Prediction of Suicide Attempts Among Persons with Depression: A Population-Based Case Cohort Study.

Studies have highlighted the potential importance of modeling interactions for suicide attempt prediction. This case-cohort study identified risk factors for suicide attempts among persons with depression in Denmark using statistical approaches that do (random forests) or do not model interactions (least absolute shrinkage and selection operator regression [LASSO]). Cases made a non-fatal suicide attempt (n = 6,032) between 1995 and 2015. The comparison subcohort was a 5% random sample of all persons in Denmark on January 1, 1995 (n = 11,963). We used random forests and LASSO for sex-stratified prediction of suicide attempts from demographic variables, psychiatric and somatic diagnoses, and treatments. Poisonings, psychiatric disorders, and medications were important predictors for both sexes. Area under the receiver operating characteristic curve (AUC) values were higher in LASSO models (0.85 [95% CI = 0.84, 0.86] in men; 0.89 [95% CI = 0.88, 0.90] in women) than random forests (0.76 [95% CI = 0.74, 0.78] in men; 0.79 [95% CI = 0.78, 0.81] in women). Automatic detection of interactions via random forests did not result in better model performance than LASSO models that did not model interactions. Due to the complex nature of psychiatric comorbidity and suicide, modeling interactions may not always be the optimal statistical approach to enhancing suicide attempt prediction in high-risk samples.

Comparative Effectiveness of Direct-Acting Antivirals for Posttraumatic Stress Disorder in Veterans Affairs Patients With Hepatitis C Virus Infection.

We recently conducted an exploratory study that indicated that several direct-acting antivirals (DAAs), highly effective medications for hepatitis C virus (HCV) infection, were also associated with improvement in posttraumatic stress disorder (PTSD) among a national cohort of US Department of Veterans Affairs (VA) patients treated between October 1, 1999, and September 30, 2019. Limiting the same cohort to patients with PTSD and HCV, we compared the associations of individual DAAs with PTSD symptom improvement using propensity score weighting. After identifying patients who had available baseline and endpoint PTSD symptom data as measured with the PTSD Checklist (PCL), we compared changes over the 8-12 weeks of DAA treatment. The DAAs most prescribed in conjunction with PCL measurement were glecaprevir/pibrentasvir (GLE/PIB; n = 54), sofosbuvir/velpatasvir (SOF/VEL; n = 54), and ledipasvir/sofosbuvir (LDV/SOF; n = 145). GLE/PIB was superior to LDV/SOF, with a mean difference in improvement of 7.3 points on the PCL (95% confidence interval (CI): 1.1, 13.6). The mean differences in improvement on the PCL were smaller between GLE/PIB and SOF/VEL (3.0, 95% CI: -6.3, 12.2) and between SOF/VEL and LDV/SOF (4.4, 95% CI: -2.4, 11.2). While almost all patients were cured of HCV (92.5%) regardless of the agent received, PTSD outcomes were superior for those receiving GLE/PIB compared with those receiving LDV/SOF, indicating that GLE/PIB may merit further investigation as a potential PTSD treatment.

The Recent Rise of Suicide Mortality in the United States.

Suicide is a major public health concern in the United States. Between 2000 and 2018, US suicide rates increased by 35%, contributing to the stagnation and subsequent decrease in US life expectancy. During 2019, suicide declined modestly, mostly owing to slight reductions in suicides among Whites. Suicide rates, however, continued to increase or remained stable among all other racial/ethnic groups, and little is known about recent suicide trends among other vulnerable groups. This article (a) summarizes US suicide mortality trends over the twentieth and early twenty-first centuries, (b) reviews potential group-level causes of increased suicide risk among subpopulations characterized by markers of vulnerability to suicide, and (c) advocates for combining recent advances in population-based suicide prevention with a socially conscious perspective that captures the social, economic, and political contexts in which suicide risk unfolds over the life course of vulnerable individuals.

The Joint Effects of Depression and Comorbid Psychiatric Disorders on Suicide Deaths: Competing Antagonism as an Explanation for Subadditivity.

