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PubMed2006

Current perspectives on the genetic analysis of autism.

Although no definitive genetic mutations leading to autism susceptibility have been established, the field has many new resources to tackle this difficult problem. Numbers of families at many research sites are now large, and new collaborations among these groups will allow for collections of subjects with enough statistical power to detect relatively small gene effects. New technological advances in genotyping will allow for more fine-grained genetic analysis, and more sophisticated techniques have been developed to address the vast amounts of data acquired. Researchers have also begun to focus on intermediate phenotypes associated with autism, such as elevated serotonin, increased head circumference, social difficulties, or language impairment or delay. These phenotypes may help to stratify affected cases into more genetically homogeneous subgroups, and may also occur in their clinically unaffected relatives. The study of intermediate phenotypes may allow investigators to find common gene variants that lead to autism susceptibility. Here we discuss the major intermediate phenotypes, and give an overview of current genetic analysis techniques.

Autistic Disorder↗

PubMed2000

The interpretation and use of statistics in assisted reproductive technologies.

This article has discussed some of the uses and limitations of currently available statistics for ART programs. A well-known example from statistics states that flipping a coin will produce "heads" 50% of the time and "tails" 50% of the time, provided that the coin is flipped enough times. Experience also shows that the result of individual flips cannot be predicted. Similarly, in ART, statistics can only give general probabilities and not meaningful predictions of the outcome of any particular cycle. Patients should be aware of the limits of statistical analysis as it applies to their individual treatment.

Adult↗

PubMed1995

The problem of the type II statistical error.

OBJECTIVE: To determine if type II statistical errors (also known as beta errors) are a common problem in published clinical research. METHODS: Type II statistical errors occur when sample sizes are too small to show an effect of treatment, even when an effect truly exists. Searching the Medline data base, we identified ten meta-analyses published during 1986-1994 in the American Journal of Obstetrics and Gynecology, Obstetrics and Gynecology, and The Journal of Reproductive Medicine. Meta-analyses were used as sources of component or individual studies for the following reason: When small component studies have negative findings that differ from the overall conclusions of the meta-analysis, the component studies may have type II statistical errors. RESULTS: We found that only 6.5% (15 of 231) of component studies provided any documentation that power calculations to determine sample sizes had been done a priori (before) the research began. Thus, many of these component studies with findings of no treatment effect may have had type II errors because of too-small sample sizes. When stratifying the component studies by year of publication, we found that 7.9% (14 of 178) of studies published in the 1980s and 1990s had any documented evidence of a priori power calculations. In the 1960s and 1970s, only one of 53 component studies had documented evidence of power calculations. CONCLUSION: To ensure that truly effective treatments are introduced into clinical practice as quickly as possible, we believe that a priori power calculations should always be done in quantitative clinical research.

Meta-Analysis as Topic↗

PubMed2002

Bayesian subset analysis: application to studying treatment-by-gender interactions.

Evaluating treatment effects within subsets of patients plays a major part of the analysis of many major clinical trials. Clinicians are often impressed by the heterogeneity of patient populations in clinical trials and hence are interested in examining subset effects. Statisticians generally discourage subset analysis or suggest that clinicians 'do subset analysis but do not believe it'. This advice, however, is a sign of the inadequacy of the analytic methods generally used for subset analysis. Separate analysis of many subsets, and basing conclusions on whether the observed treatment difference achieves significance at the 0.05 level, is likely to yield erroneous conclusions. Making the separate analysis of subsets dependent on demonstration of a statistically significant treatment-by-subset interaction is also not an effective analytic strategy because of the limited power of interaction tests. This paper describes a Bayesian approach to subset analysis developed by Simon, Dixon and Freidlin. The method avoids many of the problems of subset analysis because it is not 'separate' analysis of subsets. Instead, subset-specific treatment effects are estimated as an average of observed within-subset differences and overall differences; the two components are weighted by the a priori estimate of the likelihood of qualitative treatment by subset interactions. Hence, the Bayesian method proposed permits subset analyses incorporating the assumption that qualitative interactions are unlikely. The methodology is applied to the problem of designing and analysing clinical trials to estimate treatment effects for males and females.

Bayes Theorem↗

PubMed1983

[Multivariate analysis of beta activity in clinical material].

