A Bayesian hierarchical model for signal extraction from protein microarrays.

Protein microarrays are a promising technology that measure protein levels in serum or plasma samples. Due to their high technical variability and high variation in protein levels across serum samples in any population, directly answering biological questions of interest using protein microarray measurements is challenging. Analyzing preprocessed data and within-sample ranks of protein levels can mitigate the impact of between-sample variation. As for any analysis, ranks are sensitive to preprocessing, but loss function based ranks that accommodate major structural relations and components of uncertainty are very effective. Bayesian modeling with full posterior distributions for quantities of interest produce the most effective ranks. Such Bayesian models have been developed for other assays, for example, DNA microarrays, but modeling assumptions for these assays are not appropriate for protein microarrays. Consequently, we develop and evaluate a Bayesian model to extract the full posterior distribution of normalized protein levels and associated ranks for protein microarrays, and show that it fits well to data from two studies that use protein microarrays produced by different manufacturing processes. We validate the model via simulation and demonstrate the downstream impact of using estimates from this model to obtain optimal ranks.

A pre-processing pipeline to quantify, visualize, and reduce technical variation in protein microarray studies.

Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation, and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis.

Whole Blood Transfusion for Severe Malarial Anemia in a High Plasmodium falciparum Transmission Setting.

BACKGROUND: Severe malaria resulting from Plasmodium falciparum infection is the leading parasitic cause of death in children worldwide, and severe malarial anemia (SMA) is the most common clinical presentation. The evidence in support of current blood transfusion guidelines for patients with SMA is limited. METHODS: We conducted a retrospective cohort study of 911 hospitalized children with SMA in a holoendemic region of Zambia to examine the association of whole blood transfusion with in-hospital survival. Data were analyzed in adjusted logistic regression models using multiple imputation for missing data. RESULTS: The median age of patients was 24 months (interquartile range, 16-30) and overall case fatality was 16%. Blood transfusion was associated with 35% reduced odds of death in children with SMA (odds ratio, 0.65; 95% confidence interval, .52-.81; P = .0002) corresponding to a number-needed-to-treat (NNT) of 14 patients. Children with SMA complicated by thrombocytopenia were more likely to benefit from transfusion than those without thrombocytopenia (NNT = 5). Longer storage time of whole blood was negatively associated with survival and with the posttransfusion rise in the platelet count but was not associated with the posttransfusion change in hemoglobin concentration. CONCLUSIONS: Whole blood given to pediatric patients with SMA was associated with improved survival, mainly among those with thrombocytopenia who received whole blood stored for <4 weeks. These findings point to a potential use for incorporating thrombocytopenia into clinical decision making and management of severe malaria, which can be further assessed in prospective studies, and underline the importance of maintaining reliable blood donation networks in areas of high malaria transmission.

Accurate assignment of disease liability to genetic variants using only population data.

PURPOSE: The growing size of public variant repositories prompted us to test the accuracy of pathogenicity prediction of DNA variants using population data alone. METHODS: Under the a priori assumption that the ratio of the prevalence of variants in healthy population vs that in affected populations form 2 distinct distributions (pathogenic and benign), we used a Bayesian method to assign probability to a variant belonging to either distribution. RESULTS: The approach, termed Bayesian prevalence ratio (BayPR), accurately parsed 300 of 313 expertly curated CFTR variants: 284 of 296 pathogenic/likely pathogenic variants in 1 distribution and 16 of 17 benign/likely benign variants in another. BayPR produced an area under the receiver operating characteristic curve of 0.99 for 103 functionally confirmed missense CFTR variants, which is equal to or exceeds 10 commonly used algorithms (area under the receiver operating characteristic curve range = 0.54-0.99). Application of BayPR to expertly curated variants in 8 genes associated with 7 Mendelian conditions led to the assignment of a disease-causing probability of ≥80% to 1350 of 1374 (98.3%) pathogenic/likely pathogenic variants and of ≤20% to 22 of 23 (95.7%) benign/likely benign variants. CONCLUSION: Irrespective of the variant type or functional effect, the BayPR approach provides probabilities of pathogenicity for DNA variants responsible for Mendelian disorders using only the variant counts in affected and unaffected population samples.

Individualized treatment effects with censored data via fully nonparametric Bayesian accelerated failure time models.