BACKGROUND: Previous studies of the effect of interaction between psychiatric disorders on suicide have reported mixed results. We investigated the joint effect of depression and various comorbid psychiatric disorders on suicide. METHODS: We conducted a population-based case-cohort study with all suicide deaths occurring between 1 January 1995 and 31 December 2015 in Denmark (n = 14,103) and a comparison subcohort comprised of a 5% random sample of the source population at baseline (n = 265,183). We quantified the joint effect of pairwise combinations of depression and major psychiatric disorders (e.g., organic disorders, substance use disorders, schizophrenia, bipolar disorder, neurotic disorders, eating disorders, personality disorders, intellectual disabilities, developmental disorders, and behavioral disorders) on suicide using marginal structural models and calculated the relative excess risk due to interaction. We assessed for the presence of competing antagonism for negative relative excess risk due to interactions. RESULTS: All combinations of depression and comorbid psychiatric disorders were associated with increased suicide risk. For example, the rate of suicide among men with depression and neurotic disorders was 20 times (95% CI = 15, 26) the rate in men with neither disorder. Most disorder combinations were associated with subadditive suicide risk, and there was evidence of competing antagonism in most of these cases. CONCLUSIONS: Subadditivity may be explained by competing antagonism. When both depression and a comorbid psychiatric disorder are present, they may compete to cause the outcome such that having 2 disorders may be no worse than having a single disorder with respect to suicide risk.

Predictive models of pregnancy based on data from a preconception cohort study.

STUDY QUESTION: Can we derive adequate models to predict the probability of conception among couples actively trying to conceive? SUMMARY ANSWER: Leveraging data collected from female participants in a North American preconception cohort study, we developed models to predict pregnancy with performance of ∼70% in the area under the receiver operating characteristic curve (AUC). WHAT IS KNOWN ALREADY: Earlier work has focused primarily on identifying individual risk factors for infertility. Several predictive models have been developed in subfertile populations, with relatively low discrimination (AUC: 59-64%). STUDY DESIGN, SIZE, DURATION: Study participants were female, aged 21-45 years, residents of the USA or Canada, not using fertility treatment, and actively trying to conceive at enrollment (2013-2019). Participants completed a baseline questionnaire at enrollment and follow-up questionnaires every 2 months for up to 12 months or until conception. We used data from 4133 participants with no more than one menstrual cycle of pregnancy attempt at study entry. PARTICIPANTS/MATERIALS, SETTING, METHODS: On the baseline questionnaire, participants reported data on sociodemographic factors, lifestyle and behavioral factors, diet quality, medical history and selected male partner characteristics. A total of 163 predictors were considered in this study. We implemented regularized logistic regression, support vector machines, neural networks and gradient boosted decision trees to derive models predicting the probability of pregnancy: (i) within fewer than 12 menstrual cycles of pregnancy attempt time (Model I), and (ii) within 6 menstrual cycles of pregnancy attempt time (Model II). Cox models were used to predict the probability of pregnancy within each menstrual cycle for up to 12 cycles of follow-up (Model III). We assessed model performance using the AUC and the weighted-F1 score for Models I and II, and the concordance index for Model III. MAIN RESULTS AND THE ROLE OF CHANCE: Model I and II AUCs were 70% and 66%, respectively, in parsimonious models, and the concordance index for Model III was 63%. The predictors that were positively associated with pregnancy in all models were: having previously breastfed an infant and using multivitamins or folic acid supplements. The predictors that were inversely associated with pregnancy in all models were: female age, female BMI and history of infertility. Among nulligravid women with no history of infertility, the most important predictors were: female age, female BMI, male BMI, use of a fertility app, attempt time at study entry and perceived stress. LIMITATIONS, REASONS FOR CAUTION: Reliance on self-reported predictor data could have introduced misclassification, which would likely be non-differential with respect to the pregnancy outcome given the prospective design. In addition, we cannot be certain that all relevant predictor variables were considered. Finally, though we validated the models using split-sample replication techniques, we did not conduct an external validation study. WIDER IMPLICATIONS OF THE FINDINGS: Given a wide range of predictor data, machine learning algorithms can be leveraged to analyze epidemiologic data and predict the probability of conception with discrimination that exceeds earlier work. STUDY FUNDING/COMPETING INTEREST(S): The research was partially supported by the U.S. National Science Foundation (under grants DMS-1664644, CNS-1645681 and IIS-1914792) and the National Institutes for Health (under grants R01 GM135930 and UL54 TR004130). In the last 3 years, L.A.W. has received in-kind donations for primary data collection in PRESTO from FertilityFriend.com, Kindara.com, Sandstone Diagnostics and Swiss Precision Diagnostics. L.A.W. also serves as a fibroid consultant to AbbVie, Inc. The other authors declare no competing interests. TRIAL REGISTRATION NUMBER: N/A.

