Future Cases as Present Controls to Adjust for Exposure Trend Bias in Case-only Studies (original) (raw)
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Persistent User Bias in Case-Crossover Studies in Pharmacoepidemiology
American journal of epidemiology, 2016
Studying the effect of chronic medication exposure by means of a case-crossover design may result in an upward-biased odds ratio. In this study, our aim was to assess the occurrence of this bias and to evaluate whether it is remedied by including a control group (the case-time-control design). Using Danish data resources from 1995-2012, we conducted case-crossover and case-time-control analyses for 3 medications (statins, insulin, and thyroxine) in relation to 3 outcomes (retinal detachment, wrist fracture, and ischemic stroke), all with assumed null associations. Controls were matched on age, sex, and index date, and exposure over the preceding 12 months was ascertained. For retinal detachment, the case-crossover odds ratio was 1.60 (95% confidence interval (CI): 1.42, 1.80) for statins, 1.40 (95% CI: 1.02, 1.92) for thyroxine, and 1.53 (95% CI: 1.04, 2.24) for insulin. Estimates for the retinal detachment controls were similar, leading to near-null case-time-control estimates for ...
A weighted Cox model for modelling time-dependent exposures in the analysis of case-control studies
Statistics in Medicine, 2010
Many exposures investigated in epidemiological case-control studies may vary over time. The effects of these exposures are usually estimated using logistic regression, which does not directly account for changes in covariate values over time within individuals. By contrast, the Cox model with time-dependent covariates directly accounts for these changes over time. However, the over-sampling of cases in case-control studies, relative to controls, requires manipulating the risk sets in the Cox partial likelihood. A previous study showed that simple inclusion or exclusion of future cases in each risk set induces an under-or over-estimation bias in the regression parameters, respectively. We investigate the performance of a weighted Cox model that weights subjects according to age-conditional probabilities of developing the disease of interest in the source population. In a simulation study, the lifetime experience of a source population is first generated and a case-control study is then simulated within each population. Different characteristics of exposure are generated, including time-varying intensity. The results show that the estimates from the weighted Cox model are much less biased than the Cox models that simply include or exclude future cases, and are superior to logistic regression estimates in terms of bias and mean-squared error. An application to frequency-matched population-based case-control data on lung cancer illustrates similar differences in the estimated effects of different smoking variables. The investigated weighted Cox model is a potential alternative method to analyse matched or unmatched population-based case-control studies with time-dependent exposures.
Statistical methods in medical research, 2018
One important goal in pharmaco-epidemiology studies is to understand the causal relationship between drug exposures and their clinical outcomes, including adverse drug events. In order to achieve this goal, however, we need to resolve several challenges. Most of pharmaco-epidemiology data are observational and confounding is largely present due to many co-medications. The pharmaco-epidemiology study data set is often sampled from large medical record databases using a matched case-control design, and it may not be representative of the original patient population in the medical record databases. Data analysis method needs to handle a large sample size that cannot be handled using existing statistical analysis packages. In this paper, we tackle these challenges both methodologically and computationally. We propose a conditional causal log-odds ratio (OR) definition to characterize causal effects of drug exposures on a binary adverse drug event adjusting for individual level confounde...
Exposure stratified case-cohort designs
1998
A variant of the case-cohort design is proposed for the situation in which a correlate of the exposure (or prognostic factor) of interest is available for all cohort members, and exposure information is to be collected for a case-cohort sample. The cohort is stratified according to the correlate, and the subcohort is selected by stratified random sampling. A number of possible methods for the analysis of such exposure stratified case-cohort samples are presented and some of their statistical properties developed. The bias and efficiency of the methods are compared to each other, and to randomly sampled case-cohort studies, in a limited computer simulation study. We found that all of the proposed analysis methods performed reasonably well and were more efficient than a randomly sampled case-cohort sample. We conclude that these methods are well suited for the "clinical trials setting" in which subjects enter the study at time zero (at diagnosis or treatment) and a correlate of an expensive prognostic factor is collected for all study subjects at the time of entry to the study. In such studies, a correlate stratified subcohort can be much more cost-efficient for investigation of the expensive prognostic factor than a randomly sampled subcohort.
