The prevalence of depression in patients with mild cognitive impairment is 10%. On applying a depression rating scale with the likelihood ratio of a positive test (LR+) equal to 10, a patient with mild cognitive impairment becomes test positive.
The probability that this patient is depressed is equal:
C. This question tests one’s ability to calculate post-test probability from likelihood ratios. The probability of having a disease after testing positive with a diagnostic test depends on two factors: (a) the prevalence of the disease, (b) the likelihood of a positive test result using the instrument. It is important to remember that baseline prevalence of a disease for which a diagnostic instrument is being tested is taken as the pretest probability.
So pretest probability = 10%
Now, post-test odds = likelihood ratio × pretest odds
From a given probability odds can be calculated using the formula:
odds = (probability)/(1 – probability)
Here pretest odds = (10%)/(1 – 10%) = 10/90 = 1/9.
Now post-test odds = likelihood ratio × pretest odds = 10 × 1/9 = 10/9
Using the formula probability = odds/(1 + odds)
post-test probability = (10/9)/[1 + (10/9)] = 10/19 = 52.3%
Reference:
A multi-centre double blind pragmatic randomized controlled trial (RCT) reported remission rates for depression of 65% for fluoxetine and 60% for dosulepin.
The number of patients that must receive fluoxetine for one patient to achieve the demonstrated beneficial effect is:
B. This question tests one’s knowledge of the NNT (number needed to treat) concept. NNT is given by the inverse ratio of the absolute benefit increase (ABI) in therapeutic trials. ABI is the difference between benefit due to experimental intervention and the compared standard/ placebo. Here it is given by 65% – 60% = 5%. If ABI = 5%, NNT = 100/5 = 20.
In a randomized double-blind trial two groups of hospitalized depressed patients treated with selective serotonin reuptake inhibitors (SSRIs) are evaluated for beneficial effects on insomnia of trazodone vs temazepam.
Which of the following is NOT an important factor when evaluating the internal validity of results obtained from the above study?
C. Threats to internal validity of an experimental study include confounding, selection bias, differential attrition, and quality of measurement. Having a significant difference in baseline SSRI therapy could explain differential outcomes in the trazodone vs temazepam groups. Similarly, poor randomization may lead to selection bias and influence the differences in outcome. Failure to account for differential drop-out rates may spuriously inflate or deflate the difference in outcome. Using a scale with poor sensitivity to change will reduce the magnitude of differences that could be observed. Given both groups are recruited from the same setting (hospital), this must not influence validity; on the other hand, this might well influence generalizability of results to the non-hospitalized population (external validity).
References:
While adapting the results of an RCT (Randomized Controlled Trial) into clinical practice, a clinician wants to calculate the new NNT values for his own clinical population using the results of the RCT.
Apart from the reported RCT which of the following is needed to carry out the calculation of the new NNT (Number Needed to Treat)?
A. Published RCTs may quote impressive outcomes in terms of NNT. Applying principles of evidence-based medicine, one must check for the internal validity of a study and the degree of generalizability before adapting the results to clinical practice. One must also be aware of the fact that though clinically more meaningful, NNTs quoted in RCTs may not translate to the same extent in actual clinical practice. One way of appreciating the usefulness of a newly introduced drug is to calculate the NNT for one’s own clinical population (target population). To enable this one may estimate the patient expected event rate (PEER), which is given by the expected spontaneous resolution rate or the response rate for an existing standard treatment. This can be obtained from the local audit data or clinical experience. The product of PEER and relative benefit increase from the published RCT gives the new absolute benefit increase (ABI new) value for the target population. The inverse of the new ABI gives the new NNT for the target population. The disease prevalence rate or absolute size of the target population has no effect on the new NNT.
In an attempt to ensure equivalent distribution of potential effect-modifying factors in treating refractory depression, a researcher weighs the imbalance that might be caused whenever an individual patient enters one of the two arms of the study. Every patient is assigned to the group where the least amount of imbalance will be caused.
This method is called:
C. In most treatment trials interventions are allocated by randomization. Block randomization and stratified randomization can be used to ensure the balance between groups in size and patient characteristics. But it is very difficult to stratify using several variables in a small sample. A widely acceptable alternative approach is minimization. This method can be used to ensure very good balance between groups for several confounding factors irrespective of the size of the sample. With minimization the treatment allocated to the next participant enrolled in the trial depends (wholly or partly) on the characteristics of those participants already enrolled. This is achieved by a simple mathematical computation of magnitude of imbalance during each allocation.
Click to expand