Dataset Information

Birth size and breast cancer risk: re-analysis of individual participant data from 32 studies.

ABSTRACT: Birth size, perhaps a proxy for prenatal environment, might be a correlate of subsequent breast cancer risk, but findings from epidemiological studies have been inconsistent. We re-analysed individual participant data from published and unpublished studies to obtain more precise estimates of the magnitude and shape of the birth size-breast cancer association.Studies were identified through computer-assisted and manual searches, and personal communication with investigators. Individual participant data from 32 studies, comprising 22,058 breast cancer cases, were obtained. Random effect models were used, if appropriate, to combine study-specific estimates of effect. Birth weight was positively associated with breast cancer risk in studies based on birth records (pooled relative risk [RR] per one standard deviation [SD] [= 0.5 kg] increment in birth weight: 1.06; 95% confidence interval [CI] 1.02-1.09) and parental recall when the participants were children (1.02; 95% CI 0.99-1.05), but not in those based on adult self-reports, or maternal recall during the woman's adulthood (0.98; 95% CI 0.95-1.01) (p for heterogeneity between data sources = 0.003). Relative to women who weighed 3.000-3.499 kg, the risk was 0.96 (CI 0.80-1.16) in those who weighed < 2.500 kg, and 1.12 (95% CI 1.00-1.25) in those who weighed > or = 4.000 kg (p for linear trend = 0.001) in birth record data. Birth length and head circumference from birth records were also positively associated with breast cancer risk (pooled RR per one SD increment: 1.06 [95% CI 1.03-1.10] and 1.09 [95% CI 1.03-1.15], respectively). Simultaneous adjustment for these three birth size variables showed that length was the strongest independent predictor of risk. The birth size effects did not appear to be confounded or mediated by established breast cancer risk factors and were not modified by age or menopausal status. The cumulative incidence of breast cancer per 100 women by age 80 y in the study populations was estimated to be 10.0, 10.0, 10.4, and 11.5 in those who were, respectively, in the bottom, second, third, and top fourths of the birth length distribution.This pooled analysis of individual participant data is consistent with birth size, and in particular birth length, being an independent correlate of breast cancer risk in adulthood.

SUBMITTER: Silva Idos S

PROVIDER: S-EPMC2553821 | biostudies-other | 2008 Sep

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Birth size and breast cancer risk: re-analysis of individual participant data from 32 studies.

Silva Isabel dos Santos Idos S De Stavola Bianca B McCormack Valerie V

PLoS medicine 20080901 9

<h4>Background</h4>Birth size, perhaps a proxy for prenatal environment, might be a correlate of subsequent breast cancer risk, but findings from epidemiological studies have been inconsistent. We re-analysed individual participant data from published and unpublished studies to obtain more precise estimates of the magnitude and shape of the birth size-breast cancer association.<h4>Methods and findings</h4>Studies were identified through computer-assisted and manual searches, and personal communi ...[more]

PMID: 18828667

Similar Datasets

Project description:BackgroundRisk of the outcome is a mathematical determinant of the absolute treatment benefit of an intervention, yet this can vary substantially within a trial population, complicating the interpretation of trial results.MethodsWe developed risk models using Cox or logistic regression on a set of large publicly available randomized controlled trials (RCTs). We evaluated risk heterogeneity using the extreme quartile risk ratio (EQRR, the ratio of outcome rates in the lowest risk quartile to that in the highest) and skewness using the median to mean risk ratio (MMRR, the ratio of risk in the median risk patient to the average). We also examined heterogeneity of treatment effects (HTE) across risk strata.ResultsWe describe 39 analyses using data from 32 large trials, with event rates across studies ranging from 3% to 63% (median?=?15%, 25th-75th percentile?=?9-29%). C-statistics of risk models ranged from 0.59 to 0.89 (median?=?0.70, 25th-75th percentile?=?0.65-0.71). The EQRR ranged from 1.8 to 50.7 (median?=?4.3, 25th-75th percentile?=?3.0-6.1). The MMRR ranged from 0.4 to 1.0 (median?=?0.86, 25th-75th percentile?=?0.80-0.92). EQRRs were predictably higher and MMRRs predictably lower as the c-statistic increased or the overall outcome incidence decreased. Among 18 comparisons with a significant overall treatment effect, there was a significant interaction between treatment and baseline risk on the proportional scale in only one. The difference in the absolute risk reduction between extreme risk quartiles ranged from -3.2 to 28.3% (median?=?5.1%; 25th-75th percentile?=?0.3-10.9).ConclusionsThere is typically substantial variation in outcome risk in clinical trials, commonly leading to clinically significant differences in absolute treatment effects Most patients have outcome risks lower than the trial average reflected in the summary result. Risk-stratified trial analyses are feasible and may be clinically informative, particularly when the outcome is predictable and uncommon.

