Isaac Allen- University of Cambridge
Isaac Allen
- University of Cambridge
About
13
Publications
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Introduction
Skills and Expertise
Current institution
Publications
Publications (13)
Background
PARP inhibitors are effective in treating ovarian cancer, especially for BRCA1/2 pathogenic variant carriers and those with HRD (homologous recombination deficiency). Concerns over toxicity and costs have led to the search for predictive biomarkers. We present an updated systematic review, expanding on a previous ESMO review on PARP inhi...
Objectives
To investigate the association between bilateral salpingo-oophorectomy (BSO) and long-term health outcomes in women with a personal history of breast cancer.
Methods and analysis
We used data on women diagnosed with invasive breast cancer between 1995 and 2019 from the National Cancer Registration Dataset (NCRD) in England. The data wer...
PURPOSE
Second primary cancer (SPC) risks after breast cancer (BC) in BRCA1/BRCA2 pathogenic variant (PV) carriers are uncertain. We estimated relative and absolute risks using a novel linkage of genetic testing data to population-scale National Disease Registration Service and Hospital Episode Statistics electronic health records.
METHODS
We foll...
Background
Second primary cancers (SPCs) after breast cancer (BC) present an increasing public health burden, with little existing research on socio-demographic, tumour, and treatment effects. We addressed this in the largest BC survivor cohort to date, using a novel linkage of National Disease Registration Service datasets.
Methods
The cohort inc...
Background: Second primary cancer (SPC) risk estimates in breast cancer (BC) survivors carrying germline BRCA1/2 pathogenic variants (PVs) remain uncertain. We estimated relative and absolute SPC risks following BC in BRCA1/2 PV carriers using genetic testing laboratory data in England, linked for the first time to population-scale electronic healt...
Objective:
To provide an up-to-date systematic review on "the long-term outcomes of bilateral salpingo-oophorectomy (BSO) at the time of hysterectomy" and perform a meta-analysis for the reported associations.
Data sources:
We updated a previous systematic review by searching the literature using PubMed, Web of science and Embase for publication...
Introduction: Breast cancer is the most common cancer in women. Women with personal history of breast cancer are at increased risk of second primary cancers including ovarian cancer. Bilateral salpingo-oophorectomy (BSO) is a well-established option for ovarian cancer risk reduction. However, the benefit of ovarian cancer risk reduction should be b...
Background: Second primary cancer (SPC) incidence is rising among breast cancer (BC) survivors, but these risks remain unclear. We estimated SPC risks for male and female BC survivors using large-scale electronic health record data from a linkage of National Cancer Registration and Analysis Service data and Hospital Episode Statistics surgical reco...
Background
Second primary cancer incidence is rising among breast cancer survivors. We examined the risks of non-breast second primaries, in combination and at specific cancer sites, through a systematic review and meta-analysis.
Methods
We conducted a systematic search of PubMed, Embase, and Web of Science, seeking studies published by March 2022...
Background
With increasing survival after cancer diagnoses, second primary cancers (SPCs) are becoming more prevalent. We investigated the incidence and site of non-breast SPC risks following male breast cancer (BC).
Methods
PubMed, Embase and Web of Science were systematically searched for studies reporting standardised incidence ratios (SIRs) fo...
Background
Second primary cancer incidence is rising among breast cancer survivors. We examined the risks of non-breast second primaries, in combination and at specific cancer sites, through a systematic review and meta-analysis.
Methods
We conducted a systematic search of PubMed, Embase, and Web of Science, seeking studies published by March 2022...
Questions
Questions (3)
Hi ResearchGate,
I am trying to pool standardized incidence ratios (SIRs) in a meta-analysis using the generic inverse variance method, but the event in question is uncommon.
When I use metagen in R, the confidence intervals are estimated from a given standard error. When the event is uncommon, this is often quite wrong.
I have read several other meta-analyses of pooling SIRs with rare events using the generic inverse variance method, and they seem to have the exact confidence intervals of each study accurately generated (e.g: ).
I cannot work out how they did this. I have tried writing my own function, but I think there must be an easier way I am missing. Does anyone know?
I am writing my own function, designed to allow you to manually fix confidence intervals in metagen (rather than metagen estimating these automatically from given effect sizes).
I have everything I need to manually calculate the weights. However, I need to know how metagen creates the POOLED confidence interval to write the rest of my custom function.
That is, I already know how metagen estimates the confidence intervals of each study, I know how it calculates the weights assigned to each study, and I know how it calculates the pooled point estimate. What I don't know is exactly how it calculates the confidence interval around this pooled point estimate.
I've looked deeply into the documentation and this isn't explained. Please, could someone point me to where to find this, or tell me how it is done?
I know the standard approach is to enter the effect size in a meta-analysis (e.g: standard error) and let R do its thing. However, I am pooling standardized incidence ratios, and the event in question is rare. This means that just entering effect size actually leads to small inaccuracies in calculating confidence intervals. I would much prefer to calculate my own confidence intervals (letting me use Byar's assumption, which would get around the issue), and then enter these into a meta-analysis, weighting the studies with the inverse variance method. Is there any way of doing this in R?