Beta activity, as described by a number of parameters extracted from the power spectra, was submitted to several multivariate statistical techniques in search of interpretable subgroups. The difficulties of doing this on heterogeneous clinical material, as well as various shortcomings of the usual cluster analytical techniques, are outlined. Where longitudinal data are available, the analysis of beta activity may help in monitoring the effects of the therapy. Generally, the large variability between individuals and many sources of errors related to the processes of data collection and parameter extraction indicate that much work remains to be done before even a rough and clinically relevant typology of beta activity will become available.

Adolescent↗

PubMed2006

A one-step approach to the analysis of the QT interval in conscious telemetrized dogs.

INTRODUCTION: To account for heart rate-induced changes in the QT interval, correction formulas are generally applied to normalize the QT interval for heart rate. None of these formulas is entirely accurate because correction or normalization of any parameter in biology may introduce an additional source of variation in estimating the parameter. In this article, a one-step approach for the statistical analysis of the QT interval was proposed based on modeling the functional relationship between the QT interval and heart rate. METHODS: The QT-HR relationship was incorporated into the statistical analysis to provide a model-based correction. This was accomplished by including HR as a covariate in the QT interval analysis. The approach was demonstrated using data generated from Lilly Research Laboratories. We compared the false positive rate and statistical power of QT, QTcF, and the proposed one-step method. RESULTS: We found the one-step method demonstrated the greatest sensitivity in detecting a QT interval change without an increase in the false positive rate. It was shown that the one-step QT analysis could detect a 5%-6% increment of the QT interval. This is approximately equivalent to an increase of 11-13 ms in QT interval in beagle dogs. DISCUSSION: Several advantages and unique features of the one-step method are discussed. These include evaluating treatment effect on QT without applying a heart rate correction formula and estimating QT difference flexibly at any selected heart rate. In addition to the linear QT-HR relationship, other functional relationships can be easily implemented to this approach.

Algorithms↗

PubMed1993

Sample size calculation, power analysis and randomization: research project design in Windows.

Single estimates of sample size for a study may be easily obtained by use of a hand calculator or from published tables. In contrast, performing multiple calculations is a tedious and time-consuming task, which is greatly simplified by a computer program. The computer program presented here assists the investigator in calculating sample size estimates, determining statistical power and creating randomization tables for a study. The program is designed primarily for clinical trials and thus includes some features not found in other software packages performing similar tasks. Sample size calculation and power analysis are performed for dichotomous, continuous (parametric and non-parametric tests) and time-to-failure (exponential distribution and log-rank test) response variables, and for correlation coefficients. Sample size estimates and significance levels may be adjusted for multiple participating centers, non-compliance, interim analyses and.multiple testing. The randomization subroutine generates tables for studies with up to nine treatment arms and with any valid block size. As a Windows application, the program runs in a multitasking environment, allowing switching between programs and easy pasting of results into word-processing documents and other applications. It is very simple to use, with a completely menudriven interface and sufficient built-in help to obviate the use of a manual.

Algorithms↗

PubMed2000

N-acetyl transferase-2 and bladder cancer risk: a meta-analysis.

Interindividual differences in bladder cancer susceptibility may be partly mediated through polymorphic variability in the metabolism of carcinogens. N-acetyl transferase-2 (NAT2) has been extensively studied as a risk factor in this context, but the results are inconsistent. In some studies the failure to demonstrate a relationship may be a consequence of a lack of statistical power. To overcome lack of power, data from 21 published case-control studies were pooled in a meta-analysis using a random-effects model. The pooled odds ratio of bladder cancer associated with slow acetylator status was 1.31 (95% CI: 1.11-1.55). The results suggest that NAT2 slow acetylator status is associated with a modest increase in risk of bladder cancer. There was, however, heterogeneity between studies. It is clear from this overview that greater attention should be paid to the design of these types of study.

Arylamine N-Acetyltransferase↗

PubMed1999

How long do routine dental restorations last? A systematic review.

OBJECTIVE: To conduct a systematic review of the literature on the longevity of routine dental restorations in permanent posterior teeth, and to identify and examine factors influencing its variability. METHOD: Accepted guidelines were followed. An advisory group oversaw the project. Simple Class I and Class II amalgam, composite resin, glass ionomer and cast gold restorations were covered. Comprehensive searching of electronic databases, hand-searching, and location of 'grey' literature, generated 124 research reports. Those considered relevant were assessed for validity and quality according to agreed criteria. The analysis was descriptive. RESULTS: Eight of 58 relevant research reports were categorised, according to agreed criteria, as being of satisfactory validity and quality. They suggested that 50% of all restorations last 10 to 20 years, although both higher and lower median survival times were reported. The findings were supported by the totality of studies reviewed. However, variability was substantial. Restoration type, materials, the patient, the operator, the practice environment and type of care system appeared to influence longevity. CONCLUSIONS: Many studies were imperfect in design. Those considered to be the most appropriate for analysis were too limited to undertake a formal statistical exploration. Therefore there remains a need for definitive randomised controlled trials of restoration longevity, of sound design and adequate power, employing standardised assessments and appropriate methods of analysis.

Bicuspid↗

PubMedDate not supplied

Principles and pitfalls in the analysis of prenatal treatment effects in multiparous species.

Developmental studies often assess the effect of treatment of the pregnant mother on offspring. The use of multiparous species such as rats and mice in such studies creates a special set of design and analysis problems. These arise for two reasons. First, the availability of many offspring per litter tempts the experimenter to inflate sample size by treating scores from several pups per litter as independent observations. Second, large litter size seldom makes it practical to measure exposure effects in all offspring of an exposed dam. Such studies commonly involve two-stage sampling: Drawing a random sample of dams for treatment, then drawing a second sample of pups per dam for neurobehavioral measurements. In this article, such sampling was modeled by two different simulations. The first, a standard Monte-Carlo approach, sampled from random-normal distributions for litter mean and within-litter variability. The second simulation sampled without replacement from actual data on weight of all pups in a series of 39 nontreated rat litters. These mutually-supportive approaches demonstrate that litter effects, even over as few as three litters, are generally large and statistically meaningful. Consequently, statistical significance tests are sensitive to litter effects. Inflation of sample size by treating as few as 2 pups per litter as independent measurements can almost triple the nominal 0.05 alpha level. Furthermore, two-stage sampling increases the within-treatment error term and correspondingly reduces statistical power relative to one-stage sampling.(ABSTRACT TRUNCATED AT 250 WORDS)

Analysis of Variance↗

PubMed1997

Maternal cocaine use and low birth weight newborns: a meta-analysis.

AIM/DESIGN: Many epidemiological studies published on the association between maternal cocaine/crack use and birth weight have either lacked precision or failed to control for major confounding, predominantly by tobacco smoking. Meta-analysis enables a single summary measure of effect to be calculated by combining data from any number of individual studies, thus enhancing statistical power. We undertook a number of meta-analyses using only studies that had adjusted for tobacco smoking to estimate more precisely the effect of maternal cocaine use on birth weight. FINDINGS: A meta-analysis of five studies presenting data for 'any' prenatal cocaine exposure, adjusted for tobacco smoking but unadjusted for gestational age, produced a pooled relative risk estimate from a fixed effects analysis of 2.15 (95% CI 1.75-2.64). However, there was substantial heterogeneity among studies (p < 0.001), and the relative risk from a random effects analysis was smaller (1.65) with a confidence interval that included unity (95% CI 0.94-2.83). Addition of a further study adjusted for gestational age had minimal effect on the pooled estimate: the fixed effects relative risk was 2.14 (1.77-2.60) and the random effects estimate 1.77 (1.15-2.71). When data on more intense prenatal exposure were analysed, the fixed and random effects analysis produced the same pooled estimate of the relative risk of 4.42 (2.24-8.71), suggesting that more frequent cocaine exposure was associated with a higher relative risk for low birth weight. Data from studies on mean reduction in birth weight produced a pooled estimate of 112 g (95% CI 62-161 g). CONCLUSIONS: The current study suggests that maternal cocaine use causes low birth weight, and that the effect is greater with heavier use. However, despite the adjustment for tobacco and the adjustment by some studies for other confounders such as race, maternal age, gravidity and socio-economic status, it could be argued that other life-style factors not controlled for may account for the observed effects. While this argument is not supported by some other types of study, the issue of residual confounding can only be finally addressed by analytical studies which adequately control for important variables.

