Statistical methods for twin and sibling designs

Twin and sibling studies are valuable in that they allow adjustment for potential confounding factors that are impossible or hard to measure. By measuring associations ‘within-cluster’ it is possible to adjust for many factors that are shared between individuals in the same cluster. Using Swedish national registers, it is possible to obtain information about a large number of potential confounders. While this gives medical researchers great opportunities to control for confounding, it also increases the risk of model misspecification leading to biased estimates. One strategy to reduce the risk of such bias is to use doubly robust(DR) estimation. In DR estimation two working models are combined in such a way that the resulting estimate will remain asymptotically unbiased when one of the models is misspecified. In study I, we implement existing DR estimators for parameters in linear, log-linear and logistic regression models in the R package drgee. In study II, we propose a new class of DR estimators for ‘within-cluster’ association measures in linear and log-linear regression models. In study III we propose a DR estimator for the ‘within-cluster’ log odds ratio parameter in logistic regression models. The estimators proposed in studies II and III are also implemented in the R package drgee. In study IV, we discuss what shared factors the ‘within-cluster’ association actually is adjusted for. Using the formal theory of causal diagrams we demonstrate that the standard methods for estimating ‘within-cluster’ association parameters implicitly adjust for shared confounders, shared mediators, but not shared colliders. Therefore, the estimated parameter may have a causal interpretation as a direct effect, i.e. as the part of the causal effect that is not mediated through shared factors

Associations between individual antipsychotics and the risk of arrests and convictions of violent and other crime : a nationwide within-individual study of 74925 persons

Background Individuals diagnosed with psychiatric disorders who are prescribed antipsycho-tics have lower rates of violence and crime but the differential effects of specific antipsychotics are not known. We investigated associations between 10 specific antipsychotic medications and subsequent risks for a range of criminal outcomes. Method We identified 74 925 individuals who were ever prescribed antipsychotics between 2006 and 2013 using nationwide Swedish registries. We tested for five specific first-generation antipsychotics (levomepromazine, perphenazine, haloperidol, flupentixol, and zuclo-penthixol) and five second-generation antipsychotics (clozapine, olanzapine, quetiapine, ris-peridone, and aripiprazole). The outcomes included violent, drug-related, and any criminal arrests and convictions. We conducted within-individual analyses using fixed-effects Poisson regression models that compared rates of outcomes between periods when each individual was either on or off medication to account for time-stable unmeasured confounders. All models were adjusted for age and concurrent mood stabilizer medications. Results The relative risks of all crime outcomes were substantially reduced [range of adjusted rate ratios (aRRs): 0.50-0.67] during periods when the patients were prescribed antipsychotics v. periods when they were not. We found that clozapine (aRRs: 0.28-0.44), olanzapine (aRRs: 0.46-0.72), and risperidone (aRRs: 0.53-0.64) were associated with lower arrest and conviction risks than other antipsychotics, including quetiapine (aRRs: 0.68-0.84) and haloperidol (aRRs: 0.67-0.77). Long-acting injectables as a combined medication class were associated with lower risks of the outcomes but only risperidone was associated with lower risks of all six outcomes (aRRs: 0.33-0.69). Conclusions There is heterogeneity in the associations between specific antipsychotics and subsequent arrests and convictions for any drug-related and violent crimes.Peer reviewe

Cerebellar mutism syndrome in children with brain tumours of the posterior fossa

Background: Central nervous system tumours constitute 25% of all childhood cancers; more than half are located in the posterior fossa and surgery is usually part of therapy. One of the most disabling late effects of posterior fossa tumour surgery is the cerebellar mutism syndrome (CMS) which has been reported in up to 39% of the patients but the exact incidence is uncertain since milder cases may be unrecognized. Recovery is usually incomplete. Reported risk factors are tumour type, midline location and brainstem involvement, but the exact aetiology, surgical and other risk factors, the clinical course and strategies for prevention and treatment are yet to be determined. Methods: This observational, prospective, multicentre study will include 500 children with posterior fossa tumours. It opened late 2014 with participation from 20 Nordic and Baltic centres. From 2016, five British centres and four Dutch centres will join with a total annual accrual of 130 patients. Three other major European centres are invited to join from 2016/17. Follow-up will run for 12 months after inclusion of the last patient. All patients are treated according to local practice. Clinical data are collected through standardized online registration at pre-determined time points pre- and postoperatively. Neurological status and speech functions are examined pre- operatively and postoperatively at 1-4 weeks, 2 and 12 months. Pre- and postoperative speech samples are recorded and analysed. Imaging will be reviewed centrally. Pathology is classified according to the 2007 WHO system. Germline DNA will be collected from all patients for associations between CMS characteristics and host genome variants including pathway profiles. Discussion: Through prospective and detailed collection of information on 1) differences in incidence and clinical course of CMS for different patient and tumour characteristics, 2) standardized surgical data and their association with CMS, 3) diversities and results of other therapeutic interventions, and 4) the role of host genome variants, we aim to achieve a better understanding of risk factors for and the clinical course of CMS - with the ultimate goal of defining strategies for prevention and treatment of this severely disabling condition.Peer reviewe

Semi-parametric estimation of multi-valued treatment effects for the treated: Estimating equations and sandwich estimators

An estimand of interest in empirical studies with observational data is the average treatment effect of a multi-valued treatment in the treated subpopulation. We demonstrate three estimation approaches: outcome regression, inverse probability weighting and inverse probability weighted regression, where the latter estimator holds a so called doubly robust property. Here, we define the estimators in the framework of partial M-estimation and derive corresponding sandwich estimators of their variances. The finite sample properties of the estimators and the proposed variance estimators are evaluated in simulations that reproduce designs from a previous simulation study in the literature of multi-valued treatment effects. The proposed variance estimators are investigated and compared to a bootstrap estimator

''Studenterna lär sig det de får arbeta med. Ge dem ett bra arbetsmaterial!''

En viktig faktor för studenternas lärande i en kurs är uppgifterna de arbetar med. Idealt ska de träna studenterna på olika kompetenser, belysa teorin på olika sätt och samtidigt motivera och väcka intresse. Sedan några år tillbaka använder vi ett omfattande arbetsmaterial ''Räkna med variation'' för att undervisa i grundläggande sannolikhetslära och statistisk inferens. Vi kommer att presentera arbetsmaterialet och diskutera våra tankar kring studentens lärande vid utvecklingen av materialet. Vi ger också exempel på hur det används i kurser på LTH-programmen V, L, W, B, K, N, BMe och M, samt hur det tagits emot av studenterna

Doubly robust methods for handling confounding by cluster

In clustered designs such as family studies, the exposure-outcome association is usually confounded by both cluster-constant and cluster-varying confounders. The influence of cluster-constant confounders can be eliminated by studying the exposure-outcome association within (conditional on) clusters, but additional regression modeling is usually required to control for observed cluster-varying confounders. A problem is that the working regression model may be misspecified, in which case the estimated within-cluster association may be biased. To reduce sensitivity to model misspecification we propose to augment the standard working model for the outcome with an auxiliary working model for the exposure. We derive a doubly robust conditional generalized estimating equation (DRCGEE) estimator for the within-cluster association. This estimator combines the two models in such a way that it is consistent if either model is correct, not necessarily both. Thus, the DRCGEE estimator gives the researcher two chances instead of only one to make valid inference on the within-cluster association. We have implemented the estimator in an R package and we use it to examine the association between smoking during pregnancy and cognitive abilities in offspring, in a sample of siblings

Doubly robust conditional logistic regression

Epidemiologic research often aims to estimate the association between a binary exposure and a binary outcome, while adjusting for a set of covariates (eg, confounders). When data are clustered, as in, for instance, matched case-control studies and co-twin-control studies, it is common to use conditional logistic regression. In this model, all cluster-constant covariates are absorbed into a cluster-specific intercept, whereas cluster-varying covariates are adjusted for by explicitly adding these as explanatory variables to the model. In this paper, we propose a doubly robust estimator of the exposure-outcome odds ratio in conditional logistic regression models. This estimator protects against bias in the odds ratio estimator due to misspecification of the part of the model that contains the cluster-varying covariates. The doubly robust estimator uses two conditional logistic regression models for the odds ratio, one prospective and one retrospective, and is consistent for the exposure-outcome odds ratio if at least one of these models is correctly specified, not necessarily both. We demonstrate the properties of the proposed method by simulations and by re-analyzing a publicly available dataset from a matched case-control study on induced abortion and infertility