TY - JOUR
T1 - Cross-trial prediction in psychotherapy
T2 - External validation of the Personalized Advantage Index using machine learning in two Dutch randomized trials comparing CBT versus IPT for depression
AU - Van Bronswijk, Suzanne C.
AU - Bruijniks, Sanne J E
AU - Lorenzo-Luaces, Lorenzo
AU - Derubeis, Robert J
AU - Lemmens, Lotte H.J.M.
AU - Peeters, Frenk P.M.L.
AU - Huibers, Marcus J.H.
PY - 2021/1/2
Y1 - 2021/1/2
N2 - AbstractObjective: Optimizing treatment selection may improve treatment outcomes in depression. A promising approach is the Personalized Advantage Index (PAI), which predicts the optimal treatment for a given individual. To determine the generalizability of the PAI, models needs to be externally validated, which has rarely been done. Method: PAI models were developed within each of two independent trials, with substantial between-study differences, that both compared CBT and IPT for depression (STEPd: n = 151 and FreqMech: n = 200). Subsequently, both PAI models were tested in the other dataset. Results: In the STEPd study, post-treatment depression was significantly different between individuals assigned to their PAI-indicated treatment versus those assigned to their non-indicated treatment (d = .57). In the FreqMech study, post-treatment depression was not significantly different between patients receiving their indicated treatment versus those receiving their non-indicated treatment (d = .20). Cross-trial predictions indicated that post-treatment depression was not significantly different between those receiving their indicated treatment and those receiving their non-indicated treatment (d = .16 and d = .27). Sensitivity analyses indicated that cross-trial prediction based on only overlapping variables didn't improve the results. Conclusion: External validation of the PAI has modest results and emphasizes between-study differences and many other challenges.
AB - AbstractObjective: Optimizing treatment selection may improve treatment outcomes in depression. A promising approach is the Personalized Advantage Index (PAI), which predicts the optimal treatment for a given individual. To determine the generalizability of the PAI, models needs to be externally validated, which has rarely been done. Method: PAI models were developed within each of two independent trials, with substantial between-study differences, that both compared CBT and IPT for depression (STEPd: n = 151 and FreqMech: n = 200). Subsequently, both PAI models were tested in the other dataset. Results: In the STEPd study, post-treatment depression was significantly different between individuals assigned to their PAI-indicated treatment versus those assigned to their non-indicated treatment (d = .57). In the FreqMech study, post-treatment depression was not significantly different between patients receiving their indicated treatment versus those receiving their non-indicated treatment (d = .20). Cross-trial predictions indicated that post-treatment depression was not significantly different between those receiving their indicated treatment and those receiving their non-indicated treatment (d = .16 and d = .27). Sensitivity analyses indicated that cross-trial prediction based on only overlapping variables didn't improve the results. Conclusion: External validation of the PAI has modest results and emphasizes between-study differences and many other challenges.
KW - depression
KW - cognitive behavioural therapy
KW - interpersonal psychotherapy
KW - precision medicine
KW - prediction
KW - external validation
KW - MODELS
KW - REGULARIZATION
KW - IMPUTATION
KW - SELECTION
KW - THERAPY
U2 - 10.1080/10503307.2020.1823029
DO - 10.1080/10503307.2020.1823029
M3 - Article
C2 - 32964809
VL - 31
SP - 78
EP - 91
JO - Psychotherapy Research
JF - Psychotherapy Research
SN - 1050-3307
IS - 1
ER -