Performance of several types of beta-binomial models in comparison to standard approaches for meta-analyses with very few studies.

BACKGROUND: Meta-analyses are used to summarise the results of several studies on a specific research question. Standard methods for meta-analyses, namely inverse variance random effects models, have unfavourable properties if only very few (2 - 4) studies are available. Therefore, alternative meta-analytic methods are needed. In the case of binary data, the "common-rho" beta-binomial model has shown good results in situations with sparse data or few studies. The major concern of this model is that it ignores the fact that each treatment arm is paired with a respective control arm from the same study. Thus, the randomisation to a study arm of a specific study is disrespected, which may lead to compromised estimates of the treatment effect. Therefore, we extended this model to a version that respects randomisation. The aim of this simulation study was to compare the "common-rho" beta-binomial model and several other beta-binomial models with standard meta-analyses models, including generalised linear mixed models and several inverse variance random effects models. METHODS: We conducted a simulation study comparing beta-binomial models and various standard meta-analysis methods. The design of the simulation aimed to consider meta-analytic situations occurring in practice. RESULTS: No method performed well in scenarios with only 2 studies in the random effects scenario. In this situation, a fixed effect model or a qualitative summary of the study results may be preferable. In scenarios with 3 or 4 studies, most methods satisfied the nominal coverage probability. The "common-rho" beta-binomial model showed the highest power under the alternative hypothesis. The beta-binomial model respecting randomisation did not improve performance. CONCLUSION: The "common-rho" beta-binomial appears to be a good option for meta-analyses of very few studies. As residual concerns about the consequences of disrespecting randomisation may still exist, we recommend a sensitivity analysis with a standard meta-analysis method that respects randomisation.

No clinical utility of KRAS variant rs61764370 for ovarian or breast cancer.

OBJECTIVE: Clinical genetic testing is commercially available for rs61764370, an inherited variant residing in a KRAS 3' UTR microRNA binding site, based on suggested associations with increased ovarian and breast cancer risk as well as with survival time. However, prior studies, emphasizing particular subgroups, were relatively small. Therefore, we comprehensively evaluated ovarian and breast cancer risks as well as clinical outcome associated with rs61764370. METHODS: Centralized genotyping and analysis were performed for 140,012 women enrolled in the Ovarian Cancer Association Consortium (15,357 ovarian cancer patients; 30,816 controls), the Breast Cancer Association Consortium (33,530 breast cancer patients; 37,640 controls), and the Consortium of Modifiers of BRCA1 and BRCA2 (14,765 BRCA1 and 7904 BRCA2 mutation carriers). RESULTS: We found no association with risk of ovarian cancer (OR=0.99, 95% CI 0.94-1.04, p=0.74) or breast cancer (OR=0.98, 95% CI 0.94-1.01, p=0.19) and results were consistent among mutation carriers (BRCA1, ovarian cancer HR=1.09, 95% CI 0.97-1.23, p=0.14, breast cancer HR=1.04, 95% CI 0.97-1.12, p=0.27; BRCA2, ovarian cancer HR=0.89, 95% CI 0.71-1.13, p=0.34, breast cancer HR=1.06, 95% CI 0.94-1.19, p=0.35). Null results were also obtained for associations with overall survival following ovarian cancer (HR=0.94, 95% CI 0.83-1.07, p=0.38), breast cancer (HR=0.96, 95% CI 0.87-1.06, p=0.38), and all other previously-reported associations. CONCLUSIONS: rs61764370 is not associated with risk of ovarian or breast cancer nor with clinical outcome for patients with these cancers. Therefore, genotyping this variant has no clinical utility related to the prediction or management of these cancers.

Biometrical issues in the analysis of adverse events within the benefit assessment of drugs.

