comprehensive research and biostatistics workshop

comprehensive research and biostatistics workshop

1. WORKSHOP ON RESEARCH METHODOLOGY AND BIOSTISTICS DR PRASANNA MOHAN PROFESSOR/RESEARCH HEAD KRUPANIDHI COLLEGE OF PHYSIOTHERAPY DIRECTOR-REHAB ONE HEALTHCARE DATE :23 AUGUST 2024

2. What one thing would you do if you were invisible?

3. What is your favorite breakfast which most people dislike it?

4. Is a randomized controlled trial considered the gold standard for testing the effectiveness of an intervention ? YES/NO

5. Does a p-value less than 0.05 always indicate a meaningful and practical significance? YES/NO

6. Is it necessary to obtain informed consent from participants in all types of research involving human subjects? YES/NO

7. Can qualitative research methods be used to quantify variables? YES/NO

8. Is the null hypothesis always formulated as a statement of no effect or no difference? YES/NO

9. LEARNING OBJECTIVE

10. Why is Research Important in Physiotherapy

11. Overview Why Do We Conduct Research? Evidence- Based Practice Level of Evidence High Level Research: RCT & Systematic Review and Meta- Analysed Study

12. Why do we conduct research??

13. What is Research? Research is a systematic process of collecting and analyzing information to increase our understanding of the world in general and of the phenomenon under study in particular

14. We need to conduct research because we need to know , and knowing is important

15. Why do Research? To invent new things To solve a prevailing problem To support development programmes of a country To uplift living standards Because we are inquisitive about things around us Benefits society

16. Bachelor Master Doctor Research in Academic Level of Physiotherapy

17. Evidence- Based Practice

18. Evidence Based Practice in Physiotherapy Research is needed for clinical decision making - > physiotherapist can make decisions about therapy for their patients

19. EBP Cycle

20. ASK Background questions : mengenai pertanyaan umum tentang disease, kondisi, test atau treatment tertentu dan sering bisa dijawab cukup dengan menggunakan medical textbooks Foreground questions : mencakup aspek perawatan, pilihan terapi dan outcome yang mungkin didapatkan yang hubungannya dengan pasien dan situasi spesifik dimana dibutuhkan pemahaman dan pencarian literature yang mendalam untuk mendapatkan jawabannya. Qualitative questions look at people's experiences, attitudes, beliefs, opinions, perceptions etc. A modified framework, PICo , can be used for these types of questions. PICo stands for P opulation, I nterest and Co ntext.

21. Level of Evidence

22. Level of Evidence

23. A meta- analysis uses statistical methods to synthesise the data from individual studies in a systematic review A systematic review is a review based on clearly formulated question. It uses systematic and reproducible methods to identify, select and critically appraise all relevant research, and to collect and analyse data from the studies that are included in the review Randomised Control Trials (RCTs) are study designs that randomly assign participant to an experimental group (receives a clinical intervention) or a control group (receives placebo, the standard treatment or no intervention). Cohort studies are observational studies that identify 2 groups (cohorts) of patients, one which received the exposure of interest/intervention, and one which did not, and following them forward for the outcome Case Control Studies identify patients with a specific condition (cases) and patients without the condition (controls) and look back to see if they had an exposure of interest. They often rely on medical records and patient recall for data collection . Case reports/case series consist of a group or series of case reports involving patients with an outcome interest. As they are reports of cases and use no control groups, they have no statistical validity. Background info/Expert Opinion includes handbooks, conference proceedings and textbook which have general information. These are not backed by research studies.

24. Qualitative Research Evidence Phenomenology looks at human perception and subjectivity and focuses on the 'lived experience’ of of the individual concerned. Data of the participant's experience is collected using a focused but non- structured interview. Grounded Theory involves the discovery of a theory through the analysis of data. It is a research method that is almost opposite to the traditional social science research in that it does not begin with a hypothesis but starts with data collection through a variety of methods. Ethnography is used to study groups of people who share social and cultural characteristics; think of themselves as a group;and share common language, geographic locale and identity. Ethnography provides a 'portrait of the people'. It involves participant observation, the recording of field notes and interviewing key informants. Action research asks the question, "What is happening here and how could it be different?" It is a process of reflecting on the world to change it followed by evaluation. Data collected can be both qualitative and quantitative. Themes, issues and concerns are extracted and discussed by both the research team and the participating group.

25. Mixed- Method Research Mixed methods research is the use of quantitative and qualitative methods in a single study or series of studies. It is an emergent methodology which is increasingly used by health researchers, especially within health services research

26. Suitable design to answer a clinical question The following table outlines suitable study designs to answer a clinical question. Meta- analyses and systematic reviews where available, will often provide the best answers to clinical questions. Question Type Definition Best Study Design Therapy The effect of an intervention/s on a patient Randomised Controlled Trial (RCT) Diagnosis Ability of a test to differentiate between those with or without a condition Prospective, blind comparison to a gold standard Harm/etiology The effect of potentially harmful agents Cohort study or Case control study Prognosis The likely progression, outcome or survival time for a condition Cohort study Prevention Reducing chance of a disease by changing risk factors or early diagnosis & treatment RCT

27. Why critically appraise? To find out the validity of the study are the methods robust? To find out the reliability of the study what are the results and are they credible? To find out the applicability of the study is it important enough to change my practice?

