124 research outputs found
Supplemental material for Underestimation of treatment effects in sequentially monitored clinical trials that did not stop early for benefit
No full textSupplemental material for Underestimation of treatment effects in sequentially monitored clinical trials that did not stop early for benefit by Ian C Marschner and I Manjula Schou in Statistical Methods in Medical Research</p
sj-Rda-2-smm-10.1177_09622802231163330 - Supplemental material for Linear mixed models for investigating effect modification in subgroup meta-analysis
No full textSupplemental material, sj-Rda-2-smm-10.1177_09622802231163330 for Linear mixed models for investigating effect modification in subgroup meta-analysis by Anne Lyngholm Sørensen and Ian C Marschner in Statistical Methods in Medical Research</p
sj-pdf-1-smm-10.1177_09622802231163330 - Supplemental material for Linear mixed models for investigating effect modification in subgroup meta-analysis
No full textSupplemental material, sj-pdf-1-smm-10.1177_09622802231163330 for Linear mixed models for investigating effect modification in subgroup meta-analysis by Anne Lyngholm Sørensen and Ian C Marschner in Statistical Methods in Medical Research</p
sj-zip-2-smm-10.1177_09622802221122445 - Supplemental material for Estimation of the treatment effect following a clinical trial that stopped early for benefit
No full textSupplemental material, sj-zip-2-smm-10.1177_09622802221122445 for Estimation of the treatment effect following a clinical trial that stopped early for benefit by Ian C Marschner, Manjula Schou and Andrew J Martin in Statistical Methods in Medical Research</p
sj-pdf-1-smm-10.1177_09622802221122445 - Supplemental material for Estimation of the treatment effect following a clinical trial that stopped early for benefit
No full textSupplemental material, sj-pdf-1-smm-10.1177_09622802221122445 for Estimation of the treatment effect following a clinical trial that stopped early for benefit by Ian C Marschner, Manjula Schou and Andrew J Martin in Statistical Methods in Medical Research</p
example_search_strategy_SJOQUIST_forTAM_-_supplementary_file_S1 – Supplemental material for Progression-free survival as a surrogate endpoint for overall survival in modern ovarian cancer trials: a meta-analysis
No full textSupplemental material, example_search_strategy_SJOQUIST_forTAM_-_supplementary_file_S1 for Progression-free survival as a surrogate endpoint for overall survival in modern ovarian cancer trials: a meta-analysis by Katrin M. Sjoquist, Sarah J. Lord, Michael L. Friedlander, Robert John Simes, Ian C. Marschner and Chee Khoon Lee in Therapeutic Advances in Medical Oncology</p
Determining the size of a cross‐sectional sample to estimate the age‐specific incidence of an irreversible disease
No full textEstimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data
No full textBackgroundMortality is a key component of the natural history of COVID-19 infection. Surveillance data on COVID-19 deaths and case diagnoses are widely available in the public domain, but they are not used to model time to death because they typically do not link diagnosis and death at an individual level. This paper demonstrates that by comparing the unlinked patterns of new diagnoses and deaths over age and time, age-specific mortality and time to death may be estimated using a statistical method called deconvolution.MethodsAge-specific data were analysed on 816 deaths among 6235 cases over age 50 years in Victoria, Australia, from the period January through December 2020. Deconvolution was applied assuming logistic dependence of case fatality risk (CFR) on age and a gamma time to death distribution. Non-parametric deconvolution analyses stratified into separate age groups were used to assess the model assumptions.ResultsIt was found that age-specific CFR rose from 2.9% at age 65 years (95% CI:2.2 – 3.5) to 40.0% at age 95 years (CI: 36.6 – 43.6). The estimated mean time between diagnosis and death was 18.1 days (CI: 16.9 – 19.3) and showed no evidence of varying by age (heterogeneity P = 0.97). The estimated 90% percentile of time to death was 33.3 days (CI: 30.4 – 36.3; heterogeneity P = 0.85). The final age-specific model provided a good fit to the observed age-stratified mortality patterns.ConclusionsDeconvolution was demonstrated to be a powerful analysis method that could be applied to extensive data sources worldwide. Such analyses can inform transmission dynamics models and CFR assessment in emerging outbreaks. Based on these Australian data it is concluded that death from COVID-19 occurs within three weeks of diagnosis on average but takes five weeks in 10% of fatal cases. Fatality risk is negligible in the young but rises above 40% in the elderly, while time to death does not seem to vary by age
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