Biomedical subjects

Constantine E Frangakis

Publications and source records attributed to Constantine E Frangakis.

14 recordsLinked to original sources

PubMed2006

Covariate-based linkage analysis: application of a propensity score as the single covariate consistently improves power to detect linkage.

Successful identification of genetic risk loci for complex diseases has relied on the ability to minimize disease and genetic heterogeneity to increase the power to detect linkage. One means to account for disease heterogeneity is by incorporating covariate data. However, the inclusion of each covariate will add one degree of freedom to the allele sharing based linkage test, which may in fact decrease power. We explore the application of a propensity score, which is typically used in causal inference to combine multiple covariates into a single variable, as a means of allowing for multiple covariates with the addition of only one degree of freedom. In this study, binary trait data, simulated under various models involving genetic and environmental effects, were analyzed using a nonparametric linkage statistic implemented in LODPAL. Power and type I error rates were evaluated. Results suggest that the use of the propensity score to combine multiple covariates as a single covariate consistently improves the power compared to an analysis including no covariates, each covariate individually, or all covariates simultaneously. Type I error rates were inflated for analyses with covariates and increased with increasing number of covariates, but reduced to nominal rates with sample sizes of 1000 families. Therefore, we recommend using the propensity score as a single covariate in the linkage analysis of a trait suspected to be influenced by multiple covariates because of its potential to increase the power to detect linkage, while controlling for the increase in the type I error.

Algorithms↗

PubMed2006

Design of Depression in Alzheimer's Disease Study-2.

OBJECTIVE: Research on the efficacy of antidepressant therapy for depressive symptoms in Alzheimer disease has been hampered by lack of systematic diagnosis, small sample sizes, and short-term follow up. To address these issues, the authors present the design of the Depression in Alzheimer's Disease Study-2 (DIADS-2), a randomized, placebo-controlled multicenter trial to evaluate the efficacy and safety of the selective serotonin reuptake inhibitor sertraline for the treatment of depression in people with Alzheimer disease. METHODS: The authors present and discuss the following important aspects of the design: the inclusion of structured psychosocial therapy for the caregivers of all participants; the measurement not only of patient mood outcomes, but also of global and functional outcomes for patients and mood and burden outcomes for caregivers; the ongoing rating of multiple diagnostic criteria to allow nosologic study of depression in Alzheimer disease; the evaluation of both short-term efficacy and longer-term outcomes; the follow up of all patients regardless of whether they complete study treatment; and the unmasking of treatment assignment at the conclusion of each patient's treatment phase. CONCLUSIONS: The authors believe these design elements are important features to be included in trials of depression and other neuropsychiatric disturbances in Alzheimer disease.

Affect↗

PubMed2006

Polydesigns and causal inference.

In an increasingly common class of studies, the goal is to evaluate causal effects of treatments that are only partially controlled by the investigator. In such studies there are two conflicting features: (1) a model on the full cohort design and data can identify the causal effects of interest, but can be sensitive to extreme regions of that design's data, where model specification can have more impact; and (2) models on a reduced design (i.e., a subset of the full data), for example, conditional likelihood on matched subsets of data, can avoid such sensitivity, but do not generally identify the causal effects. We propose a framework to assess how inference is sensitive to designs by exploring combinations of both the full and reduced designs. We show that using such a "polydesign" framework generates a rich class of methods that can identify causal effects and that can also be more robust to model specification than methods using only the full design. We discuss implementation of polydesign methods, and provide an illustration in the evaluation of a needle exchange program.

Biometry↗

PubMed2005

Application of the propensity score in a covariate-based linkage analysis of the Collaborative Study on the Genetics of Alcoholism.