Project description:BackgroundPreterm birth is the leading cause of perinatal morbidity and mortality and is associated with adverse developmental and long-term health outcomes, including several cardiometabolic risk factors and outcomes. However, evidence about the association of preterm birth with later body size derives mainly from studies using birth weight as a proxy of prematurity rather than an actual length of gestation. We investigated the association of gestational age (GA) at birth with body size from infancy through adolescence.Methods and findingsWe conducted a two-stage individual participant data (IPD) meta-analysis using data from 253,810 mother-child dyads from 16 general population-based cohort studies in Europe (Denmark, Finland, France, Italy, Norway, Portugal, Spain, the Netherlands, United Kingdom), North America (Canada), and Australasia (Australia) to estimate the association of GA with body mass index (BMI) and overweight (including obesity) adjusted for the following maternal characteristics as potential confounders: education, height, prepregnancy BMI, ethnic background, parity, smoking during pregnancy, age at child's birth, gestational diabetes and hypertension, and preeclampsia. Pregnancy and birth cohort studies from the LifeCycle and the EUCAN-Connect projects were invited and were eligible for inclusion if they had information on GA and minimum one measurement of BMI between infancy and adolescence. Using a federated analytical tool (DataSHIELD), we fitted linear and logistic regression models in each cohort separately with a complete-case approach and combined the regression estimates and standard errors through random-effects study-level meta-analysis providing an overall effect estimate at early infancy (>0.0 to 0.5 years), late infancy (>0.5 to 2.0 years), early childhood (>2.0 to 5.0 years), mid-childhood (>5.0 to 9.0 years), late childhood (>9.0 to 14.0 years), and adolescence (>14.0 to 19.0 years). GA was positively associated with BMI in the first decade of life, with the greatest increase in mean BMI z-score during early infancy (0.02, 95% confidence interval (CI): 0.00; 0.05, p < 0.05) per week of increase in GA, while in adolescence, preterm individuals reached similar levels of BMI (0.00, 95% CI: -0.01; 0.01, p 0.9) as term counterparts. The association between GA and overweight revealed a similar pattern of association with an increase in odds ratio (OR) of overweight from late infancy through mid-childhood (OR 1.01 to 1.02) per week increase in GA. By adolescence, however, GA was slightly negatively associated with the risk of overweight (OR 0.98 [95% CI: 0.97; 1.00], p 0.1) per week of increase in GA. Although based on only four cohorts (n = 32,089) that reached the age of adolescence, data suggest that individuals born very preterm may be at increased odds of overweight (OR 1.46 [95% CI: 1.03; 2.08], p < 0.05) compared with term counterparts. Findings were consistent across cohorts and sensitivity analyses despite considerable heterogeneity in cohort characteristics. However, residual confounding may be a limitation in this study, while findings may be less generalisable to settings in low- and middle-income countries.ConclusionsThis study based on data from infancy through adolescence from 16 cohort studies found that GA may be important for body size in infancy, but the strength of association attenuates consistently with age. By adolescence, preterm individuals have on average a similar mean BMI to peers born at term.

Project description:BACKGROUND:Associations between circulating concentrations of oestrogens, progesterone, and androgens with breast cancer and related risk factors in premenopausal women are not well understood. We aimed to characterise these associations with a pooled analysis of data from seven studies. METHODS:Individual participant data for prediagnostic sex hormone and sex hormone-binding globulin (SHBG) concentrations were contributed from seven prospective studies. We restricted analyses to women who were premenopausal and younger than 50 years at blood collection, and to women with breast cancer diagnosed before age 50 years. We estimated odds ratios (ORs) with 95% CIs for breast cancer associated with hormone concentrations by conditional logistic regression in cases and controls matched for age, date of blood collection, and day of cycle, with stratification by study and further adjustment for cycle phase. We examined associations of hormones with risk factors for breast cancer in control women by comparing geometric mean hormone concentrations in categories of these risk factors, adjusted for study, age, phase of menstrual cycle, and body-mass index (BMI). All statistical tests were two-sided. FINDINGS:We included data for up to 767 women with breast cancer and 1699 controls in the risk analyses. Breast cancer risk was associated with a doubling in concentrations of oestradiol (OR 1·19, 95% CI 1·06-1·35), calculated free oestradiol (1·17, 1·03-1·33), oestrone (1·27, 1·05-1·54), androstenedione (1·30, 1·10-1·55), dehydroepiandrosterone sulphate (1·17, 1·04-1·32), testosterone (1·18, 1·03-1·35), and calculated free testosterone (1·08, 0·97-1·21). Breast cancer risk was not associated with luteal phase progesterone (doubling in concentration OR 1·00, 95% CI 0·92-1·09), and adjustment for other factors had little effect on any of these ORs. Cross-sectional analyses in control women showed several associations of sex hormones with breast cancer risk factors. INTERPRETATION:Circulating oestrogens and androgens are positively associated with the risk for breast cancer in premenopausal women.

