A VALIDATION STUDY OF THE RANK-PRESERVING STRUCTURAL FAILURE TIME MODEL- CONFIDENCE INTERVALS, UNIQUE, MULTIPLE AND ERRONEOUS SOLUTIONS
Author(s)
Ouwens MJ1, Hauch O2, Franzen S3
1Astrazeneca, Molndal, Sweden, 2Hauch consultancy, Saint-Gillis, Belgium, 3Registercentrum västra götaland, Göteborg, Sweden
OBJECTIVES: The rank-preserving structural failure time model (RPSFTM) is used for Health Technology Assessment submissions to adjust for switching of patients from reference to investigational treatment in cancer trials. It uses counterfactual survival (survival when only reference treatment would have been used) and assumes that, at randomization, the counterfactual survival distribution for the investigational and reference arm are identical. The validity of the on treatment version of RPSFTM at various levels of cross-over was of interest. METHODS: The RPSFTM was applied to simulated datasets differing in percentage of patients switching, time of switching, underlying acceleration factor and number of patients, using exponential distributions for the time on investigational and reference treatment. RESULTS: There were multiple scenarios where two solutions were found: one corresponding to identical counterfactual distributions, and the other to two different crossing counterfactual distributions. The same was found for the hazard ratio. No multiple potential solutions were observed only when switching patients were on investigational treatment for <40% of the time that patients in the investigational arm were on treatment. CONCLUSIONS: Automatic estimation methods to obtain point estimates and confidence intervals for the acceleration factor may be used when the time that switchers stay on investigational treatment within the trial period is short. However, multiple solutions imply that automated estimation procedures are unlikely to work when switching patients stay significantly longer on investigational treatment than direct starters.
Conference/Value in Health Info
2017-11, ISPOR Europe 2017, Glasgow, Scotland
Value in Health, Vol. 20, No. 9 (October 2017)
Code
PRM24
Topic
Clinical Outcomes, Methodological & Statistical Research
Topic Subcategory
Clinical Outcomes Assessment, Confounding, Selection Bias Correction, Causal Inference, Modeling and simulation
Disease
Oncology