A Model for the Analysis of Caries Occurrence in Primary Molar Tooth Surfaces

Recently methods of caries quantification in the primary dentition have moved away from summary ‘whole mouth’ measures at the individual level to methods based on generalised linear modelling (GLM) approaches or survival analysis approaches. However, GLM approaches based on logistic transformation fail to take into account the time-dependent process of tooth/surface survival to caries. There may also be practical difficulties associated with casting parametric survival-based approaches in a complex multilevel hierarchy and the selection of an optimal survival distribution, while non-parametric survival methods are not generally suitable for the assessment of supplementary information recorded on study participants. In the current investigation, a hybrid semi-parametric approach comprising elements of survival-based and GLM methodologies suitable for modelling of caries occurrence within fixed time periods is assessed, using an illustrative multilevel data set of caries occurrence in primary molars from a cohort study, with clustering of data assumed to occur at surface and tooth levels. Inferences of parameter significance were found to be consistent with previous parametric survival-based analyses of the same data set, with gender, socio-economic status, fluoridation status, tooth location, surface type and fluoridation status-surface type interaction significantly associated with caries occurrence. The appropriateness of the hierarchical structure facilitated by the hybrid approach was also confirmed. Hence the hybrid approach is proposed as a more appropriate alternative to primary caries modelling than non-parametric survival methods or other GLM-based models, and as a practical alternative to more rigorous survival-based methods unlikely to be fully accessible to most researchers

A randomized-controlled trial of low-dose doxycycline for periodontitis in smokers

Background/Aim : Tobacco use reduces the effect of non-surgical periodontal therapy. Host-modulation with low-dose doxycycline (LDD) might favour repair and promote an improved treatment response. The aim of this study was to investigate the effect of LDD in smokers on non-surgical periodontal therapy. Material and Methods : This was a parallel arm, randomized, identical placebo-controlled trial with masking of examiner, care-giver, participant and statistician and 6 months of follow-up. Patients received non-surgical therapy and 3 months of test or control drug. Statistical analysis used both conventional methods and multilevel modelling. Results : Eighteen control and 16 test patients completed the study. The velocity of change was statistically greater for the test group for clinical attachment level −0.19 mm/month (95% CI=−0.34, 0.04; p =0.012) and probing depth 0.30 mm/month (95% CI=−0.42, −0.17; p <0.001). However, no differences were observed for absolute change in clinical or biochemical markers at 6 months. Conclusions : This study does not provide evidence of a benefit of using LDD as an adjunct to non-surgical periodontal therapy in smokers.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74791/1/j.1600-051X.2007.01058.x.pd

Multilevel latent class (MLC) modelling of healthcare provider causal effects on patient outcomes: Evaluation via simulation

Where performance comparison of healthcare providers is of interest, characteristics of both patients and the health condition of interest must be balanced across providers for a fair comparison. This is unlikely to be feasible within observational data, as patient population characteristics may vary geographically and patient care may vary by characteristics of the health condition. We simulated data for patients and providers, based on a previously utilized real-world dataset, and separately considered both binary and continuous covariate-effects at the upper level. Multilevel latent class (MLC) modelling is proposed to partition a prediction focus at the patient level (accommodating casemix) and a causal inference focus at the provider level. The MLC model recovered a range of simulated Trust-level effects. Median recovered values were almost identical to simulated values for the binary Trust-level covariate, and we observed successful recovery of the continuous Trust-level covariate with at least 3 latent Trust classes. Credible intervals widen as the error variance increases. The MLC approach successfully partitioned modelling for prediction and for causal inference, addressing the potential conflict between these two distinct analytical strategies. This improves upon strategies which only adjust for differential selection. Patient-level variation and measurement uncertainty are accommodated within the latent classes.Comment: 19 pages, 5 figures. Abstract to be published in the conference proceedings for the Society for Social Medicine & Population Health and International Epidemiology Association European Congress Joint Annual Scientific Meeting, September 201

Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning

Prediction and causal explanation are fundamentally distinct tasks of data analysis. In health applications, this difference can be understood in terms of the difference between prognosis (prediction) and prevention/treatment (causal explanation). Nevertheless, these two concepts are often conflated in practice. We use the framework of generalized linear models (GLMs) to illustrate that predictive and causal queries require distinct processes for their application and subsequent interpretation of results. In particular, we identify five primary ways in which GLMs for prediction differ from GLMs for causal inference: (i) the covariates that should be considered for inclusion in (and possibly exclusion from) the model; (ii) how a suitable set of covariates to include in the model is determined; (iii) which covariates are ultimately selected and what functional form (i.e. parameterization) they take; (iv) how the model is evaluated; and (v) how the model is interpreted. We outline some of the potential consequences of failing to acknowledge and respect these differences, and additionally consider the implications for machine learning (ML) methods. We then conclude with three recommendations that we hope will help ensure that both prediction and causal modelling are used appropriately and to greatest effect in health research

The impact of the Calman–Hine report on the processes and outcomes of care for Yorkshire's colorectal cancer patients

The 1995 Calman–Hine plan outlined radical reform of the UK's cancer services with the aim of improving outcomes and reducing inequalities in NHS cancer care. Its main recommendation was to concentrate care into the hands of site-specialist, multi-disciplinary teams. This study aimed to determine if the implementation of Calman–Hine cancer teams was associated with improved processes and outcomes of care for colorectal cancer patients. The design included longitudinal survey of 13 colorectal cancer teams in Yorkshire and retrospective study of population-based data collected by the Northern and Yorkshire Cancer Registry and Information Service. The population was all colorectal cancer patients diagnosed and treated in Yorkshire between 1995 and 2000. The main outcome measures were: variations in the use of anterior resection and preoperative radiotherapy in rectal cancer, chemotherapy in Dukes stage C and D patients, and five-year survival. Using multilevel models, these outcomes were assessed in relation to measures of the extent of Calman–Hine implementation throughout the study period, namely: (i) each team's degree of adherence to the Manual of Cancer Service Standards (which outlines the specification of the ‘ideal’ colorectal cancer team) and (ii) the extent of site specialisation of each team's surgeons. Variation was observed in the extent to which the colorectal cancer teams in Yorkshire had conformed to the Calman–Hine recommendations. An increase in surgical site specialisation was associated with increased use of preoperative radiotherapy (OR=1.43, 95% CI=1.04–1.98, P<0.04) and anterior resection (OR=1.43, 95% CI=1.16–1.76, P<0.01) in rectal cancer patients. Increases in adherence to the Manual of Cancer Service Standards was associated with improved five-year survival after adjustment for the casemix factors of age, stage of disease, socioeconomic status and year of diagnosis, especially for colon cancer (HR=0.97, 95% CI=0.94–0.99 P<0.01). There was a similar trend of improved survival in relation to increased surgical site specialisation for rectal cancer, although the effect was not statistically significant (HR=0.93, 95% CI=0.84–1.03, P=0.15). In conclusion, the extent of implementation of the Calman–Hine report has been variable and its recommendations are associated with improvements in processes and outcomes of care for colorectal cancer patients

Model selection of the effect of binary exposures over the life course (Epidemiology (2015) 26 (719-726))

Epidemiologists are often interested in examining the effect on a later-life outcome of an exposure measured repeatedly over the life course. When different hypotheses for this effect are proposed by competing theories, it is important to identify those most supported by observed data as a first step toward estimating causal associations. One method is to compare goodness-of-fit of hypothesized models with a saturated model, but it is unclear how to judge the “best” out of two hypothesized models that both pass criteria for a good fit. We developed a new method using the least absolute shrinkage and selection operator to identify which of a small set of hypothesized models explains most of the observed outcome variation. We analyzed a cohort study with repeated measures of socioeconomic position (exposure) through childhood, early- and mid-adulthood, and body mass index (outcome) measured in mid-adulthood. We confirmed previous findings regarding support or lack of support for the following hypotheses: accumulation (number of times exposed), three critical periods (only exposure in childhood, early- or mid-adulthood), and social mobility (transition from low to high socioeconomic position). Simulations showed that our least absolute shrinkage and selection operator approach identified the most suitable hypothesized model with high probability in moderately sized samples, but with lower probability for hypotheses involving change in exposure or highly correlated exposures. Identifying a single, simple hypothesis that represents the specified knowledge of the life course association allows more precise definition of the causal effect of interest

Joint modelling compared with two stage methods for analysing longitudinal data and prospective outcomes: A simulation study of childhood growth and BP

© The Author(s) 2014. There is a growing debate with regards to the appropriate methods of analysis of growth trajectories and their association with prospective dependent outcomes. Using the example of childhood growth and adult BP, we conducted an extensive simulation study to explore four two-stage and two joint modelling methods, and compared their bias and coverage in estimation of the (unconditional) association between birth length and later BP, and the association between growth rate and later BP (conditional on birth length). We show that the two-stage method of using multilevel models to estimate growth parameters and relating these to outcome gives unbiased estimates of the conditional associations between growth and outcome. Using simulations, we demonstrate that the simple methods resulted in bias in the presence of measurement error, as did the two-stage multilevel method when looking at the total (unconditional) association of birth length with outcome. The two joint modelling methods gave unbiased results, but using the re-inflated residuals led to undercoverage of the confidence intervals. We conclude that either joint modelling or the simpler two-stage multilevel approach can be used to estimate conditional associations between growth and later outcomes, but that only joint modelling is unbiased with nominal coverage for unconditional associations

Multilevel latent class casemix modelling: a novel approach to accommodate patient casemix

<p>Abstract</p> <p>Background</p> <p>Using routinely collected patient data we explore the utility of multilevel latent class (MLLC) models to adjust for patient casemix and rank Trust performance. We contrast this with ranks derived from Trust standardised mortality ratios (SMRs).</p> <p>Methods</p> <p>Patients with colorectal cancer diagnosed between 1998 and 2004 and resident in Northern and Yorkshire regions were identified from the cancer registry database (n = 24,640). Patient age, sex, stage-at-diagnosis (Dukes), and Trust of diagnosis/treatment were extracted. Socioeconomic background was derived using the Townsend Index. Outcome was survival at 3 years after diagnosis. MLLC-modelled and SMR-generated Trust ranks were compared.</p> <p>Results</p> <p>Patients were assigned to two classes of similar size: one with reasonable prognosis (63.0% died within 3 years), and one with better prognosis (39.3% died within 3 years). In patient class one, all patients diagnosed at stage B or C died within 3 years; in patient class two, all patients diagnosed at stage A, B or C survived. Trusts were assigned two classes with 51.3% and 53.2% of patients respectively dying within 3 years. Differences in the ranked Trust performance between the MLLC model and SMRs were all within estimated 95% CIs.</p> <p>Conclusions</p> <p>A novel approach to casemix adjustment is illustrated, ranking Trust performance whilst facilitating the evaluation of factors associated with the patient journey (e.g. treatments) and factors associated with the processes of healthcare delivery (e.g. delays). Further research can demonstrate the value of modelling patient pathways and evaluating healthcare processes across provider institutions.</p

Multilevel analysis of clinical parameters in chronic periodontitis after root planing/scaling, surgery, and systemic and local antibiotics: 2-year results

Aim: Find the periodontal treatment that best maintained clinical results over time evaluated by changes in pocket depth (PD) and clinical attachment level (CAL). Methods: 229 patients with chronic periodontitis from USA (n=134) and Sweden (n=95) were randomly assigned to eight groups receiving 1 scaling+root planing (SRP) alone or combined with 2 surgery (SURG)+systemic amoxicillin (AMOX)+systemic metronidazole (MET); 3 SURG+local tetracycline (TET); 4 SURG; 5 AMOX+MET+TET; 6 AMOX+MET; 7 TET; and 8 SURG+AMOX+MET+TET. Antibiotics were given immediately after SRP. Plaque, gingival redness, bleeding on probing, suppuration, PD, and CAL were recorded at baseline and after 3, 6, 12, 18, and 24 months. Treatment effects were evaluated by linear multilevel regression and logistic multilevel regression models. We considered only data from sites with a baseline PD of at least 5 mm of 187 patients completing the study. Results: Surgically treated patients experienced most CAL loss. Adjunctive therapy including SURG was most effective in reducing PD. Combining SURG with AMOX, MET, and TET gave significant clinical benefits. Past and current smoking habits were significant predictors of deeper PD. Only current smoking was a significant predictor of CAL loss. Bleeding, accumulation of plaque, gingival redness, and suppuration were significant predictors of further CAL loss and deeper PD. Conclusions: Both surgical and non-surgical therapies can be used to arrest chronic periodontitis. SURG+AMOX+MET+TET gave best maintenance of clinical results

Simpson's Paradox, Lord's Paradox, and Suppression Effects are the same phenomenon – the reversal paradox

This article discusses three statistical paradoxes that pervade epidemiological research: Simpson's paradox, Lord's paradox, and suppression. These paradoxes have important implications for the interpretation of evidence from observational studies. This article uses hypothetical scenarios to illustrate how the three paradoxes are different manifestations of one phenomenon – the reversal paradox – depending on whether the outcome and explanatory variables are categorical, continuous or a combination of both; this renders the issues and remedies for any one to be similar for all three. Although the three statistical paradoxes occur in different types of variables, they share the same characteristic: the association between two variables can be reversed, diminished, or enhanced when another variable is statistically controlled for. Understanding the concepts and theory behind these paradoxes provides insights into some controversial or contradictory research findings. These paradoxes show that prior knowledge and underlying causal theory play an important role in the statistical modelling of epidemiological data, where incorrect use of statistical models might produce consistent, replicable, yet erroneous results