Individuals often respond differently to identical treatments, and characterizing such variability in treatment response is an important aim in the practice of personalized medicine. In this article, we describe a nonparametric accelerated failure time model that can be used to analyze heterogeneous treatment effects (HTE) when patient outcomes are time-to-event. By utilizing Bayesian additive regression trees and a mean-constrained Dirichlet process mixture model, our approach offers a flexible model for the regression function while placing few restrictions on the baseline hazard. Our nonparametric method leads to natural estimates of individual treatment effect and has the flexibility to address many major goals of HTE assessment. Moreover, our method requires little user input in terms of model specification for treatment covariate interactions or for tuning parameter selection. Our procedure shows strong predictive performance while also exhibiting good frequentist properties in terms of parameter coverage and mitigation of spurious findings of HTE. We illustrate the merits of our proposed approach with a detailed analysis of two large clinical trials (N = 6769) for the prevention and treatment of congestive heart failure using an angiotensin-converting enzyme inhibitor. The analysis revealed considerable evidence for the presence of HTE in both trials as demonstrated by substantial estimated variation in treatment effect and by high proportions of patients exhibiting strong evidence of having treatment effects which differ from the overall treatment effect.

Assessing and communicating heterogeneity of treatment effects for patient subpopulations: Panel discussion on considerations in design and analysis.

Clinical trials are primarily conducted to understand the average effects treatments have on patients. However, patients are heterogeneous in the severity of the condition and in ways that affect what treatment effect they can expect. It is therefore important to understand and characterize how treatment effects vary. The design and analysis of clinical studies play critical roles in evaluating and characterizing heterogeneous treatment effects. This panel discussed considerations in design and analysis under the recognition that there are heterogeneous treatment effects across subgroups of patients. Panel members discussed many questions including: What is a good estimate of the treatment effect in me, a 65-year-old, bald, Caucasian-American, male patient? What magnitude of heterogeneity of treatment effects (HTE) is sufficiently large to merit attention? What role can prior evidence about HTE play in confirmatory trial design and analysis? Is there anything described in the 21st Century Cures Act that would benefit from greater attention to HTE? An example of a Bayesian approach addressing multiplicity when testing for treatment effects in subgroups will be provided. We can do more or better at understanding heterogeneous treatment effects and providing the best information on heterogeneous treatment effects.

Subnational estimation of modern contraceptive prevalence in five sub-Saharan African countries: a Bayesian hierarchical approach.

BACKGROUND: Global monitoring efforts have relied on national estimates of modern contraceptive prevalence rate (mCPR) for many low-income countries. However, most contraceptive delivery programs are implemented by health departments at lower administrative levels, reflecting a persisting gap between the availability of and need for subnational mCPR estimates. METHODS: Using woman-level data from multiple semi-annual national survey rounds conducted between 2013 and 2016 in five sub-Saharan African countries (Burkina Faso, Ethiopia, Ghana, Kenya, and Uganda) by the Performance, Monitoring and Accountability 2020 project, we propose a Bayesian Hierarchical Model with a standard set of covariates and temporally correlated random effects to estimate the level and trend of mCPR for first level administrative divisions in each country. RESULTS: There is considerable narrowing of the uncertainty interval (UI) around the model-based estimates, compared to the estimates directly based on the survey data. We find substantial variations in the estimated subnational mCPRs. Uganda, for example, shows a gain in mCPR of 6.4% (95% UI: 4.5-8.3) based on model estimates of 20.9% (19.6-22.2) in mid-2014 and 27.3% (26.0-28.8) in mid-2016, with change across 10 regions ranging from - 0.6 points in Karamoja to 9.4 points in Central 2 region. The lower bound of the UIs of the change over four rounds was above 0 in 6 regions. Similar upward trends are observed for most regions in the other four countries, and there is noticeable within-country geographic variation. CONCLUSIONS: Reliable subnational estimates of mCPR empower health departments in evidence-based policy making. Despite nationally increasing mCPRs, regional disparities exist within countries suggesting uneven contraceptive access. Raising investments in disadvantaged areas may be warranted to increase equity in access to modern contraceptive methods.

Perfluoroalkyl Chemicals, Menstrual Cycle Length, and Fecundity: Findings from a Prospective Pregnancy Study.

