In this study, the combination of multi-state survival analysis and causal inference was used to estimate theprobability of an event occurring as a function of treatmenttiming. This studyfollows...Show moreIn this study, the combination of multi-state survival analysis and causal inference was used to estimate theprobability of an event occurring as a function of treatmenttiming. This studyfollows throughfrom the results and recommendations of a previous methodological researchestimating the average pregnancy probabilityas a function ofintrauterine insemination (IUI)treatment timings using observational data from a prospective cohort studyin the Netherlands. The study applied anillness-death multi-statemodel with expectation management as the initial state, IUI treatment as the transition state, and pregnancy as the final or absorbing state. To study the performance ofcausalmulti-state survival analysis,multipledatasetswitha woman’sage with a standardised normal distribution and treatment timings following an exponential distribution were generated using simulations. Fivetreatment strategies were considered: when a patient receives the treatment without delay, when treatment is delayed at three, six, nine months from follow-up,and when treatmentis delayed indefinitely i.e., the patient does not receive treatment during the observation period. For each strategy, the pregnancy probabilityfor an individual andthe group average wereestimated using the causal multi state model and compared to the calculatedtrue valuesfor an observation period of 1.5 years from the start of the follow-up. Variance, bias, and the root mean square error (RMSE) were used as performance measures toassess whether the methodcan accurately estimate the average pregnancy probabilities by treatment strategy over time.The resultsfrom the performance measuresindicate that the methodology canprovide precise and unbiasedestimates. Future work in this area includesintroducing a mechanism for censoringin the data generating stepof the simulation, exploring other probability distributions to generate the transition times, and comparing theresults for the multi-state approachwith those for other similar methodologiessuch as inverse probability weighting used to estimate the outcomesof treatment timing fromobservational data.Show less