The analysis of adverse events plays an important role in the benefit assessment of drugs. Consequently, results on adverse events are an integral part of reimbursement dossiers submitted by pharmaceutical companies to health policy decision-makers. Methods applied in the analysis of adverse events commonly include simple standard methods for contingency tables. However, the results produced may be misleading if observations are censored at the time of discontinuation due to treatment switching or noncompliance, resulting in unequal follow-up periods. In this paper, we present examples to show that the application of inadequate methods for the analysis of adverse events in the reimbursement dossier can lead to a downgrading of the evidence on a drug's benefit in the subsequent assessment, as greater harm from the drug cannot be excluded with sufficient certainty. Legal regulations on the benefit assessment of drugs in Germany are presented, in particular, with regard to the analysis of adverse events. Differences in safety considerations between the drug approval process and the benefit assessment are discussed. We show that the naive application of simple proportions in reimbursement dossiers frequently leads to uninterpretable results if observations are censored and the average follow-up periods differ between treatment groups. Likewise, the application of incidence rates may be misleading in the case of recurrent events and unequal follow-up periods. To allow for an appropriate benefit assessment of drugs, adequate survival time methods accounting for time dependencies and duration of follow-up are required, not only for time-to-event efficacy endpoints but also for adverse events. © 2016 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.

A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11.

Genome-wide association studies (GWAS) of breast cancer defined by hormone receptor status have revealed loci contributing to susceptibility of estrogen receptor (ER)-negative subtypes. To identify additional genetic variants for ER-negative breast cancer, we conducted the largest meta-analysis of ER-negative disease to date, comprising 4754 ER-negative cases and 31 663 controls from three GWAS: NCI Breast and Prostate Cancer Cohort Consortium (BPC3) (2188 ER-negative cases; 25 519 controls of European ancestry), Triple Negative Breast Cancer Consortium (TNBCC) (1562 triple negative cases; 3399 controls of European ancestry) and African American Breast Cancer Consortium (AABC) (1004 ER-negative cases; 2745 controls). We performed in silico replication of 86 SNPs at P ≤ 1 × 10(-5) in an additional 11 209 breast cancer cases (946 with ER-negative disease) and 16 057 controls of Japanese, Latino and European ancestry. We identified two novel loci for breast cancer at 20q11 and 6q14. SNP rs2284378 at 20q11 was associated with ER-negative breast cancer (combined two-stage OR = 1.16; P = 1.1 × 10(-8)) but showed a weaker association with overall breast cancer (OR = 1.08, P = 1.3 × 10(-6)) based on 17 869 cases and 43 745 controls and no association with ER-positive disease (OR = 1.01, P = 0.67) based on 9965 cases and 22 902 controls. Similarly, rs17530068 at 6q14 was associated with breast cancer (OR = 1.12; P = 1.1 × 10(-9)), and with both ER-positive (OR = 1.09; P = 1.5 × 10(-5)) and ER-negative (OR = 1.16, P = 2.5 × 10(-7)) disease. We also confirmed three known loci associated with ER-negative (19p13) and both ER-negative and ER-positive breast cancer (6q25 and 12p11). Our results highlight the value of large-scale collaborative studies to identify novel breast cancer risk loci.

Formaldehyde laser-induced fluorescence imaging with a multi-band transmission filter.

A method for laser-induced fluorescence (LIF) imaging of formaldehyde (CH2O) that discriminates against the interfering signal from polycyclic aromatic hydrocarbons (PAHs) is presented. This technique uses an interference filter with 11 transmission bands that closely match the most prominent fluorescence features of CH2O upon excitation at 355 nm. The signal increases by a factor of 12 with the multi-band filter compared to a single-band filter. Slight angle-tuning of the filter shifts the transmission maxima to the minima in between the CH2O-LIF features. The pixel-by-pixel difference between the images collected at the two filter angles thus provides CH2O images free of PAH interference, providing the capability for selective single-pulse CH2O-LIF imaging in engine combustion even under fuel-rich conditions.

A genome-wide association study to identify genetic susceptibility loci that modify ductal and lobular postmenopausal breast cancer risk associated with menopausal hormone therapy use: a two-stage design with replication.