BACKGROUND: Perfluoroalkyl substances have been associated with changes in menstrual cycle characteristics and fecundity, when modeled separately. However, these outcomes are biologically related, and we evaluate their joint association with exposure to perfluoroalkyl substances. METHODS: We recruited 501 couples from Michigan and Texas in 2005-2009 upon their discontinuing contraception and followed them until pregnancy or 12 months of trying. Female partners provided a serum sample on enrollment and completed daily journals on menstruation, intercourse, and pregnancy test results. We measured seven perfluoroalkyl substances in serum using liquid chromatography-tandem mass spectrometry. We assessed the association between perfluoroalkyl substances and menstrual cycle length using accelerated failure time models and between perfluoroalkyl substances and fecundity using a Bayesian joint modeling approach to incorporate cycle length. RESULTS: Menstrual cycles were 3% longer comparing women in the second versus first tertile of perfluorodecanoate (PFDeA; acceleration factor [AF] = 1.03, 95% credible interval [CrI] = [1.00, 1.05]), but 2% shorter for women in the highest versus lowest tertile of perfluorooctanoic acid (PFOA; AF = 0.98, 95% CrI = [0.96, 1.00]). When accounting for cycle length, relevant covariates, and remaining perfluoroalkyl substances, the probability of pregnancy was lower for women in second versus first tertile of perfluorononanoate (PFNA; odds ratio [OR] = 0.6, 95% CrI = [0.4, 1.0]) although not when comparing the highest versus lowest (OR = 0.7, 95% CrI = [0.3, 1.1]) tertile. CONCLUSIONS: In this prospective cohort study, we observed associations between two perfluoroalkyl substances and menstrual cycle length changes, and between select perfluoroalkyl substances and diminished fecundity at some (but not all) concentrations. See video abstract at, http://links.lww.com/EDE/B136.

Accounting for length-bias and selection effects in estimating the distribution of menstrual cycle length.

Prospective pregnancy studies are a valuable source of longitudinal data on menstrual cycle length. However, care is needed when making inferences of such renewal processes. For example, accounting for the sampling plan is necessary for unbiased estimation of the menstrual cycle length distribution for the study population. If couples can enroll when they learn of the study as opposed to waiting for the start of a new menstrual cycle, then due to length-bias, the enrollment cycle will be stochastically larger than the general run of cycles, a typical property of prevalent cohort studies. Furthermore, the probability of enrollment can depend on the length of time since a woman's last menstrual period (a backward recurrence time), resulting in selection effects. We focus on accounting for length-bias and selection effects in the likelihood for enrollment menstrual cycle length, using a recursive two-stage approach wherein we first estimate the probability of enrollment as a function of the backward recurrence time and then use it in a likelihood with sampling weights that account for length-bias and selection effects. To broaden the applicability of our methods, we augment our model to incorporate a couple-specific random effect and time-independent covariate. A simulation study quantifies performance for two scenarios of enrollment probability when proper account is taken of sampling plan features. In addition, we estimate the probability of enrollment and the distribution of menstrual cycle length for the study population of the Longitudinal Investigation of Fertility and the Environment Study.

Cataract Surgery Outcomes in Uveitis: The Multicenter Uveitis Steroid Treatment Trial.

PURPOSE: To assess the visual outcomes of cataract surgery in eyes that received fluocinolone acetonide implant or systemic therapy with oral corticosteroids and immunosuppression during the Multicenter Uveitis Steroid Treatment (MUST) Trial. DESIGN: Nested prospective cohort study of patients enrolled in a randomized clinical trial. PARTICIPANTS: Patients that underwent cataract surgery during the first 2 years of follow-up in the MUST Trial. METHODS: Visual outcomes of cataract surgery were evaluated 3, 6, and 9 months after surgery using logarithmic visual acuity charts. Change in visual acuity over time was assessed using a mixed-effects model. MAIN OUTCOME MEASURES: Best-corrected visual acuity. RESULTS: After excluding eyes that underwent cataract surgery simultaneously with implant surgery, among the 479 eyes in the MUST Trial, 117 eyes (28 eyes in the systemic, 89 in the implant group) in 82 patients underwent cataract surgery during the first 2 years of follow-up. Overall, visual acuity increased by 23 letters from the preoperative visit to the 3-month visit (95% confidence interval [CI], 17-29 letters; P < 0.001) and was stable through 9 months of follow-up. Eyes presumed to have a more severe cataract, as measured by inability to grade vitreous haze, gained an additional 42 letters (95% CI, 34-56 letters; P < 0.001) beyond the 13-letter gain in eyes that had gradable vitreous haze before surgery (95% CI, 9-18 letters; P < 0.001) 3 months after surgery, making up for an initial difference of -45 letters at the preoperative visit (95% CI, -56 to -34 letters; P < 0.001). Black race, longer time from uveitis onset, and hypotony were associated with worse preoperative visual acuity (P < 0.05), but did not affect postsurgical recovery (P > 0.05, test of interaction). After adjusting for other risk factors, there was no significant difference in the improvement in visual acuity between the 2 treatment groups (implant vs. systemic therapy, 2 letters; 95% CI, -10 to 15 letters; P = 0.70). CONCLUSIONS: Cataract surgery resulted in substantial, sustained, and similar visual acuity improvement in the eyes of patients with uveitis treated with the fluocinolone acetonide implant or standard systemic therapy.

