his work The Genetical Theory of Natural Selection


Sewall G. Wright used statistics in the development of modern population genetics J. B. S Haldane's book, The Causes of Evolution, reestablished natural selection as the premier mechanism of evolution by explaining it in terms of the mathematical consequences of Mendelian genetics.


These individuals and the work of other biostatisticians, mathematical biologists, and statistically inclined geneticists helped bring together evolutionary biology and genetics into a consistent, coherent whole that could begin to be quantitatively modeled.





In parallel to this overall development, the pioneering work of D'Arcy Thompson in On Growth and Form also helped to add quantitative discipline to biological study.





Despite the fundamental importance and frequent necessity of statistical reasoning, there may nonetheless have been a tendency among biologists to distrust or deprecate results which are not qualitatively apparent. One anecdote describes Thomas Hunt Morgan banning the Frieden calculator from his department at Caltech, saying "Well, I am like a guy who is prospecting for gold along the banks of the Sacramento River in 1849. With a little intelligence, I can reach down and pick up big nuggets of gold. And as long as I can do that, I'm not going to let any people in my department waste scarce resources in placer mining


Education and training programs


Almost all educational programmes in biostatistics are at postgraduate level. They are most often found in schools of public health, affiliated with schools of medicine, forestry, or agriculture or as a focus of application in departments of statistics.





In the United States, while several universities have dedicated biostatistics departments, many other top-tier universities integrate biostatistics faculty into statistics or other departments, such as epidemiology. Thus departments carrying the name "biostatistics" may exist under quite different structures. For instance, relatively new biostatistics departments have been founded with a focus on bioinformatics and computational biology, whereas older departments, typically affiliated with schools of public health, will have more traditional lines of research involving epidemiological studies and clinical trials as well as bioinformatics. In larger universities where both a statistics and a biostatistics department exist, the degree of integration between the two departments may range from the bare minimum to very close collaboration. In general, the difference between a statistics program and a biostatistics one is twofold: (i) statistics departments will often host theoretical/methodological research which are less common in biostatistics programs and (ii) statistics departments have lines of research that may include biomedical applications but also other areas such as industry (quality control), business and economics and biological areas other than medicine.








Applications of biostatistics


Public health, including epidemiology, health services research, nutrition, and environmental health


Design and analysis of clinical trials in medicine


Genomics, population genetics, and statistical genetics in populations in order to link variation in genotype with a variation in phenotype. This has been used in agriculture to improve crops and farm animals. In biomedical research, this work can assist in finding candidates for gene alleles that can cause or influence predisposition to disease in human genetics


Ecology


Biological sequence analysis


Design


A fundamental distinction in evidence-based medicine is between observational studies and randomized controlled trials. Types of observational studies in epidemiology such as the cohort study and the case-control study provide less compelling evidence than the randomized controlled trial. In observational studies, the investigators only observe associations (correlations) between the treatments experienced by participants and their health status or diseases.





A randomized controlled trial is the study design that can provide the most compelling evidence that the study treatment causes the expected effect on human health.





Currently, some Phase II and most Phase III drug trials are designed as randomized, double blind, and placebo-controlled.





Randomized: Each study subject is randomly assigned to receive either the study treatment or a placebo.


Blind: The subjects involved in the study do not know which study treatment they receive. If the study is double-blind, the researchers also do not know which treatment is being given to any given subject. This 'blinding' is to prevent biases, since if a physician knew which patient was getting the study treatment and which patient was getting the placebo, he/she might be tempted to give the (presumably helpful) study drug to a patient who could more easily benefit from it. In addition, a physician might give extra care to only the patients who receive the placebos to compensate for their ineffectiveness. A form of double-blind study called a "double-dummy" design allows additional insurance against bias or placebo effect. In this kind of study, all patients are given both placebo and active doses in alternating periods of time during the study.


Placebo-controlled: The use of a placebo (fake treatment) allows the researchers to isolate the effect of the study treatment.


Of note, during the last ten years or so it has become a common practice to conduct "active comparator" studies (also known as "active control" trials). In other words, when a treatment exists that is clearly better than doing nothing for the subject (i.e. giving them the placebo), the alternate treatment would be a standard-of-care therapy. The study would compare the 'test' treatment to standard-of-care therapy.





Although the term "clinical trials" is most commonly associated with the large, randomized studies typical of Phase III, many clinical trials are small. They may be "sponsored" by single physicians or a small group of physicians, and are designed to test simple questions. In the field of rare diseases sometimes the number of patients might be the limiting factor for a clinical trial. Other clinical trials require large numbers of participants (who may be followed over long periods of time), and the trial sponsor is a private company, a government health agency, or an academic research body such as a university.


In a clinical trial, the investigator first identifies the medication or device to be tested. Then the investigator decides what to compare it with (one or more existing treatments or a placebo), and what kind of patients might benefit from the medication/device. If the investigator cannot obtain enough patients with this specific disease or condition at his or her own location, then he or she assembles investigators at other locations who can obtain the same kind of patients to receive the treatment. During the clinical trial, the investigators: recruit patients with the predetermined characteristics, administer the treatment(s), and collect data on the patients' health for a defined time period. (These data include things like vital signs, amount of study drug in the blood, and whether the patient's health gets better or not.) The researchers send the data to the trial sponsor, who then analyzes the pooled data using statistical tests. Some examples of what a clinical trial may be designed to do:





assess the safety and effectiveness of a new medication or device on a specific kind of patient (e.g., patients who have been diagnosed with Alzheimer's disease for less than one year)


assess the safety and effectiveness of a different dose of a medication than is commonly used (e.g., 10 mg dose instead of 5 mg dose)


assess the safety and effectiveness of an already marketed medication or device for a new indication, i.e. a disease for which the drug is not approved yet


assess whether the new medication or device is more effective for the patient's condition than the already used, standard medication or device ("the gold standard" or "standard therapy")


compare the effectiveness in patients with a specific disease of two or more already approved or common interventions for that disease (e.g., Device A vs. Device B, Therapy A vs. Therapy B)





In designing a clinical trial, a sponsor must decide on the target number of patients who will participate. The sponsor's goal usually is to obtain a statistically significant result showing a significant difference in outcome (e.g., number of deaths after 28 days in the study) between the groups of patients who receive the study treatments. The number of patients required to give a statistically significant result depends on the question the trial wants to answer. (For example, to show the effectiveness of a new drug in a n