Project description:BACKGROUND:Data repositories have the potential to play an important role in the effective and safe sharing of individual-participant data (IPD) from clinical studies. We analysed the current landscape of data repositories to create a detailed description of available repositories and assess their suitability for hosting data from clinical studies, from the perspective of the clinical researcher. METHODS:We assessed repositories that enable storage, sharing, discoverability, re-use of the IPD and associated documents from clinical studies using a pre-defined set of 34 items and publicly available information from April to June 2018. For this purpose, we developed an indicator set to capture the maturity of the repositories' procedures and their suitability for the hosting of IPD. The indicators cover guidelines for data upload and data de-identification, data quality controls, contracts for upload and storage, flexibility of access, application of identifiers, availability of metadata, and long-term preservation. RESULTS:We analysed 25 repositories, from an initial set of 55 identified as possibly relevant. Half of the included repositories were generic, i.e. not limited to a specific disease or clinical area and 13 were launched in the last 8 years. The sample was extremely heterogeneous and included repositories developed by research funders, infrastructures, universities, and editors. All but three repositories do not apply a fee for uploading, storage or access to data. None of the repositories completely demonstrated all the items included in the indicator set, but three repositories (Dryad, Drum, EASY) met - fully or partially - all items. Flexibility of data-access modalities appears to be limited, being lacking in half of the repositories. CONCLUSIONS:Our evaluation, though often hampered by the lack of sufficient information, can help researchers to find a suitable repository for their datasets. Some repositories are more mature because of their support for clinical dataset preparation, contractual agreements, metadata and identifiers, different modalities of access, and long-term preservation of data. Further work is now required to achieve a more robust and accurate system for evaluation, which in turn may encourage the sharing of clinical study data. TRIAL REGISTRATION:Study protocol available at https://zenodo.org/record/1438261#.W64kW9Egrcs .

Project description:BackgroundIn non-randomized studies (NRSs) where a continuous outcome variable (e.g., depressive symptoms) is assessed at baseline and follow-up, it is common to observe imbalance of the baseline values between the treatment/exposure group and control group. This may bias the study and consequently a meta-analysis (MA) estimate. These estimates may differ across statistical methods used to deal with this issue. Analysis of individual participant data (IPD) allows standardization of methods across studies. We aimed to identify methods used in published IPD-MAs of NRSs for continuous outcomes, and to compare different methods to account for baseline values of outcome variables in IPD-MA of NRSs using two empirical examples from the Thyroid Studies Collaboration (TSC).MethodsFor the first aim we systematically searched in MEDLINE, EMBASE, and Cochrane from inception to February 2021 to identify published IPD-MAs of NRSs that adjusted for baseline outcome measures in the analysis of continuous outcomes. For the second aim, we applied analysis of covariance (ANCOVA), change score, propensity score and the naïve approach (ignores the baseline outcome data) in IPD-MA from NRSs on the association between subclinical hyperthyroidism and depressive symptoms and renal function. We estimated the study and meta-analytic mean difference (MD) and relative standard error (SE). We used both fixed- and random-effects MA.ResultsTen of 18 (56%) of the included studies used the change score method, seven (39%) studies used ANCOVA and one the propensity score (5%). The study estimates were similar across the methods in studies in which groups were balanced at baseline with regard to outcome variables but differed in studies with baseline imbalance. In our empirical examples, ANCOVA and change score showed study results on the same direction, not the propensity score. In our applications, ANCOVA provided more precise estimates, both at study and meta-analytical level, in comparison to other methods. Heterogeneity was higher when change score was used as outcome, moderate for ANCOVA and null with the propensity score.ConclusionANCOVA provided the most precise estimates at both study and meta-analytic level and thus seems preferable in the meta-analysis of IPD from non-randomized studies. For the studies that were well-balanced between groups, change score, and ANCOVA performed similarly.