BACKGROUND: Covariate-based linkage analyses using a conditional logistic model as implemented in LODPAL can increase the power to detect linkage by minimizing disease heterogeneity. However, each additional covariate analyzed will increase the degrees of freedom for the linkage test, and therefore can also increase the type I error rate. Use of a propensity score (PS) has been shown to improve consistently the statistical power to detect linkage in simulation studies. Defined as the conditional probability of being affected given the observed covariate data, the PS collapses multiple covariates into a single variable. This study evaluates the performance of the PS to detect linkage evidence in a genome-wide linkage analysis of microsatellite marker data from the Collaborative Study on the Genetics of Alcoholism. Analytical methods included nonparametric linkage analysis without covariates, with one covariate at a time including multiple PS definitions, and with multiple covariates simultaneously that corresponded to the PS definitions. Several definitions of the PS were calculated, each with increasing number of covariates up to a maximum of five. To account for the potential inflation in the type I error rates, permutation based p-values were calculated. RESULTS: Results suggest that the use of individual covariates may not necessarily increase the power to detect linkage. However the use of a PS can lead to an increase when compared to using all covariates simultaneously. Specifically, PS3, which combines age at interview, sex, and smoking status, resulted in the greatest number of significant markers identified. All methods consistently identified several chromosomal regions as significant, including loci on chromosome 2, 6, 7, and 12. CONCLUSION: These results suggest that the use of a propensity score can increase the power to detect linkage for a complex disease such as alcoholism, especially when multiple important covariates can be used to predict risk and thereby minimize linkage heterogeneity. However, because the PS is calculated as a conditional probability of being affected, it does require the presence of observed covariate data on both affected and unaffected individuals, which may not always be available in real data sets.

Alcoholism↗

PubMed2005

Exploring lag and duration effect of sunshine in triggering suicide.

BACKGROUND: Sunshine is considered to have a beneficial impact on mood. Interestingly, it has been consistently found that the incidence of suicide reaches a peak during early summer. METHODS: In order to explore the pattern of sunshine and suicide risk in a time frame of up to nine days and investigate possible lag and duration parameters of sunshine in the triggering of suicide, Greek daily suicide and solar radiance data were analyzed for a 10-year period using logistic regression models. RESULTS: The solar radiance during the day before the suicide event was significantly associated with an increased suicide risk (OR=1.020 per MW/m2). The average solar radiance during the four previous days was also significantly associated with an increased suicide risk (OR=1.031 per MW/m2). Differences among genders include the longer sunshine exposure needed in males to trigger suicide, compared to females and a lag period of three to four days that was found to lapse in females till the suicide. The increase in suicide risk in June compared to December, attributable to the daily sunshine effect, varies from 52% to 88%, thus explaining the already known suicide monthly seasonality. LIMITATIONS: No individual data on solar radiance exposure, mental disorders, alcohol consumption or suicide method were available. CONCLUSION: The effect of sunshine in the triggering of suicide may be mediated through a mechanism with a specific lag and duration effect, during the nine days preceding suicide. We hypothesize that sunshine acts as a natural antidepressant which first improves motivation, then only later improves mood, thereby creating a potential short-term increased risk of suicide initially upon its application.

Affect↗

PubMed2005

Burn injuries related to motorcycle exhaust pipes: a study in Greece.

PURPOSE: To identify measures that should reduce the incidence of burn injuries resulting from motorcycle exhaust pipes through epidemiological analysis of such injuries. BASIC PROCEDURES: During a 5-year period, 251 persons who suffered burn injuries related to motorcycle exhaust pipes have contacted four major hospitals belonging to the Emergency Department Injury Surveillance System (EDISS) operating since 1996 in Greece. These burn injuries were studied in relation to person, environment and vehicle characteristics. MAIN FINDINGS: The estimated countrywide incidence of burns from motorcycle exhaust pipes was 17 per 100,000 person-years (208 per 100,000 motorcycle-years). The incidence was two times higher for children than for older persons and among the latter it was 60% higher among females than among males. Most of burn injuries (70.5%) concerned motorcycle passengers, mainly when getting on or off motorcycle, with peak incidence during summer. The most frequent location of burn wounds was below the knee and particularly the right leg. It was estimated that the risk of motorcycle exhaust pipe burns when wearing shorts could be reduced by 46% through wearing long pants. Among the victims 65.3% experienced second degree burns. PRINCIPAL CONCLUSIONS: Motorcycle exhaust burns could be substantially reduced by systematically wearing long pants, by incorporating in the design of motorcycles external thermo resistant shields with adequate distance to the exhaust pipe, and by avoiding riding with children on motorcycles.

