Podcast
Questions and Answers
In a study investigating the association between a specific lunch and diarrheal illness, an adjusted odds ratio (OR) of 28.15 with a 95% Confidence Interval (CI) of 1.87–422.65 and a p-value < 0.05 is reported. Which of the following interpretations is MOST accurate?
In a study investigating the association between a specific lunch and diarrheal illness, an adjusted odds ratio (OR) of 28.15 with a 95% Confidence Interval (CI) of 1.87–422.65 and a p-value < 0.05 is reported. Which of the following interpretations is MOST accurate?
- The study suggests a statistically significant association between consuming the lunch and diarrheal illness, but causality cannot be definitively concluded from this analysis alone. (correct)
- The study provides strong evidence that consuming the lunch causes diarrheal illness due to the high adjusted odds ratio.
- There is a negligible association between consuming the lunch and the diarrheal illness because the confidence interval is wide.
- Consuming the lunch is unlikely to be related to diarrheal illness, as the p-value is less than 0.05, indicating a chance finding.
A researcher performs a multivariable logistic regression to analyze risk factors for a disease. What is the PRIMARY benefit of using a multivariable approach compared to univariable analysis in this context?
A researcher performs a multivariable logistic regression to analyze risk factors for a disease. What is the PRIMARY benefit of using a multivariable approach compared to univariable analysis in this context?
- Multivariable analysis simplifies the interpretation of odds ratios by isolating the effect of each variable independently.
- Multivariable analysis allows for the examination of the independent effect of each risk factor while controlling for potential confounding variables. (correct)
- Multivariable analysis broadens the confidence intervals, making the findings more generalizable to different populations.
- Multivariable analysis guarantees a lower p-value, thereby increasing the statistical significance of the findings.
In the context of hypothesis testing, failing to reject the null hypothesis (p > 0.05) implies which of the following?
In the context of hypothesis testing, failing to reject the null hypothesis (p > 0.05) implies which of the following?
- The alternative hypothesis is proven to be false.
- The study findings are clinically significant, even if not statistically significant.
- There is insufficient statistical evidence to reject the null hypothesis at the chosen significance level. (correct)
- There is strong evidence to support the null hypothesis.
An 'adjusted odds ratio' is often reported in epidemiological studies. What does the term 'adjusted' signify in this context?
An 'adjusted odds ratio' is often reported in epidemiological studies. What does the term 'adjusted' signify in this context?
A 95% confidence interval (CI) for an odds ratio (OR) is calculated to be 0.80–1.20. Assuming a significance level of 0.05, what can be concluded about the statistical significance of the association?
A 95% confidence interval (CI) for an odds ratio (OR) is calculated to be 0.80–1.20. Assuming a significance level of 0.05, what can be concluded about the statistical significance of the association?
Which statement BEST describes the relationship between p-values and 95% confidence intervals (CIs) in hypothesis testing?
Which statement BEST describes the relationship between p-values and 95% confidence intervals (CIs) in hypothesis testing?
Consider a study reporting an adjusted odds ratio (OR) of 0.5 with a 95% Confidence Interval (CI) of 0.3 - 0.7 for the association between a new dietary intervention and the risk of developing type 2 diabetes. Which of the following is the MOST appropriate interpretation?
Consider a study reporting an adjusted odds ratio (OR) of 0.5 with a 95% Confidence Interval (CI) of 0.3 - 0.7 for the association between a new dietary intervention and the risk of developing type 2 diabetes. Which of the following is the MOST appropriate interpretation?
What is the primary purpose of understanding p-values, confidence intervals, multivariable analysis, and adjusted odds ratios in the context of outbreak investigations and epidemiological studies?
What is the primary purpose of understanding p-values, confidence intervals, multivariable analysis, and adjusted odds ratios in the context of outbreak investigations and epidemiological studies?
What is the most accurate interpretation of an Odds Ratio (OR) of 63 with a 95% Confidence Interval (CI) of 62-64?
What is the most accurate interpretation of an Odds Ratio (OR) of 63 with a 95% Confidence Interval (CI) of 62-64?
In the context of epidemiological studies, what is the primary limitation of univariable analysis when assessing the relationship between exposures and outcomes?
In the context of epidemiological studies, what is the primary limitation of univariable analysis when assessing the relationship between exposures and outcomes?
Which statement accurately describes the role of a confounder in epidemiological research?
Which statement accurately describes the role of a confounder in epidemiological research?
What is the primary advantage of using multivariable analysis (MVA) compared to univariable analysis in epidemiological studies?
What is the primary advantage of using multivariable analysis (MVA) compared to univariable analysis in epidemiological studies?
In the context of multivariable analysis, what does an 'adjusted' odds ratio (aOR) represent?
In the context of multivariable analysis, what does an 'adjusted' odds ratio (aOR) represent?
Which of the following statistical techniques is most commonly used in multivariable analysis to estimate adjusted odds ratios?
Which of the following statistical techniques is most commonly used in multivariable analysis to estimate adjusted odds ratios?
