Two studies were conducted on different samples from the same population to assess the relationship between oral contraceptive use and the risk of deep venous thrombosis (DVT). Study A showed an increased risk of DVT among oral contraceptive users, with a relative risk of 2.0 and a 95% confidence interval of 1.2-2.8. Study B showed a relative risk of 2.01 and a 95% confidence interval of 0.8-3.1. Which of the following statements is most likely to be true regarding these 2 studies?

Question

Two studies were conducted on different samples from the same population to assess the relationship between oral contraceptive use and the risk of deep venous thrombosis (DVT).  Study A showed an increased risk of DVT among oral contraceptive users, with a relative risk of 2.0 and a 95% confidence interval of 1.2-2.8.  Study B showed a relative risk of 2.01 and a 95% confidence interval of 0.8-3.1.  Which of the following statements is most likely to be true regarding these 2 studies?

Quizplus · Accepted Answer

11ec25c8_d31f_66bb_bc81_55a6759e7916_MD0003_00 The relative risk (RR) is the risk of an outcome (eg, deep venous thrombosis [DVT]) in the exposed group (eg, oral contraceptive [OC] users) divided by the risk of that outcome in the unexposed group. If the RR = 1.0 (null value), then there is no association between exposure and outcome (or disease). An RR >1.0 indicates that exposure is associated with increased risk of disease. An RR <1.0 means that exposure is associated with decreased risk of disease. The RR in studies A and B was >1.0, suggesting increased DVT risk among OC users. For a result to be considered statistically significant, its corresponding confidence interval (CI) must NOT contain the null value. When the 95% CI does not include the null value, this gives a corresponding p-value <0.05 and the association between exposure and outcome is considered statistically significant. A p-value <0.05 reflects that there is a very low probability that the result was due to chance alone; formally, the p-value is the probability of observing a given (or more extreme) result due to chance alone, assuming that the null hypothesis is true. The RR obtained in study A (RR = 2.0) is statistically significant as its 95% confidence interval (1.2-2.8) does not include the null value (because it does not include 1.0) (Choice C). The 95% confidence interval for study B (0.8-3.1) includes the null value of 1.0, and thus the study is not statistically significant; in other words, the corresponding p-value would be >0.05 (Choice A). The CI in study B is wider than in study A, and it contains the null value (ie, not statistically significant). This wider CI is most likely due to a smaller sample size, which decreases the study's power (ie, its ability to detect the difference between exposed and unexposed subjects). Increasing sample size would increase the study's power and make the CI narrower. By doing so, the null value would likely have been excluded (as in study A), thus increasing statistical significance. Although there are several causes of a wide CI, small sample size is a common one. (Choices B and D) Measurements that lack accuracy (proximity to the true value) generally reflect a bias (systematic deviation from the true value). The accuracy of the studies cannot be judged because there is no information on how they were designed and conducted. Educational objective: The power of a study represents its ability to detect a difference between 2 groups (eg, exposed versus unexposed) when there truly is a difference. Increasing the sample size increases the power of a study and consequently narrows the confidence interval surrounding the point estimate. Confidence intervals express statistical significance and are interrelated with p-values.

Two Studies Were Conducted on Different Samples from the Same