Want to find out how much you already know? Take the quiz, Health statistics are used to understand risk factors for communities, track and monitor diseases, see the impact of policy changes, and assess the quality and safety of health care. Health statistics are a form of evidence, or facts that can support a conclusion.
Evidence-informed policy-making, “an approach to policy decisions that is intended to ensure that decision making is well-informed by the best available research evidence 1,” and evidence-based medicine (EBM), or “the conscientious, explicit, judicious and reasonable use of modern, best evidence in making decisions about the care of individual patients” 2 are essential to informing how best to provide health care and promote population health.
Not all evidence is, or should be, equally convincing in the support of a conclusion. Evidence varies in quality and whether it is applicable to a given situation. It is therefore essential that health researchers and policy makers understand how to assess evidence in a systematic way, including how to access transparent, high quality health statistics and information.
Correlates : See how to measure the risk factors and protective factors that impact our health. Conditions : Learn to assess how often and how badly diseases impact a community. Care : Dig into how health care is delivered to the communities that need it, to treat disease and illness. Costs : Get more information on what health care costs, and why.
Oxman, A.D., Laivs, J.N., Lewin, S., and A. Fretheim. SUPPORT Tools for evidence-informed health Policymaking (STP)1: What is evidence-informed policymaking? Health Research Policy and Systems, 7(S1).2009. Masic, I., Miokovic, M. and B. Muhamadagic. Evidence Based Medicine- New Approaches and Challenges.
What are statistical methods for health care?
Description – Statistical Methods in Healthcare In recent years the number of innovative medicinal products and devices submitted and approved by regulatory bodies has declined dramatically. The medical product development process is no longer able to keep pace with increasing technologies, science and innovations and the goal is to develop new scientific and technical tools and to make product development processes more efficient and effective.
- Statistical Methods in Healthcare focuses on the application of statistical methodologies to evaluate promising alternatives and to optimize the performance and demonstrate the effectiveness of those that warrant pursuit is critical to success.
- Statistical methods used in planning, delivering and monitoring health care, as well as selected statistical aspects of the development and/or production of pharmaceuticals and medical devices are also addressed.
With a focus on finding solutions to these challenges, this book:
Provides a comprehensive, in-depth treatment of statistical methods in healthcare, along with a reference source for practitioners and specialists in health care and drug development. Offers a broad coverage of standards and established methods through leading edge techniques. Uses an integrated case study based approach, with focus on applications. Looks at the use of analytical and monitoring schemes to evaluate therapeutic performance. Features the application of modern quality management systems to clinical practice, and to pharmaceutical development and production processes. Addresses the use of modern statistical methods such as Adaptive Design, Seamless Design, Data Mining, Bayesian networks and Bootstrapping that can be applied to support the challenging new vision.
Practitioners in healthcare-related professions, ranging from clinical trials to care delivery to medical device design, as well as statistical researchers in the field, will benefit from this book.
What is an example of a medical statistic?
2. Quantitative Data: It is information that can be measured and presented as numbers –
- Discrete: it can only take certain values. Can only be divided into discrete values i.e. whole numbers. For example; The number of compliments your department receives per week or the weekly number of cardiac arrests represent types of quantitative-discrete data.
- Continuous: Continuous data can take any value (within a range). Everyday examples include SaO2, blood pressure and weight, height, etc.
How is statistics used in nursing?
How Nurses Use Statistics in their Job – Enhance patient care: Through statistics, nurses are able to prioritize treatments and determine whether a patient needs immediate medical attention or follow-up care. As a nurse, you will use statistics to identify specific patterns in important signs and symptoms and be able to respond better to any medical changes to your patient.
- Using frequency charts or data sheets to document the timing of medication given to patients is another way that nurses use statistics.
- Support evidence-based nursing practices: The nursing profession is mostly based on empirical evidence that establishes effective protocols for patient care.
- However, for evidence-based practice to be well established in the nursing profession, nurses need basic statistics understanding to be able to read, comprehend, and interpret relevant literature.
Compare options for nursing practice: Nurses use statistics to determine whether the nursing interventions of their choice are effective. For instance, through statistics, nurses are able to tell that neither replacing intravenous fluids, nor administration sets can reduce infections.
- In some settings, nurses have to make several decisions during critical care and statistics knowledge comes in handy.
- Experience and emotional instinct aren’t enough to help make the right decisions. The U.S.
- Health care system is set to undergo profound changes to improve patient care.
- For this reason, formal nursing education is essential and its main goal is to prepare nurses to meet patients’ needs, advance science, and to deliver safe and quality patient care.
Other important reasons for formal nursing education include: Healthcare has become more complex and challenging: Nursing has become more challenging due to the plethora of electronic information and published literature that exists. Additionally, new knowledge meant to help improve patient care is always emerging, which means that a nurse is never finished learning.
- Healthcare leadership demands knowledgeable and competent staff: Formal nursing education is an important component of the commitment to become a leader in the nursing profession.
- For one to become a nursing leader, one must be able to keep up with the current trends, be able to solve nursing related problems, and know about new technology that can improve patient care.
