PubMed Health⌕ Search

Biomedical subjects

Mark S Gilthorpe

Publications and source records attributed to Mark S Gilthorpe.

At least 19 recordsLinked to original sources
PubMed2007

Revisiting the relation between change and initial value: a review and evaluation.

The relation between initial disease status and subsequent change following treatment has attracted great interest in clinical research. However, statisticians have repeatedly warned against correlating/regressing change with baseline due to two methodological concerns known as mathematical coupling and regression to the mean. Oldham's method and Blomqvist's formula are the two most often adopted methods to rectify these problems. The aims of this article are to review briefly the proposed solutions in the statistical and psychological literature, and to clarify the popular misconception that Blomqvist's formula is superior to Oldham's method. We argue that this misconception is due to a failure to recognize that the heterogeneity of individual responses to treatment is a source of regression to the mean in the analysis of the relation between change and initial value. Furthermore, we demonstrate how each method actually answers different research questions, and how confusion arises when this is not always understood.

Antihypertensive Agents↗
PubMed2006

Associations between tooth loss and mortality patterns in the Glasgow Alumni Cohort.

OBJECTIVE: To use data from the Glasgow Alumni Cohort to investigate whether oral health in young adulthood is independently associated with later life cardiovascular disease (CVD) and cancer mortality. METHODS AND RESULTS: Of the original cohort (n = 15 322), 12 631 subjects were traced through the National Health Service Central Register. Of these, 9569 men and 2654 women were 30 years or younger at baseline. During up to 57 years of follow-up, 1432 deaths occurred among subjects with complete data, including 509 deaths from CVD and 549 from cancer. After adjusting for potential confounders, no substantial association was found between the number of missing teeth (as a continuous variable) and all-cause mortality (hazard ratio (HR) for each extra missing tooth = 1.01; 95% confidence interval (CI) 1.00 to 1.02), CVD mortality (HR = 1.01; 95% CI 0.99 to 1.03) or cancer mortality (HR = 1.00; 95% CI 0.98 to 1.02). When the number of missing teeth was treated as a categorical variable, there was evidence that students with nine or more missing teeth at baseline had an increased risk of CVD (HR = 1.35; 95% CI 1.03 to 1.77) compared with those with fewer than five missing teeth. When the number of missing teeth was transformed using fractional polynomials, there seemed to be a non-linear relation between missing teeth and CVD mortality. CONCLUSIONS: Although some evidence was found to support the relation between tooth loss and CVD mortality, causal mechanisms underlying this association remain uncertain.

Adolescent↗
PubMed2006

The impact of imprecisely measured covariates on estimating gene-environment interactions.

BACKGROUND: The effects of measurement error in epidemiological exposures and confounders on estimated effects of exposure are well described, but the effects on estimates for gene-environment interactions has received rather less attention. In particular, the effects of confounder measurement error on gene-environment interactions are unknown. METHODS: We investigate these effects using simulated data and illustrate our results with a practical example in nutrition epidemiology. RESULTS: We show that the interaction regression coefficient is unchanged by confounder measurement error under certain conditions, but biased by exposure measurement error. We also confirm that confounder measurement error can lead to estimated effects of exposure biased either towards or away from the null, depending on the correlation structure, with associated effects on type II errors. CONCLUSION: Whilst measurement error in confounders does not lead to bias in interaction coefficients, it may still lead to bias in the estimated effects of exposure. There may still be cost implications for epidemiological studies that need to calibrate all error-prone covariates against a valid reference, in addition to the exposure, to reduce the effects of confounder measurement error.

Adult↗
PubMed2006

Misuses of correlation and regression analyses in orthodontic research: the problem of mathematical coupling.