Association between post-traumatic stress disorder severity and death by suicide in US military veterans: retrospective cohort study.

BACKGROUND: There is mixed evidence regarding the direction of a potential association between post-traumatic stress disorder (PTSD) and suicide mortality. AIMS: This is the first population-based study to account for both PTSD diagnosis and PTSD symptom severity simultaneously in the examination of suicide mortality. METHOD: Retrospective study that included all US Department of Veterans Affairs (VA) patients with a PTSD diagnosis and at least one symptom severity assessment using the PTSD Checklist (PCL) between 1 October 1999 and 31 December 2018 (n = 754 197). We performed multivariable proportional hazards regression models using exposure groups defined by level of PTSD symptom severity to estimate suicide mortality rates. For patients with multiple PCL scores, we performed additional models using exposure groups defined by level of change in PTSD symptom severity. We assessed suicide mortality using the VA/Department of Defense Mortality Data Repository. RESULTS: Any level of PTSD symptoms above the minimum threshold for symptomatic remission (i.e. PCL score >18) was associated with double the suicide mortality rate at 1 month after assessment. This relationship decreased over time but patients with moderate to high symptoms continued to have elevated suicide rates. Worsening PTSD symptoms were associated with a 25% higher long-term suicide mortality rate. Among patients with improved PTSD symptoms, those with symptomatic remission had a substantial and sustained reduction in the suicide rate compared with those without symptomatic remission (HR = 0.56; 95% CI 0.37-0.88). CONCLUSIONS: Ameliorating PTSD can reduce risk of suicide mortality, but patients must achieve symptomatic remission to attain this benefit.

Changes in Alcohol Consumption following Direct-Acting Antiviral Treatment for Hepatitis C in VA Patients with Comorbid Alcohol Use Disorder and PTSD.

OBJECTIVE: To investigate whether direct-acting antivirals (DAA) for hepatitis C viral infection (HCV): glecaprevir/pibrentasvir (GLE/PIB), ledipasvir/sofosbuvir (LDV/SOF), and sofosbuvir/velpatasvir (SOF/VEL) are associated with reduced alcohol consumption among veterans with alcohol use disorder (AUD) and co-occurring post-traumatic stress disorder (PTSD). METHODS: We measured change in Alcohol Use Disorder Identification Test-Consumption Module (AUDIT-C) scores in a retrospective cohort of veterans with PTSD and AUD receiving DAAs for HCV. RESULTS: One thousand two hundred and eleven patients were included (GLE/PIB n = 174, LDV/SOF n = 808, SOF/VEL n = 229). Adjusted frequencies of clinically meaningful improvement were 30.5% for GLE/PIB, 45.5% for LDV/SOF, and 40.5% for SOF/VEL. The frequency was lower for GLE/PIB than for LDV/SOF (OR = 0.59; 95% CI [0.40, 0.87]) or SOF/VEL (OR = 0.66; 95% CI [0.42, 1.04]). CONCLUSIONS: DAA treatment for HCV was associated with a substantial reduction in alcohol use in patients with AUD and co-occurring PTSD. Further exploration of the role of DAAs in AUD treatment is warranted.

Addressing Measurement Error in Random Forests Using Quantitative Bias Analysis.

Although variables are often measured with error, the impact of measurement error on machine-learning predictions is seldom quantified. The purpose of this study was to assess the impact of measurement error on the performance of random-forest models and variable importance. First, we assessed the impact of misclassification (i.e., measurement error of categorical variables) of predictors on random-forest model performance (e.g., accuracy, sensitivity) and variable importance (mean decrease in accuracy) using data from the National Comorbidity Survey Replication (2001-2003). Second, we created simulated data sets in which we knew the true model performance and variable importance measures and could verify that quantitative bias analysis was recovering the truth in misclassified versions of the data sets. Our findings showed that measurement error in the data used to construct random forests can distort model performance and variable importance measures and that bias analysis can recover the correct results. This study highlights the utility of applying quantitative bias analysis in machine learning to quantify the impact of measurement error on study results.

Predicting Sex-Specific Nonfatal Suicide Attempt Risk Using Machine Learning and Data From Danish National Registries.

