Application of bio statistics
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Biostatistics is useful in physiology, anatomy, pharmacology, and medicine. It can determine normal and abnormal variables in populations, compare means between places or times, and analyze relationships between two or more variables. In pharmacology, biostatistics is used to assess the effects of drugs on people or animals and compare different drugs. It can also evaluate treatment efficiency in various situations, identify associations between attributes like diseases and behaviors, and help determine the best treatment for a disease.
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Application of Biostatistics
O Biostatistics is the application of statistics to biological and medical data. It plays an integral role in modern medicine by analyzing data to determine treatment efficacy and develop clinical trials. A landmark study in biostatistics was the Framingham Heart Study, which through longitudinal data collection and analysis identified major risk factors for cardiovascular disease and influenced our current understanding of heart disease as a leading cause of death. Biostatistics obtains, analyzes, and interprets quantitative medical data to further human health.
{ANOVA} PPT-1.pptx
Today’s overwhelming number of techniques applicable to data analysis makes it extremely difficult to define the most beneficial approach while considering all the significant variables.
The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the response to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear across factor levels. Interpretation is easy when data is balanced across factors but much deeper understanding is needed for unbalanced data.
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means.Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.
Sir Ronald Fisher pioneered the development of ANOVA for analyzing results of agricultural experiments.1 Today, ANOVA is included in almost every statistical package, which makes it accessible to investigators in all experimental sciences. It is easy to input a data set and run a simple ANOVA, but it is challenging to choose the appropriate ANOVA for different experimental designs, to examine whether data adhere to the modeling assumptions, and to interpret the results correctly. The purpose of this report, together with the next 2 articles in the Statistical Primer for Cardiovascular Research series, is to enhance understanding of ANVOA and to promote its successful use in experimental cardiovascular research. My colleagues and I attempt to accomplish those goals through examples and explanation, while keeping within reason the burden of notation, technical jargon, and mathematical equations.
Biostatistics
- Biostatistics refers to applying statistical methods to biological and medical problems. It is also called biometrics, which means biological measurement or measurement of life.
- There are two main types of statistics: descriptive statistics which organizes and summarizes data, and inferential statistics which allows conclusions to be made from the sample data.
- Data can be qualitative like gender or eye color, or quantitative which has numerical values like age, height, weight. Quantitative data can further be interval/ratio or discrete/continuous.
- Common measures of central tendency include the mean, median and mode. Measures of variability include range, standard deviation, variance and coefficient of variation.
- Correlation describes the relationship between two variables
Anova - One way and two way
This document provides an overview of analysis of variance (ANOVA) techniques, including one-way and two-way ANOVA. It defines ANOVA as a statistical tool used to test differences between two or more means by analyzing variance. One-way ANOVA tests the effect of one factor on the mean and splits total variation into between-groups and within-groups components. Two-way ANOVA controls for another variable as a blocking factor to reduce error variance and splits total variation into between treatments, between blocks, and residual components. The document reviews key ANOVA terms, assumptions, calculations including sum of squares, F-ratio and p-value, and provides examples of one-way and two-way ANOVA.
Student's t test
This document provides information about student's t-test. It defines the t-test as a statistical method used to determine if there is a significant difference between the means of two groups. The t-test compares the means of samples A and B and calculates a t-statistic to determine if the null hypothesis that the means are the same can be rejected. An example is provided to demonstrate how to calculate the t-statistic and compare it to critical values from a t-distribution table to conclude if the difference between the sample means is statistically significant. Both one-tailed and two-tailed tests are discussed as well as restrictions of the t-test such as its assumptions of normal distributions and requirements for certain types of data.
Parametric and non parametric test in biostatistics
This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test
Biostatistics and research methodology
This ppt will help you with introduction to biostatistics, research methodology and also detail explanation about frequency distribution
Student t test
The document discusses different types of t-tests, including the one sample t-test, independent samples t-test, and paired t-test. It explains the assumptions and equations for each test and provides examples of their applications. The key differences between the t-test and z-test are also outlined. Specifically, t-tests are used for small sample sizes when the population variance is unknown, while z-tests are for large samples when the variance is known.
Recommended
Application of Biostatistics
O Biostatistics is the application of statistics to biological and medical data. It plays an integral role in modern medicine by analyzing data to determine treatment efficacy and develop clinical trials. A landmark study in biostatistics was the Framingham Heart Study, which through longitudinal data collection and analysis identified major risk factors for cardiovascular disease and influenced our current understanding of heart disease as a leading cause of death. Biostatistics obtains, analyzes, and interprets quantitative medical data to further human health.
{ANOVA} PPT-1.pptx
Today’s overwhelming number of techniques applicable to data analysis makes it extremely difficult to define the most beneficial approach while considering all the significant variables.
The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the response to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear across factor levels. Interpretation is easy when data is balanced across factors but much deeper understanding is needed for unbalanced data.
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means.Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.
Sir Ronald Fisher pioneered the development of ANOVA for analyzing results of agricultural experiments.1 Today, ANOVA is included in almost every statistical package, which makes it accessible to investigators in all experimental sciences. It is easy to input a data set and run a simple ANOVA, but it is challenging to choose the appropriate ANOVA for different experimental designs, to examine whether data adhere to the modeling assumptions, and to interpret the results correctly. The purpose of this report, together with the next 2 articles in the Statistical Primer for Cardiovascular Research series, is to enhance understanding of ANVOA and to promote its successful use in experimental cardiovascular research. My colleagues and I attempt to accomplish those goals through examples and explanation, while keeping within reason the burden of notation, technical jargon, and mathematical equations.
Biostatistics
- Biostatistics refers to applying statistical methods to biological and medical problems. It is also called biometrics, which means biological measurement or measurement of life.
- There are two main types of statistics: descriptive statistics which organizes and summarizes data, and inferential statistics which allows conclusions to be made from the sample data.
- Data can be qualitative like gender or eye color, or quantitative which has numerical values like age, height, weight. Quantitative data can further be interval/ratio or discrete/continuous.
- Common measures of central tendency include the mean, median and mode. Measures of variability include range, standard deviation, variance and coefficient of variation.
- Correlation describes the relationship between two variables
Anova - One way and two way
This document provides an overview of analysis of variance (ANOVA) techniques, including one-way and two-way ANOVA. It defines ANOVA as a statistical tool used to test differences between two or more means by analyzing variance. One-way ANOVA tests the effect of one factor on the mean and splits total variation into between-groups and within-groups components. Two-way ANOVA controls for another variable as a blocking factor to reduce error variance and splits total variation into between treatments, between blocks, and residual components. The document reviews key ANOVA terms, assumptions, calculations including sum of squares, F-ratio and p-value, and provides examples of one-way and two-way ANOVA.
Student's t test
This document provides information about student's t-test. It defines the t-test as a statistical method used to determine if there is a significant difference between the means of two groups. The t-test compares the means of samples A and B and calculates a t-statistic to determine if the null hypothesis that the means are the same can be rejected. An example is provided to demonstrate how to calculate the t-statistic and compare it to critical values from a t-distribution table to conclude if the difference between the sample means is statistically significant. Both one-tailed and two-tailed tests are discussed as well as restrictions of the t-test such as its assumptions of normal distributions and requirements for certain types of data.
Parametric and non parametric test in biostatistics
This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test
Biostatistics and research methodology
This ppt will help you with introduction to biostatistics, research methodology and also detail explanation about frequency distribution
Student t test
The document discusses different types of t-tests, including the one sample t-test, independent samples t-test, and paired t-test. It explains the assumptions and equations for each test and provides examples of their applications. The key differences between the t-test and z-test are also outlined. Specifically, t-tests are used for small sample sizes when the population variance is unknown, while z-tests are for large samples when the variance is known.
importance of biostatics in modern reasearch
This document discusses the role of biostatistics in modern research. It begins by defining statistics and biostatistics, noting that biostatistics is the application of statistical methods to biological problems. It then provides examples of how biostatistics is used across various fields, including community medicine, cancer research, pharmacology, ecology, demography, genetics, and more. Key applications mentioned include clinical trials and research, determining treatment efficacy, analyzing data from advanced technologies, and making inferences from genetic and population data.
Correlation
Correlation and regression analysis are statistical tools used to analyze relationships between variables. Correlation measures the strength and direction of association between two variables on a scale from -1 to 1. Regression analysis uses one variable to predict the value of another variable and draws a best-fit line to represent their relationship. There are always two lines of regression - one showing the regression of x on y and the other showing the regression of y on x. Regression coefficients from these lines indicate the slope and intercept of the lines and can help estimate unknown variable values based on known values.
Application of excel and spss programme in statistical
This slide includes information about application of EXCEL and SPSS programme in statistical analysis in biostatistics.
Analysis of variance (anova)
Analysis of variance (ANOVA) is a method for testing the hypothesis that there is no difference between two or more population means.
factorial design
This document discusses factorial design for pharmaceutical experiments. It defines factorial design as an experiment where two or more factors are each studied at different levels or values. The document then describes different types of factorial designs, including full factorial designs with two or three levels, and fractional factorial designs used when there are more than five factors. It also explains how factors and levels are coded numerically for the experiments.
scope and need of biostatics
This powerpoint presentation gives a brief explanation about the biostatic data .this is quite helpful to individuals to understand the basic research methodology terminologys
Introduction to biostatistics
This document provides an introduction to biostatistics. It defines biostatistics as applying statistics to biology, medicine, and public health. Some key points covered include:
- Francis Galton is considered the father of biostatistics.
- There are two main types of data: primary data collected directly and secondary data collected previously.
- Variables can be qualitative (categorical) or quantitative (numeric).
- Biostatistics is applied in areas like medicine, public health, and research to analyze data and draw conclusions.
- Common sources of health data include censuses, vital records, surveys, and hospital/disease records.