When interpreting the output of a multivariable regression, how should researchers treat variables included in the model primarily as confounder controls rather than exposures of primary interest?
When interpreting the output of a multivariable regression, how should researchers treat variables included in the model primarily as confounder controls rather than exposures of primary interest?
In a study examining the relationship between a new medication (exposure) and patient recovery (outcome), researchers identify age as a potential confounder. How does including age in a multivariable regression model address this confounding?
In a study examining the relationship between a new medication (exposure) and patient recovery (outcome), researchers identify age as a potential confounder. How does including age in a multivariable regression model address this confounding?
A clinical trial with an exceptionally large sample size reports a statistically significant p-value of 0.03 for a minimal difference in blood pressure reduction between a new drug and a placebo. Which of the following interpretations is MOST accurate regarding this finding?
A clinical trial with an exceptionally large sample size reports a statistically significant p-value of 0.03 for a minimal difference in blood pressure reduction between a new drug and a placebo. Which of the following interpretations is MOST accurate regarding this finding?
In evaluating the results of a study, researchers often prefer to examine confidence intervals in addition to p-values. What is the PRIMARY advantage of using confidence intervals over relying solely on p-values?
In evaluating the results of a study, researchers often prefer to examine confidence intervals in addition to p-values. What is the PRIMARY advantage of using confidence intervals over relying solely on p-values?
A research team calculates a 95% confidence interval for the odds ratio of developing a disease in an exposed group compared to an unexposed group. Which of the following statements BEST describes what this 95% confidence interval represents?
A research team calculates a 95% confidence interval for the odds ratio of developing a disease in an exposed group compared to an unexposed group. Which of the following statements BEST describes what this 95% confidence interval represents?
Consider a scenario where multiple studies investigate the same effect, and one study reports a 99% confidence interval while another reports a 95% confidence interval. Assuming all other factors are equal (sample size, variability, etc.), how will the width of the 99% confidence interval compare to the 95% confidence interval?
Consider a scenario where multiple studies investigate the same effect, and one study reports a 99% confidence interval while another reports a 95% confidence interval. Assuming all other factors are equal (sample size, variability, etc.), how will the width of the 99% confidence interval compare to the 95% confidence interval?
A study examining the difference in exam scores between two teaching methods reports a non-significant p-value (p = 0.15) and a remarkably narrow 95% confidence interval for the mean difference. What is the MOST appropriate conclusion based on these results?
A study examining the difference in exam scores between two teaching methods reports a non-significant p-value (p = 0.15) and a remarkably narrow 95% confidence interval for the mean difference. What is the MOST appropriate conclusion based on these results?
In contrast to the previous scenario, another study investigating a similar research question also finds a non-significant p-value (p = 0.20), but this time reports a very wide 95% confidence interval for the effect size. What is the MOST likely reason for the wide confidence interval in this context?
In contrast to the previous scenario, another study investigating a similar research question also finds a non-significant p-value (p = 0.20), but this time reports a very wide 95% confidence interval for the effect size. What is the MOST likely reason for the wide confidence interval in this context?
An epidemiological study reports an odds ratio (OR) of 1.5 for the association between a certain dietary factor and disease risk, with a 95% confidence interval of 0.8 – 2.8. Based on this confidence interval, what is the appropriate conclusion regarding statistical significance at the p=0.05 level?
An epidemiological study reports an odds ratio (OR) of 1.5 for the association between a certain dietary factor and disease risk, with a 95% confidence interval of 0.8 – 2.8. Based on this confidence interval, what is the appropriate conclusion regarding statistical significance at the p=0.05 level?
Consider two different studies examining the same intervention. Study 1 reports an odds ratio of 0.75 with a 95% CI of 0.60 – 0.93, while Study 2 reports an odds ratio of 0.70 with a 95% CI of 0.45 – 1.09. Which of the following statements BEST compares the statistical significance of the findings from these two studies at p=0.05?
Consider two different studies examining the same intervention. Study 1 reports an odds ratio of 0.75 with a 95% CI of 0.60 – 0.93, while Study 2 reports an odds ratio of 0.70 with a 95% CI of 0.45 – 1.09. Which of the following statements BEST compares the statistical significance of the findings from these two studies at p=0.05?
Flashcards
What is a P-value?
What is a P-value?
The probability of observing a test statistic as extreme as, or more extreme than, the result obtained, assuming the null hypothesis is true.
What is a Confidence Interval?
What is a Confidence Interval?
A range of values that, with a certain degree of confidence (usually 95%), contains the true population parameter.
What is Multivariable Analysis?
What is Multivariable Analysis?
Statistical analysis that examines the relationship between multiple independent variables and a dependent variable.
What is an Adjusted Odds Ratio?
What is an Adjusted Odds Ratio?
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What is the Null Hypothesis?
What is the Null Hypothesis?
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What does a p-value > 0.05 mean?
What does a p-value > 0.05 mean?
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Does p < 0.05 establish causation?
Does p < 0.05 establish causation?
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What are Microbiological and Epidemiological investigations for?
