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Related Concept Videos

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Trimmed Mean01:10

Trimmed Mean

While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first column of the Routh...
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
Bias01:22

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...

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Assessment of Cerebral Lateralization in Children using Functional Transcranial Doppler Ultrasound (fTCD)
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Published on: September 27, 2010

Conditions for bias from differential left truncation.

Penelope P Howards1, Irva Hertz-Picciotto, Charles Poole

  • 1Division of Epidemiology, Statistics and Prevention Research, National Institute of Child Health and Human Development, Rockville, MD 20852, USA. howardsp@mail.nih.gov

American Journal of Epidemiology
|December 8, 2006
PubMed
Summary

Logistic regression may bias spontaneous abortion study results due to left truncation. Survival analysis (Cox models) corrects for this bias, offering more accurate insights into pregnancy loss risk factors.

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Spontaneous abortion studies often face left truncation, where outcomes occur before enrollment, biasing results.
  • Unconditional logistic regression commonly used in these studies does not account for left truncation.

Purpose of the Study:

  • To assess bias from logistic versus Cox models in left-truncated spontaneous abortion data.
  • To explore conditions that generate bias using simulated exposure data.

Main Methods:

  • Analysis of a 1998 US pregnancy cohort study (n=5,104) on trihalomethanes and spontaneous abortion.
  • Comparison of logistic regression (ignoring left truncation) and Cox regression (accommodating left truncation).
  • Simulation of exposure data to investigate bias-generating conditions.

Main Results:

  • Odds ratios and hazard ratios from the actual study differed by 10% or less.
  • Bias magnitude depended on average gestational age at entry differences between exposed and unexposed groups.
  • Simulations indicated >20% bias in odds ratio when entry differences were ≥10 days.

Conclusions:

  • Cox regression is more appropriate than logistic regression for left-truncated spontaneous abortion studies.
  • Differences in entry gestational age, influenced by factors like education and ethnicity, can cause significant bias.
  • Cox regression offers a more accurate and equally feasible alternative to logistic regression for analyzing such data.