Formal education is a mandatory requirement to become a nurse: For you to become a registered nurse, you will need to successfully complete a formal nursing education program to qualify for the NCLEX-RN (National Council Licensure Examination for Registered Nurses) exam to acquire your nursing license.
What is applied statistics for health care professionals?
Applied Statistics for Healthcare Professional | Viterbo University This course focuses on scientific research as it applies to disciplines in healthcare. Students will learn about the statistical techniques associated with collecting and analyzing data, to make informed decisions based on current evidence.
Why statistical methods are important in health research?
The use of statistics allows clinical researchers to draw reasonable and accurate inferences from collected information and to make sound decisions in the presence of uncertainty. Mastery of statistical concepts can prevent numerous errors and biases in medical research.
Why are statistical tests used in health research?
When writing or reading articles, one should be aware whether the statistical tests performed were appropriate for the type of data collected and used, thereby avoiding misleading conclusions. The goal of all statistical tests is to determine whether two (or more) variables are associated with one another or independent from each other at the population level.
- In this issue of IJEM, our Clinical Research Capsule reviews the most common tests used in published literature and some of the pitfalls associated with their use.
- This article is intended for non-statisticians.
- One of the first things to keep in mind is the type of data and outcomes the author wants to measure and correlate.
In order to do this, one must define the variables of the study. Are we looking at a continuous variable, one that can be quantified on an infinite scale, such as temperature or age, or is it a categorical variable, one that has to be grouped in classes.
- Categorical variables can be nominal or ordinal.
- Nominal variables are data that can be counted, but not ordered or measured.
- Nominal data can be further broken down into dichotomous (e.g., dead or alive) or have several categories (e.g., blood type).
- Ordinal data are numerical values that have a natural order and thus can be ranked and ordered.
However, the distance between two values on an ordinal scale may not represent an equal degree of difference. For example, the modified Rankin score is a measure of outcome after stroke where a value of 0 is no functional deficit, and a value of 6 is dead.
- The difference between 0 and 1 is slight; however, the difference between a 2 and a 3 is very significant, as it distinguishes being functionally independent (2) versus dependent (3).
- Other examples of ordinal variables include birth order and pain severity scales.
- The second important thing to keep in mind is how the results are distributed,
Do they follow a “bell curve” (also called a Gaussian distribution), similar to biological phenomena and exam grading techniques, or do the results tend to cluster resulting in a skewed distribution? (Fig.1 ). With normally distributed data, mean and SD are reported. Left panel demonstrates a normal distribution of mean arterial pressure (MAP) in patients with acute ischemic stroke; right panel demonstrate skewed distribution of door to computed tomography time in patients with acute ischemic stroke Sometimes, it may be desirable to “normalize” skewed data.
- This is known as transformation.
- When data are skewed, the commonly applied transformations are 1/ x, log( x ), and sqrt( x ), exponentiating, squaring, or cubing x, where the x ‘s are the data values.
- Also to be considered is whether the data are matched —meaning are sample subjects or data points related to one another, or are they independent? Once these three questions are answered, one is able to choose to the appropriate statistical test and, thus, decipher if an inappropriate test is used.
For two dichotomous or binary variables, one will be able to build a 2 × 2 table. If the data follow a normal distribution, the most common test will be Chi-square test. It is used to compare the proportion of subjects in two groups, and verify the independence of each other.
For example, if a study about a certain treatment obtains data that shows that it reduces mortality more than placebo for a given disease, one would like to know if the results are true or merely a coincidence. Therefore, we perform a Chi-square test and obtain the p value. One limitation of the Chi-square testing is that its distribution breaks down as the frequencies decrease.
If in one of the cells of your table there are five or less observations, the data is considered skewed. In this case, you need to use Fisher’s exact test, specifically designed for small samples. For two continuous variables (e.g., respiratory rate versus age), one can use linear regression or correlation.
What are the two data types in medical statistics?
The two main types of data are numerical and categorical, both of which can, in turn, be further sub-divided (Table 1). Numerical variables fall into two main subtypes: discrete data and continuous data.
What are common statistical tests in medical research?
Some common statistical tests include t-tests, ANOVA and chi-square tests. Examples: race, smoking status, demographic group Examples: age, weight, heart rate, white blood cell count Table 1.
What is an example of statistics in nursing?
Reason 1: Understand How to Interpret Descriptive Statistics – Descriptive statistics are used to describe data. In a medical setting, a nurse might have access to the following descriptive statistics for a patient:
The mean weight of the patient during a given time interval. The standard deviation of weight of the patient during a given time interval. The percentile of height, weight, blood pressure, and heart rate for an patient.
Using these metrics, the nurse can gain a better understanding of the overall health of a given patient and give recommendations to the patient for improving their health. For example, suppose a nurse can see that a patient is in the 93rd percentile of weight for their age group.
What is the role of statistics in clinical practice?
