Robust Inference for the Stepped Wedge Design

Based on a permutation argument, we derive a closed form expression for an estimate of the treatment effect, along with its standard error, in a stepped wedge design. We show that these estimates are robust to misspecification of both the mean and covariance structure of the underlying data-generating mechanism, thereby providing a robust approach to inference for the treatment effect in stepped wedge designs. We use simulations to evaluate the type I error and power of the proposed estimate and to compare the performance of the proposed estimate to the optimal estimate when the correct model specification is known. The limitations, possible extensions, and open problems regarding the method are discussed

Long-term safety of unopposed estrogen used by women surviving myocardial infarction : 14 year follow-up of the ESPRIT randomised controlled trial

Peer reviewedPostprin

Analysis of recently identified dyslipidemia alleles reveals two loci that contribute to risk for carotid artery disease

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies have identified numerous single nucleotide polymorphisms (SNPs) affecting high density lipoprotein (HDL) or low density lipoprotein (LDL) cholesterol levels; these SNPs may contribute to the genetic basis of vascular diseases.</p> <p>Results</p> <p>We assessed the impact of 34 SNPs at 23 loci on dyslipidemia, key lipid sub-phenotypes, and severe carotid artery disease (CAAD) in a case-control cohort. The effects of these SNPs on HDL and LDL were consistent with those previously reported, and we provide unbiased estimates of the percent variance in HDL (3.9%) and LDL (3.3%) explained by genetic risk scores. We assessed the effects of these SNPs on HDL subfractions, apolipoprotein A-1, LDL buoyancy, apolipoprotein B, and lipoprotein (a) and found that rs646776 predicts apolipoprotein B level while rs2075650 predicts LDL buoyancy. Finally, we tested the role of these SNPs in conferring risk for ultrasonographically documented CAAD stenosis status. We found that two loci, chromosome 1p13.3 near CELSR2 and PSRC1 which contains rs646776, and 19q13.2 near TOMM40 and APOE which contains rs2075650, harbor risk alleles for CAAD.</p> <p>Conclusion</p> <p>Our analysis of 34 SNPs contributing to dyslipidemia at 23 loci suggests that genetic variation in the 1p13.3 region may increase risk of CAAD by increasing LDL particle number, whereas variation in the 19q13.2 region may increase CAAD risk by promoting formation of smaller, denser LDL particles.</p

Advanced cardiovascular risk prediction in the emergency department: updating a clinical prediction model - a large database study protocol.

From Europe PMC via Jisc Publications RouterHistory: ppub 2021-10-01, epub 2021-10-07Publication status: PublishedFunder: Department of Health; Grant(s): NIHR300246Funder: national institute for health research; Grant(s): NIHR300246BackgroundPatients presenting with chest pain represent a large proportion of attendances to emergency departments. In these patients clinicians often consider the diagnosis of acute myocardial infarction (AMI), the timely recognition and treatment of which is clinically important. Clinical prediction models (CPMs) have been used to enhance early diagnosis of AMI. The Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid is currently in clinical use across Greater Manchester. CPMs have been shown to deteriorate over time through calibration drift. We aim to assess potential calibration drift with T-MACS and compare methods for updating the model.MethodsWe will use routinely collected electronic data from patients who were treated using TMACS at two large NHS hospitals. This is estimated to include approximately 14,000 patient episodes spanning June 2016 to October 2020. The primary outcome of acute myocardial infarction will be sourced from NHS Digital's admitted patient care dataset. We will assess the calibration drift of the existing model and the benefit of updating the CPM by model recalibration, model extension and dynamic updating. These models will be validated by bootstrapping and one step ahead prequential testing. We will evaluate predictive performance using calibrations plots and c-statistics. We will also examine the reclassification of predicted probability with the updated TMACS model.DiscussionCPMs are widely used in modern medicine, but are vulnerable to deteriorating calibration over time. Ongoing refinement using routinely collected electronic data will inevitably be more efficient than deriving and validating new models. In this analysis we will seek to exemplify methods for updating CPMs to protect the initial investment of time and effort. If successful, the updating methods could be used to continually refine the algorithm used within TMACS, maintaining or even improving predictive performance over time.Trial registrationISRCTN number: ISRCTN41008456

Susceptibility to multiple cutaneous basal cell carcinomas: significant interactions between glutathione S-transferase GSTM1 genotypes, skin type and male gender.

