Two independent research groups are studying the effect of a new anticoagulant on the incidence of deep-vein thrombosis after hip replacement surgery. The first group designs a 1:1 randomized trial with 1000 patients comparing the new drug with the standard of care (500 patients in each group). The second group designs a similar randomized trial but intends to recruit 500 patients (250 patients in each group). Assuming similar populations for patient recruitment, the small study has a higher chance of which of the following?
A)Early termination
B)Outcome misclassification
C)Selection bias
D)Type I error
E)Type II error

Question

Two independent research groups are studying the effect of a new anticoagulant on the incidence of deep-vein thrombosis after hip replacement surgery.  The first group designs a 1:1 randomized trial with 1000 patients comparing the new drug with the standard of care (500 patients in each group).  The second group designs a similar randomized trial but intends to recruit 500 patients (250 patients in each group).  Assuming similar populations for patient recruitment, the small study has a higher chance of which of the following?
A)Early termination
B)Outcome misclassification
C)Selection bias
D)Type I error
E)Type II error

Quizplus · Accepted Answer

Explanation: A type II error occurs when a study fails to reject a null hypothesis (H₀) that is false. The probability of a type II error (β) is related to how much power a study has to detect a difference when a difference actually exists. Studies with a larger sample size have greater power to detect differences. The formula for power is: power = 1 - β (or, equivalently, β = 1 - power), with β ranging from 0−1. As power increases, β decreases; and as power decreases, β increases. For this example, the smaller study has less power than the larger study and so would be less likely to detect a difference between the groups, increasing the chance of a type II error. (Choice A) Early termination often occurs when a study shows an overwhelmingly positive or negative effect on a treatment group before the study's planned conclusion; negating the need for continuation. This can theoretically occur more often in larger studies as they have more power to detect differences than smaller studies. (Choice B) Outcome misclassification is an error that occurs in epidemiologic studies when the outcome of interest is misclassified. For example, in a study examining the effect of smoking on rates of stomach cancer, outcome misclassification would be present if researchers could not accurately ascertain whether participants actually have stomach cancer. This error is not influenced by sample size. (Choice C) Selection bias refers to the inappropriate selection or poor retention of study participants. As the populations in the 2 studies are similar, this would not be a likely source of bias in this example. Selection bias (Choice D) Type I errors (false positives) occur when a study incorrectly rejects a null hypothesis that is true. The rate of type I errors is denoted by α and usually reflects the significance level of a test. A higher α increases the likelihood of a type I error and decreases the likelihood of a type II error. The main effect of a smaller sample size is to increase the probability of a type II error rather than a type I error. Educational objective: The failure to detect a difference between groups when it exists is referred to as type II error. The probability of type II errors (β) is related to the power of a study (calculated as 1 − β). Power is increased with larger sample sizes, so a smaller study would be less effective in detecting differences between groups, increasing the chance of a type II error.

Two Independent Research Groups Are Studying the Effect of a New