Simple descriptive missing data indicators in longitudinal studies with attrition, intermittent missing data and a high number of follow-ups.
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ABSTRACT:
SUBMITTER: Wærsted M
PROVIDER: S-EPMC5809924 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Multiple imputation is a well-established general technique for analyzing data with missing values. A convenient way to implement multiple imputation is sequential regression multiple imputation, also called chained equations multiple imputation. In this approach, we impute missing values using regression models for each variable, conditional on the other variables in the data. This approach, however, assumes that the missingness mechanism is missing at random, and it is not well-justified under not-at-random missingness without additional modification. In this paper, we describe how we can generalize the sequential regression multiple imputation imputation procedure to handle missingness not at random in the setting where missingness may depend on other variables that are also missing but not on the missing variable itself, conditioning on fully observed variables. We provide algebraic justification for several generalizations of standard sequential regression multiple imputation using Taylor series and other approximations of the target imputation distribution under missingness not at random. Resulting regression model approximations include indicators for missingness, interactions, or other functions of the missingness not at random missingness model and observed data. In a simulation study, we demonstrate that the proposed sequential regression multiple imputation modifications result in reduced bias in the final analysis compared to standard sequential regression multiple imputation, with an approximation strategy involving inclusion of an offset in the imputation model performing the best overall. The method is illustrated in a breast cancer study, where the goal is to estimate the prevalence of a specific genetic pathogenic variant.
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Project description:In some large clinical studies, it may be impractical to perform the physical examination to every subject at his/her last monitoring time in order to diagnose the occurrence of the event of interest. This gives rise to survival data with missing censoring indicators where the probability of missing may depend on time of last monitoring and some covariates. We present a fully Bayesian semi-parametric method for such survival data to estimate regression parameters of the proportional hazards model of Cox. Theoretical investigation and simulation studies show that our method performs better than competing methods. We apply the proposed method to analyze the survival data with missing censoring indicators from the Orofacial Pain: Prospective Evaluation and Risk Assessment study.
| S-EPMC7733523 | biostudies-literature
Project description:Missing genotype data arise in association studies when the single-nucleotide polymorphisms (SNPs) on the genotyping platform are not assayed successfully, when the SNPs of interest are not on the platform, or when total sequence variation is determined only on a small fraction of individuals. We present a simple and flexible likelihood framework to study SNP-disease associations with such missing genotype data. Our likelihood makes full use of all available data in case-control studies and reference panels (e.g., the HapMap), and it properly accounts for the biased nature of the case-control sampling as well as the uncertainty in inferring unknown variants. The corresponding maximum-likelihood estimators for genetic effects and gene-environment interactions are unbiased and statistically efficient. We developed fast and stable numerical algorithms to calculate the maximum-likelihood estimators and their variances, and we implemented these algorithms in a freely available computer program. Simulation studies demonstrated that the new approach is more powerful than existing methods while providing accurate control of the type I error. An application to a case-control study on rheumatoid arthritis revealed several loci that deserve further investigations.
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Project description:Longitudinal data are very popular in practice, but they are often missing in either outcomes or time-dependent risk factors, making them highly unbalanced and complex. Missing data may contain various missing patterns or mechanisms, and how to properly handle it for unbiased and valid inference still presents a significant challenge. Here, we propose a novel semiparametric framework for analyzing longitudinal data with both missing responses and covariates that are missing at random and intermittent, a general and widely encountered situation in observational studies. Within this framework, we consider multiple robust estimation procedures based on innovative calibrated propensity scores, which offers additional relaxation of the misspecification of missing data mechanisms and shows more satisfactory numerical performance. Also, the corresponding robust information criterion on consistent variable selection for our proposed model is developed based on empirical likelihood-based methods. These advocated methods are evaluated in both theory and extensive simulation studies in a variety of situations, showing competing properties and advantages compared to the existing approaches. We illustrate the utility of our approach by analyzing the data from the HIV Epidemiology Research Study.
