Exact parametric confidence intervals for Bland-Altman limits of agreement

Purpose The previous literature on Bland-Altman analysis only describes approximate methods for calculating confidence intervals for 95% Limits of Agreement (LoAs). This paper describes exact methods for calculating such confidence intervals, based on the assumption that differences in measurement pairs are normally distributed. Methods Two basic situations are considered for calculating LoA confidence intervals: the first where LoAs are considered individually (i.e. using one-sided tolerance factors for a normal distribution); and the second, where LoAs are considered as a pair (i.e. using two-sided tolerance factors for a normal distribution). Equations underlying the calculation of exact confidence limits are briefly outlined. Results To assist in determining confidence intervals for LoAs (considered individually and as a pair) tables of coefficients have been included for degrees of freedom between 1 and 1000. Numerical examples, showing the use of the tables for calculating confidence limits for Bland-Altman LoAs, have been provided. Conclusions Exact confidence intervals for LoAs can differ considerably from Bland and Altman’s approximate method, especially for sample sizes that are not large. There are better, more precise methods for calculating confidence intervals for LoAs than Bland and Altman’s approximate method, although even an approximate calculation of confidence intervals for LoAs is likely to be better than none at all. Reporting confidence limits for LoAs considered as a pair is appropriate for most situations, however there may be circumstances where it is appropriate to report confidence limits for LoAs considered individually.

Dominant negative ATM mutations in breast cancer families

BACKGROUND: The ATM gene encoding a putative protein kinase is mutated in ataxia-telangiectasia (A-T), an autosomal recessive disorder with a predisposition for cancer. Studies of A-T families suggest that female heterozygotes have an increased risk of breast cancer compared with noncarriers. However, neither linkage analyses nor mutation studies have provided supporting evidence for a role of ATM in breast cancer predisposition. Nevertheless, two recurrent ATM mutations, T7271G and IVS10-6T-->G, reportedly increase the risk of breast cancer. We examined these two ATM mutations in a population-based, case-control series of breast cancer families and multiple-case breast cancer families. METHODS: Five hundred twenty-five or 262 case patients with breast cancer and 381 or 68 control subjects, respectively, were genotyped for the T7271G and IVS10-6T-->G ATM mutations, as were index patients from 76 non-BRCA1/2 multiple-case breast cancer families. Linkage and penetrance were analyzed. ATM protein expression and kinase activity were analyzed in lymphoblastoid cell lines from mutation carriers. All statistical tests were two-sided. RESULTS: In case and control subjects unselected for family history of breast cancer, one case patient had the T7271G mutation, and none had the IVS10-6T-->G mutation. In three multiple-case families, one of these two mutations segregated with breast cancer. The estimated average penetrance of the mutations was 60% (95% confidence interval [CI] = 32% to 90%) to age 70 years, equivalent to a 15.7-fold (95% CI = 6.4-fold to 38.0-fold) increased relative risk compared with that of the general population. Expression and activity analyses of ATM in heterozygous cell lines indicated that both mutations are dominant negative. CONCLUSION: At least two ATM mutations are associated with a sufficiently high risk of breast cancer to be found in multiple-case breast cancer families. Full mutation analysis of the ATM gene in such families could help clarify the role of ATM in breast cancer susceptibility.

Effects of different practice task constraints on fluctuations of player heart rate in small-sided football games

This paper analyzes effects of different practice task constraints on heart rate (HR) variability during 4v4 smallsided football games. Participants were sixteen football players divided into two age groups (U13, Mean age: 12.4±0.5 yrs; U15: 14.6±0.5). The task consisted of a 4v4 sub-phase without goalkeepers, on a 25x15 m field, of 15 minutes duration with an active recovery period of 6 minutes between each condition. We recorded players’ heart rates using heart rate monitors (Polar Team System, Polar Electro, Kempele, Finland) as scoring mode was manipulated (line goal: scoring by dribbling past an extended line; double goal: scoring in either of two lateral goals; and central goal: scoring only in one goal). Subsequently, %HR reserve was calculated with the Karvonen formula. We performed a time-series analysis of HR for each individual in each condition. Mean data for intra-participant variability showed that autocorrelation function was associated with more short-range dependence processes in the “line goal” condition, compared to other conditions, demonstrating that the “line goal” constraint induced more randomness in HR response. Relative to inter-individual variability, line goal constraints demonstrated lower %CV and %RMSD (U13: 9% and 19%; U15: 10% and 19%) compared with double goal (U13: 12% and 21%; U15: 12% and 21%) and central goal (U13: 14% and 24%; U15: 13% and 24%) task constraints, respectively. Results suggested that line goal constraints imposed more randomness on cardiovascular stimulation of each individual and lower inter-individual variability than double goal and central goal constraints.