Project description:BackgroundMenarche and menopause mark the onset and cessation, respectively, of ovarian activity associated with reproduction, and affect breast cancer risk. Our aim was to assess the strengths of their effects and determine whether they depend on characteristics of the tumours or the affected women.MethodsIndividual data from 117 epidemiological studies, including 118 964 women with invasive breast cancer and 306 091 without the disease, none of whom had used menopausal hormone therapy, were included in the analyses. We calculated adjusted relative risks (RRs) associated with menarche and menopause for breast cancer overall, and by tumour histology and by oestrogen receptor expression.FindingsBreast cancer risk increased by a factor of 1·050 (95% CI 1·044-1·057; p<0·0001) for every year younger at menarche, and independently by a smaller amount (1·029, 1·025-1·032; p<0·0001), for every year older at menopause. Premenopausal women had a greater risk of breast cancer than postmenopausal women of an identical age (RR at age 45-54 years 1·43, 1·33-1·52, p<0·001). All three of these associations were attenuated by increasing adiposity among postmenopausal women, but did not vary materially by women's year of birth, ethnic origin, childbearing history, smoking, alcohol consumption, or hormonal contraceptive use. All three associations were stronger for lobular than for ductal tumours (p<0·006 for each comparison). The effect of menopause in women of an identical age and trends by age at menopause were stronger for oestrogen receptor-positive disease than for oestrogen receptor-negative disease (p<0·01 for both comparisons).InterpretationThe effects of menarche and menopause on breast cancer risk might not be acting merely by lengthening women's total number of reproductive years. Endogenous ovarian hormones are more relevant for oestrogen receptor-positive disease than for oestrogen receptor-negative disease and for lobular than for ductal tumours.FundingCancer Research UK.

Project description:BACKGROUND:Much controversy surrounds the association of traditional cardiovascular disease risk factors with venous thromboembolism (VTE). METHODS:We performed an individual level random-effect meta-analysis including 9 prospective studies with measured baseline cardiovascular disease risk factors and validated VTE events. Definitions were harmonized across studies. Traditional cardiovascular disease risk factors were modeled categorically and continuously using restricted cubic splines. Estimates were obtained for overall VTE, provoked VTE (ie, VTE occurring in the presence of 1 or more established VTE risk factors), and unprovoked VTE, pulmonary embolism, and deep-vein thrombosis. RESULTS:The studies included 244?865 participants with 4910 VTE events occurring during a mean follow-up of 4.7 to 19.7 years per study. Age, sex, and body mass index-adjusted hazard ratios for overall VTE were 0.98 (95% confidence interval [CI]: 0.89-1.07) for hypertension, 0.97 (95% CI: 0.88-1.08) for hyperlipidemia, 1.01 (95% CI: 0.89-1.15) for diabetes mellitus, and 1.19 (95% CI: 1.08-1.32) for current smoking. After full adjustment, these estimates were numerically similar. When modeled continuously, an inverse association was observed for systolic blood pressure (hazard ratio=0.79 [95% CI: 0.68-0.92] at systolic blood pressure 160 vs 110 mm?Hg) but not for diastolic blood pressure or lipid measures with VTE. An important finding from VTE subtype analyses was that cigarette smoking was associated with provoked but not unprovoked VTE. Fully adjusted hazard ratios for the associations of current smoking with provoked and unprovoked VTE were 1.36 (95% CI: 1.22-1.52) and 1.08 (95% CI: 0.90-1.29), respectively. CONCLUSIONS:Except for the association between cigarette smoking and provoked VTE, which is potentially mediated through comorbid conditions such as cancer, the modifiable traditional cardiovascular disease risk factors are not associated with increased VTE risk. Higher systolic blood pressure showed an inverse association with VTE.

Project description:BackgroundPrognostic factors are associated with the risk of a subsequent outcome in people with a given disease or health condition. Meta-analysis using individual participant data (IPD), where the raw data are synthesised from multiple studies, has been championed as the gold-standard for synthesising prognostic factor studies. We assessed the feasibility and conduct of this approach.MethodsA systematic review to identify published IPD meta-analyses of prognostic factors studies, followed by detailed assessment of a random sample of 20 articles published from 2006. Six of these 20 articles were from the IMPACT (International Mission for Prognosis and Analysis of Clinical Trials in traumatic brain injury) collaboration, for which additional information was also used from simultaneously published companion papers.ResultsForty-eight published IPD meta-analyses of prognostic factors were identified up to March 2009. Only three were published before 2000 but thereafter a median of four articles exist per year, with traumatic brain injury the most active research field. Availability of IPD offered many advantages, such as checking modelling assumptions; analysing variables on their continuous scale with the possibility of assessing for non-linear relationships; and obtaining results adjusted for other variables. However, researchers also faced many challenges, such as large cost and time required to obtain and clean IPD; unavailable IPD for some studies; different sets of prognostic factors in each study; and variability in study methods of measurement. The IMPACT initiative is a leading example, and had generally strong design, methodological and statistical standards. Elsewhere, standards are not always as high and improvements in the conduct of IPD meta-analyses of prognostic factor studies are often needed; in particular, continuous variables are often categorised without reason; publication bias and availability bias are rarely examined; and important methodological details and summary results are often inadequately reported.ConclusionsIPD meta-analyses of prognostic factors are achievable and offer many advantages, as displayed most expertly by the IMPACT initiative. However such projects face numerous logistical and methodological obstacles, and their conduct and reporting can often be substantially improved.

Dataset Information

Birth size and breast cancer risk: re-analysis of individual participant data from 32 studies.

Publications

Birth size and breast cancer risk: re-analysis of individual participant data from 32 studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets