The effects of teenaged motherhood and maternal age on offspring intelligence.
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Adolescent↗
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Results for “Family Size--statistics”
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Patterns of fertility among Africans in Glen Norah Township, Salisbury.
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Adolescent↗
Early childbearing and later economic well-being.
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Adolescent↗
Regional variations in family size in the Republic of Ireland.
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Family Characteristics↗
A discordant-sibship test for disequilibrium and linkage: no need for parental data.
The sibship disequilibrium test (SDT) is designed to detect both linkage in the presence of association and association in the presence of linkage (linkage disequilibrium). The test does not require parental data but requires discordant sibships with at least one affected and one unaffected sibling. The SDT has many desirable properties: it uses all the siblings in the sibship; it remains valid if there are misclassifications of the affectation status; it does not detect spurious associations due to population stratification; asymptotically it has a chi2 distribution under the null hypothesis; and exact P values can be easily computed for a biallelic marker. We show how to extend the SDT to markers with multiple alleles and how to combine families with parents and data from discordant sibships. We discuss the power of the test by presenting sample-size calculations involving a complex disease model, and we present formulas for the asymptotic relative efficiency (which is approximately the ratio of sample sizes) between SDT and the transmission/disequilibrium test (TDT) for special family structures. For sib pairs, we compare the SDT to a test proposed both by Curtis and, independently, by Spielman and Ewens. We show that, for discordant sib pairs, the SDT has good power for testing linkage disequilibrium relative both to Curtis's tests and to the TDT using trios comprising an affected sib and its parents. With additional sibs, we show that the SDT can be more powerful than the TDT for testing linkage disequilibrium, especially for disease prevalence >.3.
Alleles↗
Parent-offspring resemblance of palmar and plantar dermatoglyphic patterns in Down syndrome.
With the aim of investigating the influence of trisomy 21 on the expression of heritable morphological features, we recorded the palmar and plantar dermatoglyphic patterns in 48 children with Down syndrome (DS), in both their parents, and, as a control, in 57 of their siblings. Using the Kendall tau rank correlation test, a considerable parental influence on the frequency of true patterns in the interdigital areas (IDA) III and IV of the palms (P = 0.3%) and soles (P = 1.0% and 3.8%, respectively) is demonstrated for the control siblings. In the children with DS, this influence is discernible as well, although with no statistical significance. A greater number of families may be required to settle the question.
Down Syndrome↗
General practitioner participants in a telemedicine trial: comparisons with their peers.
A study was conducted to determine whether general practitioners (GPs) participating in a telemedicine trial were self-selected enthusiasts for information technology compared with GPs in general. We compared two experimental groups of GPs in London and Wales (n = 126) who had volunteered to participate in a telemedicine trial and two randomly selected groups of GPs from the same areas (total n = 300) who acted as controls. A postal questionnaire was used and achieved a 79% response rate. There were no significant differences in the demographic characteristics of the experimental and control groups in London and Wales, except that the London telemedicine GPs (mean age 41.9 years) were significantly younger than the London controls (mean age 46.6 years). The control GPs were similar to the telemedicine GPs in terms of frequency of computer use and their responses to statements about their attitudes to computers. In the combined telemedicine and control groups, 85% and 77% respectively said that they enjoyed using computers. Telemedicine GPs used computers more frequently for administrative purposes but they shared the same attitudes towards information technology as GPs in general. In all situations where GPs could exercise personal choice, the control and experimental groups were similar.
Adult↗
A survey of affected-sibship statistics for nonparametric linkage analysis.
We have compared the power of a large number of allele-sharing statistics for "nonparametric" linkage analysis with affected sibships. Our rationale was that there is an extensive literature comparing statistics for sibling pairs but that there has not been much guidance on how to choose statistics for studies that include sibships of various sizes. We concentrated on statistics that can be described as assigning scores to each identity-by-descent-sharing configuration that a pedigree might take on (Whittemore and Halpern 1994). We considered sibships of sizes two through five, 27 different genetic models, and varying recombination fractions between the marker and the trait locus. We tried to identify statistics whose power was robust over a wide variety of models. We found that the statistic that is probably used most often in such studies-S(all)-performs quite well, although it is not necessarily the best. We also found several other statistics (such as the R criterion, S(robdom), and the Sobel-and-Lange statistic C) that perform well in most situations, a few (such as S(-#geno) and the Feingold-and-Siegmund version of S(pairs)) that have high power only in very special situations, and a few (such as S(-#geno), the N criterion, and the Sobel-and-Lange statistic B) that seem to have low power for the majority of the trait models. For the most part, the same statistics performed well for all sibship sizes. We also used our results to give some suggestions regarding how to weight sibships of different sizes, in forming an overall statistic.
Alleles↗
Comparison of Mauthner cell size in teleosts.
The Mauthner cells (M-cells) are a pair of neurons found in the medulla oblongata of most fish and amphibians. This neuron is not the same size in all teleosts. Based on the hypothesis that differences in M-cell size might be related to fish family and possibly fish habitat, M-cell and nuclear size were compared between fish families. In order to minimize effects of fish length on cell or nuclear size, statistical treatment of the data was made for four separate length classes. The results indicate at least three groupings by cell or nuclear size. The cell is large in fish from the Salmonidae, Catostomidae and Cyprinidae, small in the Stichaeidae, Cottidae and Pleuronectidae and absent in Batrachoididae, Lophiidae, Ogcocephalidae and Cylopteridae. In general a correlation exists between fish that lack or have small M-cells and a demersal habitat. Since these fish may rely on camouflage for protection against predation, the M-cells may have altered excitability or function as compared to more active fish in which the M-cell is known to function in initiating the startle response (larval zebrafish and goldfish). Differences in input to M-cells of different sizes in conjunction with known electrophysiological properties of the goldfish M-cell are discussed in relation to the function of this neuron.
Animals↗
Estimating sample sizes for continuous, binary, and ordinal outcomes in paired comparisons: practical hints.
Paired data occur in crossover trials and matched case-control studies, and it is rare to find studies reporting sample size calculations associated with these types of studies, despite recommendations from editors that sample size calculations should be justified. In this article we describe some simple formulas and strategies for calculating the number of patients that should be entered into a matched or paired study when the outcome measures are continuous, binary, or ordinal.
Aged↗
Removing the sampling restrictions from family-based tests of association for a quantitative-trait locus.
One strategy for localization of a quantitative-trait locus (QTL) is to test whether the distribution of a quantitative trait depends on the number of copies of a specific genetic-marker allele that an individual possesses. This approach tests for association between alleles at the marker and the QTL, and it assumes that association is a consequence of the marker being physically close to the QTL. However, problems can occur when data are not from a homogeneous population, since associations can arise irrespective of a genetic marker being in physical proximity to the QTL-that is, no information is gained regarding localization. Methods to address this problem have recently been proposed. These proposed methods use family data for indirect stratification of a population, thereby removing the effect of associations that are due to unknown population substructure. They are, however, restricted in terms of the number of children per family that can be used in the analysis. Here we introduce tests that can be used on family data with parent and child genotypes, with child genotypes only, or with a combination of these types of families, without size restrictions. Furthermore, equations that allow one to determine the sample size needed to achieve desired power are derived. By means of simulation, we demonstrate that the existing tests have an elevated false-positive rate when the size restrictions are not followed and that a good deal of information is lost as a result of adherence to the size restrictions. Finally, we introduce permutation procedures that are recommended for small samples but that can also be used for extensions of the tests to multiallelic markers and to the simultaneous use of more than one marker.
Alleles↗
Trials which randomize practices II: sample size.
BACKGROUND: When practices are randomized in a trial and observations are made on the patients to assess the relative effectiveness of the different interventions, sample size calculations need to estimate the number of practices required, not just the total number of patients. OBJECTIVE: Our aims were to introduce the methodology for appropriate sample size calculation and discuss the implications for power. METHOD: A worked example from general practice is used. DISCUSSION: Designs which randomize practices are less powerful than designs which randomize patients to intervention groups, particularly where a large number of patients is recruited from each practice. Studies which randomize few practices should be avoided if possible, as the loss of power is considerable and simple randomization may not ensure comparability of intervention groups.
Family Practice↗
A test for linkage and association in general pedigrees: the pedigree disequilibrium test.
Family-based tests of linkage disequilibrium typically are based on nuclear-family data including affected individuals and their parents or their unaffected siblings. A limitation of such tests is that they generally are not valid tests of association when data from related nuclear families from larger pedigrees are used. Standard methods require selection of a single nuclear family from any extended pedigrees when testing for linkage disequilibrium. Often data are available for larger pedigrees, and it would be desirable to have a valid test of linkage disequilibrium that can use all potentially informative data. In this study, we present the pedigree disequilibrium test (PDT) for analysis of linkage disequilibrium in general pedigrees. The PDT can use data from related nuclear families from extended pedigrees and is valid even when there is population substructure. Using computer simulations, we demonstrated validity of the test when the asymptotic distribution is used to assess the significance, and examined statistical power. Power simulations demonstrate that, when extended pedigree data are available, substantial gains in power can be attained by use of the PDT rather than existing methods that use only a subset of the data. Furthermore, the PDT remains more powerful even when there is misclassification of unaffected individuals. Our simulations suggest that there may be advantages to using the PDT even if the data consist of independent families without extended family information. Thus, the PDT provides a general test of linkage disequilibrium that can be widely applied to different data structures.
