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What was the research about?

Researchers can use data from patient registries to look at which medicines or other treatments work best. Registries store data about people with a specific health problem. The data may include the health care and medicines patients receive over time and patient reports of their health status.

To find out patients’ health status, registries ask patients to fill out surveys at different times during treatment. Researchers can compare survey results from when patients first take the survey with results from surveys taken after treatment. They can then find out how well a medicine works. But patients may not always take the first survey before they start a new medicine. Sometimes, they don’t take the first survey until after starting treatment. When this happens, it is hard to know how well the medicine works.

In this study, the research team looked at different ways to use data from patient surveys in registries. The team wanted to learn which way would give the most accurate understanding of the effects of a new medicine. The study also looked at patients’ views on taking part in registries.

What were the results?

Estimating the effects of medicines. The most accurate understanding of a medicine’s effects came from using surveys completed at the time closest to the start of a new medicine. It didn’t matter if patients completed the survey before or just after they started to take the new medicine. Both ways worked well when used for predicting if the medicine will work.

Patient views on taking part in registries. Patients said they would take part in registries if

• They knew they were helping others
• Their own care might benefit
• Taking the survey was easy

Patients who spoke Spanish reported concerns about taking part in registries. Concerns included being guinea pigs and not trusting the translators.

Patients under age 45 preferred receiving surveys by email, internet, or phone app. Those older than age 65 preferred mailed paper surveys. Patients aged 45–65 had mixed preferences.

What did the research team do?

The research team created a computer program. The program compared 13 different ways to include data from patient surveys in registries. The team wanted to see which way worked best to learn a medicine’s effects. To check the results of the computer program, the team looked at real registry data for patients with rheumatoid arthritis, or RA.

Then, the team gave a survey to 150 registry patients with RA and 169 registry patients with inflammatory bowel disease, or IBD. The survey asked for patients’ views about taking part in registries. In each group, 95 percent of people who took the survey were non-Hispanic white. In addition, 83 percent of patients with RA were women, and 62 percent of patients with IBD were women. The average age of patients with RA was 62; the average age of patients with IBD was 43. Patients lived in Boston, Massachusetts.

What were the limits of the study?

The study only looked at one type of data from patient surveys for two health problems. The results may differ for other types of data. Future studies could look at using patient survey data from patient registries for other health problems.

Most people who took the survey were non-Hispanic white. Results may be different for people of other races and ethnicities. Future research could ask people from other races and ethnicities about taking part in patient registries.

How can people use the results?

The results of this study may help researchers use patient survey data from patient registries to get the most accurate understanding of a medicine’s effects. Researchers may also use the results to encourage people to take part in patient registries.

Objective

(1) To identify and test the best methods for incorporating potential confounders of patient-reported outcomes that researchers did not measure at the same time as treatment initiation into the analysis of clinical registry data; (2) To assess patient preferences regarding clinical registry participation

Study Design

Using patient registry data in comparative effectiveness research can help researchers examine changes in patient outcomes after starting a new treatment. Registries include patient-reported outcomes, such as measures of overall health and fatigue, to monitor patients’ well-being over time. Baseline measures capture confounders, which are variables that may affect the treatment results. Patients often report these outcomes at regular intervals, but these intervals rarely coincide with treatment initiation. Researchers must adjust for baseline confounders that they did not measure at true baseline, prior to treatment initiation.

This simulation study compared the ability of computational methods to incorporate patient-reported outcomes as confounders into the analysis of comparative drug safety research. The research team then applied these adjustment methods to rheumatoid arthritis (RA) patient registry data to validate results. The simulation study compared 13 confounder adjustment methods to model the total comparative effect of drug treatments on risk ratios for infection. The adjustment methods included, for example, using the most recent measurement prior to treatment initiation and using the arithmetic mean of the two nearest measurements. Simulation scenarios assumed a 5,000-patient sample, used different distributions of patient global assessment scores, and employed varying time intervals between patient global assessment measurement and treatment initiation.

The team empirically validated the tested confounder adjustment methods using data from 294 eligible patients with RA who began treatment with a tumor necrosis factor-α inhibitor or other medicine. Researchers assessed comparative safety based on infection risk following treatment.

To obtain patient preferences for participating in registry research, the research team conducted focus groups and administered surveys to 150 patients with RA and 169 patients with inflammatory bowel disease (IBD). In each survey group, 95% of respondents were non-Hispanic white. The mean age was 62 for the RA respondents and 43 for the IBD respondents, and 83% of RA respondents and 62% of IBD respondents were women.

Patients, clinicians, and patient registry researchers helped develop focus group questions and study design elements.

Results

Confounder adjustment

• For the simulation studies, the confounder-adjustment method that produced the least biased risk ratio used patient-reported outcomes measured at the timepoint closest to—either before or after—treatment initiation.
• All tested approaches using empirical data gave similar estimates of treatment effect. Using patient-reported outcomes measured at the timepoint closest to treatment initiation—either before or after—yielded the most consistent and least biased estimates.

Patient preferences about registry participation

• The top three motivating factors for registry participation across all focus group and survey respondents were altruism, possible benefits for one’s own care, and convenience.
• Spanish-speaking focus group participants expressed less interest in registry participation, due to worries about being guinea pigs and distrust of translators.
• Survey respondents under 45 years preferred surveys via e-mail, internet, or phone apps, and those over 65 years preferred mailed surveys. Respondents aged 45–65 had mixed preferences.

Limitations

The simulation study assessed a single continuous confounder; results may not apply to studies with multiple confounders. Incomplete, missing, or differentially reported data may alter the accuracy of the confounder adjustments.

Most survey respondents were non-Hispanic white; preferences may be different for other populations or for those who chose not to participate.

Conclusions and Relevance

Confounder measurement timing relative to treatment initiation may influence estimates of associations between treatment and clinical outcomes in comparative effectiveness research. Using patient-reported outcomes measured at the timepoint nearest an intervention’s initiation, even if measured after treatment begins, may control for confounders most effectively.

Because registry participants prefer different methods for completing surveys, researchers should consider tailoring their approach for delivering surveys.

Future Research Needs

Future research could examine multiple potential confounders and other health conditions relevant to registry data sets to determine whether these results generalize. Studies could also examine preferences for registry participation among racially and ethnically diverse patients.