Menopausal hormone therapy (MHT) is associated with an elevated risk of breast cancer in postmenopausal women. To identify genetic loci that modify breast cancer risk related to MHT use in postmenopausal women, we conducted a two-stage genome-wide association study (GWAS) with replication. In stage I, we performed a case-only GWAS in 731 invasive breast cancer cases from the German case-control study Mammary Carcinoma Risk Factor Investigation (MARIE). The 1,200 single nucleotide polymorphisms (SNPs) showing the lowest P values for interaction with current MHT use (within 6 months prior to breast cancer diagnosis), were carried forward to stage II, involving pooled case-control analyses including additional MARIE subjects (1,375 cases, 1,974 controls) as well as 795 cases and 764 controls of a Swedish case-control study. A joint P value was calculated for a combined analysis of stages I and II. Replication of the most significant interaction of the combined stage I and II was performed using 5,795 cases and 5,390 controls from nine studies of the Breast Cancer Association Consortium (BCAC). The combined stage I and II yielded five SNPs on chromosomes 2, 7, and 18 with joint P values <6 × 10(-6) for effect modification of current MHT use. The most significant interaction was observed for rs6707272 (P = 3 × 10(-7)) on chromosome 2 but was not replicated in the BCAC studies (P = 0.21). The potentially modifying SNPs are in strong linkage disequilibrium with SNPs in TRIP12 and DNER on chromosome 2 and SETBP1 on chromosome 18, previously linked to carcinogenesis. However, none of the interaction effects reached genome-wide significance. The inability to replicate the top SNP × MHT interaction may be due to limited power of the replication phase. Our study, however, suggests that there are unlikely to be SNPs that interact strongly enough with MHT use to be clinically significant in European women.

Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status.

OBJECTIVE: There is increasing interest in adding common genetic variants identified through genome wide association studies (GWAS) to breast cancer risk prediction models. First results from such models showed modest benefits in terms of risk discrimination. Heterogeneity of breast cancer as defined by hormone-receptor status has not been considered in this context. In this study we investigated the predictive capacity of 32 GWAS-detected common variants for breast cancer risk, alone and in combination with classical risk factors, and for tumours with different hormone receptor status. MATERIAL AND METHODS: Within the Breast and Prostate Cancer Cohort Consortium, we analysed 6009 invasive breast cancer cases and 7827 matched controls of European ancestry, with data on classical breast cancer risk factors and 32 common gene variants identified through GWAS. Discriminatory ability with respect to breast cancer of specific hormone receptor-status was assessed with the age adjusted and cohort-adjusted concordance statistic (AUROC(a)). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement was used to measure improvements in risk prediction. RESULTS: We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROC(a) going from 2.7% to 4%). Discriminatory ability for all models varied strongly by hormone receptor status. DISCUSSION AND CONCLUSIONS: Adding information on common polymorphisms provides small but statistically significant improvements in the quality of breast cancer risk prediction models. We consistently observed better performance for receptor-positive cases, but the gain in discriminatory quality is not sufficient for clinical application.

Comprehensive analysis of common genetic variation in 61 genes related to steroid hormone and insulin-like growth factor-I metabolism and breast cancer risk in the NCI breast and prostate cancer cohort consortium.

There is extensive evidence that increases in blood and tissue concentrations of steroid hormones and of insulin-like growth factor I (IGF-I) are associated with breast cancer risk. However, studies of common variation in genes involved in steroid hormone and IGF-I metabolism have yet to provide convincing evidence that such variants predict breast cancer risk. The Breast and Prostate Cancer Cohort Consortium (BPC3) is a collaboration of large US and European cohorts. We genotyped 1416 tagging single nucleotide polymorphisms (SNPs) in 37 steroid hormone metabolism genes and 24 IGF-I pathway genes in 6292 cases of breast cancer and 8135 controls, mostly Caucasian, postmenopausal women from the BPC3. We also imputed 3921 additional SNPs in the regions of interest. None of the SNPs tested was significantly associated with breast cancer risk, after correction for multiple comparisons. The results remained null when cases and controls were stratified by age at diagnosis/recruitment, advanced or nonadvanced disease, body mass index, with or without in situ cases; or restricted to Caucasians. Among 770 estrogen receptor-negative cases, an SNP located 3' of growth hormone receptor (GHR) was marginally associated with increased risk after correction for multiple testing (P(trend) = 1.5 × 10(-4)). We found no significant overall associations between breast cancer and common germline variation in 61 genes involved in steroid hormone and IGF-I metabolism in this large, comprehensive study. Although previous studies have shown that variations in these genes can influence endogenous hormone levels, the magnitude of the effect of single SNPs does not appear to be sufficient to alter breast cancer risk.