Before a medicine is approved for use, it has to undergo A clinical trial is a clinical study in which participants are assigned according to(.) ” href=”https://toolbox.eupati.eu/glossary/clinical-trial/” data-gt-translate-attributes=””>clinical trials to test its Efficacy refers to the ability of a medicine to provide a beneficial effect (a(.) ” href=”https://toolbox.eupati.eu/glossary/efficacy/” data-gt-translate-attributes=””>efficacy and The condition of being protected against consequences of failure, error or any other(.) ” href=”https://toolbox.eupati.eu/glossary/safety/” data-gt-translate-attributes=””>safety, Clinical research involves investigating proposed medical treatments, assessing the relative Benefit is a positive outcome (such as the relief of symptoms, cure, or prevention)(.) ” href=”https://toolbox.eupati.eu/glossary/benefit/” data-gt-translate-attributes=””>benefits of competing therapies, and establishing optimal treatment combinations. Clinical research attempts to answer questions such as “should a man with prostate cancer undergo radical prostatectomy or radiation or wait and see?” and “is the The number of new cases of a health event (such as development of a disease, or(.) ” href=”https://toolbox.eupati.eu/glossary/incidence/” data-gt-translate-attributes=””>incidence of serious adverse effects among patients receiving a new pain-relieving therapy greater than the The number of new cases of a health event (such as development of a disease, or(.) ” href=”https://toolbox.eupati.eu/glossary/incidence/” data-gt-translate-attributes=””>incidence of serious adverse effects in patients receiving the standard therapy?” Statistics are a mathematical methods of describing and drawing conclusions from(.) ” href=”https://toolbox.eupati.eu/glossary/statistics/” data-gt-translate-attributes=””>Statistics play a very important role in any A clinical trial is a clinical study in which participants are assigned according to(.) ” href=”https://toolbox.eupati.eu/glossary/clinical-trial/” data-gt-translate-attributes=””>clinical trial from design, conduct, analysis, and reporting in terms of controlling for and minimising biases, confounding factors, and measuring random errors. A grasp of Statistics are a mathematical methods of describing and drawing conclusions from(.) ” href=”https://toolbox.eupati.eu/glossary/statistics/” data-gt-translate-attributes=””>statistical methods is fundamental to understanding randomised trial methods and results. Statistics are a mathematical methods of describing and drawing conclusions from(.) ” href=”https://toolbox.eupati.eu/glossary/statistics/” data-gt-translate-attributes=””>Statistical methods provide formal accounting for sources of variability in patients’ responses to treatment. The use of Statistics are a mathematical methods of describing and drawing conclusions from(.) ” href=”https://toolbox.eupati.eu/glossary/statistics/” data-gt-translate-attributes=””>statistics in A clinical trial is a clinical study in which participants are assigned according to(.) ” href=”https://toolbox.eupati.eu/glossary/clinical-trial/” data-gt-translate-attributes=””>clinical trials allows the clinical researcher to form reasonable and accurate inferences from collected information, and sound decisions in the presence of uncertainty. Statistics are a mathematical methods of describing and drawing conclusions from(.) ” href=”https://toolbox.eupati.eu/glossary/statistics/” data-gt-translate-attributes=””>Statistics are key in preventing errors and biases in medical research.
What statistical test is used in nursing?
Abstract – A working understanding of the major fundamentals of statistical analysis is required to incorporate the findings of empirical research into nursing practice. The primary focus of this article is to describe common statistical terms, present some common statistical tests, and explain the interpretation of results from inferential statistics in nursing research.
- An overview of major concepts in statistics, including the distinction between parametric and nonparametric statistics, different types of data, and the interpretation of statistical significance, is reviewed.
- Examples of some of the most common statistical techniques used in nursing research, such as the Student independent t test, analysis of variance, and regression, are also discussed.
Nursing knowledge based on empirical research plays a fundamental role in the development of evidence-based nursing practice. The ability to interpret and use quantitative findings from nursing research is an essential skill for advanced practice nurses to ensure provision of the best care possible for our patients.
What is descriptive statistics in healthcare?
Descriptive statistics – Descriptive statistics aggregate data that are grouped into variables to examine typical values and the spread of values for each variable in a data set. Statistics summarising typical values are referred to as measures of central tendency and include the mean, median and mode.
The spread of values is represented through measures of variability, including the variance, SD and range. Together, descriptive statistics provide indicators of the distribution of data, or the frequency of values through the data set as in a histogram plot. Table 1 summarises commonly used descriptive statistics.
For consistency, I use the terms independent variable and dependent variable, but in some fields and types of research such as correlational studies the preferred terms may be predictor and outcome variable. An independent variable influences, affects or predicts a dependent variable,
What is applied statistics used for?
Applied Statistics And with the knowledge gained on how to best learn from this data in the Applied Statistics program, you’ll be able to choose to work in a variety of fields — engineering, environment, finance, healthcare, government, retail, social sciences and more.
- Applied Statistics includes planning for the collection of data, managing data, analyzing, interpreting and drawing conclusions from data, and identifying problems, solutions and opportunities using the analysis.
- This major builds critical thinking and problem solving skills in data analysis and empirical research.
In addition to career goals, it will prepare you for advanced degree programs in statistics and quantitative fields. 
Understand the fundamentals of probability theory Understand statistical and inferential reasoning Become proficient at statistical computing Understand the fundamentals of statistical modeling and understand its limitations Become skilled in the description, interpretation and exploratory analysis of data by graphical and other means Learn how to effectively communicate statistics
Full list of Applied Statistics program goals can be found on the,
What is vital statistics in applied statistics?
