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Related Concept Videos

Relative Risk01:12

Relative Risk

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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
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z Scores and Area Under the Curve01:17

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z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Related Experiment Video

Updated: Sep 24, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Nomogram-based risk prediction of macrosomia: a case-control study.

Jing Du1, Xiaomei Zhang2, Sanbao Chai1

  • 1Department of Endocrinology and metabolism, Peking University International Hospital, No. 1 Life Garden Road Zhongguancun Life Science Garden Changping District, Beijing, 102206, China.

BMC Pregnancy and Childbirth
|May 5, 2022
PubMed
Summary

This study developed a nomogram to predict macrosomia, a condition linked to poor birth outcomes, in early pregnancy. The model effectively identifies at-risk pregnancies using first-trimester data.

Keywords:
MacrosomiaNomogramRisk factorScreening

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Area of Science:

  • Obstetrics and Gynecology
  • Maternal-Fetal Medicine
  • Reproductive Health

Background:

  • Macrosomia is associated with adverse maternal and fetal outcomes.
  • Limited research exists on predicting macrosomia in early pregnancy.
  • Early prediction is crucial for timely intervention and improved outcomes.

Purpose of the Study:

  • To develop a predictive nomogram for macrosomia during the first trimester of pregnancy.
  • To identify key risk factors for macrosomia in early pregnancy.
  • To provide clinicians with a tool for early risk assessment.

Main Methods:

  • A case-control study involving 1549 pregnant women.
  • Analysis of risk factors for macrosomia using multivariate logistic regression.
  • Development and validation of a nomogram for predicting first-trimester macrosomia risk.

Main Results:

  • The prevalence of macrosomia was 6.13%.
  • Identified risk factors include pre-pregnancy overweight/obesity, multiparity, history of macrosomia, history of gestational diabetes mellitus/diabetes mellitus, and elevated HbA1c and TC levels in the first trimester.
  • The nomogram demonstrated good predictive performance with an AUC of 0.807, sensitivity of 0.716, and specificity of 0.777.

Conclusions:

  • A nomogram model effectively predicts macrosomia risk in the first trimester.
  • This tool can aid clinicians in early identification and management of pregnancies at risk for macrosomia.
  • Early prediction facilitates timely interventions to improve maternal and fetal outcomes.