Stages of drug development
This document outlines the stages of drug development, beginning with drug discovery and ending with regulatory approval. It discusses the history of drug development from ancient times to modern pharmaceutical industry. The key stages are described as pre-clinical development involving non-human testing, clinical development involving human trials to test safety and efficacy, and regulatory approval. Drug discovery involves target identification, validation, lead discovery through screening, and lead optimization to improve desired properties. Recent advances like genomics, high-throughput screening, and bioinformatics have accelerated the drug development process.
Chi -square test
The chi-square test is used to compare observed data with expected data. It was developed by Karl Pearson in 1900. The chi-square test calculates the sum of the squares of the differences between the observed and expected frequencies divided by the expected frequency. The chi-square value is then compared to a critical value to determine if there is a significant difference between the observed and expected results. The degrees of freedom, which determine the critical value, are calculated based on the number of rows and columns in a contingency table. The chi-square test can be used to test goodness of fit, independence of attributes, and other hypotheses.
Null hypothesis
1. A statistical hypothesis represents the mathematical relationship between two or more population parameters. It can be directional, specifying the exact relationship, or nondirectional, anticipating a difference but not specifying the direction.
2. The null hypothesis predicts no difference between population parameters, while the alternative or experimental hypothesis predicts a difference. Through statistical testing, we can either reject the null hypothesis in favor of the alternative, or accept the null hypothesis.
3. Significance testing uses statistical tests and probabilities to determine if sample data can be used to reject the null hypothesis involving population parameters. If the difference is unlikely to have occurred by chance when the null hypothesis is true, it is considered statistically significant.
Karl pearson's correlation
this ppt gives you adequate information about Karl Pearsonscoefficient correlation and its calculation. its the widely used to calculate a relationship between two variables. The correlation shows a specific value of the degree of a linear relationship between the X and Y variables. it is also called as The Karl Pearson‘s product-moment correlation coefficient. the value of r is alwys lies between -1 to +1. + 0.1 shows Lower degree of +ve correlation, +0.8 shows Higher degree of +ve correlation.-0.1 shows Lower degree of -ve correlation. -0.8 shows Higher degree of -ve correlation.
introduction to biostatistics.pptx
Introduction to biostatistics and its application in various sectors.
Introduction to variables and variation.
Different types of variables and their introduction.
Use of biostatistics in various fields.
Correlation biostatistics
Correlation measures the strength and direction of the linear relationship between two variables. A correlation coefficient value close to 1 indicates a strong positive linear relationship, while a value close to -1 indicates a strong negative linear relationship. A value of 0 indicates no linear relationship between the variables. Some examples of correlation types include positive correlation between income and expenses, and negative correlation between price and demand. Rank correlation measures the correspondence between rankings of the same items. The coefficient of rank correlation is used to assess if rankings correspond statistically significantly. Applications of correlation include understanding dependency between variables to build linear models for prediction and analyzing relationships like the impact of population on poverty rates.
Introduction and Applications of Biostatistics.pdf
Biostatistics is the application of statistics to biological and health data. It involves collecting, organizing, summarizing, analyzing, interpreting and drawing valid conclusions from data to make reasonable decisions. Some key areas where biostatistics is applied include medicine, epidemiology, public health, genetics, pharmacology, and environmental science. It helps define normal ranges, identify disease signs and symptoms, evaluate health programs, and conduct genetic and epidemiological studies.
Analysis of variance (ANOVA)
This document discusses various types of analysis of variance (ANOVA) statistical tests. It begins with an introduction to one-way ANOVA for comparing the means of three or more independent groups. Requirements for one-way ANOVA include a nominal independent variable with three or more levels and a continuous dependent variable. Assumptions of one-way ANOVA include normality and homogeneity of variances. The document then briefly discusses two-way ANOVA, MANOVA, ANOVA with repeated measures, and related statistical tests. Examples of each type of ANOVA are provided.
Stages of drug discovery
The document outlines the key stages of drug discovery:
1. Target identification involves finding the specific biomolecule target of a drug.
2. Target validation helps evaluate the potential of a target through assays and screening to find initial hits.
3. Lead discovery uses screening methods like molecular modeling or combinational chemistry to identify initial compounds ("leads") that interact with the target.
4. Lead optimization chemically modifies the leads to improve efficacy, safety and other drug properties.
5. Pre-clinical safety involves animal testing, toxicity studies and formulation before clinical trials in humans.
Chi square test
The chi-square test is used to determine if an observed frequency distribution differs from an expected theoretical distribution. It can test goodness of fit, independence of attributes, and homogeneity. The test involves calculating chi-square by taking the sum of the squares of the differences between observed and expected frequencies divided by expected frequencies. For the test to be valid, certain conditions must be met regarding sample size, expected frequencies, independence, and randomness. The test has some limitations such as not measuring strength of association and being unreliable with small expected frequencies.