From Wikipedia, the free encyclopedia For bust/waist/hip measurement, see BWH, Vital statistics is accumulated data gathered on live births, deaths, migration, foetal deaths, marriages and divorces. The most common way of collecting information on these events is through civil registration, an administrative system used by governments to record vital events which occur in their populations.
What is basic applied stats?
Applied Statistics: Basic Principles and Application International Journal of Innovation and Economic Development Volume 7, Issue 3, August 2021, Pages 27-33 DOI: 10.18775/ijied.1849-7551-7020.2015.73.2003 URL: Alisa Bilal Zorić Polytechnic Baltazar Zaprešić, Zaprešić, Croatia Abstract: Statistics is an old scientific discipline, but its application has never been more topical.
- Statistics is a branch of mathematics that deals with the collection, analysis, interpretation and presentation of data.
- Increased computing power had a huge impact on the popularisation of the practice of statistical science.
- With new technologies such as the internet of things, we start to collect data from various sources like web server logs, online transaction records, tweet streams, social media, data from all kinds of sensors.
With increased access to big data, there is a need for professionals with applied statistics knowledge who can visualize and analyze data, make sense of it, and use it to solve real complex problems. Applied statistics is the root of data analysis, and the practice of applied statistics involves analyzing data to help define and determine organizational needs.
Why is statistical significance more important than clinical significance?
Common pitfalls in statistical analysis: Clinical versus statistical significance In clinical research, study results, which are statistically significant are often interpreted as being clinically important. While statistical significance indicates the reliability of the study results, clinical significance reflects its impact on clinical practice. The third article in this series exploring pitfalls in statistical analysis clarifies the importance of differentiating between statistical significance and clinical significance. Keywords: Biostatistics, confidence intervals, data interpretation, statistical One of the common problems faced by readers (and authors!) of medical articles is in the interpretation of the word “significance.” The term “statistical significance” is often misinterpreted as a “clinically important” result. The confusion stems from the fact that many people equate “significance” with its literal meaning of “importance,” whereas in statistics, it has a far more restrictive connotation. This article explains the idea of the statistical significance and differentiates it from clinical relevance or importance, which is an entirely different concept. In the previous article, in this series, we looked at different ways of expressing statistical significance (” P ” values versus confidence intervals). Measures of statistical significance quantify the probability of a study’s results being due to chance. Clinical significance, on the other hand, refers to the magnitude of the actual treatment effect (i.e., the difference between the intervention and control groups, also known as the “treatment effect size”), which will determine whether the results of the trial are likely to impact current medical practice. The ” P ” value, frequently used to measure statistical significance, is the probability that the study results are due to chance rather than to a real treatment effect. The conventional cut off for the ” P ” value to be considered statistically significant is of 0.05 (or 5%). What a P < 0.05 implies is that the possibility of the results in a study being due to chance is <5%. In clinical practice, the "clinical significance" of a result is dependent on its implications on existing practice-treatment effect size being one of the most important factors that drives treatment decisions. LeFort suggests that the clinical significance should reflect "the extent of change, whether the change makes a real difference to subject lives, how long the effects last, consumer acceptability, cost-effectiveness, and ease of implementation". While there are established, traditionally accepted values for statistical significance testing, this is lacking for evaluating clinical significance. More often than not, it is the judgment of the clinician (and the patient) which decides whether a result is clinically significant or not. Statistical significance is heavily dependent on the study's sample size; with large sample sizes, even small treatment effects (which are clinically inconsequential) can appear statistically significant; therefore, the reader has to interpret carefully whether this "significance" is clinically meaningful. A study published in the Journal of Clinical Oncology compared overall survival in 569 patients with advanced pancreatic cancer who were randomised to receive erlotinib plus gemcitabine versus gemcitabine alone. Median survival was found to be "significantly" prolonged in the erlotinib/gemcitabine arm (6.24 months vs.5.91 months, P = 0.038). The P = 0.038 means that there is only a 3.8% chance that this observed difference between the groups occurred by chance (which is less than the traditional cut-off of 5%) and therefore, statistically significant. In this example, the clinical relevance of this "positive" study is the "treatment effect" or difference in median survival between 6.24 and 5.91 months – a mere 10 days, which most oncologists would agree is a clinically irrelevant "improvement" in outcomes, especially when considering the added toxicity and costs involved with the combination. Most journals now endorse the use of the CONSORT statement for reporting of parallel-group randomized trials, which emphasizes the need for reporting of the estimated effect size and its precision (such as 95% confidence interval) for each primary and secondary outcome. Readers should bear in mind that interpretation of study results should take into account the clinical significance by looking at the actual treatment effect (with confidence intervals) and should not just be based on " P " values and statistical significance. Source of Support: Nil. Conflict of Interest: None declared.1. Ranganathan P, Pramesh CS, Buyse M. Common pitfalls in statistical analysis: "P" values, statistical significance and confidence intervals. Perspect Clin Res.2015; 6 :116–7.2. LeFort SM. The statistical versus clinical significance debate. Image J Nurs Sch.1993; 25 :57–62.3. Fethney J. Statistical and clinical significance, and how to use confidence intervals to help interpret both. Aust Crit Care.2010; 23 :93–7.4. Moore MJ, Goldstein D, Hamm J, Figer A, Hecht JR, Gallinger S, et al. Erlotinib plus gemcitabine compared with gemcitabine alone in patients with advanced pancreatic cancer: A phase III trial of the National Cancer Institute of Canada Clinical Trials Group. J Clin Oncol.2007; 25 :1960–6.5. Schulz KF, Altman DG, Moher D. CONSORT Group. CONSORT 2010 statement: Updated guidelines for reporting parallel group randomized trials. Ann Intern Med.2010; 152 :726–32. : Common pitfalls in statistical analysis: Clinical versus statistical significance
Why is statistical and clinical significance important?