Diagnostic accuracy of rapid point-of-care tests for diagnosis of current SARS-CoV-2 infections in children: a systematic review and meta-analysis.

OBJECTIVE: To systematically assess the diagnostic accuracy of rapid point-of-care tests for diagnosis of current SARS-CoV-2 infections in children under real-life conditions. DESIGN: Systematic review and meta-analysis. DATA SOURCES: MEDLINE, Embase, Cochrane Database for Systematic Reviews, INAHTA HTA database, preprint servers (via Europe PMC), ClinicalTrials.gov, WHO ICTRP from 1 January 2020 to 7 May 2021; NICE Evidence Search, NICE Guidance, FIND Website from 1 January 2020 to 24 May 2021. REVIEW METHODS: Diagnostic cross-sectional or cohort studies were eligible for inclusion if they had paediatric study participants and compared rapid point-of care tests for diagnosing current SARS-CoV-2 infections with reverse transcription polymerase chain reaction (RT-PCR) as the reference standard. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was used to assess the risk of bias and the applicability of the included studies. Bivariate meta-analyses with random effects were performed. Variability was assessed by subgroup analyses. RESULTS: 17 studies with a total of 6355 paediatric study participants were included. All studies compared antigen tests against RT-PCR. Overall, studies evaluated eight antigen tests from six different brands. Only one study was at low risk of bias. The pooled overall diagnostic sensitivity and specificity in paediatric populations was 64.2% (95% CI 57.4% to 70.5%) and 99.1% (95% CI 98.2% to 99.5%), respectively. In symptomatic children, the pooled diagnostic sensitivity was 71.8% (95% CI 63.6% to 78.8%) and the pooled diagnostic specificity was 98.7% (95% CI 96.6% to 99.5%). The pooled diagnostic sensitivity in asymptomatic children was 56.2% (95% CI 47.6% to 64.4%) and the pooled diagnostic specificity was 98.6% (95% CI 97.3% to 99.3%). CONCLUSIONS: The performance of current antigen tests in paediatric populations under real-life conditions varies broadly. Relevant data were only identified for very few antigen tests on the market, and the risk of bias was mostly unclear due to poor reporting. Additionally, the most common uses of these tests in children (eg, self-testing in schools or parents testing their toddlers before kindergarten) have not been addressed in clinical performance studies yet. The observed low diagnostic sensitivity may impact the planned purpose of the broad implementation of testing programmes. PROSPERO REGISTRATION NUMBER: CRD42021236313.

Entropy-supported marker selection and Mantel statistics for haplotype sharing analysis.

Haplotype sharing analysis is a well-established option for the investigation of the etiology of complex diseases. The statistical power of haplotype association methods depends strongly on how the information of unobserved haplotypes can be captured by multilocus genotypes. In this study we combine an entropy-based marker selection algorithm (EMS), with a haplotype sharing-based Mantel statistics into a new algorithm. Genetic markers are iteratively selected by their multilocus linkage disequilibrium (LD), which is assessed by a normalized entropy difference. The initial marker set is gradually enlarged to increase the available information on the amount of sharing around a potential susceptibility marker. Markers are rejected from joint phasing if they do not increase the multilocus LD. In simulated candidate gene studies, the Mantel statistics combined with the new EMS performs as well or better at detecting the disease single nucleotide polymorphism-or in indirect association analysis its flanking markers-than the Mantel statistics without selection of markers prior to haplotype estimation and the Mantel statistics using sliding windows of size five. It is therefore appealing to apply our selection approach for haplotype-based association analysis, since marker selection driven by the observed data avoids both the arbitrary choice of markers when using a fixed window size, as well as the estimation of haplotype block structure.

Association of JAK-STAT pathway related genes with lymphoma risk: results of a European case-control study (EpiLymph).

