Bio-Statistics in Bio-Medical research
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This document discusses key concepts in biostatistics used in biomedical research. It covers topics like types of variables, measures of central tendency and dispersion, distributions of data, statistical tests for different situations, hypotheses testing and errors, measures of association, diagnostic tests, and regression analysis. Understanding biostatistics is important for evidence-based medicine and improving patient lives through rigorous research. Sample size, confidence intervals, and avoiding bias and confounding are important considerations in study design and interpretation.
![• Absolute risk = a/(a + b) or c/ ( c+ d)
• Relative risk/ Risk Ratio = (a/[a + b]) / (c/[c +
d])
• Hazard ratio is a measure of relative risk over
time in circumstances in which we are
interested not only in the total number of
events, but in their timing as well.](https://image.slidesharecdn.com/biostatsinbiomedicalresearchendo-201021040546/85/Bio-Statistics-in-Bio-Medical-research-45-320.jpg)
Bio-Statistics in Bio-Medical research
- 1. Bio-statistics in Bio-medical Research Dr. Shinjan Patra D.M. ( Endocrinology) Resident All India Institute of Medical Sciences Jodhpur
- 2. Introduction • Collecting, analyzing, and interpreting data are essential components of biomedical research and require biostatistics. • Doing various statistical tests has been made easy by sophisticated computer software.
- 3. First learning with Bio-statistics
- 4. Evidence Based!! Clinical Expertise Research Evidence Patient Preferences EBM Are our patients always able to judge what is best for them? Our clinical experience alone may not improve our patient’s lives A lot of research is aimed at improving the lives of patients! 4
- 5. Basic need for Bio-statistics • To choose the right statistical test for the computer to perform based on the nature of data derived from one’s own research • To understand if an analysis was performed appropriately during review and interpretation of others’ research
- 6. Drawing Conclusions Designing & Implementing TRUTH IN THE UNIVERSE Research Question TRUTH IN THE STUDY Study Plan FINDINGS IN THE STUDY Actual Study Infer Infer Design Implement Process of Research Project 6
- 7. Methods of Analysis I. Descriptive Methods : Tables Diagrams Charts II. Inferential Methods : Estimation Point Estimation Mean / proportion etc. Interval Estimation i.e. Confidence interval of point estimate Hypotheses Testing Comparison between the treatments Association 7
- 8. Overview • Variables • Distribution of Data • Statistical Tests • Hypothesis testing and Error • Confounding/Bias • Measures of Association • Diagnostic Tests • Regression analysis
- 9. Variables • Any characteristic that can be observed, measured, or categorized • Divided into Two- Categorical Continuous
- 10. Categorical • Not suitable for quantification; classified into categories Nominal- Named categories, with no implied value- like- blood groups. Existential, no inherent order or superiority. Nominal data with only two groups are referred to as dichotomous or binary (male or female) Ordinal: Named with an order/ superiority like grades of GO
- 11. Continuous • can have an infinite number of possible values Interval: Equal interval between values but no meaningful zero point ( body temp) Ratio: Equal intervals with a meaningful zero point and all mathematical operations are functional ( amount of secretions from a diabetic wound)
- 13. 13 Intervening Variable • An intervening variable is on the causal pathway to the outcome
- 14. Choice of Statistical test • Parametric tests can be used with interval and ratio data but not with nominal or ordinal data • Nonparametric tests can be used with any type of variable, including nominal or ordinal data
- 15. Distribution of the Data
- 16. Central Tendency • Estimates the “center” of the Distribution Mean- representative of all data points and is the most efficient estimator of the middle of a normal (Gaussian) distribution; However, inappropriate as a measure of central tendency if data are skewed. Influenced by outlying values Commonly used for interval and ratio data.
- 17. Median- not influenced by outlying values more appropriate for skewed data commonly used for ordinal data. Mode- particularly useful while describing data distributed in a bimodal pattern when mean and median are not appropriate. commonly used with nominal data.
- 18. Normal Distribution • Gaussian. Symmetric bell-shaped frequency distribution, in which mean, median, and mode all have the same value • Appropriate statistical test would be a parametric test such as a t-test or an analysis of variance (ANOVA). • Data are usually represented as mean (SD).
- 20. Skewed Distribution • Positive/Right and Negative/Left • Appropriate statistical test would be a nonparametric test, such as Wilcoxon test or Mann-Whitney test. • Data are usually represented as median, interquartile range (IQR)
- 22. Measures of Dispersion • Range- difference between the highest and the lowest values. Range can change drastically when the study is repeated • INTERQUARTILE RANGE (IQR) is the range between the 25th and 75th percentiles or the difference between the medians of the lower half and upper half of the data and comprises the middle 50% of the data
- 23. • VARIANCE is a measure of dispersion or average deviation from the mean • STANDARD DEVIATION (SD) is the square root of variance and is the most common measure of dispersion used for normally distributed data • STANDARD ERROR OF MEAN (SEM) is calculated by dividing the SD by the square root of n.