Statistical Significance – In clinical research, such as an, statistical significance helps us determine whether the results of a treatment group are different than that of a control group. To do this, we select the appropriate based on our sample and then calculate the corresponding P-values. It is generally accepted that for P-values < 0.05, the differences between the groups are statistically significant. For more information on which tests you need for your analysis, please refer to our article on, But what exactly is a P-value, and what does it really represent? In order to understand P-values, we first have to review the concept of the null hypothesis. In every study, there is at least one key hypothesis that is being tested. For example, you hypothesize that a new eye drop formulation will significantly improve comfort for dry eye patients (as rated by a questionnaire) compared to another commercial product. In this scenario, there are three possible outcomes in this study:
The new eye drop is more comfortable than the commercial eye drop The new eye drop is less comfortable than the commercial eye drop The new eye drop has the same level of comfort as the commercial eye drop (null hypothesis)
The third outcome in this scenario is considered the null hypothesis, in which there was no difference between the two tested groups. Let’s hypothetically assume that the results of the study showed that were no differences in comfort scores between the two eye drops. However, due to random sample error, the sample did not represent the true population. If you were to repeat this study several more times, you would have found that there were differences between the two eye drops. To account for the potential errors in random sampling, we need to determine the P-values. The P-values tell us how well the sample data reflects the null hypothesis. The higher the P-value, the more likely your data supports the null hypothesis. The lower the P-value, the more likely your data rejects the null hypothesis. For instance, a P-value less than 0.05 would suggest that there was less than a 5% chance that the null hypothesis was true, in which case you would reject the null hypothesis. In other words, for P < 0.05, the statistics would support that your initial hypothesis was true and that there was a difference in comfort between the two eye drops.
Why is using statistical evidence important?
1500.0 – A guide for using statistics for evidence based policy, 2010 HOW GOOD STATISTICS CAN ENHANCE THE DECISION MAKING PROCESS Statistics are a vital source of evidence as they provide us with clear, objective, numerical data on important aspects of Australian life including the growth and characteristics of our population, economic performance, levels of health and wellbeing and the condition of our surrounding environment.
The Australian Bureau of Statistics (ABS) plays an important role in this process by providing data ‘to assist and encourage informed decision making, research and discussion within governments and the community, by leading a high quality, objective and responsive national statistical service’ (ABS Mission Statement).
When we are able to understand and interpret this data correctly, our ability to identify key areas which require change are enhanced, and our proposals for change are likely to respond to the ‘real’ needs of the Australian community. Statistics can also aid the decision making process by enabling us to establish numerical benchmarks and monitor and evaluate the progress of our policy or program.
- This is essential in ensuring that policies are meeting initial aims and identifying any areas which require improvement.
- Statistics can be used to inform decision making throughout the different stages of the policy-making process.
- The following framework has been adapted from different approaches to the policy making cycle, outlined in Disability Services, Queensland, 2008; Edwards, 2004; Othman, 2005.
The framework highlights the importance of using statistical information at each of the stages of the policy cycle.
| STAGE 1 | Identify and understand the issue |
| The first phase involves identifying and understanding the issue at hand. Statistics can assist policy makers to identify existing economic, social or environmental issues that need addressing. For example, statistical analysis could identify issues concerning the aging of the population or the implications of rising inflation. They are also vital for developing a better understanding of the issue by analysing trends over time, or patterns in the data. |
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1500.0 – A guide for using statistics for evidence based policy, 2010
What are the statistical methods?
Selection of Appropriate Statistical Methods for Data Analysis Department of Biostatistics and Health Informatics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India Find articles by Department of Biostatistics and Health Informatics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India Find articles by Department of Biostatistics and Health Informatics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India Find articles by 1 Department of Neuro-otology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India Find articles by 2 Department of Endocrine Surgery, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India Find articles by
Department of Biostatistics and Health Informatics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India 1 Department of Neuro-otology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India 2 Department of Endocrine Surgery, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India
Address for correspondence: Dr. Prabhaker Mishra, Department of Biostatistics and Health Informatics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India. E-mail: : © 2019 Annals of Cardiac Anaesthesia This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
- In biostatistics, for each of the specific situation, statistical methods are available for analysis and interpretation of the data.
