%0 Journal Article %1 Schmidt2016 %A Schmidt, A F %A Klungel, O H %A Nielen, M %A de Boer, A %A Groenwold, R H H %A Hoes, A W %D 2016 %J Pharmacoepidemiology and drug safety %K effectmodification generalizability interaction nonrandomizedstudydesign observationalstudydesign pharmacoepidemiology randomizedcontrolledtrial statistics %N 4 %P 355-62 %R 10.1002/pds.3965 %T Tailoring treatments using treatment effect modification. %U http://www.ncbi.nlm.nih.gov/pubmed/26877168 %V 25 %X BACKGROUND AND OBJECTIVE Applying results from clinical studies to individual patients can be a difficult process. Using the concept of treatment effect modification (also referred to as interaction), defined as a difference in treatment response between patient groups, we discuss whether and how treatment effects can be tailored to better meet patients' needs. RESULTS First we argue that contrary to how most studies are designed, treatment effect modification should be expected. Second, given this expected heterogeneity, a small number of clinically relevant subgroups should be a priori selected, depending on the expected magnitude of effect modification, and prevalence of the patient type. Third, by defining generalizability as the absence of treatment effect modification we show that generalizability can be evaluated within the usual statistical framework of equivalence testing. Fourth, when equivalence cannot be confirmed, we address the need for further analyses and studies tailoring treatment towards groups of patients with similar response to treatment. Fifth, we argue that to properly frame, the entire body of evidence on effect modification should be quantified in a prior probability.

@article{Schmidt2016, abstract = {BACKGROUND AND OBJECTIVE Applying results from clinical studies to individual patients can be a difficult process. Using the concept of treatment effect modification (also referred to as interaction), defined as a difference in treatment response between patient groups, we discuss whether and how treatment effects can be tailored to better meet patients' needs. RESULTS First we argue that contrary to how most studies are designed, treatment effect modification should be expected. Second, given this expected heterogeneity, a small number of clinically relevant subgroups should be a priori selected, depending on the expected magnitude of effect modification, and prevalence of the patient type. Third, by defining generalizability as the absence of treatment effect modification we show that generalizability can be evaluated within the usual statistical framework of equivalence testing. Fourth, when equivalence cannot be confirmed, we address the need for further analyses and studies tailoring treatment towards groups of patients with similar response to treatment. Fifth, we argue that to properly frame, the entire body of evidence on effect modification should be quantified in a prior probability.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Schmidt, A F and Klungel, O H and Nielen, M and de Boer, A and Groenwold, R H H and Hoes, A W}, biburl = {https://www.bibsonomy.org/bibtex/24fb72af75ecbce52b3b495351add679f/jepcastel}, doi = {10.1002/pds.3965}, interhash = {54b720134dbc3454637acf0bea41cab2}, intrahash = {4fb72af75ecbce52b3b495351add679f}, issn = {1099-1557}, journal = {Pharmacoepidemiology and drug safety}, keywords = {effectmodification generalizability interaction nonrandomizedstudydesign observationalstudydesign pharmacoepidemiology randomizedcontrolledtrial statistics}, month = {4}, note = {Mesures d'associació; Interacció; Generalització}, number = 4, pages = {355-62}, pmid = {26877168}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Tailoring treatments using treatment effect modification.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/26877168}, volume = 25, year = 2016 }