Friday, March 11, 2011

Use of SF-36 in Clinical Trials

The SF-36 is a multi-purpose, short-form health survey with 36 questions. SF-36 is one of the most popular instruments for generic health surveys and it can be used across age, disease, and treatment group, and are appropriate for a wide variety of applications. Conversely to generic health surveys, disease specific health surveys are focused on a particular condition or disease. In clinical trials, SF-36 remains as one of the most common instruments for assessing the Health Related Quality of Life (HQOL), especially in diseases where there is no valid disease-specific tool.
SF-36 yields an 8-scale profile of functional health and well-being scores (so called domain scores) as well as psychometrically-based physical and mental health summary measures [physical component summary (PCS) and mental component summary (MCS)] and a preference-based health utility index (question #2).
The mapping from the original questions -> 8 domains -> PCS or MCS is sketched in the diagram below. Notice that only 35 out of 36 questions are used in this diagram. The question 2 asks about the general health status and does not contribute to the calculation of domain scores and component summaries. A good use of question 2 is to use its responses as anchor in identifying the minimal clinically important difference (MCID). In one of our publications in J Neurol Neurosurg Psychiatry, we indeed used this approach to identify the MCID.
For these 36 questions, the response categories vary depending on the question. The response categories range from 2 (yes, no) to 6 (all of the time, most of the time, a good bit of the time, some of the time, a little of the time, none of the time). Therefore, in order to calculate the domain score, a scoring method or algorithm has to be employed. For PCS and MCS, the calculation will be based on equations with coefficients from the regression models generated from the General Healthy Populatoin. In US, it is the Healthy General US Population. If different healthy population is used, the factor score coefficients for the Z_scores will be different and PCS and MCS values will be different.
The details about scoring method can be found at QualityMetric’s website. The scoring and calculation of component summaries require the programming. Some of the example programs (but not validated) can be found from the web:
Some questions and answers on using SF-36 in clinical trials:
Q: Is SF-36 free for using in clinical trials?
A: It is not free. License has to be obtained for using in industry-sponsored clinical trials. See qualitymetric website for detail.
Q: Why do we have question #2 that is used in calculation of any domain score and component summary?
A: It can be used as an assessment of general health status and also as an anchor for identifying MCID.
Q: Which general health population should be used for norm-based scoring?
A: The advantage of norm-based scoring is to facilitate the comparisons. If a study is a US domestic study, General Healthy US Population should be used. If it is an international study, the country-specific General Healthy Populations are preferred. SF-36 has been validated in many languages.
Q: What will be language to describe the statistical analysis plan for SF-36
A: For study protocol or for journal article statistical method section, analysis plan for SF-36 should be kept simple. In one of our publications on SF-36, we simply said:
"The corresponding physical component summary and mental component summary values for the randomized participants were calculated using the reported means, SDs, and factor score coefficients that came from the healthy general US population in 1990. A linear T-score transformation method was used so that both the physical component summary and the mental component summary scores were standardized with a range of 0 (lowest) to 100 (highest)"
Q: Could SF-36 be used in cost utility analysis?
A: No. SF-36 is not a utility score. However, Sf-36 can be converted to utility score (such as EQ-5D). See my previous blog
Q: Could we have one overall score for SF-36?
A: No. PCS and MCS have to be analyzed separately. You can not add PCS and MCS to have a single overall score.
Q: How to analyze the domain scores and component summaries?
A: Typically, 8 domain scores and 2 component summaries can be analyzed separately using analysis of variance or analysis of covariance or other methods such as repeat measurement depending on the study design.
A good approach in analyzing the SF-36 is to compare the each domain score with the General Healthy Population to show how much difference between the patients in the study and the General Healthy Population for pre-treatment and for end treatment visits. This approach was utilized in our SF-36 publication in Neurology.

Thursday, March 03, 2011

Incidence Rate (IR) – How could this be wrongly calculated?

I am very surprised to see how a simple concept of ‘incidence rate’ can be wrongly calculated in documents  submitted to regulatory agencies (such as FDA). In a briefing document titled “Tiotropium (SPIRIVA): Pulmonary Allergy Drug Advisory Meeting – November 2009” submitted by a sponsor, there were wrong statements every where about the calculation of the incidence rate for safety variables.