- To select the appropriate statistical method, one need to know the assumption and conditions of the statistical methods, so that proper statistical method can be selected for data analysis.
Two main statistical methods are used in data analysis: descriptive statistics, which summarizes data using indexes such as mean and median and another is inferential statistics, which draw conclusions from data using statistical tests such as student’s t -test.
Selection of appropriate statistical method depends on the following three things: Aim and objective of the study, Type and distribution of the data used, and Nature of the observations (paired/unpaired). All type of statistical methods that are used to compare the means are called parametric while statistical methods used to compare other than means (ex-median/mean ranks/proportions) are called nonparametric methods.
In the present article, we have discussed the parametric and non-parametric methods, their assumptions, and how to select appropriate statistical methods for analysis and interpretation of the biomedical data. Keywords: Diagnostic accuracy, parametric and nonparametric methods, regression analysis, statistical method, survival analysis Selection of appropriate statistical method is very important step in analysis of biomedical data.
A wrong selection of the statistical method not only creates some serious problem during the interpretation of the findings but also affects the conclusion of the study. In statistics, for each specific situation, statistical methods are available to analysis and interpretation of the data. To select the appropriate statistical method, one need to know the assumption and conditions of the statistical methods, so that proper statistical method can be selected for data analysis.
Other than knowledge of the statistical methods, another very important aspect is nature and type of the data collected and objective of the study because as per objective, corresponding statistical methods are selected which are suitable on given data.
- Practice of wrong or inappropriate statistical method is a common phenomenon in the published articles in biomedical research.
- Incorrect statistical methods can be seen in many conditions like use of unpaired t -test on paired data or use of parametric test for the data which does not follow the normal distribution, etc., At present, many statistical software like SPSS, R, Stata, and SAS are available and using these softwares, one can easily perform the statistical analysis but selection of appropriate statistical test is still a difficult task for the biomedical researchers especially those with nonstatistical background.
Two main statistical methods are used in data analysis: descriptive statistics, which summarizes data using indexes such as mean, median, standard deviation and another is inferential statistics, which draws conclusions from data using statistical tests such as student’s t-test, ANOVA test, etc.
Selection of appropriate statistical method depends on the following three things: Aim and objective of the study, Type and distribution of the data used, and Nature of the observations (paired/unpaired). Selection of statistical test depends upon our aim and objective of the study. Suppose our objective is to find out the predictors of the outcome variable, then regression analysis is used while to compare the means between two independent samples, unpaired samples t-test is used.
For the same objective, selection of the statistical test is varying as per data types. For the nominal, ordinal, discrete data, we use nonparametric methods while for continuous data, parametric methods as well as nonparametric methods are used. For example, in the regression analysis, when our outcome variable is categorical, logistic regression while for the continuous variable, linear regression model is used.
- The choice of the most appropriate representative measure for continuous variable is dependent on how the values are distributed.
- If continuous variable follows normal distribution, mean is the representative measure while for non-normal data, median is considered as the most appropriate representative measure of the data set.
Similarly in the categorical data, proportion (percentage) while for the ranking/ordinal data, mean ranks are our representative measure. In the inferential statistics, hypothesis is constructed using these measures and further in the hypothesis testing, these measures are used to compare between/among the groups to calculate significance level.
Suppose we want to compare the diastolic blood pressure (DBP) between three age groups (years) ( 50). If our DBP variable is normally distributed, mean value is our representative measure and null hypothesis stated that mean DB P values of the three age groups are statistically equal. In case of non-normal DBP variable, median value is our representative measure and null hypothesis stated that distribution of the DB P values among three age groups are statistically equal.
In above example, one-way ANOVA test is used to compare the means when DBP follows normal distribution while Kruskal-Wallis H tests/median tests are used to compare the distribution of DBP among three age groups when DBP follows non-normal distribution.
Similarly, suppose we want to compare the mean arterial pressure (MAP) between treatment and control groups, if our MAP variable follows normal distribution, independent samples t-test while in case follow non-normal distribution, Mann-Whitney U test are used to compare the MAP between the treatment and control groups.
Another important point in selection of the statistical test is to assess whether data is paired (same subjects are measures at different time points or using different methods) or unpaired (each group have different subject). For example, to compare the means between two groups, when data is paired, paired samples t-test while for unpaired (independent) data, independent samples t-test is used.
Inferential statistical methods fall into two possible categorizations: parametric and nonparametric. All type of statistical methods those are used to compare the means are called parametric while statistical methods used to compare other than means (ex-median/mean ranks/proportions) are called nonparametric methods.
Parametric tests rely on the assumption that the variable is continuous and follow approximate normally distributed. When data is continuous with non-normal distribution or any other types of data other than continuous variable, nonparametric methods are used.
- Fortunately, the most frequently used parametric methods have nonparametric counterparts.
- This can be useful when the assumptions of a parametric test are violated and we can choose the nonparametric alternative as a backup analysis.
- All type of the t -test, F test are considered parametric test.
- Student’s t -test (one sample t -test, independent samples t -test, paired samples t -test) is used to compare the means between two groups while F test (one-way ANOVA, repeated measures ANOVA, etc.) which is the extension of the student’s t -test are used to compare the means among three or more groups.
