Table 3. Statistical methods of the 11 studies included in the systematic review.
Study | Data used | Intervention | Aim of analysis | Analytical approach | Variables controlled for | Method used for missing data | Considerations in model selection | Study quality |
---|---|---|---|---|---|---|---|---|
Christensen et al, 2011 (29) | Secondary (RCT) | Preventive intervention for perinatal depression | Trajectories, risk profiles and outcomes | Trajectories: growth mixture modelling (Mplus); Risk factors: multinomial logistic regression | Demographic, psychosocial characteristics and randomization status for adjusted logistic regression models | Not specified | Information criteria fit indices: BIC and ABIC Estimated posterior probabilities: entropy, sample size of latent trajectories Likelihood ratio tests: BLRT | + |
Glasheen et al, 2013 (21) | Secondary | None | Trajectories, risk profiles and outcomes | Trajectories: growth mixture modelling (Mplus); Risk factors: logistic and multinomial regression analyses | Demographic characteristics, social support, pregnancy, labour and delivery complications, substance use for regression models | Not specified | Information criteria fit indices: BIC, AIC, Estimated posterior probabilities: entropy Likelihood ratio tests: LMR* | +++ |
Kuo et al, 2012 (26) | Primary | None | Trajectories and risk profiles | Trajectories: growth Mixture modelling (SAS); Risk factors: multinomial logistic regression | Parity, education, prenatal employment, prenatal exercise, mode of birth, sleep quality for regression models | PROC TRAJ (SAS): maximum likelihood estimation | Information criteria fit indices: BIC | ++ |
Kuo et al, 2014 (25) | Primary | None | Trajectories and risk profiles | Trajectories: group-based trajectory modelling (SAS); Risk factors: logistic regressions | Age, parity, education, pregnancy BMI, use of patient controlled analgesics (PCAs) and sleep quality for regression models | PROCTRAJ (SAS): maximum likelihood estimation | Information criteria fit indices: BIC Sample size of latent trajectories | +++ |
Lee et al, 2014 (30) | Secondary (RCT) | Weight loss (diet and exercise) | Trajectories and risk profiles | Trajectories: latent growth modelling and latent class growth analysis (Mplus); Risk factors: multinomial logistic regression | Maternal BMI, parity, study (KAN-DO vs. AMP) and arm (control vs. intervention) for LCGA and regression models | Full information maximum likelihood OR expectation maximization algorithm | Information criteria fit indices: BIC Estimated posterior probabilities: entropy Sample size of latent trajectories | ++ |
Marcus et al, 2011 (22) | Primary | None | Trajectories and outcomes | Trajectories: mixture growth curve approach (SAS) | n/a | Imputed missing values using Proc MI (SAS) | Information criteria fit indices: BIC | + |
Mora et al, 2009 (24) | Secondary (Cohort) | None | Trajectories and risk profiles | Trajectories: growth mixture modelling (MPlus); Risk factors: bivariate analyses (chi square and analysis of variance), multinomial regression | Adjusting for all covariates (maternal characterist ics) in regression models | Expectati on-maximizat ion algorithm | Estimated posterior probabilities: entropy Information criteria fit indices: BIC, AIC, ABIC Likelihood ratio tests: BLRT Sample size of latent trajectories | +++ |
Parade et al, 2014 (23) | Primary | None | Trajectories and risk profiles | Trajectories: unconditional latent class growth analysis (using Mplus); Risk factors: analyses not specified | Education in latent class growth analysis; education, family income, romantic relationship length and type in regression models | Not specified | Information criteria fit indices: BIC Likelihood ratio tests: BLRT | + |
Ramos-Marcuse et al, 2010 (31) | Secondary (RCT) | Promoting Parenting and adolescent development | Trajectories and outcomes | Trajectories: group-based modelling (semiparametric – using PROCTRAJ, in SAS); Risk factors: polynomial function, analysis of variance and pairwise comparisons | RCT arm not controlled for (no difference in depressive symptoms); arm allocation in post-hoc analyses | Not specified | Information criteria fit indices: BIC Sample size of latent trajectories | ++ |
Sutter-Dallay et al, 2012 (27) | Secondary (Cohort) | None | Trajectories and risk profiles | Trajectories: semiparametric mixture models using PROC TRAJ (SAS); Risk factors: multinomial logistic regression | Adjusting for all covariates in the regression model; education excluded from regression due to collinearity | Not specified | Information criteria fit indices: BIC Average posterior probability Sample size of latent trajectories | ++ |
Vänskä et al, 2011 (28) | Primary | None | Trajectories, risk profiles and outcomes | Trajectories: mixture modelling (Mplus); Risk factors: ANCOVA | Current psychological distress (based on GHQ36) and parity at recruitment in ANCOVA | Missing-data method (Mplus) | Information criteria fit indices: BIC, AIC, ABIC, Likelihood ratio tests: VLMR, LMR and BLRT Individual and average posterior probabilities | ++ |
ABIC sample size adjusted Bayesian information criterion; AIC: Akaike information criterion; BIC: Bayesian information criterion; BLRT: bootstrap likelihood ratio test; CBT: cognitive behavioural therapy; LMR: Lo-Mendell-Rubin adjusted likelihood ratio test; RCT: randomised controlled trial; VLMR: Vuong-Lo-Mendell-Rubin likelihood ratio test.