Search results

Based on the state of the scientific evidence, the Sequenced Treatment Effectiveness for Posttraumatic Stress (STEPS) Trial was designed to address three specific aims. The first aim was to quantitatively compare engagement, self-reported PTSD symptom severity (primary outcome), quality of life, and recovery outcomes of primary care patients randomized to initially receive brief psychotherapy (Written Exposure Therapy - WET) or their choice of the three selective serotonin reuptake inhibitors (SSRIs). The second aim was, among patients not responding to initial treatment, to quantitatively compare outcomes of primary care patients randomized to: 1) augment the SSRI with WET, 2) switch from the SSRI to another class of antidepressants (serotonin-norepinephrine reuptake inhibitors - SNRI), or 3) switch from WET to the choice of the three SSRIs. The third specific aim was to quantitatively examine treatment heterogeneity among subgroups of primary care patients receiving pharmacotherapy and psychotherapy, including veterans, women, and those using cannabis.

Researchers often have trouble collecting complete information on patient health, as patients may receive care at different places. Linking patient records from different places may help researchers get a more complete picture.

One way to link records is through personal information, such as names and birth dates. But this method increases risks to patient privacy. Another way, known as privacy-preserving record linkage, or PPRL, masks personal information. But current PPRL methods only work when linking entire sets of patient data, including data that have already been shared and linked. Linking entire data sets takes a long time. Also, sharing the same records multiple times increases data privacy risks.

In this study, the research team developed and tested a new PPRL method called incremental PPRL. This method links only new or updated data rather than re-linking entire data sets.

Sometimes a study can get better results using data from different sites. In these cases, researchers may want to share patient data, including personal and private information such as dates of birth and addresses. However, researchers may not want to share data across sites because of worries about patient privacy. Some statistical methods can change patients' sensitive individual data into summary data that hides individuals' personal information. These privacy-protecting methods, or PPMs, make it safe to share data across sites. But researchers don't know if PPMs produce accurate results.

In this study, the research team compared combinations of PPMs with methods that use patients' individual data.

To access the methods, software, and R package, please visit the distributed GitHub.