For example, on page 50, it says “Incidence rates of adverse events were computed as the number of patients experiencing an event divided by the person-years at risk”; In Section 8.1.5 (Statistical methods), it says “For each event, an incidence rate (IR) was calculated from the number of patients with an event divided by the cumulative time at risk within a treatment group and expressed as patient-years.”  In their summary tables, they footnoted “the number of patients with an event” (instead of the number of total events) was used in calculating the incidence rate. They never listed the total number of patient year (the denominator) for their Incidence rate calculation. In ‘Statistical method’ section, they even tried to justify the use of “the difference in incidence rate” because “most Tiotropium trials have significantly greater number of patients in the placebo group discontinuing the trial early compared to tiotropium treated patients.”
“Incidence Rate” is a basic concept from epidemiology studies and is calculated as the number of events divided by the number of patient years. According to free medical dictionary, “incidence rate is the probability of developing a particular disease during a given period of time; the numerator is the number of new cases during the specified time period and the denominator is the population at risk during the period. “   According to Wikipedia, “The incidence rate is the number of new cases per population in a given time period. When the denominator is the sum of the person-time of the at risk population, it is also known as the incidence density rate or person-time incidence rate. In the same example as above, the incidence rate is 14 cases per 1000 person-years, because the incidence proportion (28 per 1,000) is divided by the number of years (two). Using person-time rather than just time handles situations where the amount of observation time differs between people, or when the population at risk varies with time. Use of this measure implicitly implies the assumption that the incidence rate is constant over different periods of time, such that for an incidence rate of 14 per 1000 persons-years, 14 cases would be expected for 1000 persons observed for 1 year or 50 persons observed for 20 years.”
In an article by Marco et al “Incidence of Chronic Obstructive Pulmonary Disease in a Cohort of Young Adults According to the Presence of Chronic Cough and Phlegm”, the incidence rate is correctly defined for calculation.
“Incidence rates of COPD were estimated as the ratio of the number of new cases and the number of person-years at risk (per 1,000), which were considered equal to the length of the follow-up for each member of the cohort.”

The key is that if you calculate the ‘incidence rate’, your numerator must be ‘number of events’, not ‘number of patients with an event’. For events that can only occur once in a lifetime for a specific patient (such as cancer), there may not be much difference between ‘number of events” and “number of patients with an event”. However, for events occurr more than one time for a specific patient, “number of events” and “number of patients with an event” are very different concepts.

In Tiotropium briefing document, the correct calculation for incidence rate should be ‘number of events (AEs or COPDs)’ divided by ‘the patient year’. It was simply wrong when they used ‘number of patients with an event’ as the numerator in their calculation of incidence rate. Their justification for using the difference in incidence rate is just the opposite of their statement. If placebo group has more dropouts, their way of calculating the incidence rate will overestimate the rate for placebo group and underestimate the rate for Tiotropium group. This can be easily illustrated using an example below:

Assuming 10 patients in Tiotropium and 10 subjects in Placebo group, 5 patients in Tiotropium group and 5 patients in Placebo group had at least one COPD during the study. The incidence of COPD will be 5/10 = 50% in both groups. Suppose it is a one-year trial, all patients in Tiotropium group completed the one-year and all patients in Placebo group completed only 6 months. The patient year will be 10X1 = 10 for Tiotropium group and 10x0.5 = 5 for Placebo group. The incidence rates now become 5/10 = 50% in Tiotropium group and 5/5 = 100% in Placebo group – this is just simply wrong. In this case, when the patient year (or person year) is used as denominator, the numerator used in the calculation should be the number of events, not the number of patients with an event.    

It is unfortunate this simple concept of ‘incidence rate’ has been wrongly calculated in Tiotropium studies. This wrong calculation may have been embedded in their paper published in prestigious New England Journal of Medicine.

If ‘number of patients with an event’ is used in the numerator, the denominator has to be the total number of patients (not the number of patient year). ‘Number of patients with an event’ divided by ‘number of total patients’ is called ‘incidence of events’ – this is a typical way when we summarize the adverse events in clinical trials.