Regression
Multiple regression analysis allows researchers to examine the relationship between one dependent or outcome variable and two or more independent or predictor variables. It extends simple linear regression to model more complex relationships. Stepwise regression is a technique that automates the process of building regression models by sequentially adding or removing variables based on statistical criteria. It begins with no variables in the model and adds variables one at a time based on their contribution to the model until none improve it significantly.
Biostatistics: Classification of data
Statistics is the collection, analysis, and interpretation of raw data. There are two main types of data: attributes data which cannot be measured but can be compared, and variables data which is measurable like height or weight. Data classification organizes data into categories to make essential data easy to find. There are several types of data classification including geographical by location, chronological by time, qualitative by attributes, quantitative by measurable characteristics, and alphabetical by name. Classification condenses data, prepares it for tabulation, facilitates comparison and relationship study.
Parametric and nonparametric test
Parametric tests make specific assumptions about the population parameter and use distributions to determine test statistics. They apply to interval/ratio variables where the population is completely known. Nonparametric tests do not make assumptions about the population or its distribution and use arbitrary test statistics. They apply to nominal/ordinal variables where the population is unknown. The key differences are in the basis of the test statistic, measurement level, measure of central tendency, population information known, and applicability to variables versus attributes.
Biostatics introduction history and application
Biostatistics is the application of statistics to biological and medical problems. Francis Galton is considered the father of biostatistics, developing concepts like correlation and using statistical tools and surveys to study human populations. Biostatistics has various applications in fields like physiology, pharmacology, and medicine. It is used to determine normal vs abnormal values in populations, compare variables between groups, assess drug efficacy and safety, identify disease signs and symptoms, and help determine optimal treatments.
Pharma 2016
This document provides an introduction to pharmacology. It discusses that pharmacology is the study of drugs and their interaction with living systems. The two main divisions of pharmacology are pharmacodynamics, which refers to what the drug does to the body, and pharmacokinetics, which refers to what the body does to the drug. It also discusses key concepts in pharmacology including the essential drug concept, clinical pharmacology, and how pharmacology relates to other fields like pharmacy, toxicology, and biotechnology.
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importance of biostatics in modern reasearch
This document discusses the role of biostatistics in modern research. It begins by defining statistics and biostatistics, noting that biostatistics is the application of statistical methods to biological problems. It then provides examples of how biostatistics is used across various fields, including community medicine, cancer research, pharmacology, ecology, demography, genetics, and more. Key applications mentioned include clinical trials and research, determining treatment efficacy, analyzing data from advanced technologies, and making inferences from genetic and population data.
Correlation
Correlation and regression analysis are statistical tools used to analyze relationships between variables. Correlation measures the strength and direction of association between two variables on a scale from -1 to 1. Regression analysis uses one variable to predict the value of another variable and draws a best-fit line to represent their relationship. There are always two lines of regression - one showing the regression of x on y and the other showing the regression of y on x. Regression coefficients from these lines indicate the slope and intercept of the lines and can help estimate unknown variable values based on known values.
Application of excel and spss programme in statistical
This slide includes information about application of EXCEL and SPSS programme in statistical analysis in biostatistics.
Analysis of variance (anova)
Analysis of variance (ANOVA) is a method for testing the hypothesis that there is no difference between two or more population means.
factorial design
This document discusses factorial design for pharmaceutical experiments. It defines factorial design as an experiment where two or more factors are each studied at different levels or values. The document then describes different types of factorial designs, including full factorial designs with two or three levels, and fractional factorial designs used when there are more than five factors. It also explains how factors and levels are coded numerically for the experiments.
scope and need of biostatics
This powerpoint presentation gives a brief explanation about the biostatic data .this is quite helpful to individuals to understand the basic research methodology terminologys
Introduction to biostatistics
This document provides an introduction to biostatistics. It defines biostatistics as applying statistics to biology, medicine, and public health. Some key points covered include:
- Francis Galton is considered the father of biostatistics.
- There are two main types of data: primary data collected directly and secondary data collected previously.
- Variables can be qualitative (categorical) or quantitative (numeric).
- Biostatistics is applied in areas like medicine, public health, and research to analyze data and draw conclusions.
- Common sources of health data include censuses, vital records, surveys, and hospital/disease records.
Stages of drug development
This document outlines the stages of drug development, beginning with drug discovery and ending with regulatory approval. It discusses the history of drug development from ancient times to modern pharmaceutical industry. The key stages are described as pre-clinical development involving non-human testing, clinical development involving human trials to test safety and efficacy, and regulatory approval. Drug discovery involves target identification, validation, lead discovery through screening, and lead optimization to improve desired properties. Recent advances like genomics, high-throughput screening, and bioinformatics have accelerated the drug development process.
Chi -square test
The chi-square test is used to compare observed data with expected data. It was developed by Karl Pearson in 1900. The chi-square test calculates the sum of the squares of the differences between the observed and expected frequencies divided by the expected frequency. The chi-square value is then compared to a critical value to determine if there is a significant difference between the observed and expected results. The degrees of freedom, which determine the critical value, are calculated based on the number of rows and columns in a contingency table. The chi-square test can be used to test goodness of fit, independence of attributes, and other hypotheses.
Null hypothesis
1. A statistical hypothesis represents the mathematical relationship between two or more population parameters. It can be directional, specifying the exact relationship, or nondirectional, anticipating a difference but not specifying the direction.
2. The null hypothesis predicts no difference between population parameters, while the alternative or experimental hypothesis predicts a difference. Through statistical testing, we can either reject the null hypothesis in favor of the alternative, or accept the null hypothesis.
3. Significance testing uses statistical tests and probabilities to determine if sample data can be used to reject the null hypothesis involving population parameters. If the difference is unlikely to have occurred by chance when the null hypothesis is true, it is considered statistically significant.
Karl pearson's correlation
this ppt gives you adequate information about Karl Pearsonscoefficient correlation and its calculation. its the widely used to calculate a relationship between two variables. The correlation shows a specific value of the degree of a linear relationship between the X and Y variables. it is also called as The Karl Pearson‘s product-moment correlation coefficient. the value of r is alwys lies between -1 to +1. + 0.1 shows Lower degree of +ve correlation, +0.8 shows Higher degree of +ve correlation.-0.1 shows Lower degree of -ve correlation. -0.8 shows Higher degree of -ve correlation.
introduction to biostatistics.pptx
Introduction to biostatistics and its application in various sectors.
Introduction to variables and variation.
Different types of variables and their introduction.
Use of biostatistics in various fields.
Correlation biostatistics
Correlation measures the strength and direction of the linear relationship between two variables. A correlation coefficient value close to 1 indicates a strong positive linear relationship, while a value close to -1 indicates a strong negative linear relationship. A value of 0 indicates no linear relationship between the variables. Some examples of correlation types include positive correlation between income and expenses, and negative correlation between price and demand. Rank correlation measures the correspondence between rankings of the same items. The coefficient of rank correlation is used to assess if rankings correspond statistically significantly. Applications of correlation include understanding dependency between variables to build linear models for prediction and analyzing relationships like the impact of population on poverty rates.
Introduction and Applications of Biostatistics.pdf
Biostatistics is the application of statistics to biological and health data. It involves collecting, organizing, summarizing, analyzing, interpreting and drawing valid conclusions from data to make reasonable decisions. Some key areas where biostatistics is applied include medicine, epidemiology, public health, genetics, pharmacology, and environmental science. It helps define normal ranges, identify disease signs and symptoms, evaluate health programs, and conduct genetic and epidemiological studies.
Analysis of variance (ANOVA)
This document discusses various types of analysis of variance (ANOVA) statistical tests. It begins with an introduction to one-way ANOVA for comparing the means of three or more independent groups. Requirements for one-way ANOVA include a nominal independent variable with three or more levels and a continuous dependent variable. Assumptions of one-way ANOVA include normality and homogeneity of variances. The document then briefly discusses two-way ANOVA, MANOVA, ANOVA with repeated measures, and related statistical tests. Examples of each type of ANOVA are provided.
Stages of drug discovery
The document outlines the key stages of drug discovery:
1. Target identification involves finding the specific biomolecule target of a drug.
2. Target validation helps evaluate the potential of a target through assays and screening to find initial hits.
3. Lead discovery uses screening methods like molecular modeling or combinational chemistry to identify initial compounds ("leads") that interact with the target.
4. Lead optimization chemically modifies the leads to improve efficacy, safety and other drug properties.
5. Pre-clinical safety involves animal testing, toxicity studies and formulation before clinical trials in humans.
Chi square test
The chi-square test is used to determine if an observed frequency distribution differs from an expected theoretical distribution. It can test goodness of fit, independence of attributes, and homogeneity. The test involves calculating chi-square by taking the sum of the squares of the differences between observed and expected frequencies divided by expected frequencies. For the test to be valid, certain conditions must be met regarding sample size, expected frequencies, independence, and randomness. The test has some limitations such as not measuring strength of association and being unreliable with small expected frequencies.
Regression
Multiple regression analysis allows researchers to examine the relationship between one dependent or outcome variable and two or more independent or predictor variables. It extends simple linear regression to model more complex relationships. Stepwise regression is a technique that automates the process of building regression models by sequentially adding or removing variables based on statistical criteria. It begins with no variables in the model and adds variables one at a time based on their contribution to the model until none improve it significantly.
Biostatistics: Classification of data
Statistics is the collection, analysis, and interpretation of raw data. There are two main types of data: attributes data which cannot be measured but can be compared, and variables data which is measurable like height or weight. Data classification organizes data into categories to make essential data easy to find. There are several types of data classification including geographical by location, chronological by time, qualitative by attributes, quantitative by measurable characteristics, and alphabetical by name. Classification condenses data, prepares it for tabulation, facilitates comparison and relationship study.
Parametric and nonparametric test
Parametric tests make specific assumptions about the population parameter and use distributions to determine test statistics. They apply to interval/ratio variables where the population is completely known. Nonparametric tests do not make assumptions about the population or its distribution and use arbitrary test statistics. They apply to nominal/ordinal variables where the population is unknown. The key differences are in the basis of the test statistic, measurement level, measure of central tendency, population information known, and applicability to variables versus attributes.
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1. Introduction to biostatistics
This document provides an overview of biostatistics. It defines biostatistics as the application of statistical principles and methods to biological and health data. Biostatistics is used in various fields including public health, epidemiology, medicine, anatomy, physiology, pharmacology, and preventive medicine. It involves collecting, presenting, analyzing and interpreting numerical data to evaluate public health programs, compare medical treatments, define normal health ranges, and study associations between biological factors. The document also discusses limitations of statistics and the various steps involved in biostatistical analysis including collecting and presenting data, descriptive and inferential analysis, and interpretation.
AGING· The human lifespan seems to be limited to 80 -100 year.docx
AGING:
· The human lifespan seems to be limited to 80 -100 years (cross-cultural, multi-ethnic) with some exceptions (to 115+ years)
· Life expectancy among genders and races (& socio-economic groups) varies due to standard of living, cultural behaviors (diet, risk taking behaviors, etc)
· In general, women have higher life expectancy than men (possibly due to cardiovascular disease developing later in life).
· Multiple theories of aging:
· Somatic mutation theory: cells are "programmed" to mutate & die after a limited number of divisions (ceiling to possible number of cell divisions of all human cells); possibly due to accumulated defects in mitochondria over time (cells can no longer extract energy from foodstuffs).
· The molecular clock and the Hayflick Limit: after each cell division, the chromosome becomes shortened at the telomere (the tip of the chromosome). Eventually, the shortening is so great that the replicating enzymes can't "read" the chromosome to replicate it, and the cell can no longer divide – this is sometimes called the “Hayflick limit” … this prevents cells from indefinite reproduction, otherwise called the "molecular clock”
· Catastrophic theory: also called the "complexity theory (really a "chaos" theory) of accumulated mistakes in DNA transcription & translation & the inability of the cells and organs to function together in response to the normal stresses of the environment. This results in adaptive dysfunction and organ derangements leading to organism disease as well as the "normal" process of aging & death. Helps explain associated neurological changes with advanced age.
· Neuroendocrine theory: the brain is "programmed" to stop producing needed supportive hormonal factors.
· Extracellular degenerative theory: accumulation of disease over time due to environmental factors.
· Modifiable factors? Can we turn back the hands of time (or at least slow them down)?
· Pay attention to diet
· Fruit and vegetable consumption and mortality (Wang, et al., 2014, BMJ): http://www.bmj.com/content/349/bmj.g4490 (Links to an external site.)Links to an external site.
· Dietary protein sources and cancer (Farvid, et al., 2014, BMJ): http://www.bmj.com/content/348/bmj.g3437 (Links to an external site.)Links to an external site.
· The “Mediterranean” diet and telomere length (Crous-Bou, et al., 2014): http://www.bmj.com/content/349/bmj.g6674 (Links to an external site.)Links to an external site.
· Increase physical activity
· Reduce exposure to environmental pollutants
View this video:2012 AMMG lecture: Telomeres and a new theory of aging (Links to an external site.)Links to an external site.
Park, E. (2012, June, 9). 2012 AMMG lecture: Telomeres and a new theory of aging [Video file]. Retrieved from https://www.youtube.com/watch?v=m0DZ1-WVtao&feature=youtu.be
Definitions of health:
Definitions and perspectives on the concepts of health, wellness, and illness/disease, psychology topics in health/wellness/illness.
various approaches to drug discovery
clinical and preclinical approaches to drug discovery.Here we mainly deals with preclinical approaches, ie. Pharmacological approach and toxicological approach
ALTERNATIVE MEDICINE.docx PTT. Slide share
This document provides an overview of alternative and traditional medicine. It defines key terms like complementary medicine, alternative medicine, and integrative medicine. It describes the main categories of alternative medicine practices including natural products, mind-body medicine, manipulative practices, and energy or whole medical systems. Specific alternative therapies like herbal medicine, acupuncture, chiropractic, massage and meditation are discussed. The document contrasts alternative medicine with conventional Western medicine and notes alternative medicine focuses more on holism, spirituality and vital energy forces while conventional medicine is more materialistic.
A story of drug development
The document provides an overview of the drug development process from discovery of a lead compound through clinical trials and regulatory approval. Key steps include identifying a biological target, using high-throughput screening to discover lead compounds, conducting preclinical testing on lead compounds, and then Phase I-III clinical trials on humans to test safety and efficacy before regulatory approval and marketing. The process takes over a decade and costs billions due to extensive testing required to bring a new medicine to patients.
Biostatistics
Biostatistics applies statistical tools to biological and medical data. It is used (1) to establish what is considered normal in areas like physiology and anatomy, (2) to determine whether observed differences are due to chance or other factors in areas like comparing drug effectiveness and public health measures, and (3) to identify disease characteristics and evaluate health programs by comparing data from experiment and control groups.
Clinical Trials of Nutraceuticals and Health Foods
Clinical Testing of Clinical Trials of Nutraceuticals and Health Foods is an important process for studying the efficacy of the product.
Animal toxicity studies
This document outlines the principles and types of non-clinical toxicity studies conducted in animals prior to testing pharmaceuticals in humans. It discusses general principles like complying with Good Laboratory Practice, using standardized equipment and protocols. It also describes the different types of toxicology studies including toxicokinetic, reproductive toxicity, teratogenicity and perinatal studies. The goal is to assess safety and predict potential adverse effects in humans by administering test substances to animals and observing toxicity.
Pharmacology Terms (RCVetS.com)
Pharmacology is the study of the interaction of chemicals with living systems. It includes the study of drugs and their actions in the body, properties of drugs and how the body affects them, and turning new chemicals into medications. Important terms include pharmacodynamics, which is the study of drug actions in the body, and pharmacokinetics, which is the study of how the body affects drugs through absorption, distribution, metabolism, and excretion.
Animals in ResearchThe Importance of Animals in the Sc.docx
Animals
in Research
The Importance of
Animals in the Science
of Toxicology
2
Research involving laboratory animals is important to people and to our quality of life. In the past century, most inhabitants
of this planet have experienced an unprecedented
rise in living standards, life expectancy and personal
opportunity, in large part due to the many ways
chemicals have been put to work for us. For example,
drugs whose effects range from curing previously
fatal bacterial infections, reducing the impact of
AIDS, minimizing heart disease, decreasing age-
related wrinkles, to reducing hair loss are widely
available today. The many benefits of the diverse
uses of our natural resources are an outcome of
careful scientific research and of using chemicals
in an appropriate and safe manner. Toxicologists,
the scientists who help determine the limits for safe
use of materials, use modern technological research
methods, including tests on animals, to protect
human and animal health and the environment.
What is toxicology?
Toxicology is the study of how chemical substances
interact with living systems and affect normal
processes, and the use of this information to predict
safe exposure levels. Toxicological research and
testing helps us to live safely and to derive benefit
from natural and synthetic substances while avoiding
harm. Toxicologists are involved in the evaluation
of household products, medicines and the effects of
incidental and occupational exposure to natural and
manufactured substances. Toxicology also helps
us develop the best treatments in the event that
accidental overexposure does occur.
What is safe?
Toxicologists know that no substance is risk-free.
One fundamental tenet of the science of toxicology
is that all chemicals can cause harm at some level of
exposure, summed up in the phrase "the dose makes
the poison." This means that exposure to a specific
small amount of any substance will have no detectable
impact on normal biological processes and is
considered safe. Some doses actually have beneficial
effects, as we all know from use of medicines. But
increasing exposure to most substances will, at
some point, cause harmful effects. Substances are
considered toxic at that level. For example, digitalis is
a plant product that has been used with great benefit
to treat heart irregularities, but too large a dose will
cause death. Oxygen provides another example of how
increasing the dose can turn a safe compound into a
toxic one. Oxygen is essential to life and part of the air
we breathe, but when given at high concentrations it
can cause lung and eye damage in infants.
Sometimes the possible negative effects of a
substance are outweighed by the positive benefits
at that dose. Dogs are treated with heartworm
medication because the risk of death from
heartworms is much greater than the risk of toxicity
of the medication. Similarly, chemotherapeutic
agents ar.
Animals in ResearchThe Importance of Animals in the Sc.docx
Animals
in Research
The Importance of
Animals in the Science
of Toxicology
2
Research involving laboratory animals is important to people and to our quality of life. In the past century, most inhabitants
of this planet have experienced an unprecedented
rise in living standards, life expectancy and personal
opportunity, in large part due to the many ways
chemicals have been put to work for us. For example,
drugs whose effects range from curing previously
fatal bacterial infections, reducing the impact of
AIDS, minimizing heart disease, decreasing age-
related wrinkles, to reducing hair loss are widely
available today. The many benefits of the diverse
uses of our natural resources are an outcome of
careful scientific research and of using chemicals
in an appropriate and safe manner. Toxicologists,
the scientists who help determine the limits for safe
use of materials, use modern technological research
methods, including tests on animals, to protect
human and animal health and the environment.
What is toxicology?
Toxicology is the study of how chemical substances
interact with living systems and affect normal
processes, and the use of this information to predict
safe exposure levels. Toxicological research and
testing helps us to live safely and to derive benefit
from natural and synthetic substances while avoiding
harm. Toxicologists are involved in the evaluation
of household products, medicines and the effects of
incidental and occupational exposure to natural and
manufactured substances. Toxicology also helps
us develop the best treatments in the event that
accidental overexposure does occur.
What is safe?
Toxicologists know that no substance is risk-free.
One fundamental tenet of the science of toxicology
is that all chemicals can cause harm at some level of
exposure, summed up in the phrase "the dose makes
the poison." This means that exposure to a specific
small amount of any substance will have no detectable
impact on normal biological processes and is
considered safe. Some doses actually have beneficial
effects, as we all know from use of medicines. But
increasing exposure to most substances will, at
some point, cause harmful effects. Substances are
considered toxic at that level. For example, digitalis is
a plant product that has been used with great benefit
to treat heart irregularities, but too large a dose will
cause death. Oxygen provides another example of how
increasing the dose can turn a safe compound into a
toxic one. Oxygen is essential to life and part of the air
we breathe, but when given at high concentrations it
can cause lung and eye damage in infants.
Sometimes the possible negative effects of a
substance are outweighed by the positive benefits
at that dose. Dogs are treated with heartworm
medication because the risk of death from
heartworms is much greater than the risk of toxicity
of the medication. Similarly, chemotherapeutic
agents ar.
Clinical Trial.pptx
Clinical trials are research studies that test medical, surgical, or behavioral interventions in people to determine if a new treatment or prevention method is safe and effective; the first recorded clinical trial was conducted in 1747 by James Lind to identify a treatment for scurvy; clinical trials generally have multiple phases and involve many participants including volunteers, physicians, nurses, and data managers to thoroughly study a potential new treatment.
Medical research methodology
This document defines and classifies different types of medical research. It begins by defining research as a systematic and organized method for finding answers to questions. Medical research is then classified into primary research, which involves collecting new data, and secondary research, which involves collecting existing data from other researchers. Observational research is further divided into descriptive studies, which observe characteristics without intervention, and analytical studies, which attempt to establish causes or risk factors. Specific types of descriptive and analytical studies discussed include case reports, case series, cross-sectional studies, and case-control studies. Key aspects and examples of each type of study are provided.
Extrapolation of in vitro data to preclinical and.pptx
The document discusses extrapolating data from preclinical in vitro and in vivo animal studies to humans in clinical trials. It provides information on different types of studies and explains how data from animal models is used to estimate safe starting doses for human subjects. The key points are:
1) Preclinical studies test drugs in animal and cell models before human trials to evaluate toxicity and effects. Data from these studies is extrapolated using mathematical processes to estimate appropriate human doses.
2) The no-observed-adverse-effect level (NOAEL) from animal studies is used to calculate a human equivalent dose (HED) based on body surface area, accounting for differences between species.
3) Additional safety factors are applied
Drug development process
Pre-discovery
Understand the disease
Target Identification
Choose a molecule to target with a drug
Target Validation
Test the target and confirm its role in the disease
Drug Discovery
Find a promising molecule (a “lead compound”)
that could become a drug
final pathophysiology.pptx
This document discusses the basics of pathophysiology. It begins by defining anatomy, physiology, and pathophysiology, noting that pathophysiology builds on knowledge of normal structure and function by exploring how disease develops and changes anatomy and physiology. It then discusses several key pathophysiology topics: the causes and mechanisms of disease; how pathologists study tissues and cells to determine disease cause; how normal and abnormal structure and function relate to disease signs and symptoms; and how specific diseases affect individual organ systems based on normal structure and function. The document emphasizes that understanding pathophysiology helps physicians develop effective prevention, diagnostic, treatment, and management strategies for disease.
Similar to Application of bio statistics (20)
AGING· The human lifespan seems to be limited to 80 -100 year.docx
AGING· The human lifespan seems to be limited to 80 -100 year.docx
Clinical Trials of Nutraceuticals and Health Foods
Clinical Trials of Nutraceuticals and Health Foods
Animals in ResearchThe Importance of Animals in the Sc.docx
Animals in ResearchThe Importance of Animals in the Sc.docx
Animals in ResearchThe Importance of Animals in the Sc.docx
Animals in ResearchThe Importance of Animals in the Sc.docx
Extrapolation of in vitro data to preclinical and.pptx
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Application of bio statistics
- 1. Application of Biostatistics Biostatistics is useful in the field of Science in many ways like: In Physiology and Anatomy: To find or to determine the normality and abnormality of variables (such as health, weight or height) of targeted population. It is also use to find out the mean differences of same variable of different places, time or period occur due to external or internal factor. For E.g. to finding out mean difference of ages or weight in Karachi and Lahore occur due to external or internal factor. It is also applicable in determining relationship between 2 or more variables such as weight and height. In Pharmacology: In Pharmacology, it is use to determine the effect or action of drug given to either a person or an animal. It is also use to determine the comparison between the effects, result or action of 2 or more drug either between new one and older one. In Medical: It is also use to determine the efficiency of drug(s) in particular or different situations like in treatments, emergencies or operations. It is also use to find association between 2 or more attributes such as TB and Smoking.
- 2. It is also use to find out association of one disease with another. It is also applied to determine the usefulness or un-usefulness of a particular drug in a particular time, place or period. It is also help in determining to opt best possible treatment among all for a disease.