Previous studies have suggested an important role for the Janus kinase-signal transducer and activator of transcription (JAK-STAT) signalling pathway in tumour development. Therefore, we explored genetic variants in JAK-STAT pathway associated genes with lymphoma risk. In samples of the EpiLymph case-control study we genotyped 1536 single nucleotide polymorphisms (SNPs) using GoldenGate BeadArray™ Technology (Illumina, San Diego, CA, USA). Here, we report the associations between selected SNPs and haplotypes of the JAK-STAT pathway and risk of Hodgkin lymphoma (HL), B-cell non-Hodgkin lymphoma (B-NHL) and most frequent B-NHL subtypes. Among 210 relevant JAK-STAT pathway-related SNPs, polymorphisms in nine genes (BMF, IFNG, IL12A, SOCS1, STAT1, STAT3, STAT5A, STAT6, TP63) were significantly associated with lymphoma risk. At a study-wise significance level, we obtained a risk reduction of 28% among carriers of the heterozygous genotype of the STAT3 variant (rs1053023) for B-NHL. For six other variants within the STAT3 gene we observed an inverse association with different lymphoma subtypes. A reduced risk for HL was observed for the heterozygous genotype of the STAT6 SNP (rs324011). This is an explorative investigation to examine associations between JAK-STAT signalling related genes and lymphoma risk. The results implicate a relevant role of certain pathway-related genes in lymphomagenesis, but still need to be approved by independent studies.

Polymorphisms in fatty-acid-metabolism-related genes are associated with colorectal cancer risk.

Colorectal cancer (CRC) is the third most common malignant tumor and the fourth leading cause of cancer death worldwide. The crucial role of fatty acids for a number of important biological processes suggests a more in-depth analysis of inter-individual differences in fatty acid metabolizing genes as contributing factor to colon carcinogenesis. We examined the association between genetic variability in 43 fatty acid metabolism-related genes and colorectal risk in 1225 CRC cases and 2032 controls participating in the European Prospective Investigation into Cancer and Nutrition study. Three hundred and ninety two single-nucleotide polymorphisms were selected using pairwise tagging with an r(2) cutoff of 0.8 and a minor allele frequency of >5%. Conditional logistic regression models were used to estimate odds ratios and corresponding 95% confidence intervals. Haplotype analysis was performed using a generalized linear model framework. On the genotype level, hydroxyprostaglandin dehydrogenase 15-(NAD) (HPGD), phospholipase A2 group VI (PLA2G6) and transient receptor potential vanilloid 3 were associated with higher risk for CRC, whereas prostaglandin E receptor 2 (PTGER2) was associated with lower CRC risk. A significant inverse association (P < 0.006) was found for PTGER2 GGG haplotype, whereas HPGD AGGAG and PLA2G3 CT haplotypes were significantly (P < 0.001 and P = 0.003, respectively) associated with higher risk of CRC. Based on these data, we present for the first time the association of HPGD variants with CRC risk. Our results support the key role of prostanoid signaling in colon carcinogenesis and suggest a relevance of genetic variation in fatty acid metabolism-related genes and CRC risk.

Genetic polymorphisms of MPO, GSTT1, GSTM1, GSTP1, EPHX1 and NQO1 as risk factors of early-onset lung cancer.

Early-onset lung cancer diagnosed up to the age of 50 is a very rare disease, with an increasing incidence rate. Differences in aetiology, characteristics and epidemiology of early and older onset lung cancer have been described previously, suggesting the importance of genetic factors in early-onset lung cancer aetiology. A case-control study was conducted to investigate the effects of genetic polymorphisms in the MPO, EPHX1, GSTT1, GSTM1, GSTP1 and NQO1 genes on the risk of early-onset lung cancer development. Six hundred thirty-eight Caucasian patients under the age of 51 with confirmed primary lung cancer and 1,300 cancer free control individuals, matched by age and sex, were included in this analysis. Seventeen single nucleotide polymorphisms and two deletion polymorphisms were genotyped. No significant association was found for any of the analyzed polymorphisms and overall lung cancer risk. Nonsignificantly decreased risk of lung cancer was observed for carriers of 1 or 2 copies of GSTM1. Subgroup analysis revealed gender- and/or smoking-specific effects of EPHX1 rs2854455 (IV-1464C > T) and rs2234922 (His139Arg), GSTT1 deletion, GSTP1 rs1695 (Ile105Val), rs947895 (+991C > A) and rs4891 (Ser185Ser) and NQO1 rs1800566 (Pro187Ser) polymorphisms. However, none of the observed effects were confirmed by interaction tests nor were they significant after Bonferroni correction for multiple testing. In summary, our study suggested a modifying effect of polymorphisms in EPHX1, GSTP1, GSTT1, GSTM1 and NQO1 genes on the risk of early-onset lung cancer. To confirm these observations and to eliminate possible bias in our analyses, larger studies are warranted.

