Lugo-Palacios et al. (2024)<\/a> aims to answer. The goal of their study is to determine which second-line treatment for type 2 diabetes is most effective in the real world. To do this, the authors estimate the average treatment effect (ATEs) and conditional average treatment effect (CATE) for the use of dipeptidyl peptidase\u20104 inhibitors (DPP4i) and sulfonylureas (SU) as \u2018add on\u2019 therapies to metformin for the treatment of patients with type 2 diabetes in England. The primary endpoint of interest was glycemic control.\u00a0 One challenge is, that published RCTs report do not have a consensus recommendation; some find superior improvement with SUs and others with DPP4i.\u00a0 As mentioned above, one problem is that RCTs evaluating these treatments is that they often exclude patients with very poor glycemic control and thus the extent to which different types of real-world patients would benefit from each treatment is unclear. <\/p>\nThe study approach identified subpopulations from within the target population into two groups: those who met a published RCT\u2019s eligibility criteria (\u2018RCT eligible\u2019) and those who did not (\u2018RCT ineligible\u2019).\u00a0 The authors compare the ATE for the \u2018RCT eligible\u2019 to the RCT with the same eligibility criteria (the \u2018RCT benchmark\u2019) to examine how well real-world data imitates RCT data.\u00a0 Next, the authors compared CATEs for the overall target population(i.e., \u2018RCT eligible\u2019 and \u2018RCT ineligible\u2019 groups).\u00a0 CATEs were estimated separately by age, ethnicity, baseline HbA1c, and body mass index (BMI). Covariates used in the analysis included demographics and clinical factors (i.e., baseline HbA1c, systolic blood pressure (SBP), diastolic blood pressure (DBP), estimated glomerular filtration rate (eGFR), and BMI)<\/p>\n
The econometric approach was to use local instrumental variables (LIV). The instrument used was <\/p>\n
\u2026clinical commissioning groups (CCG)\u2019s tendency to prescribe (TTP) DPP4i as second\u2010line treatment. Over the study time\u2010frame, general practitioners (GPs) worked within a CCG which informed health funding decisions for its respective geographic region. For example, some CCGs tended to recommend \u2013to their affiliated GPs\u2013 the prescription of either DPP4i or SU<\/em><\/p>\nUsing this instrument, the authors conducted the LIV estimate as follows:<\/p>\n
\u2026the first stage models estimated the probability that each person was prescribed DDP4i given their baseline covariates and their CCG\u2019s TTP. The second\u2010stage outcome models then included the predicted probabilities from the first\u2010stage (propensity score) models, covariates and their interactions. Probit regression models were used to estimate the initial propensity score (first stage), while generalised linear models were applied to the outcome data, with the most appropriate family (gaussian) and link function (identity) chosen according to root mean squared error, with Hosmer\u2010Lemeshow and Pregibon tests also used to check model fit and appropriateness.<\/p>\n
Using this approach the authors found the following:<\/p>\n
The IV was the clinical commissioning groups (CCG)\u2019s tendency to prescribe (TTP) DPP4i as second\u2010line treatment. Over the study time\u2010frame, general practitioners (GPs) worked within a CCG which informed health funding decisions for its respective geographic region. For example, some CCGs tended to recommend \u2013to their affiliated GPs\u2013 the prescription of either DPP4i or SU as second\u2010line treatment.<\/em><\/p>\nThe authors
\nuse this approach and find that: <\/p>\n
The estimated ATEs for the \u2018RCT\u2010eligible\u2019 population are similar to those from a published RCT. The estimated CATEs are in the same direction for the subpopulations included versus excluded from the RCT, but differ in magnitude. The variation in the estimated individual treatment effects is greater across the broader sample of people who do not meet the RCT inclusion criteria than for those who do.<\/p>\n
The graphs show the results overall for RCT eligible and ineligible as well as for the specific subgroups of interest. <\/p>\n
https:\/\/pubmed.ncbi.nlm.nih.gov\/39327529\/<\/p>\n
https:\/\/pubmed.ncbi.nlm.nih.gov\/39327529\/<\/p>\n
Learning Point<\/h3>\n
What are the 4 conditions for a valid instrument must meet? The authors describe these as follows.<\/p>\n
First, the instrument must predict the treatment prescribed\u2026Second, the instrument must be independent of unmeasured covariates that predict the outcomes of interest, which can be partially evaluated through its relationship with measured covariates\u2026Third, the instrument must have an effect on the outcomes only through the treatment received\u2026Fourth, we assume that the average treatment choice must increase or decrease monotonically with the level of the IV.<\/em><\/p>","protected":false},"excerpt":{"rendered":"Randomized controlled trials are the gold standard for evaluating treatment efficacy, but effectiveness in the real-world may vary. One reason for this is that clinical trials often have stricter inclusion criteria than is the case for the target treated population. Policymakers, payers, and clinicians may wonder how well the results from the narrower clinical trial…<\/p>\n","protected":false},"author":0,"featured_media":1549,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-1548","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles"],"_links":{"self":[{"href":"https:\/\/medical-article.com\/index.php?rest_route=\/wp\/v2\/posts\/1548"}],"collection":[{"href":"https:\/\/medical-article.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/medical-article.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/medical-article.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1548"}],"version-history":[{"count":0,"href":"https:\/\/medical-article.com\/index.php?rest_route=\/wp\/v2\/posts\/1548\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/medical-article.com\/index.php?rest_route=\/wp\/v2\/media\/1549"}],"wp:attachment":[{"href":"https:\/\/medical-article.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1548"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/medical-article.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1548"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/medical-article.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1548"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}