INTRODUCTION: The aim of this article was to encourage good practice in the statistical analyses of orthodontic research data. Our objective was to highlight the statistical problems caused by mathematical coupling (MC) in correlation and regression analyses. These statistical problems are among the most common pitfalls in orthodontic research when exploring associations among clinical variables. This article will show why these problems arise and how they can be avoided and overcome. METHODS: Four orthodontic journals were electronically and manually searched for articles that used correlation and regression analyses. Studies that seemed to suffer from MC in their statistical analyses were identified and carefully examined. RESULTS: Several examples from our search illustrate that MC in correlation and regression analyses can potentially cause misleading results. More appropriate statistical methods are available and should be used to eliminate confusing results and improve any subsequent interpretations. Because many clinical and radiographic variables used in orthodontic research are correlated due to direct or indirect MC, interpretation of studies in the literature needs to be cautious. CONCLUSIONS: Correlation and regression analyses are useful tools in orthodontic research when their assumptions and limitations are recognized. However, greater care is required in formulating research questions and experimental designs. It is prudent to seek statistical advice when orthodontic research involves complex data analyses.

Analysis of Variance↗
PubMed2006

Evaluating the quality of active-control trials in periodontal research.

AIM: The increasing popularity of randomized-controlled trials (RCTs) has raised the issue of their quality. Frequently overlooked are the differences between superiority and equivalence trials. The purpose of this study was to apply specific methodological criteria to evaluate the quality of active-control trials using studies that compared guided tissue regeneration (GTR) with enamel matrix derivatives (EMD). MATERIALS AND METHODS: Seven RCTs were identified in the literature. Standard methodological criteria and seven additional criteria for trials using active-control groups were used to evaluate the quality of the seven RCTs. RESULTS: Two trials were considered as superiority trials. The remaining five provided no clear statement of their research aim. However, two claimed that EMD and GTR were equally effective, because their results failed to show a significant difference between EMD and GTR. Most trials did not meet the majority of the design criteria. CONCLUSIONS: The general lack of compliance with quality criteria might place doubt on the value of these trials and may render any conclusions questionable. It is therefore important to distinguish clearly between superiority trials and equivalence trials, and to incorporate appropriate additional criteria in the design of future RCTs with active-control groups.

Dental Enamel Proteins↗
PubMed2005

Detecting small-area similarities in the epidemiology of childhood acute lymphoblastic leukemia and diabetes mellitus, type 1: a Bayesian approach.

Childhood acute lymphoblastic leukemia and diabetes mellitus, type 1, have common epidemiologic and etiologic features, including correlated international incidence and associations with infections. The authors examined whether the diseases' similar large-scale distributions are reflected in small geographic areas while also examining the influence of sociodemographic characteristics. Details of 299 children (0-14 years) with acute lymphoblastic leukemia and 1,551 children with diabetes diagnosed between 1986 and 1998 were extracted from two registers in Yorkshire, United Kingdom. Standardized incidence ratios across 532 electoral wards were compared using Poisson regression, confirming significant associations between population mixing and the geographic heterogeneity of both conditions. Bayesian methods analysis of spatial correlation between diseases by modeling a bivariate outcome based on their standardized incidence ratios was applied; spatial and heterogeneity components were included within a hierarchical random effects model. A positive correlation between diseases of 0.33 (95% credible interval: -0.20, 0.74) was observed, and this was reduced after control for population mixing (r = 0.18), population density (r = 0.14), and deprivation (r = 0.06). The Bayesian approach showed a modest but nonsignificant joint spatial correlation between diseases, only partially suggesting that the risk of both was associated within some electoral wards. With Bayesian methodology, population mixing remained significantly associated with both diseases. The links between diabetes and acute lymphoblastic leukemia observed for large regions are weaker for small areas. More powerful replications are needed for confirmation of these findings.

Adolescent↗
PubMed2005

Why evidence for the fetal origins of adult disease might be a statistical artifact: the "reversal paradox" for the relation between birth weight and blood pressure in later life.

Some researchers have recently questioned the validity of associations between birth weight and health in later life. They argue that these associations might be due in part to inappropriate statistical adjustment for variables on the causal pathway (such as current body size), which creates an artifactual statistical effect known as the "reversal paradox." Computer simulations were conducted for three hypothetical relations between birth weight and adult blood pressure. The authors examined the effect of statistically adjusting for different correlations between current weight and birth weight and between current weight and adult blood pressure to assess their impact on associations between birth weight and blood pressure. When there was no genuine relation between birth weight and blood pressure, adjustment for current weight created an inverse association whose size depended on the magnitude of the positive correlations between current weight and birth weight and between current weight and blood pressure. When there was a genuine inverse relation between birth weight and blood pressure, the association was exaggerated following adjustment for current weight, whereas a positive relation between birth weight and blood pressure could be reversed after adjusting for current weight. Thus, researchers must consider the reversal paradox when adjusting for variables that lie within causal pathways.

Adult↗
PubMed2005

The problem of analysing the relationship between change and initial value in oral health research.

The relationship between initial disease status and subsequent change following treatment has attracted great interest in dental research. However, medical statisticians have repeatedly warned against correlating/regressing change with baseline because of two methodological concerns known as mathematical coupling and regression to the mean. In general, mathematical coupling occurs when one variable directly or indirectly contains the whole or part of another, and the two variables are then analyzed by using correlation or regression. Consequently, the statistical procedure of testing the null hypothesis - that the coefficient of correlation or the slope of regression is zero - may become inappropriate. Regression to the mean occurs with any variable that fluctuates within an individual or a population, either owing to measurement error and/or to physiological variation. The aim of this article was to clarify the conceptual confusion around mathematical coupling and regression to the mean within the statistical literature, and to correct a popular misconception about the correct analysis of the relationship between change and initial value. As examples that use inappropriate methods to analyze the relationship between change and baseline are still found in leading dental journals, this article seeks to help oral health researchers understand these problems and explain how to overcome them.

Algorithms↗
PubMed2005

The relationship between baseline value and its change: problems in categorization and the proposal of a new method.

Oral health researchers have shown great interest in the relationship between the initial status of diseases and subsequent changes following treatment. Two main approaches have been adopted to provide evidence of a positive association between baseline values and their changes following treatment. One approach is to use correlation or regression to test the relationship between baseline measurements and subsequent change (correlation/regression approach). The second approach is to categorize the lesions into subgroups, according to threshold values, and subsequently compare the treatment effects across the two (or more) subgroups (categorization approach). However, the correlation/regression approach suffers a methodological weakness known as mathematical coupling. Consequently, the statistical procedure of testing the null hypothesis becomes inappropriate. Categorization seems to avoid the problem of mathematical coupling, although it still suffers regression to the mean. We show, first, how the appropriate null hypothesis may be established to analyze the relationship between baseline values and change in the correlation approach and, second, we use computer simulations to investigate the impact of regression to the mean on the significance testing of the differences in the average treatment effects (or average baseline values) in the categorization approach. Data available from previous literature are reanalyzed by testing the appropriate null hypotheses and the results are compared to those from testing the usual (incorrect) null hypothesis. The results indicate that both the correlation and categorization approaches can give rise to misleading conclusions and that more appropriate methods, such as Oldham's method and our new approach of deriving the correct null hypothesis, should be adopted.

Algorithms↗
PubMed2005

A multilevel modelling solution to mathematical coupling.

Owing to mathematical coupling, statistical analyses relating change to baseline values using correlation or regression are erroneous, where the statistical procedure of testing the null hypothesis becomes invalid. Alternatives, such as Oldham's method and the variance ratio test, have been advocated, although these are limited in the presence of measurement errors with non-constant variance. Furthermore, such methods prohibit the consideration of additional covariates (e.g., treatment group within trials) or confounders (e.g., age and gender). This study illustrates the more sophisticated approach of multilevel modelling (MLM) which overcomes these limitations and provides a comprehensive solution to the analysis of change with respect to baseline values. Although mathematical coupling is widespread throughout applied research, one particular area where several studies have suggested a strong relationship between baseline disease severity and treatment effect is guided tissue regeneration (GTR) within dental research. For illustration, we use GTR studies where the original data were found to be available in the literature for reanalysis. We contrast the results from an MLM approach and Oldham's method with the standard (incorrect) approach that suffers from mathematical coupling. MLM provides a robust solution when relating change to baseline and is capable of simultaneously dealing with complex error structures and additional covariates and/or potential confounders.

Data Interpretation, Statistical↗
PubMed2005

A Bayesian analysis of amalgam restorations in the Royal Air Force using the counting process approach with nested frailty effects.

Survival analysis methods are increasingly used in dental research to measure risk of tooth eruption and caries as well as life spans of amalgam restorations. Analyses have been extended to account for lack of independence in the data, which arises from the clustering of observations within units such as tooth-surfaces, teeth and subjects. There are various analytical strategies and modelling approaches now available to us in dealing with clustered dental data. In this article, the modelling strategy of Cox's proportional hazards regression is formulated using the counting process approach, which can easily be extended to include time-variant covariates as well as nested random frailty effects. A semi-parametric Bayesian method is presented for the analysis of the proposed model. The methodology is applied to an analysis of nested clustered data on life-span of amalgam restorations in the UK Royal Air Force. These data have previously been analysed using a non-Bayesian approach. The Gibbs sampler, a Markov chain Monte Carlo method, is used to generate samples from the marginal posterior distribution of the parameters of this Bayesian model.

Bayes Theorem↗
PubMed2005

Capnocytophaga spp. in periodontitis patients manifesting diabetes mellitus.

BACKGROUND: The subgingival microflora in patients presenting concurrently with periodontitis and diabetes mellitus (DM) are poorly understood. While traditional putative periodontal pathogens are implicated, research involving other oral organisms; e.g., Capnocytophaga spp., is lacking. These organisms produce a range of bacterial enzymes relevant to periodontal breakdown. It is inferred that periodontal bacteria acquire systemic access through the ulcerated periodontal pocket surface; conclusive evidence supporting this notion is limited. The aims of this investigation were to: 1) quantify and identify Capnocytophaga spp. present in healthy and diseased sites in periodontitis patients with and without DM, and 2) isolate periodontal pathogens from these patients' blood. METHODS: Twenty-one DM-periodontitis and 25 periodontitis patients were recruited. Subgingival plaque was collected from three healthy and three diseased sites per subject. Capnocytophaga spp. and total (facultative and obligate) anaerobic counts from each site were estimated. Capnocytophaga spp. were identified using 16S rRNA polymerase chain reaction (PCR) restriction fragment length polymorphism (RFLP). Statistical analyses were performed using multilevel modeling. Blood samples were subjected to HbA(1c) estimation and bacterial culture. RESULTS: A total of 848 Capnocytophaga spp. were isolated and identified. Significantly higher numbers of Capnocytophaga spp. (P <0.001) and anaerobes (P <0.001) were present in diseased sites in DM-periodontitis subjects compared to healthy sites in non-DM-periodontitis and DM-periodontitis subjects. C. ochracea (and variant) and C. granulosa were the most prevalent species. Blood samples were negative for Capnocytophaga spp. CONCLUSIONS: Total mean counts for Capnocytophaga spp. were significantly higher in DM-periodontitis subjects versus non-DM-periodontitis (P = 0.025) and at diseased sites versus healthy sites (P <0.001). Analysis of individual species revealed that the outcome varied with site status and DM status.

Adult↗
PubMed2004

Ratio variables in regression analysis can give rise to spurious results: illustration from two studies in periodontology.

OBJECTIVES: For over a century, statisticians have highlighted concerns about the inappropriate use of ratio variables in correlation and regression analysis. However, little attention has been paid to these concerns in medical and dental research. The use of ratio variables in correlation and regression analysis can give rise to spurious results due to inappropriate model specification and mathematical coupling, leading to serious misinterpretation of data and consequently to incorrect study conclusions. METHODS: Data were reanalysed from two recently published articles: one on the efficacy of guided tissue regeneration on root coverage; the other a randomised controlled trial comparing three surgical approaches in the treatment of periodontal infrabony defects. The reanalysis was performed to examine whether the assumptions behind the correlation/regression analyses have been seriously violated in these two studies, and to see if the interpretation of results is tenable. RESULTS: Use of ratio variables seriously violated the assumptions underpinning the statistical methods utilised in these two studies, and consequently the conclusions were substantially misleading. Recommendations made in these studies were not tenable. CONCLUSIONS: The reanalyses illustrate how the inappropriate use of ratio variables remains prevalent in dental research, leading to incorrect interpretation of the evidence. This emphasises the need for collaboration between clinicians and statisticians to avoid the risk of yielding erroneous conclusions from flawed statistical analyses.

Alveolar Bone Loss↗
PubMed2004

Mathematical coupling can undermine the statistical assessment of clinical research: illustration from the treatment of guided tissue regeneration.

OBJECTIVES: Previous periodontal literature has shown that there is a strong relationship between treatment effects, such as guided tissue regeneration (GTR), and baseline disease severity. However, relating change to baseline values using correlation or regression is methodologically flawed due to mathematical coupling, where the statistical procedure of testing the null hypothesis-that the coefficient of correlation or slope of regression is equal to zero-becomes erroneous. The aim of this study is to investigate if baseline disease severity is genuinely associated with the treatment outcome of intrabony defects using GTR after adjustment for mathematical coupling. In particular, we seek to demonstrate the potential effect that mathematical coupling has in distorting the results from the statistical analyses of trials of dental treatment, using data from the periodontal literature on GTR. The erroneous results arising from the use of simple correlation and regression techniques to analyse this association will be demonstrated, also the methodological flaw where the statistical procedure tests the null hypothesis-that the coefficient of correlation or the slope of regression is equal to zero. METHODS: Three main periodontal journals were electronically and manually searched to extract the data for the clinical outcomes of pocket probing depth (PPD) and lifetime cumulative attachment loss (LCAL) in the studies using GTR. The relationship between clinical outcomes and baseline measurements were reanalysed using Oldham's method and the variance ratio test. RESULTS: The results of these analyses were compared with those from the papers where the authors used the standard approach of correlation or regression. This shows that mathematical coupling caused spurious correlations between baseline disease severity and treatment effect. Ten out of 12 studies for PPD and nine out of 14 for LCAL initially claimed a significant positive relationship; after using either of the more appropriate statistical methods of adjustment, only three correlations in each group of studies remained significant. CONCLUSIONS: Previous evidence suggesting an association between baseline disease severity and treatment effect for GTR is challenged and therefore needs to be critically reviewed. All future clinical research should avoid using mathematically coupled data in correlation or regression analysis. In seeking to examine the bivariate association between baseline and subsequent change, Oldham's method is recommended.

Alveolar Bone Loss↗
PubMed2004

Passive tactile sensibility in edentulous subjects treated with dental implants: a pilot study.

STATEMENT OF PROBLEM: Edentulous patients treated with implant-supported prostheses have shown increased passive tactile sensibility compared with those using conventional complete dentures. This is thought to be due to the close mechanical coupling between the implant and bone via the osseointegrated interface, yet the phenomenon has received little attention. PURPOSE: The purpose of this study was to measure passive tactile sensibility in a group of edentulous subjects treated with dental implants, and to relate the measured sensibility to a range of factors thought to be of possible relevance, namely, patient age, gender, time since implant placement, implant length, and implant separation. MATERIAL AND METHODS: Twenty edentulous subjects successfully treated with 2 or more Nobel Biocare dental implants in the anterior mandible were studied. The inclusion criteria were : (1) age of less than 50 years, (2) a period of at least 12 months since implant placement, (3) implant length of at least 10 mm and of standard diameter (excluding narrow and wide platform designs), and (4) implant separation of at least 18 mm. Using a computer-controlled custom-made device, pushing forces (2.1, 2.4, 2.7, and 3.0 N/s) were applied directly and perpendicular to the long axes of the implant abutments until the subjects felt the first sensation of pressure. The magnitude of these forces was measured with an integral transducer. The applied force had a ramped staircase pattern, and force application rates were varied between 2.1 and 3.0 N/s. Multilevel modeling was used to analyze the collected data (alpha=.05). RESULTS: The threshold values of passive tactile sensibility ranged between 3.1 and 15.7 N (mean 10.9; SD 3.9). Analysis failed to show any significant association between passive tactile sensibility and the variables studied. CONCLUSION: Within the limitations of this study, which included a small sample size, no relationship was found between passive tactile sensibility associated with long-standing implants and any of the variables studied (age, gender, time since implant placement, implant length, and implant separation).

Age Factors↗