Adolescent↗

PubMed2005

Designs in partially controlled studies: messages from a review.

The ability to evaluate effects of factors on outcomes is increasingly important for studies that control some but not all of the factors. Although important advances have been made in methods of analysis for such partially controlled studies, work on designs has been limited. To help understand why, we review the main designs that have been used for such partially controlled studies. Based on the review, we give two complementary reasons that explain the limited work on such designs, and suggest a new direction in this area.

Cluster Analysis↗

PubMed2004

Revealing and addressing length bias and heterogeneous effects in frequency case-crossover studies.

The case-crossover design is useful for assessing whether a recurrent exposure (e.g., drug) triggers an event (e.g., myocardial infarction), using only cases, when finding good controls is impractical. In the basic frequency design, the observed exposure odds among cases, during a period immediately before the event, are compared with the expected exposure odds, based on their usual frequency of past exposures. This is equivalent to comparing observed gap times between the event and the last exposure with the expected gap times based on the subjects' exposure experience under the null hypothesis of no exposure-event relation. Such a comparison reveals two problems in the usual-frequency analyses: 1) length bias that exists even under the null hypothesis; and 2) loss of efficiency when exposure effects do exist. The first problem arises because the event will more likely fall on a longer-than-average period between exposures, even under the null hypothesis, resulting in a systematic downward bias of risk ratios. The second problem arises from categorizing cases as exposed or unexposed and from not fully using the data on gap times between events and preceding exposures. A new method of analysis is presented that is free from length bias and that efficiently uses gap time data.

Bias↗

PubMed2004

Estimating the population burden of injuries: a comparison of household surveys and emergency department surveillance.

BACKGROUND: Injuries represent an important public health problem but their incidence is difficult to estimate. METHODS: We conducted a population-based household survey in Greece covering 4079 interviewed individuals. The interviewees reported, for themselves and for cohabitating adults (age 15 years and older; n = 7157), injuries that occurred during the preceding year. Major injuries were defined as those requiring contact with a health institution. We compared these survey data with data obtained through a national Emergency Department Injury Surveillance System (EDISS). RESULTS: For the month closest to the survey interview, the incidence reported for the responders was 5.9 per 100 person-year, whereas the incidence for cohabitating adults was 3.7 per 100 person-years. These incidence rates declined for months more remote to the interview. Comparison of survey and EDISS data suggested that survey reporting was less accurate for nontraffic-related injuries. Taking into account possible recall and telescoping biases, the best survey estimate of the national annual number of major injuries is 525,000 (5.9 per 100 person-year), whereas the EDISS data yielded an estimate of 1,150,000 major injuries (12.9 per 100 person-years) CONCLUSIONS: Comparison of survey and EDISS data systems provides quantitative assessment of accuracy of the survey data in relation to time of injury before report date, to severity of injury, and to whether the injury is to the interviewee or to a cohabitant. The 2 systems could be used in a complementary way, although EDISS generates information that is medically more accurate and is a more cost-effective data collection system.

Adolescent↗

PubMed2003

Modelling risk factors for injuries from dog bites in Greece: a case-only design and analysis.

We conducted a study using a newly developed dataset based on Emergency Departments records of a network of hospitals from Greece on injuries from dog bites. Our goal is three-fold: (a) to investigate if surrogate factors of leisure time are associated with increased risk of injury from bites; (b) to address recently reported contradictory results on putative association of lunar periods and injuries from dog bites; and (c) to offer a general methodology for addressing similar case-only designs with combined factors of which some can exhibit cyclical patterns. To address these goals, we used a case-only design of our dataset, and conducted an analysis where we controlled simultaneously for weekday/weekend effects, season of year (winter, spring/fall, summer), and lunar periods, because any one of these factors can contribute to the degree of exposure to injuries from dog bites. We found that increased risk of injury from bites was associated with weekends versus weekdays (RR=1.19, 95% CI: 1.10-1.29), summer versus winter (RR=1.24, 95% CI: 1.11-1.39), and fall or spring versus winter (RR=1.31, 95% CI: 1.19-1.45). The results support the hypothesis that longer leisure time at these levels of factors does increase the risk of having a bite injury. Moreover, after controlling for these factors, risk of bite injury was not associated with moon periods, thereby also helping settle a longstanding argument.