Suicide attempts are a leading cause of injury globally. Accurate prediction of suicide attempts might offer opportunities for prevention. This case-cohort study used machine learning to examine sex-specific risk profiles for suicide attempts in Danish nationwide registry data. Cases were all persons who made a nonfatal suicide attempt between 1995 and 2015 (n = 22,974); the subcohort was a 5% random sample of the population at risk on January 1, 1995 (n = 265,183). We developed sex-stratified classification trees and random forests using 1,458 predictors, including demographic factors, family histories, psychiatric and physical health diagnoses, surgery, and prescribed medications. We found that substance use disorders/treatment, prescribed psychiatric medications, previous poisoning diagnoses, and stress disorders were important factors for predicting suicide attempts among men and women. Individuals in the top 5% of predicted risk accounted for 44.7% of all suicide attempts among men and 43.2% of all attempts among women. Our findings illuminate novel risk factors and interactions that are most predictive of nonfatal suicide attempts, while consistency between our findings and previous work in this area adds to the call to move machine learning suicide research toward the examination of high-risk subpopulations.

Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark.

BACKGROUND: Suicide risk is high in the 30 days after discharge from psychiatric hospital, but knowledge of the profiles of high-risk patients remains limited. AIMS: To examine sex-specific risk profiles for suicide in the 30 days after discharge from psychiatric hospital, using machine learning and Danish registry data. METHOD: We conducted a case-cohort study capturing all suicide cases occurring in the 30 days after psychiatric hospital discharge in Denmark from 1 January 1995 to 31 December 2015 (n = 1205). The comparison subcohort was a 5% random sample of all persons born or residing in Denmark on 1 January 1995, and who had a first psychiatric hospital admission between 1995 and 2015 (n = 24 559). Predictors included diagnoses, surgeries, prescribed medications and demographic information. The outcome was suicide death recorded in the Danish Cause of Death Registry. RESULTS: For men, prescriptions for anxiolytics and drugs used in addictive disorders interacted with other characteristics in the risk profiles (e.g. alcohol-related disorders, hypnotics and sedatives) that led to higher risk of postdischarge suicide. In women, there was interaction between recurrent major depression and other characteristics (e.g. poisoning, low income) that led to increased risk of suicide. Random forests identified important suicide predictors: alcohol-related disorders and nicotine dependence in men and poisoning in women. CONCLUSIONS: Our findings suggest that accurate prediction of suicide during the high-risk period immediately after psychiatric hospital discharge may require a complex evaluation of multiple factors for men and women.

Stress Disorders and the Risk of Nonfatal Suicide Attempts in the Danish Population.

Accurate documentation of the associations between stress disorders and suicide attempts provides important information about a high-risk population and target group for preventative interventions. In this case-cohort study, cases were all individuals born or residing in Denmark who made a nonfatal suicide attempt during 1995-2015 (n = 22,974). The comparison subcohort included a 5% random sample of the Danish population on January 1, 1995 (n = 265,183). Stress disorder diagnoses and suicide attempts were identified using ICD-10 codes from national medical registries. The presence of any stress disorder substantially increased the rate of suicide attempts versus the comparison subcohort, rate per 100,000 person-years (PYs) = 604 vs. 13. We observed associations between each type of stress disorder and suicide attempts, hazard ratios (HRs) = 10.1-37.6, even after adjustment for potential confounders, adjusted HRs = 1.8-8.3, with the strongest associations for adjustment disorder relative to other diagnoses. After adjusting for demographic and health variables, the rate of suicide attempts among individuals with any stress disorder diagnosis was nearly 13 times the suicide attempt rate in the comparison cohort. A bias analysis demonstrated that associations remained robust despite potential differential misclassification of suicide attempts. Study strengths included the use of individual-level data linked across administrative and medical registries in the setting of universal health care and the use of longitudinal analyses capturing data over 20 years. The study demonstrated associations between the full range of stress disorders and suicide attempts, extending research specific to posttraumatic stress disorder.

Incident psychiatric comorbidity following stress disorder diagnoses in Danish school-aged children: prospective population-based study.