Similarly, Pearson correlation coefficient, linear regression is also considered parametric methods, is used to calculate using mean and standard deviation of the data. For above parametric methods, counterpart nonparametric methods are also available.
For example, Mann-Whitney U test and Wilcoxon test are used for student’s t -test while Kruskal-Wallis H test, median test, and Friedman test are alternative methods of the F test (ANOVA). Similarly, Spearman rank correlation coefficient and log linear regression are used as nonparametric method of the Pearson correlation and linear regression, respectively.
Parametric and their counterpart nonparametric methods are given in, Parametric and their Alternative Nonparametric Methods
| Description | Parametric Methods | Nonparametric Methods |
|---|---|---|
| Descriptive statistics | Mean, Standard deviation | Median, Interquartile range |
| Sample with population (or hypothetical value) | One sample t -test ( n <30) and One sample Z -test ( n ≥30) | One sample Wilcoxon signed rank test |
| Two unpaired groups | Independent samples t -test (Unpaired samples t -test) | Mann Whitney U test/Wilcoxon rank sum test |
| Two paired groups | Paired samples t -test | Related samples Wilcoxon signed-rank test |
| Three or more unpaired groups | One-way ANOVA | Kruskal-Wallis H test |
| Three or more paired groups | Repeated measures ANOVA | Friedman Test |
| Degree of linear relationship between two variables | Pearson’s correlation coefficient | Spearman rank correlation coefficient |
| Predict one outcome variable by at least one independent variable | Linear regression model | Nonlinear regression model/Log linear regression model on log normal data |
The statistical methods used to compare the proportions are considered nonparametric methods and these methods have no alternative parametric methods. Pearson Chi-square test and Fisher exact test is used to compare the proportions between two or more independent groups.
| Description | Statistical Methods | Data Type |
|---|---|---|
| Test the association between two categorical variables (Independent groups) | Pearson Chi-square test/Fisher exact test | Variable has ≥2 categories |
| Test the change in proportions between 2/3 groups (paired groups) | McNemar test/Cochrane Q test | Variable has 2 categories |
| Comparisons between proportions | Z test for proportions | Variable has 2 categories |
Intraclass correlation coefficient is calculated when both pre-post data are in continuous scale. Unweighted and weighted Kappa statistics are used to test the absolute agreement between two methods measured on the same subjects (pre-post) for nominal and ordinal data, respectively.
There are some methods those are either semiparametric or nonparametric and these methods, counterpart parametric methods, are not available. Methods are logistic regression analysis, survival analysis, and receiver operating characteristics curve. Logistic regression analysis is used to predict the categorical outcome variable using independent variable(s).
Survival analysis is used to calculate the survival time/survival probability, comparison of the survival time between the groups (Kaplan-Meier method) as well as to identify the predictors of the survival time of the subjects/patients (Cox regression analysis).
Receiver operating characteristics (ROC) curve is used to calculate area under curve (AUC) and cutoff values for given continuous variable with corresponding diagnostic accuracy using categorical outcome variable. Diagnostic accuracy of the test method is calculated as compared with another method (usually as compared with gold standard method).
Sensitivity (proportion of the detected disease cases from the actual disease cases), specificity (proportion of the detected non-disease subjects from the actual non-disease subjects), overall accuracy (proportion of agreement between test and gold standard methods to correctly detect the disease and non-disease subjects) are the key measures used to assess the diagnostic accuracy of the test method.
Other measures like false negative rate (1-sensitivity), false-positive rate (1-specificity), likelihood ratio positive (sensitivity/false-positive rate), likelihood ratio negative (false-negative rate/Specificity), positive predictive value (proportion of correctly detected disease cases by the test variable out of total detected disease cases by the itself), and negative predictive value (proportion of correctly detected non-disease subjects by test variable out of total non-disease subjects detected by the itself) are also used to calculate the diagnostic accuracy of the test method.