Cocaine↗

PubMed2003

Phylogeny estimation: traditional and Bayesian approaches.

The construction of evolutionary trees is now a standard part of exploratory sequence analysis. Bayesian methods for estimating trees have recently been proposed as a faster method of incorporating the power of complex statistical models into the process. Researchers who rely on comparative analyses need to understand the theoretical and practical motivations that underlie these new techniques, and how they differ from previous methods. The ability of the new approaches to address previously intractable questions is making phylogenetic analysis an essential tool in an increasing number of areas of genetic research.

Bayes Theorem↗

PubMed2000

Bacteriological safety and cost-effectiveness of a nonrefluxing valve in the irrigation system during outpatient flexible cystoscopy.

OBJECTIVE: To critically evaluate the infection rate associated with the use of a nonrefluxing irrigation system for outpatient flexible cystoscopy, by comparing it with conventional irrigation systems, and to determine the costs and benefits of this system of irrigation delivery. PATIENTS AND METHODS: All patients undergoing flexible cystoscopy on scheduled outpatient lists were considered for inclusion in the study; 143 patients of 220 undergoing cystoscopy fulfilled the study criteria. The study group of patients underwent cystoscopy with a new single-use nonrefluxing valve inserted into the same irrigation delivery system that was used for the whole endoscopy session, and the control group had the complete irrigation system changed after each endoscopic examination. Midstream urine samples were taken for analysis before cystoscopy and again 3-4 days later. Infection was defined as a pure growth of >/= 105 organisms/mL, with associated pyuria defined as >/= 10 pus cells per high power microscopic field. RESULTS: Complete data were available on 133 patients, with a further nine being excluded from the statistical analysis because they had a pre-existing urinary tract infection, leaving 64 patients in the study and 69 in the control groups. The overall infection rate for flexible cystoscopy was 3.2%, with no significant difference between the study and control groups. Cost savings of > 35% can be expected using the nonrefluxing valve method of irrigation delivery. CONCLUSION: The nonrefluxing valve caused no detectable increase in patient morbidity from bacterial infection when compared with conventional irrigation systems for flexible cystoscopy, and can save considerable costs.

Aged↗

PubMed2002

An introduction to statistical concepts for the analysis of EEG data and the planning of pharmaco-EEG trials.

The use of descriptive statistics and graphics for electroencephalogram (EEG) data after initial data reduction and the application of hypothesis tests to EEG data are described. In planning pharmaco-EEG trials, some notion of statistical power and variability of effect size is necessary as they are used to calculate sample size in parallel-group clinical trials. The need for sensitivity analysis during the study planning phase is demonstrated as well. Because EEG data are highly dimensional data, an outline of methods for reducing dimensionality is provided. These concepts are demonstrated using data from patients with Alzheimer's disease and from healthy controls.

Alzheimer Disease↗

PubMed2006

A workflow spatial scan statistic.

We propose a modification of the spatial scan statistic that takes account of workflow, which is the movement of individuals between home and work. The objective is to detect clusters of disease in situations where exposure occurs in the workplace, but only home address is available for analysis. In these situations, application of the usual spatial scan statistic does not account for possible differences between home and work address, thereby reducing the power of detection. We describe an extension to the usual spatial scan statistic that uses workflow data to search for disease clusters resulting from workplace exposure. We also present results from simulations that demonstrate the increased power of the workflow scan statistic over the usual scan statistic for detecting clusters arising from exposures in the workplace.

Anthrax↗

PubMed1988

Overweight and pregnancy complications.

