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
Using individual participant data (IPD) has many advantages over using aggregate data (AD) inclinical meta-analysis. However, access to the IPD is often limited, yet the aggregate data is...Show moreUsing individual participant data (IPD) has many advantages over using aggregate data (AD) inclinical meta-analysis. However, access to the IPD is often limited, yet the aggregate data is availablefrom most clinical trials. Papadimitropoulou’s et al. [4] propose a method for studies with continuousoutcomes at baseline and follow-up measurement to generate pseudo-IPD from the aggregate data,which can be analyzed as IPD, using analysis of covariance (ANCOVA) models and linear mixed mod-els. The pseudo-IPD is generated based on the mean, standard deviation at baseline and follow-up, andthe correlation between baseline and follow-up, which are sufficient statistics of the linear mixed model.This thesis exemplified the pseudo-IPD models, standard meta-analysis models, and a Trowman meta-regression model on Obstructive Sleep Apnea Data with 2 treatment groups. We further exploredthe performance of the models under different conditions by a simulation study. The estimates of theTrowman meta-regression suffered from significant variance, and the standard AD models providedbias estimation when baseline imbalance exists. The ANCOVA models for pseudo-IPD and AD offeredmore accurate and stable results. The pseudo-IPD ANCOVA model is the most preferred since it canaccount for baseline difference and interaction between treatment and baseline, and different residualstructures can be used.Show less