Adjusting for selection bias in retrospective, case–control studies
2009
Retrospective case control studies are more susceptible to selection bias than other epidemiologic studies as by design they require that both cases and controls are representative of the same population. However, as cases and control recruitment processes are often different, it is not always obvious that the necessary exchangeability conditions hold. Selection bias typically arises when the selection criteria are associated with the risk factor under investigation. We develop a method which produces biasadjusted estimates for the odds ratio. Our method hinges on two conditions. The first is that a variable that separates the risk factor from the selection criteria can be identified. This is termed the bias breaking variable. The second condition is that data can be found such that a bias-corrected estimate of the distribution of the bias breaking variable can be obtained. We show by means of a set of examples that such bias breaking variables are not uncommon in epidemiologic settings. We demonstrate using simulations that the estimates of the odds ratios produced by our method are consistently closer to the true odds ratio than standard odds ratio estimates using logistic regression. Further, by applying it to a case control study, we show that our method can help to determine whether selection bias is present and thus confirm the validity of study conclusions when no evidence of selection bias can be found. selection bias, directed acyclic graphs, conditional independence, confounding, retrospective case control studies, post-stratification, weighting
Case-cohort and nested case-control designs are often used to select an appropriate subsample of individuals from prospective cohort studies. Despite the great attention that has been given to the calculation of association estimators, no formal methods have been described for estimating risk prediction measures from these 2 sampling designs. Using real data from the Swedish Twin Registry (2004)(2005)(2006)(2007)(2008)(2009), the authors sampled unstratified and stratified (matched) case-cohort and nested case-control subsamples and compared them with the full cohort (as ''gold standard''). The real biomarker (high density lipoprotein cholesterol) and simulated biomarkers (BIO1 and BIO2) were studied in terms of association with cardiovascular disease, individual risk of cardiovascular disease at 3 years, and main prediction metrics. Overall, stratification improved efficiency, with stratified case-cohort designs being comparable to matched nested case-control designs. Individual risks and prediction measures calculated by using case-cohort and nested case-control designs after appropriate reweighting could be assessed with good efficiency, except for the finely matched nested case-control design, where matching variables could not be included in the individual risk estimation. In conclusion, the authors have shown that case-cohort and nested case-control designs can be used in settings where the research aim is to evaluate the prediction ability of new markers and that matching strategies for nested case-control designs may lead to biased prediction measures. cardiovascular diseases; case-cohort studies; nested case-control studies; risk prediction; sampling design Abbreviations: BIO1 and BIO2, simulated biomarkers 1 and 2; CVD, cardiovascular disease; HDL-C, high density lipoprotein cholesterol; SD, standard deviation.
Modelling Association Among Bivariate Exposures In Matched Case-Control Studies
Sankhya Ser A
The paper considers the problem of modelling association between two exposure variables in a matched case-control study, where both the exposures may be partially missing. The exposure variables could all be categorical or continuous or could be a mixed set of some categorical and some continuous variables. Association models for the missing exposure variables using the completely observed covariates and disease status are proposed for each of the three scenarios. The models account for varying stratum heterogeneity in different matched sets. Three real data examples accompany the proposed models. The examples as well as a small scale simulation study indicate that in presence of missingness and association, modelling the association between the exposures rather than ignoring it, often leads to better estimates of the relative risk parameters with smaller standard errors. Estimation of the model parameters is carried out in a Bayesian framework and the estimates are compared with classical conditional logistic regression estimates.
Pharmacoepidemiology and Drug Safety, 2016
Purpose-The objective of this study is to evaluate regression, matching and stratification on propensity score (PS) or disease risk score (DRS) in a setting of sequential analyses where statistical hypotheses are tested multiple times. Methods-In a setting of sequential analyses, we simulated incident users and binary outcomes with different confounding strength, outcome incidence, and the adoption rate of treatment. We compared type I error rate, empirical power, and time to signal using the following confounder adjustments: 1) regression; 2) treatment matching (1:1, 1:4) on PS or DRS, and 3) stratification on PS or DRS. We estimated PS and DRS using lookwise and cumulative methods (all data up to the current look). We applied these confounder adjustments in examining the association between NSAIDS and bleeding. Results-PS and DRS methods had similar empirical power and time to signal. However DRS methods yielded type I error rates up to 17% for 1:4 matching and 15.3% for stratification methods when treatment and outcome were common and confounding strength with treatment was stronger. When treatment and outcome were not common, stratification on PS and DRS and regression yielded 8-10% type I error rates and inflated empirical power. However when outcome and treatment were common both regression and stratification on PS outperformed other matching methods with type I error rates close to 5%.