28. Critical Appraisal tools Critical appraisal is the systematic evaluation of clinical research papers in order to establish: - Does this study address a clearly focused question? - Did the study use valid methods to address this question? - Are the valid results of this study important? - Are these valid, important results applicable to my patient or population? If the answer to any of these questions is “no”, you can save yourself the trouble of reading the rest of it.

29. High Level of Research: Randomised Controlled Trial (RCT), Systematic Review and Meta-Analysis

30. Study Design in Health Science Experimental (Randomized Controlled Trial) A new intervention is deliberately introduced and compared with standard care Quasi- experimental (non- randomized, controlled before and after) Researchers do not have full control over the implementation of the intervention Observational (Cohort, case- control, cross-sectional) Describe current practice Observed differences cannot be attributed solely to a “treatment” effect

31. Randomized Controlled Trial (RCT) Randomized controlled trial : (RCT) A study in which people are allocated at random (by chance alone) to receive one of several clinical interventions. One of these interventions is the standard of comparison or control . The control may be a standard practice, a placebo ("sugar pill"), or no intervention at all

32. Feasibility Study Acceptability Rate Recruitment Rate Adherence Rate Drop- Out Rate Adverse Event Retention Rate

33. What is Systematic Review A review that has been prepared using some kind of systematic approach to minimising biases and random errors, and that the components of the approach will be documented in a materials and methods section Systematic reviews Reviews

34. What is Meta- Analysis A statistical analysis of the results from independent studies, which generally aims to produce a single estimate of the treatment effect

35. Advantages of a Systematic Review/Meta Analysis Limits bias in identifying and excluding studies Objective Good quality evidence, more reliable and accurate conclusions Added power by synthesising individual study results Control over the volume of literature

36. In Brief Research gap is a problem, which is not covered properly. It may be stemming from deficiency of appropriate data to support the thesis title and literature gap, which means the lacking or uncomplete piece of information in the Academic research literature which is not investigated or studied.

37. What is a research gap? A research gap means, there are some areas that have significant scope for more research, but they have not been investigated by other researchers. In other words, no one has picked up or worked on these ideas. A research gap refers to such unexplored or underexplored areas that have scope for further research.

38. Introduction A research gap is a topic or field for which insufficient data restrict the ability to conclude a research question. If we are investigating a research problem , what is the best medicine for heart attacks? You can find out several researches and potential answers to the research problem. Research gap identify a knowledge gap or an unexplored area on which you can base your research.

39. How to Identify Research Gaps

40. How to Identify Research Gaps

41. How to Identify Research Gaps

42. How to Identify Gaps in the Literature

43. How to Identify Gaps in the Literature

44. How to Identify Gaps in the Literature

45. How to Identify Gaps in the Literature Check prominent journal websites Study each of the questions Using digital tools to look for common subjects or most of the research papers Make your questions a note Ask for your research advisior support Look at published literature for inspiration 01 02 03 04 05 06

46. How to Identify Gaps in the Literature

47. How to Identify Gaps in the Literature

48. How to Identify Gaps in the Literature

49. How to Identify Gaps in the Literature

50. How to Identify Gaps in the Literature

51. Normality test Normality Test : A normality test is a statistical test used to determine whether a dataset is well-modeled by a normal distribution. Examples of normality tests include the Shapiro-Wilk test, Kolmogorov-Smirnov test, Anderson-Darling test, and the Lilliefors test. Null Hypothesis (H0) : In the context of a normality test, the null hypothesis typically states that the data come from a normal distribution. Alternative Hypothesis (H1) : The alternative hypothesis states that the data do not come from a normal distribution. Significance Level (alpha) : This is the threshold used to determine whether the test result is statistically significant. Common significance levels are 0.05, 0.01, or 0.10. P-Value : The p-value is the probability of observing the test results under the null hypothesis. A small p-value (less than the significance level) indicates that the observed data is unlikely under the null hypothesis, leading to its rejection.

52. Interpreting a Non-Significant Normality Test:

53. Practical Implications :

54. One-Sample T-Test: Suppose a researcher wants to determine if a new diet affects the average cholesterol level in adults. The known average cholesterol level for the general population is 200 mg/dL. After implementing the new diet, a sample of 15 adults has an average cholesterol level of 190 mg/dL with a sample standard deviation of 12 mg/dL. The researcher wants to test if the diet significantly changes the average cholesterol level at a 0.05 significance level.