BACKGROUND: Prospective population-based studies of psychiatric comorbidity following trauma and severe stress exposure in children are limited. AIMS: To examine incident psychiatric comorbidity following stress disorder diagnoses in Danish school-aged children using Danish national healthcare system registries. METHOD: Children (6-15 years of age) with a severe stress or adjustment disorder (ICD-10) between 1995 and 2011 (n = 11 292) were followed prospectively for an average of 5.8 years. Incident depressive, anxiety and behavioural disorder diagnoses were examined relative to an age- and gender-matched comparison cohort (n = 56 460) using Cox proportional hazards regression models. Effect modification by gender was examined through stratified analyses. RESULTS: All severe stress and adjustment disorder diagnoses were associated with increased rates for all incident outcome disorders relative to the comparison cohort. For instance, adjustment disorders were associated with higher rates of incident depressive (rate ratio RR = 6.8; 95% CI 6.0-7.7), anxiety (RR = 5.3; 95% CI 4.5-6.4), and behavioural disorders (RR = 7.9; 95% CI 6.6-9.3). Similarly, PTSD was also associated with higher rates of depressive (RR = 7.4; 95% CI 4.2-13), anxiety (RR = 7.1; 95% CI 3.5-14) and behavioural disorder (RR = 4.9; 95% CI 2.3-11) diagnoses. There was no evidence of gender-related differences. CONCLUSIONS: Stress disorders varying in symptom constellation and severity are associated with a range of incident psychiatric disorders in children. Transdiagnostic assessments within a longitudinal framework are needed to characterise the course of post-trauma or severe stressor psychopathology.

Posttraumatic Stress Disorder and Incident Infections: A Nationwide Cohort Study.

BACKGROUND: It is unknown whether posttraumatic stress disorder (PTSD) is associated with incident infections. This study's objectives were to examine (1) the association between PTSD diagnosis and 28 types of infections and (2) the interaction between PTSD diagnosis and sex on the rate of infections. METHODS: The study population consisted of a longitudinal nationwide cohort of all residents of Denmark who received a PTSD diagnosis between 1995 and 2011, and an age- and sex-matched general population comparison cohort. We fit Cox proportional hazards regression models to examine associations between PTSD diagnosis and infections. To account for multiple estimation, we adjusted the hazard ratios (HRs) using semi-Bayes shrinkage. We calculated interaction contrasts to assess the presence of interaction between PTSD diagnosis and sex. RESULTS: After semi-Bayes shrinkage, the HR for any type of infection was 1.8 (95% confidence interval: 1.6, 2.0), adjusting for marital status, non-psychiatric comorbidity, and diagnoses of substance abuse, substance dependence, and depression. The association between PTSD diagnosis and some infections (e.g., urinary tract infections) were stronger among women, whereas other associations were stronger among men (e.g., skin infections). CONCLUSIONS: This study's findings suggest that PTSD diagnosis is a risk factor for numerous infection types and that the associations between PTSD diagnosis and infections are modified by sex.

Adjustment disorder and type-specific cancer incidence: a Danish cohort study.

BACKGROUND: Although adjustment disorder is common, there is a dearth of research on its physical health consequences. Earlier studies, biological mechanisms and stress-related behaviors suggest that cancer may be a potential sequelae of adjustment disorder. This study examined the association between adjustment disorder and type-specific cancer incidence in a nationwide cohort. METHODS: Data were obtained from the comprehensive nationwide medical and administrative registries of Denmark. We calculated the incidence of type-specific cancers from 1995 to 2013 in patients with a prior adjustment disorder diagnosis (n = 58,712), and compared it with the incidence in the general population by calculating standardized incidence ratios (SIRs) with accompanying 95% confidence intervals (CIs). SIRs were adjusted using semi-Bayes shrinkage. RESULTS: The SIR for any type of cancer was 1.0 (95% CI: 0.99, 1.1). Adjustment disorder was associated with a 10% lower rate of immune-related cancers (SIR = 0.9, 95% CI: 0.84, 0.97) and with a 20% higher rate of smoking- and alcohol-related cancers (SIR = 1.2, 95% CI: 1.1, 1.3). We found null associations for hematological (SIR = 1.1, 95% CI: 0.89, 1.3) and hormone-related (SIR = 0.98, 95% CI: 0.91, 1.1) malignancies. After semi-Bayes adjustment, type-specific cancer SIRs indicated no association between adjustment disorder and cancer incidence. CONCLUSIONS: This study provides persuasive evidence for a null association between adjustment disorder and type-specific cancer incidence in a nationwide study cohort.

Predictive models of miscarriage on the basis of data from a preconception cohort study.