The factors that determine development of single and multiple primary cutaneous basal cell carcinomas (BCCs) are unclear. We describe a case-control study firstly, to examine the influence of allelism at the glutathione S-transferase GSTM1 and GSTT1 and cytochrome P450 CYP2D6 loci on susceptibility to these tumours and, secondly, to identify interactions between genotypes and relevant individual characteristics, such as skin type and gender. Frequency distributions for GSTM1 genotypes in cases and controls were not different, although the frequency of GSTM1 A/B was significantly lower (P = 0.048) in the multiple BCCs than in controls. We found no significant differences in the frequencies of GSTT1 and CYP2D6 genotypes in cases and controls. Interactions between genotypes were studied by comparing multinomial frequency distributions in mutually exclusive groups. These identified no differences between cases and controls for combinations of the putatively high risk GSTM1 null, GSTT1 null, CYP2D6 EM genotypes. Interactions between GSTM1 A/B and the CYP2D6 PM and GSTT1-positive genotypes were also not different. Frequency distributions of GSTM1 A/B with CYP2D6 EM in controls and multiple BCCs were significantly different (P = 0.033). The proportion of males in the multiple BCC group (61.3%) was greater than in controls (47.0%) and single BCC (52.2%), and the frequency of the combination GSTM1 null/male gender was significantly greater in patients with multiple tumours (P = 0.002). Frequency distributions of GSTM1 null/skin type 1 were also significantly different (P = 0.029) and the proportion of subjects who were GSTM1 null with skin type 1 was greater (P = 0.009) in the multiple BCC group. We examined the data for interactions between GSTM1 null/skin type 1/male gender by comparing frequency distributions of these factors in the single and multiple BCC groups. The distributions were almost significantly different (exact P = 0.051). No significant interactions between GSTT1 null or CYP2D6 EM and skin type 1 were identified. Comparisons of frequency distributions of smoking with the GSTM1 null, GSTT1 null and CYP2D6 EM genotypes identified no differences between patients with single and multiple tumours

Siamese Survival Analysis with Competing Risks

Survival analysis in the presence of multiple possible adverse events, i.e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.). Since only one event is typically observed, the incidence of an event of interest is often obscured by other related competing events. This nonidentifiability, or inability to estimate true cause-specific survival curves from empirical data, further complicates competing risk survival analysis. We introduce Siamese Survival Prognosis Network (SSPN), a novel deep learning architecture for estimating personalized risk scores in the presence of competing risks. SSPN circumvents the nonidentifiability problem by avoiding the estimation of cause-specific survival curves and instead determines pairwise concordant time-dependent risks, where longer event times are assigned lower risks. Furthermore, SSPN is able to directly optimize an approximation to the C-discrimination index, rather than relying on well-known metrics which are unable to capture the unique requirements of survival analysis with competing risks

Advanced cardiovascular risk prediction in the emergency department: updating a clinical prediction model – a large database study protocol

From Springer Nature via Jisc Publications RouterHistory: received 2021-07-27, accepted 2021-09-27, registration 2021-09-28, online 2021-10-07, pub-electronic 2021-10-07, collection 2021-12Publication status: PublishedFunder: national institute for health research; doi: http://dx.doi.org/10.13039/501100000272; Grant(s): NIHR300246Abstract: Background: Patients presenting with chest pain represent a large proportion of attendances to emergency departments. In these patients clinicians often consider the diagnosis of acute myocardial infarction (AMI), the timely recognition and treatment of which is clinically important. Clinical prediction models (CPMs) have been used to enhance early diagnosis of AMI. The Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid is currently in clinical use across Greater Manchester. CPMs have been shown to deteriorate over time through calibration drift. We aim to assess potential calibration drift with T-MACS and compare methods for updating the model. Methods: We will use routinely collected electronic data from patients who were treated using TMACS at two large NHS hospitals. This is estimated to include approximately 14,000 patient episodes spanning June 2016 to October 2020. The primary outcome of acute myocardial infarction will be sourced from NHS Digital’s admitted patient care dataset. We will assess the calibration drift of the existing model and the benefit of updating the CPM by model recalibration, model extension and dynamic updating. These models will be validated by bootstrapping and one step ahead prequential testing. We will evaluate predictive performance using calibrations plots and c-statistics. We will also examine the reclassification of predicted probability with the updated TMACS model. Discussion: CPMs are widely used in modern medicine, but are vulnerable to deteriorating calibration over time. Ongoing refinement using routinely collected electronic data will inevitably be more efficient than deriving and validating new models. In this analysis we will seek to exemplify methods for updating CPMs to protect the initial investment of time and effort. If successful, the updating methods could be used to continually refine the algorithm used within TMACS, maintaining or even improving predictive performance over time. Trial registration: ISRCTN number: ISRCTN4100845