Semi-parametric and non-parametric methods
| Description | Statistical methods | Data type |
|---|---|---|
| To predict the outcome variable using independent variables | Binary Logistic regression analysis | Outcome variable (two categories), Independent variable (s): Categorical (≥2 categories) or Continuous variables or both |
| To predict the outcome variable using independent variables | Multinomial Logistic regression analysis | Outcome variable (≥3 categories), Independent variable (s): Categorical (≥2 categories) or continuous variables or both |
| Area under Curve and cutoff values in the continuous variable | Receiver operating characteristics (ROC) curve | Outcome variable (two categories), Test variable : Continuous |
| To predict the survival probability of the subjects for the given equal intervals | Life table analysis | Outcome variable (two categories), Follow-up time : Continuous variable |
| To compare the survival time in ≥2 groups with P | Kaplan-Meier curve | Outcome variable (two categories), Follow-up time : Continuous variable, One categorical group variable |
| To assess the predictors those influencing the survival probability | Cox regression analysis | Outcome variable (two categories), Follow-up time : Continuous variable, Independent variable(s): Categorical variable(s) (≥2 categories) or continuous variable(s) or both |
| To predict the diagnostic accuracy of the test variable as compared to gold standard method | Diagnostic accuracy (Sensitivity, Specificity etc.) | Both variables (gold standard method and test method) should be categorical (2 × 2 table) |
| Absolute Agreement between two diagnostic methods | Unweighted and weighted Kappa statistics/Intra class correlation | Between two Nominal variables (unweighted Kappa), Two Ordinal variables (Weighted kappa), Two Continuous variables (Intraclass correlation) |
Parametric methods are stronger test to detect the difference between the groups as compared with its counterpart nonparametric methods, although due to some strict assumptions, including normality of the data and sample size, we cannot use parametric test in every situation and resultant its alternative nonparametric methods are used. As mean is used to compare parametric method, which is severally affected by the outliers while in nonparametric method, median/mean rank is our representative measures which do not affect from the outliers. In parametric methods like student’s t-test and ANOVA test, significance level is calculated using mean and standard deviation, and to calculate standard deviation in each group, at least two observations are required. If every group did not have at least two observations, its alternative nonparametric method to be selected works through comparisons of the mean ranks of the data. For small sample size (average ≤15 observations per group), normality testing methods are less sensitive about non-normality and there is chance to detect normality despite having non-normal data. It is recommended that when sample size is small, only on highly normally distributed data, parametric method should be used otherwise corresponding nonparametric methods should be preferred. Similarly on sufficient or large sample size (average >15 observations per group), most of the statistical methods are highly sensitive about non-normality and there is chance to wrongly detect non-normality, despite having normal data. It is recommended that when sample size is sufficient, only on highly non-normal data, nonparametric method should be used otherwise corresponding parametric methods should be preferred. To detect the significant difference between the means/medians/mean ranks/proportions, at minimum level of confidence (usually 95%) and power of the test (usually 80%), how many individuals/subjects (sample size) are required depends on the detected effect size. The effect size and corresponding required sample size are inversely proportional to each other, that is, on the same level of confidence and power of the test, when effect size is increasing, required sample size is decreasing. Summary is, no minimum or maximum sample size is fix for any particular statistical method and it is subject to estimate based on the given inputs including effect size, level of confidence, power of the study, etc., Only on the sufficient sample size, we can detect the difference significantly. In case lack of the sample size than actual required, our study will be under power to detect the given difference as well as result would be statistically insignificant. As for each and every situation, there are specific statistical methods. Failing to select appropriate statistical method, our significance level as well as their conclusion is affected. For example in a study, systolic blood pressure (mean ± SD) of the control (126.45 ± 8.85, n 1 =20) and treatment (121.85 ± 5.96, n 2 =20) group was compared using Independent samples t -test (correct practice). Result showed that mean difference between two groups was statistically insignificant ( P = 0.061) while on the same data, paired samples t -test (incorrect practice) indicated that mean difference was statistically significant ( P = 0.011). Due to incorrect practice, we detected the statistically significant difference between the groups although actually difference did not exist. Selection of the appropriate statistical methods is very important for the quality research. It is important that a researcher knows the basic concepts of the statistical methods used to conduct research study that produce a valid and reliable results. There are various statistical methods that can be used in different situations. Each test makes particular assumptions about the data. These assumptions should be taken into consideration when deciding which the most appropriate test is. Wrong or inappropriate use of statistical methods may lead to defective conclusions, finally would harm the evidence-based practices. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important for improving and producing quality biomedical research. However, it is extremely difficult for a biomedical researchers or academician to learn the entire statistical methods. Therefore, at least basic knowledge is very important so that appropriate selection of the statistical methods can decide as well as correct/incorrect practices can be recognized in the published research. There are many softwares available online as well as offline for analyzing the data, although it is fact that which set of statistical tests are appropriate for the given data and study objective is still very difficult for the researchers to understand. Therefore, since planning of the study to data collection, analysis and finally in the review process, proper consultation from statistical experts may be an alternative option and can reduce the burden from the clinicians to go in depth of statistics which required lots of time and effort and ultimately affect their clinical works. These practices not only ensure the correct and appropriate use of the biostatistical methods in the research but also ensure the highest quality of statistical reporting in the research and journals. There are no conflicts of interest. Authors would like to express their deep and sincere gratitude to Dr. Prabhat Tiwari, Professor, Department of Anaesthesiology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, for his encouragement to write this article. His critical reviews and suggestions were very useful for improvement in the article.1. Nayak BK, Hazra A. How to choose the right statistical test.? Indian J Ophthalmol.2011; 59 :85–6.2. Karan J. How to select appropriate statistical test.? J Pharm Negative Results.2010; 1 :61–3.3. Mishra P, Mayilvaganan S, Agarwal A. Statistical methods in endocrine surgery journal club. World J Endoc Surg.2015; 7 :21–3.4. Mishra P, Pandey CM, Singh U, Gupta A. Scales of measurement and presentation of statistical data. Ann Card Anaesth.2018; 21 :419–22.5. Campbell MJ, Swinscow TDV. Wiley-Blackwell: BMJ Books; 2009. Statistics at Square One 11th ed.6. Sundaram KR, Dwivedi SN, Sreenivas V. 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