Carcinogen metabolism, cigarette smoking, and breast cancer risk: a Bayes model averaging approach.

BACKGROUND: Standard logistic regression with or without stepwise selection has the disadvantage of not incorporating model uncertainty and the dependency of estimates on the underlying model into the final inference. We explore the use of a Bayes Model Averaging approach as an alternative to analyze the influence of genetic variants, environmental effects and their interactions on disease. METHODS: Logistic regression with and without stepwise selection and Bayes Model Averaging were applied to a population-based case-control study exploring the association of genetic variants in tobacco smoke-related carcinogen pathways with breast cancer. RESULTS: Both regression and Bayes Model Averaging highlighted a significant effect of NAT1*10 on breast cancer, while regression analysis also suggested a significant effect for packyears and for the interaction of packyears and NAT2. CONCLUSIONS: Bayes Model Averaging allows incorporation of model uncertainty, helps reduce dimensionality and avoids the problem of multiple comparisons. It can be used to incorporate biological information, such as pathway data, into the analysis. As with all Bayesian analysis methods, careful consideration must be given to prior specification.

Comparison of different haplotype-based association methods for gene-environment (GxE) interactions in case-control studies when haplotype-phase is ambiguous.

OBJECTIVE: We compared four haplotype-based approaches for the analysis of gene-environment interactions when haplotype-phase is ambiguous. The methods employ different versions of the expectation maximization algorithm and differ in the choice of the reference group and in the way the risk of disease is modeled (retrospective versus prospective). Furthermore, the methods are based on distinct assumptions (such as Hardy Weinberg equilibrium). The haplotype-based methods were also compared to single-marker logistic regression (LR). METHODS: We simulated case-control scenarios where the risk variant was directly genotyped (direct scenario) or in linkage disequilibrium with the genotyped markers (indirect scenario). RESULTS: The retrospective methods tended to be more powerful for detecting interactions than the prospective methods. In the indirect scenarios, the power of all methods was decreased. However, the power of the retrospectives methods was high in some scenarios and the interactions may only be detectable when using these approaches. Furthermore, we observed that the precision of one prospective method was clearly lower than the precision of the retrospective methods. CONCLUSION: For the analysis of gene-environment (GxE) interactions in case-control data, the investigated retrospective methods can be an attractive alternative to haplotype-based methods which do not account for the retrospective sampling design.

CYP450 polymorphisms as risk factors for early-onset lung cancer: gender-specific differences.

Cytochrome P450 (CYP) enzymes, involved in metabolism of tobacco carcinogens, are also involved in estrogen metabolism and many are regulated by estrogens. These genes may thus be of relevance to gender-specific differences in lung cancer risk, particularly in early-onset lung cancer, where a high proportion of women is observed. We conducted a case-control study to investigate genetic polymorphisms in cytochromes that might modify the risk of developing early-onset lung cancer. In total, 638 Caucasian patients under the age of 51 with primary lung cancer and 1300 cancer-free control individuals, matched by age and sex, were included in this analysis. Thirteen polymorphisms in the CYP1A1, CYP1B1, CYP2A13, CYP3A4 and CYP3A5 genes were analyzed. No significant association was found for any of the analyzed polymorphisms and lung cancer risk overall. However, among women, a significantly increased risk of early-onset lung cancer was observed for carriers of the minor allele of CYP1B1 SNP rs1056836 [odds ratio (OR) 1.97; 95% confidence interval (CI) 1.32-2.94; P < 0.001]. Also, a non-significant increase in lung cancer risk was observed in the group of women carriers of the minor allele of CYP2A13 SNP rs1709084 (OR 1.64; 95% CI 1.00-2.70; P = 0.05). The effect of these two polymorphisms was shown to be modified by smoking. Haplotype analysis was performed for CYP1B1 and CYP2A13. No differences between cases and controls were observed for both genes (P = 0.63 and P = 0.42 for CYP1B1 and CYP2A13, respectively). Our results suggest that the CYP1B1 and the CYP2A13 genotypes may contribute to individual susceptibility to early-onset lung cancer in women.