A Bayesian joint model of menstrual cycle length and fecundity.

Menstrual cycle length (MCL) has been shown to play an important role in couple fecundity, which is the biologic capacity for reproduction irrespective of pregnancy intentions. However, a comprehensive assessment of its role requires a fecundity model that accounts for male and female attributes and the couple's intercourse pattern relative to the ovulation day. To this end, we employ a Bayesian joint model for MCL and pregnancy. MCLs follow a scale multiplied (accelerated) mixture model with Gaussian and Gumbel components; the pregnancy model includes MCL as a covariate and computes the cycle-specific probability of pregnancy in a menstrual cycle conditional on the pattern of intercourse and no previous fertilization. Day-specific fertilization probability is modeled using natural, cubic splines. We analyze data from the Longitudinal Investigation of Fertility and the Environment Study (the LIFE Study), a couple based prospective pregnancy study, and find a statistically significant quadratic relation between fecundity and menstrual cycle length, after adjustment for intercourse pattern and other attributes, including male semen quality, both partner's age, and active smoking status (determined by baseline cotinine level 100 ng/mL). We compare results to those produced by a more basic model and show the advantages of a more comprehensive approach.

Comparative effectiveness of weight-loss interventions in clinical practice.

BACKGROUND: Obesity and its cardiovascular complications are extremely common medical problems, but evidence on how to accomplish weight loss in clinical practice is sparse. METHODS: We conducted a randomized, controlled trial to examine the effects of two behavioral weight-loss interventions in 415 obese patients with at least one cardiovascular risk factor. Participants were recruited from six primary care practices; 63.6% were women, 41.0% were black, and the mean age was 54.0 years. One intervention provided patients with weight-loss support remotely--through the telephone, a study-specific Web site, and e-mail. The other intervention provided in-person support during group and individual sessions, along with the three remote means of support. There was also a control group in which weight loss was self-directed. Outcomes were compared between each intervention group and the control group and between the two intervention groups. For both interventions, primary care providers reinforced participation at routinely scheduled visits. The trial duration was 24 months. RESULTS: At baseline, the mean body-mass index (the weight in kilograms divided by the square of the height in meters) for all participants was 36.6, and the mean weight was 103.8 kg. At 24 months, the mean change in weight from baseline was -0.8 kg in the control group, -4.6 kg in the group receiving remote support only (P<0.001 for the comparison with the control group), and -5.1 kg in the group receiving in-person support (P<0.001 for the comparison with the control group). The percentage of participants who lost 5% or more of their initial weight was 18.8% in the control group, 38.2% in the group receiving remote support only, and 41.4% in the group receiving in-person support. The change in weight from baseline did not differ significantly between the two intervention groups. CONCLUSIONS: In two behavioral interventions, one delivered with in-person support and the other delivered remotely, without face-to-face contact between participants and weight-loss coaches, obese patients achieved and sustained clinically significant weight loss over a period of 24 months. (Funded by the National Heart, Lung, and Blood Institute and others; ClinicalTrials.gov number, NCT00783315.).

Cost-effectiveness of fluocinolone acetonide implant versus systemic therapy for noninfectious intermediate, posterior, and panuveitis.