55. Steps to Perform a One-Sample T-Test: State the hypotheses : Define the null and alternative hypotheses. Null Hypothesis (H0): The mean cholesterol level after the diet is 200 mg/dL. 𝐻0:𝜇=200 H 0​: μ =200 Alternative Hypothesis (H1): The mean cholesterol level after the diet is not 200 mg/dL. 𝐻1:𝜇≠200 H 1​: μ =200 Choose the significance level ( α) : 0.05. Calculate the test statistic : 𝑥ˉ x ˉ = 190 (sample mean) 𝜇0 = 200 ( hypothesized population mean) 𝑠 s = 12 (sample standard deviation) 𝑛 n = 15 (sample size) 𝑡=190−20012/15=−1012/3.873=−103.1=−3.226 t =12/15​190−200​=12/3.873−10​=3.1−10​=−3.226

56. Steps to Perform a One-Sample T-Test: Determine the degrees of freedom : 𝑑𝑓=𝑛−1=15−1=14 df = n −1=15−1=14 Find the critical t-value : For a two-tailed test with α = 0.05 and df = 14, the critical t-values are approximately ±2.145 (from the t-distribution table). Compare the test statistic to the critical t-value : Test statistic 𝑡=−3.226 t =−3.226 Critical t-values ±2.145±2.145 Since -3.226 is less than -2.145, the test statistic falls in the critical region.p =0.006 Make a decision : Reject the null hypothesis.

57. D ependent t-test A dependent t-test, also known as a paired samples t-test or matched pairs t-test, is used to compare the means of two related groups. This test is appropriate when you have two measurements taken on the same subjects, such as before and after an intervention, or when you have matched pairs of subjects. Purpose The dependent t-test evaluates whether the mean difference between the paired observations is significantly different from zero.

58. D ependent t-test Hypotheses: Null Hypothesis (H0) : The mean difference between the paired observations is zero. 𝐻0:𝜇𝑑=0 Alternative Hypothesis (H1) : The mean difference between the paired observations is not zero. 𝐻1:𝜇𝑑≠0 Test Statistic: The test statistic for the dependent t-test is calculated as: Where: 𝑑ˉ is the mean of the differences between paired observations. 𝑠𝑑 is the standard deviation of the differences. 𝑛 is the number of pairs.

59. Degrees of Freedom: The degrees of freedom ( df ) for the dependent t-test is 𝑛−1 . P-Value: The p-value is the probability of observing a test statistic as extreme as, or more extreme than, the value observed under the null hypothesis. The p-value is compared to the significance level (α, typically 0.05). If the p-value ≤ α, reject the null hypothesis. If the p-value > α, fail to reject the null hypothesis.

60. Steps to Perform a Dependent T-Test: State the hypotheses : Define the null and alternative hypotheses. Choose the significance level (α) : Common choices are 0.05, 0.01, or 0.10. Calculate the differences : Compute the difference between each pair of observations. Calculate the mean and standard deviation of the differences . Calculate the test statistic : Use the formula provided above. Determine the degrees of freedom : 𝑑𝑓=𝑛−1 df = n −1 . Find the critical t-value : Refer to the t-distribution table or use statistical software. Compare the test statistic to the critical t-value : Alternatively, compare the p-value to α. Make a decision : Based on the comparison, decide to reject or fail to reject the null hypothesis. Draw a conclusion : Interpret the result in the context of the research question.

61. I ndependent t-test An independent t-test, also known as a two-sample t-test or unpaired t-test, is used to determine whether there is a significant difference between the means of two independent groups. This test is appropriate when you have two separate groups and want to compare their means. Purpose: The independent t-test evaluates whether the means of two independent groups are significantly different from each other.

62. D ependent t-test When to Use: You have two independent groups. The data in each group should be approximately normally distributed. The variances of the two groups should be approximately equal (homogeneity of variances). Hypotheses: Null Hypothesis (H0) : The means of the two groups are equal. 𝐻0:𝜇1=𝜇2 Alternative Hypothesis (H1) : The means of the two groups are not equal. 𝐻1:𝜇1≠𝜇2

63. Steps to Perform an Independent T-Test: State the hypotheses : Define the null and alternative hypotheses. Choose the significance level (α) : Common choices are 0.05, 0.01, or 0.10. Calculate the sample means and variances : Compute the means and variances for both groups. Calculate the test statistic : Use the formula provided above. Determine the degrees of freedom : Use the degrees of freedom formula provided above. Find the critical t-value : Refer to the t-distribution table or use statistical software. Compare the test statistic to the critical t-value : Alternatively, compare the p-value to α. Make a decision : Based on the comparison, decide to reject or fail to reject the null hypothesis. Draw a conclusion : Interpret the result in the context of the research question.

64. Conclusion It would help if you used the suggestions given in this blog to find out what works for you because there is no particular method to pick out outstanding or fascinating research problems. Keep reading and asking questions before the specific issue you've been looking for is found!