{"id":4278,"date":"2023-08-01T12:22:36","date_gmt":"2023-08-01T12:22:36","guid":{"rendered":"https:\/\/www.editage.com\/insights\/6-common-mistakes-made-by-biomedical-researchers-while-testing-the-relationships-between-variables\/"},"modified":"2025-01-15T06:20:02","modified_gmt":"2025-01-15T06:20:02","slug":"6-common-mistakes-made-by-biomedical-researchers-while-testing-the-relationships-between-variables","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/6-common-mistakes-made-by-biomedical-researchers-while-testing-the-relationships-between-variables","title":{"rendered":"6 Common mistakes made by biomedical researchers while testing the relationships between variables"},"content":{"rendered":"

Typically, relationships between variables are tested using correlation, association, or regression analysis<\/a> (we\u2019ve covered special relationships like odds and risks<\/a> in another post). Today, we\u2019ll look at some important precautions for such analyses, to ensure your findings are reliable and provide high-quality evidence.\u00a0\u00a0<\/p>\n

1. Not clearly identifying and summarizing the variables tested:<\/strong> Readers always need to know exactly what you\u2019re studying and how you\u2019ve summarized these variables<\/a>. For example, if you\u2019re examining the relationship between exercise and glucose control, you\u2019ll need to define glucose control in the context of your study (are you considering HbA1c levels, post-prandial glucose levels, a combination of the two, etc.)?\u00a0<\/p>\n

2. Not checking data type:<\/strong> Before running any kind of analysis, you need to verify what type of data you\u2019ve collected. Data can be continuous (e.g., BMI), categorical (e.g., race), or ordinal (e.g., cancer stage). The type of test you run depends on the variables you have. Correlations can be calculated between continuous variables, whereas tests of association (e.g., chi-square<\/a>) can be calculated between categorical variables. \u00a0<\/p>\n

3. Not checking data distribution:<\/strong> Understanding distribution<\/a> of diverse datasets can prevent misleading conclusions. By recognizing this, you can choose appropriate statistical tests or consider transforming the data to ensure accurate results. For example, Pearson\u2019s r can be calculated if the two continuous variables are normally distributed, whereas Spearman\u2019s rho may be a more suitable option for non-normally distributed variables. \u00a0<\/p>\n

4. Not checking for missing data and outliers: <\/strong>In any research study, missing data<\/a> and outliers are sneaky little devils that can skew your analysis. Imagine you are investigating the relationship between age and cholesterol levels, using electronic health records (EHRs). You might find that some EHRs don\u2019t include VLDL cholesterol levels, leading to missing data.\u00a0<\/p>\n

Addressing missing data and handling outliers with care is essential. You could use imputation methods to replace missing data or consider robust statistical techniques if outliers are present. Ignoring them or using inappropriate methods like Last Observation Carried Forward might introduce bias into your results, and we definitely don’t want that!\u00a0<\/p>\n

5. Using vague descriptors: <\/strong>When it comes to presenting your findings, clarity is key! Using terms like “strong” or “weak” to describe relationships<\/a> between variables can be vague and open to interpretation. Instead, report actual coefficients obtained from your analysis.\u00a0<\/p>\n

For example, if you are studying the correlation between exercise and cardiovascular health, mentioning a specific correlation coefficient (like Pearson’s r) gives a clear picture of the relationship’s strength and direction. This approach leaves no room for confusion and improves the rigor of your conclusions.\u00a0<\/p>\n

Pro tip: <\/strong>Don\u2019t report just p values either! In both your abstract and main text, include correlation coefficients (with the \u2013 sign if needed), to give readers a clear picture of the key relationships you\u2019re studying.\u00a0<\/p>\n

6. Drawing inferences beyond what the analysis may substantiate: <\/strong>Biomedical researchers use a variety of statistical methods<\/a>, from regression analysis<\/a> to t-tests<\/a> and ANOVA<\/a>s. Each analysis type yields specific insights, and it’s essential to draw inferences accordingly.\u00a0<\/p>\n

Suppose you find that dietary fiber intake is inversely correlated with acne severity. Based on your findings, you could recommend that acne management programs include a component on getting participants to consume more fiber. However, you can\u2019t claim that a low-fiber diet causes acne, because you\u2019ve only done a correlation analysis.\u00a0\u00a0<\/p>\n

Conclusion\u00a0<\/strong><\/p>\n

So there you have it, six crucial precautions to keep in mind while testing relationships between variables in biomedical research. By following the above best practices, you’ll be well-equipped to produce high-quality, robust research<\/a> that advances our understanding of human health.\u00a0<\/p>\n

\u00a0<\/p>\n

Our expert biostatisticians can help you make sure you’re using the right methods to analyze relationships in your study, in keeping with your study design, research question, and data characteristics. Check out Editage’s Statistical Analysis & Review Services<\/a>.\u00a0\u00a0\u00a0<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Typically, relationships between variables are tested using correlation, association, or regression analysis (we\u2019ve covered special relationships like odds and risks in another post). Today, we\u2019ll look at some important precautions for such analyses, to ensure your findings are reliable and provide high-quality evidence.\u00a0\u00a0 1. Not clearly identifying and summarizing the variables tested: Readers always need […]<\/p>\n","protected":false},"author":15,"featured_media":33313,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2420,2403],"tags":[2622,1319,2778,366],"new_categories":[],"new_tags":[],"series":[],"class_list":["post-4278","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analysis","category-publication-support-services","tag-analysisofdata","tag-statistical-analysis","tag-statistical-analysis-and-review","tag-statistical-reporting"],"acf":[],"yoast_head":"\n6 Common mistakes made by biomedical researchers while testing the relationships between variables | Editage Insights<\/title>\n<meta name=\"description\" content=\"Relationships between variables are tested using correlation, association, or regression analysis. 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