| S-EPMC8587776 | biostudies-literature
Project description:ObjectiveMissing data is a ubiquitous problem in studies using patient-reported measures, decreasing sample sizes and causing possible bias. In longitudinal studies, special problems relate to attrition and death during follow-up. We describe a methodological approach for the use of multiple imputation (MI) to meet these challenges.MethodsIn a cohort of patients treated with percutaneous coronary intervention followed with use of repetitive questionnaires and information from national registers over 3 years, only 417 out of 1,726 patients had complete data on all measure points and covariates. We suggest strategies for use of MI and different methods for dealing with death along with sensitivity analysis of deviations from the assumption of missing at random, all with the use of standard statistical software. The Mental Component Summary from Short Form 12-item survey was used as an example.ConclusionIgnoring missing data may cause bias of unknown size and direction in longitudinal studies. We have illustrated that MI is a feasible method to try to deal with bias due to missing data in longitudinal studies, including attrition and nonresponse, and should be considered in combination with analysis of sensitivity in longitudinal studies. How to handle dropout due to death is still open for debate.
| S-EPMC4303367 | biostudies-literature
Project description:BACKGROUND: Many randomized trials involve missing binary outcomes. Although many previous adjustments for missing binary outcomes have been proposed, none of these makes explicit use of randomization to bound the bias when the data are not missing at random. METHODS: We propose a novel approach that uses the randomization distribution to compute the anticipated maximum bias when missing at random does not hold due to an unobserved binary covariate (implying that missingness depends on outcome and treatment group). The anticipated maximum bias equals the product of two factors: (a) the anticipated maximum bias if there were complete confounding of the unobserved covariate with treatment group among subjects with an observed outcome and (b) an upper bound factor that depends only on the fraction missing in each randomization group. If less than 15% of subjects are missing in each group, the upper bound factor is less than.18. RESULTS: We illustrated the methodology using data from the Polyp Prevention Trial. We anticipated a maximum bias under complete confounding of.25. With only 7% and 9% missing in each arm, the upper bound factor, after adjusting for age and sex, was.10. The anticipated maximum bias of.25 x.10 =.025 would not have affected the conclusion of no treatment effect. CONCLUSION: This approach is easy to implement and is particularly informative when less than 15% of subjects are missing in each arm.
| S-EPMC194902 | biostudies-literature
Project description:IntroductionTelemedicine is an attractive option for the follow-up of paediatric patients with SARS-CoV-2 infection. The aim of this article is to describe the experience with telephone consultations in a tertiary hospital.Patients and methodsRetrospective descriptive study of children with confirmed or probable diagnosis of COVID-19 attended by telephone consultations in Hospital La Paz (Madrid) between March and June 2020. Patients were referred from the Emergency Department after being discharged from the hospital. Telephone consultations were made every 48hours until symptoms resolved, then weekly until completing 14 days without symptoms.ResultsA total of 72 children were included, with median age of 83.5 months (IQR=16.3-157.5). Of those 46 (63.9%) were male, and 14 (19.4%) had comorbidities. There were 32 (44.4%) hospital admissions. COVID-19 diagnosis was confirmed in 33 children by RT-PCR, and in 7 by serology tests. The seroconversion rate was 67.7% in those patients with a positive RT-PCR. Other infections were found in 7 patients (5 Mycoplasma pneumoniae, 1 parvovirus, and 1 cytomegalovirus). Median symptom duration was 25.5 days (IQR=13.8-37), while median follow-up duration was 28 days (IQR=21-39). The median number of telephone consultations per patient was 6 (IQR=4-8). Clinical worsening was reported in 19 (26.4%) during follow-up, and 14 (19.4%) were re-evaluated in the Emergency Department. One patient required hospital admission, but he had a favourable outcome.ConclusionsChildren with suspected SARS-CoV-2 infection should be followed-up due to prolonged duration of symptoms, and the risk of clinical deterioration. Telephone consultations are a useful and safe alternative for the follow-up of patients with mild symptoms, and for children discharged from the hospital.