OBJECTIVE: To evaluate the 3-year incremental cost-effectiveness of fluocinolone acetonide implant versus systemic therapy for the treatment of noninfectious intermediate, posterior, and panuveitis. DESIGN: Randomized, controlled, clinical trial. PARTICIPANTS: Patients with active or recently active intermediate, posterior, or panuveitis enrolled in the Multicenter Uveitis Steroid Treatment Trial. METHODS: Data on cost and health utility during 3 years after randomization were evaluated at 6-month intervals. Analyses were stratified by disease laterality at randomization (31 unilateral vs 224 bilateral) because of the large upfront cost of the implant. MAIN OUTCOME MEASURES: The primary outcome was the incremental cost-effectiveness ratio (ICER) over 3 years: the ratio of the difference in cost (in United States dollars) to the difference in quality-adjusted life-years (QALYs). Costs of medications, surgeries, hospitalizations, and regular procedures (e.g., laboratory monitoring for systemic therapy) were included. We computed QALYs as a weighted average of EQ-5D scores over 3 years of follow-up. RESULTS: The ICER at 3 years was $297,800/QALY for bilateral disease, driven by the high cost of implant therapy (difference implant - systemic [Δ]: $16,900; P < 0.001) and the modest gains in QALYs (Δ = 0.057; P = 0.22). The probability of the ICER being cost-effective at thresholds of $50,000/QALY and $100,000/QALY was 0.003 and 0.04, respectively. The ICER for unilateral disease was more favorable, namely, $41,200/QALY at 3 years, because of a smaller difference in cost between the 2 therapies (Δ = $5300; P = 0.44) and a larger benefit in QALYs with the implant (Δ = 0.130; P = 0.12). The probability of the ICER being cost-effective at thresholds of $50,000/QALY and $100,000/QALY was 0.53 and 0.74, respectively. CONCLUSIONS: Fluocinolone acetonide implant therapy was reasonably cost-effective compared with systemic therapy for individuals with unilateral intermediate, posterior, or panuveitis but not for those with bilateral disease. These results do not apply to the use of implant therapy when systemic therapy has failed or is contraindicated. Should the duration of implant effect prove to be substantially >3 years or should large changes in therapy pricing occur, the cost-effectiveness of implant versus systemic therapy would need to be reevaluated.

Estimation of treatment effects in matched-pair cluster randomized trials by calibrating covariate imbalance between clusters.

We address estimation of intervention effects in experimental designs in which (a) interventions are assigned at the cluster level; (b) clusters are selected to form pairs, matched on observed characteristics; and (c) intervention is assigned to one cluster at random within each pair. One goal of policy interest is to estimate the average outcome if all clusters in all pairs are assigned control versus if all clusters in all pairs are assigned to intervention. In such designs, inference that ignores individual level covariates can be imprecise because cluster-level assignment can leave substantial imbalance in the covariate distribution between experimental arms within each pair. However, most existing methods that adjust for covariates have estimands that are not of policy interest. We propose a methodology that explicitly balances the observed covariates among clusters in a pair, and retains the original estimand of interest. We demonstrate our approach through the evaluation of the Guided Care program.

Evaluating local vegetation cover as a risk factor for malaria transmission: a new analytical approach using ImageJ.

BACKGROUND: In places where malaria transmission is unstable or is transmitted under hypoendemic conditions, there are periods where limited foci of cases still occur and people become infected. These residual "hot spots" are likely reservoirs of the parasite population and so are fundamental to the seasonal spread and decline of malaria. It is, therefore, important to understand the ecological conditions that permit vector mosquitoes to survive and forage in these specific areas. Features such as local waterways and vegetation, as well as local ecology, particularly nocturnal temperature, humidity, and vegetative sustainability, are important for modeling local mosquito behavior. Vegetation around a homestead likely provides refuge for outdoor resting of these insects and may be a risk factor for malaria transmission. Analysis of this vegetation can be done using satellite information and mapping programs, such as Google Earth, but manual quantification is difficult and can be tedious and subjective. A more objective method is required. METHODS: Vegetation cover in the environment is reasonably static, particularly in and around homesteads. In order to evaluate and enumerate such information, ImageJ, an image processing software, was used to analyse Google Earth satellite imagery. The number of plants, total amount of vegetation around a homestead and its percentage of the total area were calculated and related to homesteads where cases of malaria were recorded. RESULTS: Preliminary results were obtained from a series of field trials carried out in South East Zambia in the Choma and Namwala districts from a base at the Macha District Hospital. CONCLUSIONS: This technique is objective, clear and simple to manipulate and has potential application to determine the role that vegetation proximal to houses may play in affecting mosquito behaviour, foraging and subsequent malaria incidence.

Efficient simulation of epistatic interactions in case-parent trios.