Alleles↗
Does penis size influence condom slippage and breakage?
This study examined the effect of penis dimensions on the probability of complete condom slippage and condom breakage in actual use. Men were recruited through advertising, used the condoms supplied and completed a diary sheet for each condom used. A total of 3658 condoms were used by 184 men of which 1.34% broke and 2.05% slipped off. No significant effect was demonstrated for penile dimensions on the probability of complete condom slippage. However, condom breakage was strongly associated with penile circumference. These findings suggest that condom manufacturers may need to increase the range of condom sizes available, or some aspects of their performance, in order to ensure that condoms meet the needs of all men without unduly exposing them to risk.
Condoms↗
Sample size in guidelines trials.
In clinical trials, the statistical concepts of significance and power are used in the determination of sample size for trials. The trialist must provide an estimate of standard deviation and a hypothetical population difference to be detected. This must be modified to deal with the designs encountered in guideline research. These are cluster randomized trials, because the patients of a single doctor or practice form a cluster. The trialist must be able to provide information about the effects of clustering, in the form of an intraclass correlation coefficient.
Family Practice↗
How interchangeable are different estimators of effect size?
The computation of effect sizes is a key feature of meta-analysis. In treatment outcome meta-analyses, the standardized mean difference statistic on posttest scores (d) is usually the effect size statistic used. However, when primary studies do not report the statistics needed to compute d, many methods for estimating d from other data have been developed. Little is known about the accuracy of these estimates, yet meta-analysts frequently use them on the assumption that they are estimating the same population parameter as d. This study investigates that assumption empirically. On a sample of 140 psychosocial treatment or prevention studies from a variety of areas, the present study shows that these estimates yield results that are often not equivalent to d in either mean or variance. The frequent mixing of d and other estimates of d in past meta-analyses, therefore, may have led to biased effect size estimates and inaccurate significance tests.
Electronic Data Processing↗
Sample size for comparing linear growth curves.
Assuming a linear growth curve model under a suitable link function, we compute the sample size for comparing two treatment groups when the repeated measurements marginally follow exponential family distributions. From the treatment profiles of the chosen link function, we compute the common intercept beta0 and the regression slopes beta1 and beta2 to define delta = beta1 - beta2, the difference to be detected, under a specified alternative hypothesis. The dispersion matrices of the generalized estimating equations estimators are obtained under the null and alternative hypotheses using a suitable working correlation matrix. We compute the sample size assuming that delta is asymptotically normal. Details are worked out for repeated measures designs with binary and count data along with numerical examples.
Female↗
Evidence for the sensitivity of the SF-36 health status measure to inequalities in health: results from the Oxford healthy lifestyles survey.
OBJECTIVES: The short form 36 (SF-36) health questionnaire may not be appropriate for population surveys assessing health gain because of the low responsiveness (sensitivity to change) of domains on the measure. An hypothesised health gain of respondents in social class V to that of those in social class I indicated only marginal improvement in self reported health. Subgroup analysis, however, showed that the SF-36 would indicate dramatic changes if the health of social class V could be improved to that of social class I. DESIGN: Postal survey using a questionnaire booklet containing the SF-36 and a number of other items concerned with lifestyles and illness. A letter outlining the purpose of the study was included. SETTING: The sample was drawn from family health services authority (FHSA) computerised registers for Berkshire, Buckinghamshire, Northamptonshire, and Oxfordshire. SAMPLE: The questionnaire was sent to 13,042 randomly selected subjects between the ages of 17-65. Altogether 9332 (72%) responded. OUTCOME MEASURES: Scores for the eight dimensions of the SF-36. STATISTICS: The sensitivity of the SF-36 was tested by hypothesising that the scores of those in the bottom quartile of the SF-36 scores in class V could be improved to the level of the scores from the bottom quartile of SF-36 scores in class I using the effect size statistic. RESULTS: SF-36 scores for the population at the 25th, 50th, and 75th centiles were provided. Those who reported worse health on each dimension of the SF-36 (ie in the lowest 25% of scores) differ dramatically between social class I and V. Large effect sizes were gained on all but one dimension of the SF-36 when the health of those in the bottom quartile of the SF-36 scores in class V were hypothesised to have improved to the level of the scores from the bottom quartile of SF-36 scores in class I. CONCLUSIONS: Analysis of SF-36 data at a population level is inappropriate; subgroup analysis is more appropriate. The data suggest that if it were possible to improve the functioning and wellbeing of those in worst health in class V to those reporting the worst health in class I the improvement would be dramatic. Furthermore, differences between the classes detected by the SF-36 are substantial and more dramatic than might previously have been imagined.
Adolescent↗