| S-EPMC7605723 | biostudies-literature
Project description:IntroductionTelemedicine is an attractive option for the follow-up of paediatric patients with SARS-CoV-2 infection. The aim of this article is to describe the experience with telephone consultations in a tertiary hospital.Patients and methodsRetrospective descriptive study of children with confirmed or probable diagnosis of COVID-19 attended by telephone consultations in Hospital La Paz (Madrid) between March and June 2020. Patients were referred from the Emergency Department after being discharged from the hospital. Telephone consultations were made every 48 h until symptoms resolved, then weekly until completing 14 days without symptoms.ResultsA total of 72 children were included, with median age of 83.5 months [IQR = 16.3-157.5]. Of those 46 (63.9%) were male, and 14 (19.4%) had comorbidities. There were 32 (44.4%) hospital admissions. COVID-19 diagnosis was confirmed in 33 children by RT-PCR, and in 7 by serology tests. The seroconversion rate was 67.7% in those patients with a positive RT-PCR. Other infections were found in 7 patients (5 Mycoplasma pneumoniae, 1 parvovirus, and 1 CMV). Median symptom duration was 25.5 days [IQR = 13.8-37], while median follow-up duration was 28 days [IQR = 21-39]. The median number of telephone consultations per patient was 6 [IQR = 4-8]. Clinical worsening was reported in 19 (26.4%) during follow-up, and 14 (19.4%) were re-evaluated in the Emergency Department. One patient required hospital admission, but he had a favourable outcome.ConclusionsChildren with suspected SARS-CoV-2 infection should be followed-up due to prolonged duration of symptoms, and the risk of clinical deterioration. Telephone consultations are a useful and safe alternative for the follow-up of patients with mild symptoms, and for children discharged from the hospital.
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Project description:Background & aimsThere are currently no validated claims-based indicators for identifying a worsening of disease in patients with inflammatory bowel disease (IBD). Therefore, we aimed to develop and validate indicators that identify flare-ups of IBD using data from Danish nationwide registries.MethodsUsing Danish nationwide administrative data, we identified all patients with Crohn's disease (CD) or ulcerative colitis (UC) who had at least one measurement of faecal calprotectin between 1 January 2015 and 31 June 2017. We tested several different claims-based indicators of disease flare-ups against levels of faecal (F-)calprotectin (no flare-up: <250 mg/kg; mild flare-up: 250-1000 mg/kg; severe flare-up: ⩾1000 mg/kg). A generalised estimating equation was used to evaluate whether the proposed indicators could predict disease activity.ResultsA total of 890 children and 4719 adults with CD, and 592 children and 5467 adults with UC were included in the study. During the observation period, 48-61% and 48-55% of the CD and UC patients, respectively, had no flare-up, 26-29% (CD) and 24-26% (UC) experienced a mild flare-up, and 12-23% (CD) and 21-27% (UC) experienced a severe flare-up. Combinations of indicators that could predict a flare-up in CD and UC adults included hospitalisation, surgery, initiation or switch of biological therapy, treatment with systemic steroids, locally acting steroids or topical 5-aminosalicylates, colonoscopy/sigmoidoscopy, and magnetic resonance imaging/computed tomography. In children, only the number of gastroenterology visits was significant as an indicator among UC patients, and none were seen in children with CD. Overall, the indicator combinations resulted in a predictive ability of 0.62-0.67.ConclusionAdministrative claims data can be useful for identifying patients exhibiting (F-calprotectin defined) flare-ups of their IBD. Clinically relevant events captured in the Danish national patient registry are associated with increased levels of calprotectin and hence increased disease activity, and can be used as valid outcomes in future studies.
| S-EPMC8020737 | biostudies-literature
Project description:Visceral leishmaniasis (VL) is a parasitic disease transmitted by sandflies and is fatal if left untreated. Phase II trials of new treatment regimens for VL are primarily carried out to evaluate safety and efficacy, while pharmacokinetic data are also important to inform future combination treatment regimens. The efficacy of VL treatments is evaluated at two time points, initial cure, when treatment is completed and definitive cure, commonly 6 months post end of treatment, to allow for slow response to treatment and detection of relapses. This paper investigates a generalization of the triangular design to impose a minimum sample size for pharmacokinetic or other analyses, and methods to estimate efficacy at extended follow-up accounting for the sequential design and changes in cure status during extended follow-up.We provided R functions that generalize the triangular design to impose a minimum sample size before allowing stopping for efficacy. For estimation of efficacy at a second, extended, follow-up time, the performance of a shrinkage estimator (SHE), a probability tree estimator (PTE) and the maximum likelihood estimator (MLE) for estimation was assessed by simulation.The SHE and PTE are viable approaches to estimate an extended follow-up although the SHE performed better than the PTE: the bias and root mean square error were lower and coverage probabilities higher.Generalization of the triangular design is simple to implement for adaptations to meet requirements for pharmacokinetic analyses. Using the simple MLE approach to estimate efficacy at extended follow-up will lead to biased results, generally over-estimating treatment success. The SHE is recommended in trials of two or more treatments. The PTE is an acceptable alternative for one-arm trials or where use of the SHE is not possible due to computational complexity.NCT01067443 , February 2010.
| S-EPMC4647805 | biostudies-literature