- 24. Statistical tests • Parametric- assume the underlying population to be normally distributed and are based on means and SDs • Non-parametric- No assumption of population distribution
- 25. t Tests • Student’s t test is a simple, commonly used parametric test to compare two groups of continuous variables • Paired : Each patient/subject serves as his/her own control before and after an intervention • Unpaired: Two groups of patients/subjects are compared with each other
- 26. Analysis of Variance ( ANOVA) • One-way ANOVA is an extension of the two- sample t test to three or more samples and deals with statistical test on more than two groups • Other methods, such as planned or post hoc comparisons, are conducted to examine specific comparisons among individual means
- 27. Wilcoxon Rank-Sum test • Mostly for Skewed distribution and Ordinal data. • Others- x2 test (Chi-squared test)- common test used to compare categorical data. Data first entered into a 2*2 contingency table • If the numbers are small (expected value is <5), an alternative test called Fisher’s exact test.
- 29. Simplified form • Nominal variable- Chi Square / Fischers • Ordinal Variable- Wilcoxon/ Mann-Whitney U • Interval/Ratio- Normal distribution- t test/ANOVA • Interval/Ratio- Skewed distribution- Wilcoxon/ Mann-Whitney U
- 30. Hypothesis testing • Null hypothesis refers to restating the research hypothesis to one that proposes no difference between groups being compared • An alternative hypothesis proposes an association- One-sided/two-sided
- 31. Errors • TYPE I ERROR (false-positive, also known as a rejection error) is rejection of a null hypothesis that is actually true in the population • TYPE II ERROR (false-negative, also known as an acceptance error) is failure to reject a null hypothesis that is actually false
- 33. Interpretation of p Value • p VALUE is the probability of the null hypothesis being true by chance alone. • It is also the probability of committing a type I error. • p value of 0.05 or less is commonly used to denote significance • This value informs the investigator that there is at least a 95% chance that the two samples represent different populations
- 34. • lower p value (<0.01) indicates a lower likelihood (1%) that the null hypothesis may be true due to chance alone. • lower p value does not infer a higher strength of association or clinical importance of an association • Factors that tend to decrease p value and increase significance are increased sample size, increased difference in control and experimental means and less variance
- 36. Confidence interval • Range of values that you expect to include the actual mean of the true population • Typically 95% confidence intervals are used in research • Values at either extreme of this range are called confidence limits
- 37. Sample Size and C.I • The larger the sample size, the narrower the C.I . • p value and C.I together provide the best information about the role of chance • Sample size is an important determinant of the power of the study to detect significant differences
- 38. Confounding • Distortion in a measure of effect that may arise because we fail to control for other variables that are previously known risk factors for the health outcome being studied • Can lead to the observation of apparent differences between the study groups when they do not truly exist, or conversely, the observation of no difference when they do exist. 38
- 39. Confounding variable • Independent risk factor (cause) of outcome • Unevenly distributed among exposed and unexposed • Not on the causal pathway between exposure and outcome 39
- 40. THE DIFFERENCE BETWEEN BIAS AND CONFOUNDING Bias creates an association that is not true, Confounding describes an association that is true, but potentially misleading. 40
- 41. Example • 2 groups of bottle-fed babies and breast-milk fed babies compared for Gastro-enteritis ( GE) • Bias- Finding less GE in bottle-fed babies due to less follow-up to the doctors • Confounding- Better-hygiene and less crowding can prevent GE in any of the groups; but that doesn’t correspond to true protective effects of breast-milk
- 42. Least square mean changes • When there is study imbalance such that some blocks of experimental units are under- represented relative to other block • Least square means (estimated population marginal means) provide an opportunity to obtain an unbiased estimate of averages in the face of this kind of study imbalance
- 45. • Absolute risk = a/(a + b) or c/ ( c+ d) • Relative risk/ Risk Ratio = (a/[a + b]) / (c/[c + d]) • Hazard ratio is a measure of relative risk over time in circumstances in which we are interested not only in the total number of events, but in their timing as well.
- 46. Diagnostic tests
- 49. Key points • Sensitivity and specificity are prevalence independent. • Increased prevalence of disease will increase PPV • Reduced disease prevalence increase NPV • Increasing the threshold for a positive test reduces false-positives and increases specificity • Reducing the threshold or cutoff value for a positive test reduces false-negatives and increases sensitivity
- 51. Gold standard • Unambiguous method of determining whether or not a patient has a particular disease or outcome
- 52. Regression Analysis • Simple Regression- Many relationships between variables can be fit to a straight line. Like duration of treatment • Logistics Regression- In situations in which the response of interest is dichotomous (binary) rather than continuous. Like outcome of death or alive
- 53. Summary • Bio-statistics is integral part of understanding any bio-medical research and Evidence-based medicines. • The concept of various variable and understanding the various statistical tests are equally important • Sample size and the confidence limit concepts hold the key
- 54. Thank you
- 55. CAS-3 & CAS-4 • CAS-3- PPV- 61% • NPV- 60% • Sensitivity- 64% • Specificity- 57% • CAS-4- PPV- 80% • NPV- 64% • Sensitivity- 55% • Specificity- 86% • M. Ph. Mourits et al.