Animals↗

PubMed2002

Clustered encouragement designs with individual noncompliance: bayesian inference with randomization, and application to advance directive forms.

In many studies comparing a new 'target treatment' with a control target treatment, the received treatment does not always agree with assigned treatment-that is, the compliance is imperfect. An obvious example arises when ethical or practical constraints prevent even the randomized assignment of receipt of the new target treatment but allow the randomized assignment of the encouragement to receive this treatment. In fact, many randomized experiments where compliance is not enforced by the experimenter (e.g. with non-blinded assignment) may be more accurately thought of as randomized encouragement designs. Moreover, often the assignment of encouragement is at the level of clusters (e.g. doctors) where the compliance with the assignment varies across the units (e.g. patients) within clusters. We refer to such studies as 'clustered encouragement designs' (CEDs) and they arise relatively frequently (e.g. Sommer and Zeger, 1991; McDonald et al., 1992; Dexter et al., 1998) Here, we propose Bayesian methodology for causal inference for the effect of the new target treatment versus the control target treatment in the randomized CED with all-or-none compliance at the unit level, which generalizes the approach of Hirano et al. (2000) in important and surprisingly subtle ways, to account for the clustering, which is necessary for statistical validity. We illustrate our methods using data from a recent study exploring the role of physician consulting in increasing patients' completion of Advance Directive forms.

Journal Article↗

PubMed2002

A role of sunshine in the triggering of suicide.

Several reports indicate that suicide follows a seasonal pattern with a dominant peak during the month of maximum daylight. The purpose of this study was to evaluate the hypothesis that sunshine exposure may trigger suicidal behavior. We found a remarkably consistent pattern of seasonality with peak incidence around June in the northern hemisphere and December in the southern hemisphere. Moreover, there was a positive association between the seasonal amplitude of suicide (measured by relative risk) and total sunshine in the corresponding country. These findings indicate that sunshine may have a triggering effect on suicide, and suggests further research in the field of sunshine-regulated hormones, particularly melatonin.

Humans↗

PubMed2002

Confidence intervals for seasonal relative risk with null boundary values.

In evaluating the relative risk of seasonality, the null value is a boundary value. In this case, tests for the null hypothesis exist, but standard methods for confidence intervals are not appropriate. We provide a method for constructing confidence intervals under the circular normal model. The proposed confidence intervals are valid for all values of the underlying seasonal risk if the model is correct and for the null boundary value of the seasonal risk, regardless of model assumptions and sample size. We apply our method to seasonal suicide data from a recent report.

Algorithms↗

PubMed2002

Principal stratification in causal inference.

Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, but the estimands underlying standard methods are not causal effects. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal stratification. Principal stratification with respect to a posttreatment variable is a cross-classification of subjects defined by the joint potential values of that posttreatment variable tinder each of the treatments being compared. Principal effects are causal effects within a principal stratum. The key property of principal strata is that they are not affected by treatment assignment and therefore can be used just as any pretreatment covariate. such as age category. As a result, the central property of our principal effects is that they are always causal effects and do not suffer from the complications of standard posttreatment-adjusted estimands. We discuss briefly that such principal causal effects are the link between three recent applications with adjustment for posttreatment variables: (i) treatment noncompliance, (ii) missing outcomes (dropout) following treatment noncompliance. and (iii) censoring by death. We then attack the problem of surrogate or biomarker endpoints, where we show, using principal causal effects, that all current definitions of surrogacy, even when perfectly true, do not generally have the desired interpretation as causal effects of treatment on outcome. We go on to forrmulate estimands based on principal stratification and principal causal effects and show their superiority.

Child↗