Statistical approaches to evaluate interactions between single nucleotide polymorphisms (SNPs) and SNP-environment interactions are of great importance in genetic association studies, as susceptibility to complex disease might be related to the interaction of multiple SNPs and/or environmental factors. With these methods under active development, algorithms to simulate genomic data sets are needed to ensure proper type I error control of newly proposed methods and to compare power with existing methods. In this paper we propose an efficient method for a haplotype-based simulation of case-parent trios when the disease risk is thought to depend on possibly higher-order epistatic interactions or gene-environment interactions with binary exposures.

Longitudinal accuracy of web-based self-reported weights: results from the Hopkins POWER Trial.

BACKGROUND: Websites and phone apps are increasingly used to track weights during weight loss interventions, yet the longitudinal accuracy of these self-reported weights is uncertain. OBJECTIVE: Our goal was to compare the longitudinal accuracy of self-reported weights entered online during the course of a randomized weight loss trial to measurements taken in the clinic. We aimed to determine if accuracy of self-reported weight is associated with weight loss and to determine the extent of misclassification in achieving 5% weight loss when using self-reported compared to clinic weights. METHODS: This study examined the accuracy of self-reported weights recorded online among intervention participants in the Hopkins Practice-Based Opportunities for Weight Reduction (POWER) trial, a randomized trial examining the effectiveness of two lifestyle-based weight loss interventions compared to a control group among obese adult patients with at least one cardiovascular risk factor. One treatment group was offered telephonic coaching and the other group was offered in-person individual coaching and group sessions. All intervention participants (n=277) received a digital scale and were asked to track their weight weekly on a study website. Research staff used a standard protocol to measure weight in the clinic. Differences (self-reported weight - clinic weight) indicate if self-report under (-) or over (+) estimated clinic weight using the self-reported weight that was closest in time to the clinic weight and was within a window ranging from the day of the clinic visit to 7 days before the 6-month (n=225) and 24-month (n=191) clinic visits. The absolute value of the differences (absolute difference) describes the overall accuracy. RESULTS: Underestimation of self-reported weights increased significantly from 6 months (mean -0.5 kg, SD 1.0 kg) to 24 months (mean -1.1 kg, SD 2.0 kg; P=.002). The average absolute difference also increased from 6 months (mean 0.7 kg, SD 0.8 kg) to 24 months (mean 1.3, SD 1.8 kg; P<.001). Participants who achieved the study weight loss goal at 24 months (based on clinic weights) had lower absolute differences (P=.01) compared to those who did not meet this goal. At 24 months, there was 9% misclassification of weight loss goal success when using self-reported weight compared to clinic weight as an outcome. At 24 months, those with self-reported weights (n=191) had three times the weight loss compared to those (n=73) without self-reported weights (P<.001). CONCLUSIONS: Underestimation of weight increased over time and was associated with less weight loss. In addition to intervention adherence, weight loss programs should emphasize accuracy in self-reporting. TRIAL REGISTRATION: ClinicalTrials.gov: NCT00783315; http://clinicaltrials.gov/show/NCT00783315 (Archived by WebCite at http://www.webcitation.org/6R4gDAK5K).

Incorporating genotype uncertainties into the genotypic TDT for main effects and gene-environment interactions.

Genotype imputation has become a standard option for researchers to expand their genotype datasets to improve signal precision and power in tests of genetic association with disease. In imputations for family-based studies however, subjects are often treated as unrelated individuals: currently, only BEAGLE allows for simultaneous imputation for trios of parents and offspring; however, only the most likely genotype calls are returned, not estimated genotype probabilities. For population-based SNP association studies, it has been shown that incorporating genotype uncertainty can be more powerful than using hard genotype calls. We here investigate this issue in the context of case-parent family data. We present the statistical framework for the genotypic transmission-disequilibrium test (gTDT) using observed genotype calls and imputed genotype probabilities, derive an extension to assess gene-environment interactions for binary environmental variables, and illustrate the performance of our method on a set of trios from the International Cleft Consortium. In contrast to population-based studies, however, utilizing the genotype probabilities in this framework (derived by treating the family members as unrelated) can result in biases of the test statistics toward protectiveness for the minor allele, particularly for markers with lower minor allele frequencies and lower imputation quality. We further compare the results between ignoring relatedness in the imputation and taking family structure into account, based on hard genotype calls. We find that by far the least biased results are obtained when family structure is taken into account and currently recommend this approach in spite of its intense computational requirements.