Impact of genotyping errors on the type I error rate and the power of haplotype-based association methods.

BACKGROUND: We investigated the influence of genotyping errors on the type I error rate and empirical power of two haplotype based association methods applied to candidate regions. We compared the performance of the Mantel Statistic Using Haplotype Sharing and the haplotype frequency based score test with that of the Armitage trend test.Our study is based on 1000 replication of simulated case-control data settings with 500 cases and 500 controls, respectively. One of the examined markers was set to be the disease locus with a simulated odds ratio of 3. Differential and non-differential genotyping errors were introduced following a misclassification model with varying mean error rates per locus in the range of 0.2% to 15.6%. RESULTS: We found that the type I error rate of all three test statistics hold the nominal significance level in the presence of non-differential genotyping errors and low error rates. For high and differential error rates, the type I error rate of all three test statistics was inflated, even when genetic markers not in Hardy-Weinberg Equilibrium were removed. The empirical power of all three association test statistics remained high at around 89% to 94% when genotyping error rates were low, but decreased to 48% to 80% for high and non-differential genotyping error rates. CONCLUSION: Currently realistic genotyping error rates for candidate gene analysis (mean error rate per locus of 0.2%) pose no significant problem for the type I error rate as well as the power of all three investigated test statistics.

Limitations of the incidence density ratio as approximation of the hazard ratio.

BACKGROUND: Incidence density ratios (IDRs) are frequently used to account for varying follow-up times when comparing the risks of adverse events in two treatment groups. The validity of the IDR as approximation of the hazard ratio (HR) is unknown in the situation of differential average follow up by treatment group and non-constant hazard functions. Thus, the use of the IDR when individual patient data are not available might be questionable. METHODS: A simulation study was performed using various survival-time distributions with increasing and decreasing hazard functions and various situations of differential follow up by treatment group. HRs and IDRs were estimated from the simulated survival times and compared with the true HR. A rule of thumb was derived to decide in which data situations the IDR can be used as approximation of the HR. RESULTS: The results show that the validity of the IDR depends on the survival-time distribution, the difference between the average follow-up durations, the baseline risk, and the sample size. For non-constant hazard functions, the IDR is only an adequate approximation of the HR if the average follow-up durations of the groups are equal and the baseline risk is not larger than 25%. In the case of large differences in the average follow-up durations between the groups and non-constant hazard functions, the IDR represents no valid approximation of the HR. CONCLUSIONS: The proposed rule of thumb allows the use of the IDR as approximation of the HR in specific data situations, when it is not possible to estimate the HR by means of adequate survival-time methods because the required individual patient data are not available. However, in general, adequate survival-time methods should be used to analyze adverse events rather than the simple IDR.

Gene-environment interactions for complex traits: definitions, methodological requirements and challenges.

Genetic and environmental risk factors and their interactions contribute to the development of complex diseases. In this review, we discuss methodological issues involved in investigating gene-environment (G x E) interactions in genetic-epidemiological studies of complex diseases and their potential relevance for clinical application. Although there are some important examples of interactions and applications, the widespread use of the knowledge about G x E interaction for targeted intervention or personalized treatment (pharmacogenetics) is still beyond current means. This is due to the fact that convincing evidence and high predictive or discriminative power are necessary conditions for usefulness in clinical practice. We attempt to clarify conceptual differences of the term 'interaction' in the statistical and biological sciences, since precise definitions are important for the interpretation of results. We argue that the investigation of G x E interactions is more rewarding for the detailed characterization of identified disease genes (ie at advanced stages of genetic research) and the stratified analysis of environmental effects by genotype or vice versa. Advantages and disadvantages of different epidemiological study designs are given and sample size requirements are exemplified. These issues as well as a critical appraisal of common methodological concerns are finally discussed.