OBJECTIVE: To use self-reported preconception data to derive models that predict the risk of miscarriage. DESIGN: Prospective preconception cohort study. SETTING: Not applicable. PATIENTS: Study participants were female, aged 21-45 years, residents of the United States or Canada, and attempting spontaneous pregnancy at enrollment during 2013-2022. Participants were followed for up to 12 months of pregnancy attempts; those who conceived were followed through pregnancy and postpartum. We restricted analyses to participants who conceived during the study period. EXPOSURE: On baseline and follow-up questionnaires completed every 8 weeks until pregnancy, we collected self-reported data on sociodemographic factors, reproductive history, lifestyle, anthropometrics, diet, medical history, and male partner characteristics. We included 160 potential predictor variables in our models. MAIN OUTCOME MEASURES: The primary outcome was a miscarriage, defined as pregnancy loss before 20 weeks of gestation. We followed participants from their first positive pregnancy test until miscarriage or a censoring event (induced abortion, ectopic pregnancy, loss of follow-up, or 20 weeks of gestation), whichever occurred first. We fit both survival and static models using Cox proportional hazards models, logistic regression, support vector machines, gradient-boosted trees, and random forest algorithms. We evaluated model performance using the concordance index (survival models) and the weighted F1 score (static models). RESULTS: Among the 8,720 participants who conceived, 20.4% reported miscarriage. In multivariable models, the strongest predictors of miscarriage were female age, history of miscarriage, and male partner age. The weighted F1 score ranged from 73%-89% for static models and the concordance index ranged from 53%-56% for survival models, indicating better discrimination for the static models compared with the survival models (i.e., the ability of the model to discriminate between individuals with and without miscarriage). No appreciable differences were observed across strata of miscarriage history or among models restricted to ≥8 weeks of gestation. CONCLUSION: Our findings suggest that miscarriage is not easily predicted on the basis of preconception lifestyle characteristics and that advancing age and a history of miscarriage are the most important predictors of incident miscarriage.

Introduction to the special section on causal inference and agent-based modeling.

This is an introduction to the special section "Causal Inference and Agent-Based Modeling" in trauma research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

An introduction to directed acyclic graphs in trauma research.

OBJECTIVE: Directed acyclic graphs (DAGs) are visual representations of the presumed causal structure of an empirical research data set. They are important tools for researchers but have been used rarely in the psychological trauma literature. The purpose of this article is to explain what DAGs are and why (and how) they are useful for trauma researchers. METHOD: We first describe the utility of DAGs for making causal assumptions explicit, identifying causal effects, and preventing bias. Basic definitions and rules governing the use of DAGs are presented using a hypothetical DAG. We explain why conditioning on a variable, for example, by controlling for it in a multivariable model, can in some circumstances actually introduce bias and not prevent it. We also provide references for topics related to DAGs that are beyond the scope of this introductory article. RESULTS: DAGs are illustrated using the example of the effect of posttraumatic stress disorder (PTSD) on Parkinson's disease. We demonstrate that a multivariable model controlling for all covariates that are being considered introduces bias and would make it impossible to identify the causal effect of PTSD on Parkinson's disease. CONCLUSIONS: DAGs can help trauma researchers to understand when they can and when they cannot draw causal conclusions based on research data. This introduction to DAGs should help readers understand their use in the articles on marginal structural models, causal mediation analysis, and instrumental variable methods in this special section, Causal inference and agent-based modeling in trauma research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Critical Appraisal of Matching-Adjusted Indirect Comparisons in Spinal Muscular Atrophy.

In the absence of head-to-head trials, indirect treatment comparisons (ITCs) are often used to compare the efficacy of different therapies to support decision-making. Matching-adjusted indirect comparison (MAIC), a type of ITC, is increasingly used to compare treatment efficacy when individual patient data are available from one trial and only aggregate data are available from the other trial. This paper examines the conduct and reporting of MAICs to compare treatments for spinal muscular atrophy (SMA), a rare neuromuscular disease. A literature search identified three studies comparing approved treatments for SMA including nusinersen, risdiplam, and onasemnogene abeparvovec. The quality of the MAICs was assessed on the basis of the following principles consolidated from published MAIC best practices: (1) justification for the use of MAIC is clearly stated, (2) the included trials with respect to study population and design are comparable, (3) all known confounders and effect modifiers are identified a priori and accounted for in the analysis, (4) outcomes should be similar in definition and assessment, (5) baseline characteristics are reported before and after adjustment, along with weights, and (6) key details of a MAIC are reported. In the three MAIC publications in SMA to date, the quality of analysis and reporting varied greatly. Various sources of bias in the MAICs were identified, including lack of control for key confounders and effect modifiers, inconsistency in outcome definitions across trials, imbalances in important baseline characteristics after weighting, and lack of reporting key elements. These findings highlight the importance of evaluating MAICs according to best practices when assessing the conduct and reporting of MAICs.