Data analysis Part III �Comparative analysis

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Tigist W. (MD, MPH)

January, 2023

Research

• A systematic collection, analysis and interpretation of data to answer a certain question or to solve a problem.

In other words, research has the following flow

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• Observing Question/ problem🡪 designing a way to answer/solve the Question/ problem (proposal) 🡪 To implement the proposal we need data🡪 systematically collect the data 🡪 Manage the data 🡪 Analyze the data🡪 interpret the result of the analysis (thesis/ manuscript)🡪 Apply the result to the practice

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Data analysis: Definition

• It is a systematic process of applying statistical techniques and logic (scientific knowledge) to a well managed data to answer our research question.

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OR

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• Making a meaning out of a raw data.
• Research question? 🡪 Raw data 🡪 Data management 🡪 Data analysis (Answers the research question) 🡪 Input for informed decision.

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• How do we make a meaning???
• Data analysis levels/steps

Data analysis: levels/ Steps

• Describing the data
• Characterizing the study population based on all the studied variables (exposures and outcome/s).
• All research types (Qualitative and Quantitative)
• Descriptive statistical analysis
• Comparing groups
• Comparing the characteristics of the study population based on their outcome status or particular exposure of interest.
• Mostly in analytic comparative research designs
• Inferential statistical analysis (Comparison analysis)
• Building relationships
• Identifying significant exposures that affect the outcome.
• Only in analytic research designs
• Inferential statistical analysis (regression analysis)

Comparing groups

• Comparing the characteristics of the study population based on their outcome status (cohort or experimental studies) or particular exposure of interest (comparative cross sectional or case control studies) to see if they have a statistically significant difference or not.

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Article review

• Factors associated with development of symptomatic disease in Ethiopian COVID-19 patients: a case-control study.

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(Check Tables 1 and 2 for comparative tests)

https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-021-06465-1

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Data analysis tool selection criteria: �For all analysis levels

• Selecting the appropriate data analysis tool for all the three levels
• Always depends on the scale of measurement of the variable to be analyzed.

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Selecting the comparison test

Steps

1. Identify the variables scale of measurement
2. Select the test
3. Test assumptions
4. If it fails use the parallel non-parametric test

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Two types

• Parametric tests
• Make assumption about data distribution
• Always test assumptions
• Greater statistical power

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• Non-parametric tests
• Distribution free tests

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Example

• Is there association between disease severity at admission (Mild Vs Moderate Vs Severe) and disease outcome (Improved Vs Complicated)?

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• Chi-square test

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• Is there a difference in blood glucose level between different group of patients taking Metformin, glipizide, pioglitazone and miglitol?

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• Independent group ANOVA

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Practice 1

https://t.me/Medical_Research_Lounge/857

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Practice 2

https://t.me/Medical_Research_Lounge/890

Practice 3

https://t.me/Medical_Research_Lounge/904

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Article review

• Factors associated with development of symptomatic disease in Ethiopian COVID-19 patients: a case-control study.

​

(Check Tables 1 and 2 for comparative tests)

https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-021-06465-1

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End

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