The study looked at how regression to the mean affects the results of diabetes Type II medication effectiveness using data from the Netherlands Epidemiology of Obesity (NEO) study. This research...Show moreThe study looked at how regression to the mean affects the results of diabetes Type II medication effectiveness using data from the Netherlands Epidemiology of Obesity (NEO) study. This research showed that RTM can happen before and after starting medication. In simulations where there was no treatment effect, the HbA1c levels before starting medication were unexpectedly lower than the true average. This suggests that choosing different times to compare in the study can really change how effective the medication seems. When applying the same methods to the real NEO study data, there were some big differences compared to the simulations. For example, the estimated average HbA1c level before starting medication in the NEO study was higher than what was found using certain statistical models. This points out some possible issues with these methods and shows how complex the NEO study data is. The findings suggest that RTM is an important factor to consider in studies about medication effectiveness. Because the NEO study data is so complex, future research might need more detailed methods to properly understand RTM and how it affects the results. Expanding the types of scenarios studied, including different criteria for when treatment starts, and looking at more patient data could help give a fuller picture of RTM and improve how we evaluate medication effectiveness in real-world studiesShow less
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
Matching methods are often used by researchers to improve causal inferences made from observational data. They aim to reduce confounding by improving covariate balance between treatment groups....Show moreMatching methods are often used by researchers to improve causal inferences made from observational data. They aim to reduce confounding by improving covariate balance between treatment groups. Allowing more accurate estimates of causal effects. This research compares three commonly used matching methods; Nearest neighbour matching with replacement and without replacement, and propensity score matching using a caliper (PSM). Monte Carlo simulations were performed to assess the strengths and weaknesses of the methods in estimating the true treatment effect under different conditions. Two sources of bias were identified. The first occurs when there is incomplete overlap of the control and treated groups, even when there is theoretical positivity. The second is introduced when there are interactions between the outcome and the covariates. The two sources of bias combine and can either exaggerate or mitigate the total bias. The results show that when bias from overlap can be mostly removed by using PSM, this increases the number of treated units discarded. This means that only the average treatment effect on the matched (ATM) can be calculated, not the usual target estimand the average treatment effect on treated (ATT). For higher sample sizes and a 1:1 ratio of controls to treated units, matching with replacement performed better than PSM. Using matching with replacement also removed a lot of the overlap bias without discarding any treated units. It was found that when using PSM the caliper is sample size dependent; a fixed caliper on the logit scale varies in size on the original covariate scale especially for low sample sizes. It was also found that the ideal caliper size, especially for smaller sample sizes and low levels of overlap can be between 0.3 and 0.7, higher than the 0.2 that is commonly recommended. The results show how even a small lack of positivity in the form of overlap of < 100% can introduce bias. They also highlight the importance of interaction as an additional source of bias when in combination with lack of positivity. Additionally, this study demonstrates the need to pay attention to the proportions of discarded units when using PSM or matching with replacement as this affects the variance.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
Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
Currently, platelet transfusion is the main treatment for patients with thrombocytopenia due to haematological malignancy and intensive chemotherapy. When the platelet count is low, transfusion is...Show moreCurrently, platelet transfusion is the main treatment for patients with thrombocytopenia due to haematological malignancy and intensive chemotherapy. When the platelet count is low, transfusion is given to prevent bleedings. However, the platelet count is not the only determinant of bleeding (Ypma et al., 2019). Other biomarkers might additionally or even better predict bleeding such as the albumincreatinine ratio measured in urine. This thesis project will determine the predictive value of these new biomarkers where we would like to predict the ”untreated risk” of bleeding: the risk of bleeding if patients would not receive a transfusion. We used a real dataset that contains 88 patients with 116 thrombocytopenic episodes in which patients’ platelet counts are low and they may develop a bleeding. A problem is that the patients who received transfusions cause diculty in predicting the “untreated risk”. Another problem is that transfusions were given partly based on the platelet counts, which makes the e↵ect of transfusion on bleeding confounded by platelet count. We considered two situations. One was to predict the bleeding during the day based on the platelet count that was measured in the morning (the one-day situation). The two-day situation was to predict bleeding in the next two days, but before the second day-night based on the platelet count that was measured on the first day morning. In the first part of this thesis, we structured the relationship between biomarkers, transfusions and bleeding by expressing them in causal diagrams. Using the causal diagrams, we found the reason why the conventional models failed to predict untreated risk in the two-day situation. We found that the marginal structural model might be a solution. In the second part, we set up a simulation to verify whether the marginal structural model or conventional regression models can handle the confounding in the one-day situation and the time-dependent confounding in the two-day situation. Based on our simulation studies, we concluded that for the one-day situation the regression model including treatment and predictor was well equipped, while in the two-day situation the marginal structure model is recommended to estimate the “untreated” risk. In the third part, we applied the models to the dataset. We found that in the one-day situation urine albumin/creatinine ratio and the platelet count have potential predictive value for predicting same day bleeding, while, for the two-day situation, only the urine albumin/creatinine ratio was significantly associated with the risk of bleeding in all models. Additionally, there was not a clear e↵ect of transfusion detected in the one-day situation and two-day situation.Show less