The association between increased prepregnancy weight for height and seven pregnancy complications was studied in a multi-racial sample of more than 4100 recent deliveries. Body mass indices were calculated and used to classify women as average weight (90-119 percent of ideal or BMI 19.21-25.60), moderately overweight (120-135 percent ideal or BMI 25.61-28.90), and very overweight (greater than 135 percent ideal or BMI greater than 28.91) prior to pregnancy. Compared to women of average weight for height, very overweight women had a higher risk of diabetes, hypertension, pregnancy-induced hypertension and primary cesarean section delivery. Moderately overweight women were also at higher risk than average for diabetes, pregnancy-induced hypertension and primary cesarean deliveries but the relative risks were of a smaller magnitude than for very overweight women. With women of average prepregnancy body mass as reference, moderately elevated, but not significant relative risks were found for perinatal mortality in the very overweight group, for urinary tract infections in both overweight groups, and a decreased risk for anemia was found in the very overweight group. However, post-hoc power analyses indicated that the number of overweight women in the sample did not allow adequate statistical power to detect these small differences in risk. To overcome limitations associated with low statistical power, the results of three recent studies of these outcomes in very overweight pregnant women were combined and summarized using Mantel-Haenzel techniques. This second, larger analysis suggested that very overweight women are at significantly higher risk for all seven outcomes studied. Summary results for moderately overweight women could not be calculated, since only two of the studies had evaluated moderately overweight women separately. These latter results support other findings that both moderate overweight and very overweight are risk factors during pregnancy, with the highest risk occurring in the heaviest group. Although these results indicate that moderate overweight is a risk factor during pregnancy, additional studies are needed to confirm the impact of being 20-35 percent above ideal weight prior to pregnancy. The results of this analysis also imply that since the baseline incidence of many perinatal complications is low, studies relating overweight and pregnancy complications should include large enough samples of overweight women so that there is adequate statistical power to reliably detect differences in complication rates.

Adolescent↗

PubMed2005

A constrained-likelihood approach to marker-trait association studies.

Marker-trait association analysis is an important statistical tool for detecting DNA variants responsible for genetic traits. In such analyses, an analysis model of the mean genetic effects of the genotypes is often specified. For instance, the effect of the disease allele on the trait is often specified to be dominant, recessive, additive, or multiplicative. Although this model-based approach is powerful when the analysis model is correctly specified, it has been found to have low power sometimes when the specified model is incorrect. We introduce an approach that does not require the specification of a particular genetic model. This approach is built upon a constrained maximum likelihood in which the mean genetic effect of the heterozygous genotype is required to not exceed those of the two homozygous genotypes. The asymptotic distribution of the likelihood-ratio statistic is derived for two special cases. A simulation study suggests that this new approach has power comparable to that of the model-based method when the analysis model is correctly specified. This approach uses one marker at a time (i.e., it is a single-marker analysis). However, given the latest findings that powerful inferential procedures for haplotype analyses can be constructed from single-marker analyses, we expect this approach to be useful for haplotype analyses.

Alleles↗

PubMed2004

Meta-analysis: apolipoprotein E genotypes and risk for coronary heart disease.

BACKGROUND: Apolipoprotein E (apoE) genotypes play critical roles in lipid metabolism and are believed to influence risk for coronary heart disease (CHD). Despite many population studies, however, the impact of apoE polymorphism on risk for CHD remains uncertain. PURPOSE: To qualitatively and quantitatively assess the evidence regarding the relation of apoE polymorphism to CHD risk. DATA SOURCES: All relevant reports and references from original and review papers published from 1966 to January 2004. STUDY SELECTION: Predefined criteria were used to identify 48 relevant studies. DATA EXTRACTION: A summary database that contained variables of study design, study sample and ethnicity, sex, apoE genotypes, CHD end points, plasma lipid levels, and other CHD risk factors was developed. DATA SYNTHESIS: The authors qualitatively evaluated many potential sources of heterogeneity. To quantify the extent of heterogeneity and assess the consistency of apoE-CHD associations, stratified analyses were conducted using the classic random-effects model. To further incorporate uncertainty due to between-study variation, the pooled odds ratios (ORs) and 95% credible intervals (CrIs) were estimated by using a Bayesian hierarchical model. Finally, the robustness of the pooled estimates was tested in multiple sensitivity analyses. Compared with individuals with the epsilon3/3 genotype, carriers of the apoE epsilon4 allele had a 42% higher risk for CHD (OR, 1.42 [95% CrI, 1.26 to 1.61]). The epsilon2 allele had no significant association with CHD risk (OR, 0.98 [CrI, 0.66 to 1.46]). LIMITATIONS: This meta-analysis did not include unpublished data or studies published in languages other than English. CONCLUSIONS: Inadequate statistical power, differences in geographic and ethnic background, allele frequency, sex, CHD phenotypes, study design, and potential gene-environment interactions may have contributed to the conflicting results of previous studies. The apoE epsilon4 allele is a significant risk factor for CHD.

Apolipoproteins E↗