Dataset Information

Analysis of in vitro fertilization data with multiple outcomes using discrete time-to-event analysis.

ABSTRACT: In vitro fertilization (IVF) is an increasingly common method of assisted reproductive technology. Because of the careful observation and follow-up required as part of the procedure, IVF studies provide an ideal opportunity to identify and assess clinical and demographic factors along with environmental exposures that may impact successful reproduction. A major challenge in analyzing data from IVF studies is handling the complexity and multiplicity of outcome, resulting from both multiple opportunities for pregnancy loss within a single IVF cycle in addition to multiple IVF cycles. To date, most evaluations of IVF studies do not make use of full data because of its complex structure. In this paper, we develop statistical methodology for analysis of IVF data with multiple cycles and possibly multiple failure types observed for each individual. We develop a general analysis framework based on a generalized linear modeling formulation that allows implementation of various types of models including shared frailty models, failure-specific frailty models, and transitional models, using standard software. We apply our methodology to data from an IVF study conducted at the Brigham and Women's Hospital, Massachusetts. We also summarize the performance of our proposed methods on the basis of a simulation study.

SUBMITTER: Maity A

PROVIDER: S-EPMC3981885 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Analysis of in vitro fertilization data with multiple outcomes using discrete time-to-event analysis.

Maity Arnab A Williams Paige L PL Ryan Louise L Missmer Stacey A SA Coull Brent A BA Hauser Russ R

Statistics in medicine 20131208 10

In vitro fertilization (IVF) is an increasingly common method of assisted reproductive technology. Because of the careful observation and follow-up required as part of the procedure, IVF studies provide an ideal opportunity to identify and assess clinical and demographic factors along with environmental exposures that may impact successful reproduction. A major challenge in analyzing data from IVF studies is handling the complexity and multiplicity of outcome, resulting from both multiple opport ...[more]

PMID: 24317880

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Project description:BackgroundTime-to-event data is frequently reported in both clinical and preclinical research spheres. Systematic review and meta-analysis is a tool that can help to identify pitfalls in preclinical research conduct and reporting that can help to improve translational efficacy. However, pooling of studies using hazard ratios (HRs) is cumbersome especially in preclinical meta-analyses including large numbers of small studies. Median survival is a much simpler metric although because of some limitations, which may not apply to preclinical data, it is generally not used in survival meta-analysis. We aimed to appraise its performance when compared with hazard ratio-based meta-analysis when pooling large numbers of small, imprecise studies.MethodsWe simulated a survival dataset with features representative of a typical preclinical survival meta-analysis, including with influence of a treatment and a number of covariates. We calculated individual patient data-based hazard ratios and median survival ratios (MSRs), comparing the summary statistics directly and their performance at random-effects meta-analysis. Finally, we compared their sensitivity to detect associations between treatment and influential covariates at meta-regression.ResultsThere was an imperfect correlation between MSR and HR, although the opposing direction of treatment effects between summary statistics appeared not to be a major issue. Precision was more conservative for HR than MSR, meaning that estimates of heterogeneity were lower. There was a slight sensitivity advantage for MSR at meta-analysis and meta-regression, although power was low in all circumstances.ConclusionsWe believe we have validated MSR as a summary statistic for use in a meta-analysis of small, imprecise experimental survival studies-helping to increase confidence and efficiency in future reviews in this area. While assessment of study precision and therefore weighting is less reliable, MSR appears to perform favourably during meta-analysis. Sensitivity of meta-regression was low for this set of parameters, so pooling of treatments to increase sample size may be required to ensure confidence in preclinical survival meta-regressions.

Project description:Multiple myeloma is a haematological malignancy typically characterised by neoplastic plasma cell infiltration of the bone marrow. Treatment for multiple myeloma consists of multi-line chemotherapy with or without autologous stem cell transplantation and has been rapidly evolving in recent years. However, clinical trials are unable to provide patients and clinicians with long-term prognostic information nor policymakers with the full body of evidence needed to perform economic evaluation of new therapies or make reimbursement decisions. To address these limitations of the available evidence, this study aimed to develop and validate the EpiMAP Myeloma model, a discrete-event simulation model of multiple myeloma disease outcomes and treatment pathways. Risk equations were estimated using the Australian and New Zealand Myeloma & Related Diseases Registry after multiple imputation of missing data. Risk equation coefficients were combined with multiple myeloma patients at diagnosis from the Registry to perform the simulation. The model was validated with 100 bootstraps of an out-of-sample prediction analysis using a 70/30 split of the 4,121 registry patients diagnosed between 2009 and 2023, resulting in 2,884 and 1,237 patients in the training and validation cohorts, respectively. For 90% of the 120 months in the 10-year post-diagnosis period, there was no significant difference in overall survival between the validation and simulated cohorts. These results highlight that the EpiMAP Myeloma model is robust at predicting multiple myeloma disease outcomes and treatment pathways in Australia & New Zealand. In the future, clinicians will be able to use the EpiMAP Myeloma model to provide personalised estimates of life expectancy to patients based on their specific characteristics, disease stage, and response to treatment. Policymakers will also be able to use the model to perform economic evaluation, to forecast the number of patients receiving treatment at different stages, and to determine the downstream impact of listing new, effective therapies.

Dataset Information

Analysis of in vitro fertilization data with multiple outcomes using discrete time-to-event analysis.

Publications

Analysis of in vitro fertilization data with multiple outcomes using discrete time-to-event analysis.

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