What are Microbiological and Epidemiological investigations for?
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Confidence Interval
Confidence Interval
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95% Confidence Interval
95% Confidence Interval
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Narrow Confidence Interval
Narrow Confidence Interval
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Wide Confidence Interval
Wide Confidence Interval
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Non-significant p-value and Narrow CI
Non-significant p-value and Narrow CI
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Non-significant p-value and Wide CI
Non-significant p-value and Wide CI
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CI Includes ONE (for RR/OR)
CI Includes ONE (for RR/OR)
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Assessing Significance with CI
Assessing Significance with CI
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Case-Control Study Output
Case-Control Study Output
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Univariable Analysis
Univariable Analysis
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Confounder
Confounder
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Multivariable Analysis (MVA)
Multivariable Analysis (MVA)
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Aims of MVA
Aims of MVA
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Adjusted Odds Ratio (aOR)
Adjusted Odds Ratio (aOR)
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aOR function
aOR function
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Study Notes
- Analytical studies and key statistical concepts part II
- By Dr Daniel Todkill
- Consultant in Public Health Medicine
- Field Epidemiology, UK-Health Security Agency / Warwick Evidence, Warwick Medical School Outbreak & Infection Module
Summary of Learning Outcomes
- P value: understand what it is
- Confidence Interval: understand what is meant by a 95% confidence interval
- Multivariable: understanding of “multivariable”
- Adjusted Odds Ratio: understanding of what it is
Application in Outbreak Investigations
- Results of outbreak investigations are often presented with statistical measures multivariable logistic regressions, odds ratios, confidence intervals, and p-values
- Example: A 2015 investigation by Public Health England and the UK Ministry of Defence into cases of diarrhoea and fever in military personnel returning from supporting the Ebola epidemic in Sierra Leone
- Tests for Ebola virus infection came back negative
- PCR tests detected the ipaH gene in 10/12 faecal specimens
- Shigella boydii serotype 20 was isolated from 7 patients
- The case control study was analyzed using multivariable logistic regression
- Conclusions showed consumption of a coronation chicken lunch at the transit camp in Sierra Leone (SL) 24-48 h prior to departure for the UK was significantly associated with disease
- adjusted odds ratio (OR) 28.15, 95% CI: 1.87-422.65, p < 0.05
- Rapid, effective microbiological and epidemiological investigations are critical for identifying the aetiological agent in patients presenting with fever and diarrhoea
P-Value
- Represents the probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is correct
- Null hypothesis states that there is no association between cases and exposures
- Low p-value, usually below 0.05 (or 5%), suggests the association is likely real, leading to rejection of the null hypothesis
- P-values help determine the significance of results when testing a hypothesis
- In a trial, the 'null hypothesis' asserts no difference between the groups being compared, and the 'alternative hypothesis' posits a difference
- P-value ranges between 0 and 1
- A small p-value (≤ 0.05) indicates strong evidence against the null hypothesis, often deemed 'statistically significant'
- Values > 0.05 are considered weak evidence, failing to reject the null
- P-value does not establish a causal relationship
- Usually generated through statistical tests like the Student's t-test or chi-squared test
- P values depend on both the size of the effect and the sample size, thus small differences can be considered ‘statistically significant’ if the sample is large
Confidence Intervals
- A range of potential values for the risk ratio or odds ratio as they are both estimates, calculated from a sample
- CI indicates the precision with which the sample estimate is likely to represent the population
- For a 95% CI, the true value for the population at large will lie within the range 19 times out of 20 (95/100)
- A narrow CI implies a large sample size or very similar results
- If a p-value is non-significant and there is a narrow CI, then there is truly no effect
- If a p-value is non-significant and there is a wide CI, this means there is not enough data for clear results
Relative Risk and Odds Ratios
- Where a 95% CI includes ONE; there is no evidence at the level of p=0.5 there is a true difference
- (for Odds Ratios - OR), if the 95% confidence interval (CI) includes 1, it means statistically significant difference between the groups being compared
- If the interval does not include 1, the result is considered statistically significant
Multivariable Analysis (MVA)
- Associations are looking for several X variables (exposures) simultaneously but only one Y illness
- There are different statistical techniques to do this, regression is most common
- The aims are to understand which variables are associated with an outcome, account for the effect of other variables when measuring one variable, adjust for 'confounders'
- The output of regression is usually an 'adjusted' odds ratio (aOR)
- The aOR controls for the other variables in the regression by producing the odds for each individual variable assuming that the other variables were held constant
- In the example study, aOR of 28.15 means the odds of having eaten chicken were 28.15 times higher among case-patients than controls, when all other measured variables were controlled for
- Chicken may be a risk factor for illness because the OR is greater than 1
- In this case the magnitude of the OR suggests a strong association The 95% CI's do not cross 1, and p<0.05, suggesting that this is a statistically significant result
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Description
This quiz covers key concepts in statistics and epidemiology, including odds ratios, confidence intervals, p-values, and hypothesis testing. Questions focus on interpreting statistical results and understanding the benefits of multivariable analysis.
